Don't expect them to use the "R" word.
None of these NFL teams will admit to rebuilding. And some might not even be willing to undergo the process itself, since that'd essentially concede to fans that a run to Super Bowl LIV isn't on the table.
But somewhere between five and 10 NFL franchises are typically in rebuilding mode at any given time, and these six would be best-served by approaching the 2019 offseason with a rebuild in mind.
It looks as though the Miami Dolphins are ready to move on from veteran quarterback Ryan Tannehill under new head coach Brian Flores, and general manager Chris Grier is calling the shots with Mike Tannenbaum out. The franchise is transitioning in the spots that matter, and there's no indication it's on the verge of contending after back-to-back losing seasons in a lopsided AFC East.
The Dolphins still have some strong pieces, but an NFL rebuild doesn't require a purge of the entire roster. They're just not positioned to compete, and they're better off parting ways with expensive veterans and focusing on the draft.
1. Cut or trade Tannehill and draft a quarterback: They hold the No. 13 overall pick, which should give them a shot at some of the draft's top signal-callers. They can combine the quarterback they draft with a cheap veteran and focus on progress during what is likely to be a losing season.
2. Say goodbye to Cameron Wake (37 years old), Frank Gore (35), Robert Quinn (28) and Kiko Alonso (28): Will those aging vets be part of the equation in 2020 and beyond? If the Dolphins have any doubt, they should save their money.
3. But keep Kenny Stills and Danny Amendola: For the sake of the young quarterback they'll groom, it's worth paying experienced, reliable complementary offensive players. They might even want to pay up to keep impending free-agent right tackle Ja'Wuan James, who is only 26.
Really, the Dolphins will just be waiting on players to develop. Most rookie quarterbacks need time, and recent first-round picks Charles Harris and Minkah Fitzpatrick are still maturing as professional football players. Plus, there'll likely be growing pains for both Flores and Grier.
Veteran quarterback Andy Dalton has regressed on paper and empirically the last couple of seasons, and it's become apparent that both Dalton, 31, and top wide receiver A.J. Green, 30, won't carry the Cincinnati Bengals to a Super Bowl. The team can save $28.4 million by parting ways with those two post-prime key players right now, and the timing would be perfect as rookie head coach Zac Taylor takes over.
Taylor has endorsed Dalton, but that's the politically correct thing to do. The reality is he and his team would be better off cutting bait and drafting/grooming a higher-upside quarterback who best suits what Taylor wants to do. It's not as though this team is on the brink of contending. The Bengals had the league's lowest-ranked defense last year; even with a healthy Dalton and Green, they're clearly the worst team in the AFC North.
1. Cut and/or trade Dalton and Green: They'd suddenly have more salary-cap space than every team in football except the Indianapolis Colts and New York Jets.
2. Use those savings to support the quarterback of the future: Upgrade that horrendous offensive line with two new guards and a new right tackle in free agency, bring in a good safety-valve receiver on whom Dalton's replacement can depend, and spruce up the defensive front seven.
3. Draft a quarterback in Round 1: The Bengals should then join the Dolphins and New York Giants (and possibly the Jacksonville Jaguars, Denver Broncos and Washington Redskins) in sweepstakes for Dwayne Haskins, Drew Lock, Kyler Murray and Daniel Jones. We won't recommend one over the other at pick No. 11 because they're all playing craps.
Just like Miami, Cincinnati will have to wait for its young, still-unknown quarterback to develop while potentially encountering growing pains with a first-year head coach. That defense can't be fixed overnight, either, and Cordy Glenn's $9.3 million cap hit should probably remain on the books for said quarterback's sake—even if the veteran left tackle is overpaid and unlikely to be part of the next era of Bengals football.
The same thing applies to Geno Atkins, who is 30 but is too much of a force and would be too costly to release with a $10.4 million dead-cap number.
The Buffalo Bills have their quarterback of the future in Josh Allen, but the rest of the roster is a sick joke. LeSean McCoy is practically toast, they cut Charles Clay, Allen doesn't have a single accomplished receiver to throw to, and the offensive line contributed to a scenario in which Allen was one of just eight qualified quarterbacks to be sacked on at least eight percent of his dropbacks in 2018.
Allen needs and deserves so much more, and it doesn't help that his defense lacks playmakers and an overall identity.
The only Bills players immune to the coming purge should be recent first-round picks Allen, Tremaine Edmunds and Tre'Davious White.
1. Cut everybody: OK not everybody, but the Bills should cut anybody whose departure can save them money without jeopardizing Allen's progress and/or health. In other words, forget about 30-year-old Jerry Hughes ($7.5 million in savings), Clay ($4.5 million, already banked), Trent Murphy (nearly $5 million) and McCoy (a no-brainer at $6.4 million). Time to see what the kids can do.
2. Sign Adam Humphries: The impending free-agent receiver from the Tampa Bay Buccaneers would be a perfect slot security blanket for Allen. He's still only 26 years old, so he can be part of the future while bringing some experience to that receiving corps at a potentially reasonable price because he's yet to become a star. An alternative is Jamison Crowder from the Washington Redskins.
3. Draft the left tackle of the future: That's really what Allen needs out of that No. 9 pick. Alabama's Jonah Williams is the obvious name to watch, but that could change over the next 10 weeks.
Left tackle Dion Dawkins probably isn't suited to be a long-term pillar on Allen's blind side, but he's still extremely cheap, and the Bills would be foolish to ditch him until they have a better option there. It would ideally be nice to give Allen some continuity among the guys charged with protecting him, so I also wouldn't blame them for bringing back impending right-side free agents Jordan Mills and John Miller. They do have the cap space.
Let's not pretend Alex Smith is still the long-term answer at quarterback for the Washington Redskins. It's a shame he suffered a terrible leg injury in 2018, but Smith wasn't playing well before he went down. Now the 14-year veteran might miss the entire 2019 season, which means he might not be available to start another game until he's 36 years old. What kind of shape will he be in at that point?
Smith is too expensive to cut ($42 million dead cap), but it's time to find his long-term replacement while making wholesale changes in a lot of other key spots.
1. Replace Smith in the draft: That means using the No. 15 overall pick on Jones, Haskins, Murray or Lock, because the alternative is either too expensive (Nick Foles) or will be a short-term solution at best (Tannehill or Blake Bortles) for a team that probably isn't about to make a Super Bowl run.
2. Raise cash: The Redskins can't afford Foles or any other established veteran quarterback without taking hits elsewhere because they only have about $20 million in cap space, according to Spotrac. But they should ask themselves if the injury-prone Jordan Reed or aging defenders Ryan Kerrigan (30) and Josh Norman (31) are worth keeping around for the next wave. They can save over $25 million by parting ways with those three vets.
3. Lock up Brandon Scherff and Trent Williams: The offensive line still has plenty of talent, and the Redskins could actually save money while maintaining line continuity by handing out extensions to those two key linemen now. Scherff's entering a walk year, while Williams' contract expires in 2021.
A coaching change isn't coming, apparently. The Redskins brought back Jay Gruden, and—considering the circumstances at quarterback—it might take a truly horrible season for that to change next year. That could make this rebuild tricky.
The New York Giants don't need to reconstruct to the extent that the Dolphins or Bengals do, but any team that's on the verge of a transition at quarterback is in a good position to rebuild. The Giants obviously have to replace Eli Manning soon, and the return on investment just hasn't been there for a high-priced defense.
Saquon Barkley is the future, and it looks as though the plan is to stick with Odell Beckham Jr., which is fair considering his talent and upside. But nobody else on the Giants roster should be immune to a potential trade or release.
1. Replace Manning: That's always the first step when you've got an over-the-hill signal-caller, and an argument can be made that the G-Men would have been better off doing this last April. But that ship has sailed, and now they have the No. 6 overall pick, which positions them well to land whichever quarterback they want from the 2019 class.
3. Bolster support for Manning and his successor: We're good with the Giants forgoing $17 million in savings to give Manning a goodbye tour, mainly because the 38-year-old could be a good mentor and there's no reason to put pressure on whoever they draft. But the Giants should invest in a quality young receiver to match with Beckham and Sterling Shepard. Both Humphries and Crowder would work here, too.
Again, the divorce from Manning doesn't have to happen overnight because he's off the books next offseason anyway and the Giants probably won't need to spend to the cap if they indeed take our advice on veteran defensive players such as Vernon, Jenkins and Ogletree. It's also unfortunately too early to get out of left tackle Nate Solder's inflated mistake of a contract, but some experience and continuity isn't a bad thing there.
The Oakland Raiders already started doing so last year by parting ways with Michael Crabtree, Khalil Mack, Amari Cooper and Bruce Irvin. Might as well keep it up.
All of the ingredients are there in terms of draft capital (five first-round picks in the next two years) and cap space (they're projected to have more money to spend than all but six other teams), and they already have a franchise quarterback in place. Now it's just about building up the remainder of the roster.
1. Find the new Cooper and Crabtree: We know there are a lot of punchlines available here regarding the fact that they could have just kept Mack, Irvin, Cooper and Crabtree! But Jon Gruden is building this team his way, and there's nothing that can be done about those moves now. The Raiders can at least commit less money to potential future star offensive weapons than they would have with those veterans. Oakland should use one of its three first-rounders on a wideout such as D.K. Metcalf or Marquise Brown, and it should be in on free-agent receivers John Brown and Golden Tate.
2. Find the new Mack and Irvin: Likewise, the Raiders can at least commit less money to potential future star pass-rushers than they would have with those veterans. Oakland should use one of its three first-rounders on an edge-rusher such as Josh Allen, Nick Bosa or Jachai Polite, and it should be in on free-agent pass-rushers Jadeveon Clowney, Demarcus Lawrence, Trey Flowers, Frank Clark, Dee Ford and Za'Darius Smith.
3. Keep the offensive line intact: The unit is old and expensive, but the transitioning offense needs that wisdom and stability. They've got the money to keep Kelechi Osemele, Gabe Jackson, Rodney Hudson and Donald Penn around, so they might as well have them there to help blue-chip second-year offensive tackle Kolton Miller.
This is more about who will need to wait. Quarterback Derek Carr is ready to run, but his team has yet to walk under Gruden. He'll turn 28 next month. The Raiders don't want to waste a good chunk of his prime, but they'll be relying on a lot of young players to develop in 2019 and 2020.
Salary-cap numbers provided by Spotrac.


A crystal clear consommé, packed full of flavour is a wonderful thing.
Stock is simmered and clarified with protein, vegetables and herbs, and then strained off, leaving an elegant and delicious broth.
It's a great way to start a meal or as a welcome respite between heavier courses. And it's actually not that difficult to make once you know how.
We've collected five videos that show you how to make a variety of consommés, including recipes from some of the world's best chefs.
Take a look below and for more gourmet tips, why not check out these video guides to making fluid gels and purees.


Israel continues to maintain its long-standing policy of nuclear opacity, neither officially confirming nor denying that it possesses nuclear weapons.1 However, Israel is widely believed to have used the heavy-water research reactor at the Negev Nuclear Research Center, near Dimona, to produce plutonium for a nuclear weapon arsenal. There is little publicly available information about the Dimona reactor's operating history. According to one estimate, it could have produced 690–950 kilograms of weapon-grade plutonium as of 2011 (see section X below). Only part of this plutonium may have been used to produce weapons.
Kile, Shannon N., Phillip Schell, Hans M. Kristensen, Vitaly Fedchenko, Alexander Glaser, and Zia Mian International Panel on Fissile Materials. "7. World nuclear forces." SIPRI Yearbook. SIPRI. Oxford: Oxford University Press. 2016. Web. 20 Apr. 2019. <http://www.sipriyearbook.org/view/9780199650583/sipri-9780199650583-div1-49.xml>.


text:
Retirement incomes panel announced
Lucy Dean
The government has revealed the nine experts who will advise Treasury on the development of a retirement income product framework.
A new advisory group has been created in order to assist Treasury in developing the framework for comprehensive income products for retirement (CIPRs), according to a statement.
Minister for Revenue and Financial Services Kelly O'Dwyer said the nine experts would represent consumers and industry and "bring together substantial expertise and experience from across the consumer and superannuation sector, including in asset management, aged care, consumer protection, product development and compliance, academia and law".
"Following advice from the group, the government will undertake broader consultation with all stakeholders on the detail of its proposed approach," the statement said.
They include Nick Callil, who is the head of retirement solutions Australia at Willis Towers Watson; Nerida Cole, the head of financial advisory at Dixon Advisory; and Jeremy Cooper, the chairman of retirement income at Challenger.
Chairman at LifeCircle and non-executive director at Opal Specialist Aged Care Sally Evans was named, together with senior partner at Mercer David Knox and the Reserve Bank of Australia's Deborah Ralston.
Director at the Actuaries Institute Australia, Class, SuperEd, UniSuper and OnePath Insurance Nicolette Rubinsztein, King & Wood Mallesons' Ruth Stringer and Council on the Ageing (COTA) chief executive Ian Yates make up the remaining three.
"The central task of the reference group is to provide feedback and advice to Treasury on possible options and scope of a retirement covenant in the Superannuation Industry Supervision Act 1993," the statement continued.
"This would require superannuation trustees to design and offer appropriate retirement income solutions to their members."
The group was formed following 57 submissions to Treasury on the development of the CIPRs framework.
The introduction of CIPRs was a recommendation made by the Financial System Inquiry, in a move to boost the retirement outcomes for Australian retirees.
In its report, the Financial System Inquiry said, "The product would commence on the member's instruction, or the member may choose to take their benefits in another way. Impediments to product development should be removed."
The idea of CIPRs is too reduce or remove longevity risk, which is the risk that a retiree will outlive their savings.
This risk was addressed by Jeremy Cooper at a retirement income conference last year. He explained that the super system was designed with a 75-year lifespan in mind and as such, the question of whether the retirement system is "fit for purpose" is a pertinent one.
Noting that people not often live into their late 80s, he said: "A super fund needs a retirement income philosophy. You will not be surprised to hear that I don't think this amounts to: being great investors.
"The return of a member's money in the form of regular income and better managing their risks in retirement is not the same as the time-weighted returns achieved by the fund as [a] whole."
Last Updated: 19 February 2018 Published: 20 February 2018
The review into the capabilities of APRA was released this week and it found that APRA required a cultural change among a host of other chan...
Union demands government action on credit insurance
The Finance Sector Union of Australia has urged government action on consumer credit insurance and bank cultural issues following ASIC's r...
Investment complaints surge
Consumer complaints relating to investment and advice rose by 69 per cent in the first six months of the Australian Financial Complaints Aut...

redpajama_set_name: RedPajamaCommonCrawl


Pictures simply click save link to save these pictures in your personal computer. They're ready for transfer, if you love and wish to take it, click save badge in the page, and it'll be directly down loaded to your laptop computer.
Pictures I am hoping you can think it's great. This specific impression over can be labelled using.


text:
Q: Set cookie from main domain for subdomain I have a website, consisting of three domains: domain.com, sub1.domain.com and sub2.domain.com.
I now want to get via AJAX on sub1.domain.com a page on domain.com which sets a cookie. This cookie has to be available to all three domains.
I've tried: setcookie('name','value',time()+3600,'/','.domain.com'); and I've tried: setcookie('name','value',time()+3600,'/','domain.com'); (watch the dot before the domain, I was told that old browsers wouldn't accept it.) But this didn't work. In fact, there isn't a cookie set.
How can I set a cookie on an AJAX request from a subdomain? I already added header('Access-Control-Allow-Origin: *'); to the setcookie-page.

A: There are a couple of things that are required when using credentials:

*

*withCredentials flag

The AJAX request needs to have xhr.withCredentials = true; set.

*

*Access-Control-Allow-Credentials
The server must also respond with header('Access-Control-Allow-Credentials: true');.

*

*Wildcard origin not allowed
When specifying withCredentials, the server cannot allow an origin of *. Therefore, you must respond with a list of valid domains:
header('Access-Control-Allow-Origin: http://sub1.domain.com,http://sub2.domain.com');

If you still want to have an arbitrary list of subdomains, you could do something like the following:
if (substr($_SERVER['HTTP_ORIGIN'], -11) === '.domain.com') {
    header('Access-Control-Allow-Origin: ' . $_SERVER['HTTP_ORIGIN']);
}

This sets the allowed origin to the value of the Origin request header, but only if it's on your domain.

redpajama_set_name: RedPajamaStackExchange


Self-organizing maps (SOMs) have now been applied for a number of years to identify patterns in large datasets; yet, their application in the spatiotemporal domain has been lagging. Here, we demonstrate how spatialtemporal disease diffusion patterns can be analysed using SOMs and Sammon's projection.
SOMs were applied to identify synchrony between spatial locations, to group epidemic waves based on similarity of diffusion pattern and to construct sequence of maps of synoptic states. The Sammon's projection was used to created diffusion trajectories from the SOM output. These methods were demonstrated with a dataset that reports Measles outbreaks that took place in Iceland in the period 1946–1970. The dataset reports the number of Measles cases per month in 50 medical districts.
Both stable and incidental synchronisation between medical districts were identified as well as two distinct groups of epidemic waves, a uniformly structured fast developing group and a multiform slow developing group. Diffusion trajectories for the fast developing group indicate a typical diffusion pattern from Reykjavik to the northern and eastern parts of the island. For the other group, diffusion trajectories are heterogeneous, deviating from the Reykjavik pattern.
This study demonstrates the applicability of SOMs (combined with Sammon's Projection and GIS) in spatiotemporal diffusion analyses. It shows how to visualise diffusion patterns to identify (dis)similarity between individual waves and between individual waves and an overall time-series performing integrated analysis of synchrony and diffusion trajectories.
Spatiotemporal analysis of epidemic waves can reveal important information on anomalies and trends, and provide inside into the underlying diffusion patterns [1, 2]. These patterns are categorised as contagious spread, hierarchical spread, or mixed diffusion. Contagious spread depends on direct person to person contact and results in centrifugal patterns from the source outward . Hierarchical spread refers to disease transmission through an ordered sequence of geographic locations (normally based on their size)  and it can be related to the movement of people, carrying a disease to a new centre of population via long distance travel. Due to this, hierarchical spread is typically characterized by the display of synchrony among locations that have similar size but that are geographically apart . Two or more locations are synchronized when they exhibit a parallel development in the number of disease cases.
The search for synchrony is not unique to epidemiology but originates in innovation diffusion and ecology, and it occurs in many other disciplines . Hence, multiple methods exist to quantify and to map synchrony . Among these methods wavelets are frequently used as they also allow to study non-stationary (trends) in time series . Wavelets analyse disease diffusion in the frequency domain where synchrony can be identified via the coherence in the phase of the number of diseases cases at each geographic location .
Besides synchrony, another important property of spatiotemporal disease diffusion is the trajectory of wave propagation. This trajectory captures the step by step diffusion by describing the speed and direction of spread . As waves of infectious diseases are normally a combination of contagious and hierarchical spread , this trajectory is not a single and continuous line (as a trajectory representing human movement) but a reflection of a moving front or fronts. Methods for capturing this movement range from different calculations of front velocity , to methods that capture the direction of diffusion as related to clusters of human population, network distance or travel distance [8, 9].
In this paper, we propose using self-organizing maps (SOMs) to study disease diffusion in space and time. SOMs are a well-known data-mining method, used to cluster and visualize high dimensional data by projecting it into a low-dimensional (typically 2D) space . This projection makes it easier to understand spatiotemporal datasets and the patterns that they might contain [11, 12]. In spatial-epidemiology, SOMs are mostly used as a non-linear analytical method to study multivariate patterns [13–15] but here we show that they also enable the integrated analysis of both synchrony and diffusion trajectories. Moreover, this data mining methods is advantageous because it does not require transforming the data to a new "data space" (like wavelets). This greatly facilitates the interpretation of the results as the shape of the epidemiological curve (number of cases as a function of time) is preserved so that the time of infection and intensity (persistence) can be studied for each geographic location.
The detection of synchrony using SOMs is based on the fact that they maintain the topological characteristics of the input data. This ensures that locations with a high level of synchronisation in the timing and intensity are mapped near to each other forming clusters. The study of diffusion trajectories can be achieved by further applying the Sammon's projection to the previous SOM results. In short, in this study we illustrate the following issues: identification of locations (spatial units) with similar diffusion processes – synchrony (1) and characterization of spatial temporal diffusion patterns – diffusion trajectories (2).
To illustrate this study, we used data on eight historical Measles epidemics in Iceland . The epidemics span the period November 1946 (wave 8) to December 1970 (wave 15). Prior outbreaks took place (waves 1–7), but are not included in this research for reason of data incompleteness and re-organization of medical districts. After 1970, outbreaks have different characteristics due to the introduction of mass vaccination.
The data reports monthly Measles cases for each of the 50 medical districts of the country. Figure 1A shows the log transformed Measles cases (log10 (cases +1)) for all of the eight epidemics per medical district, with the medical districts sorted in ascending order of population size. The colour representing the epidemic intensity shows that for all waves, there are only few medical districts with high intensity and that these are always the centres with the higher population (bottom of the graph). The number of cases per epidemic outbreak ranges between 6000 cases for wave 9 and less than 1900 for wave 10 (Figure 1B) . Because of the low number of cases wave 10 is excluded from further analysis. Inter wave periods are evenly distributed showing no significant changes in pattern over the studied time period. All presented analyses are performed on log transformed input data to ensure a normal distribution and are scaled (0–1).
Measles dataset. Measles cases in Iceland (1946–1970). (A) Spatial distribution of log10 (cases +1) per medical district, with medical districts arranged in ascending order of population size and colour representing epidemic intensity. (B) Time series of total number of notified cases for the complete country (log10 (cases +1)).
This dataset was selected because Iceland has proven to be an excellent study example for disease diffusion processes for a number of reasons including: the isolation of the country which creates a self-contained system with few external influences; the stability of the population and spatial structure (medical districts), and length of the available time series. This has led to the well-documented and extensively studied Measles dataset [2, 17].
SOMs are a type of un-supervised artificial neural network used to cluster high dimensional data by projecting it onto a low-dimensional lattice. This lattice consists of neurons that are trained iteratively to extract patterns from the input data. These patterns are generalizations of the input data and are referred to as codebook vectors. At the start of the training phase, each neuron is assigned a codebook vector that is updated at each iteration, in such a way that topological properties in the input training data are preserved.
The size (number of neurons, including number of rows and columns) and type (rectangular or hexagonal) of the SOM lattice were chosen.
Each neuron was assigned a random vector of weights or codebook vector (m k ) with the same dimensionality as the input data.
in which mk is an n-dimensional codebook vector, α (t) is the learning rate, h ck (t) is the neighbourhood kernel of the BMU neuron and x is a randomly chosen input vector from the training dataset.
For the training of the SOM we used a hexagonal SOM lattice, using a standard linearly declining learning rate from 0.05 to 0.01 over 1000 iterative updates. The radius of the neighbourhood kernel uses the starting value of 2/3 of all unit-to-unit distances using a square neighbourhood.
After the training, a secondary clustering can be performed on the SOM lattice, using visual analytics or a different clustering algorithm. Especially when the training lattice is large (larger than the number of clusters needed), a secondary clustering is known to outperform the initial SOM . Secondary clustering can be performed via visual analytics or by using a second clustering algorithm.
A relatively simple way of identifying SOM clusters is by using the U-matrix. The U-matrix displays the Euclidean distance between the codebook vectors of neighbouring SOM neurons. High values in the U-matrix visually separate clusters. However, this method has proven to be difficult, especially with complex datasets. Therefore, several authors have proposed graph-based technics to enhanced the U-matrix for cluster interpretation . Here we used an enhanced U-matrix as proposed by Hamel and Brown . In this method, the centres of the lattice neurons are used as the vertices of a planar graph (a graph without crossing edges). The edges in the graph connect nodes to the neighbouring node with the maximum gradient. In this way, subgraphs are created, that indicate the clustered neurons. When displaying the graph on top of the U-matrix, an easy visual interpretation of the number and composition of the clusters is possible.
Besides the visual identification of clusters based on the U-matrix, a different clustering algorithm can be used for secondary clustering. A range of options exist including k-means and hierarchical clustering . In hierarchical clustering, neurons are first assigned to their own cluster, the distance between clusters is calculated and then, iteratively, the most similar clusters are joined. A disadvantage of these methods is that user has to decide the number of clusters to be found.
After training the SOM and performing the secondary clustering, the third step in the SOM process is the mapping of the data onto the trained SOM, identifying for each input vector the BMU neuron and cluster. The training dataset and mapping dataset can be the same, subsets, or mapping data may consist of new data not included in the training sample. Here, we trained the SOMs with the complete dataset to ensure that all existing patterns are represented in the codebook vectors of the lattice. However, different subsets of the training data are mapped back onto the lattice for evaluation. These subsets correspond to single epidemic waves, making it possible to compare the mapping of the total dataset to the mapping of the individual waves.
The standard way to quantify error for trained SOMs is the quantization error, which measures the distance between the mapping data and the codebook vector. In this research, the quantization error is used to evaluate the "goodness of fit" of the mapped data. The smaller the quantization error, the better the mapping.
When applying SOMs for spatiotemporal analyses, the data used for training and mapping needs to be considered in a dual fashion: from a spatial perspective and from a temporal perspective [22, 23]. A data organisation of the type space over time (SxT) allows the detection of spatial units (medical districts) that show similar behaviour over time; that are synchronized. Here, we would like to find both synchronies over the total time series and over single waves. Therefore, two different types of space over time datasets are used: the space over time (SxT) dataset where T includes the complete time series (Figure 2A) and the space over wave (SxW) dataset where T covers a single wave (W) (Figure 2B). To study the diffusion of the disease over time, a time over space (TxS) data organization is used (Figure 2C).
Data organisation. Data organisation, Space in Time (SxT) SOM (A), Space over Wave (SxW) SOM (B) and Time in Space (TxS) SOM (C).
Finding synchronies based on SOMs makes use of the combined ability of the SOM method to produce a generalized prototype vector from the input data and to order these vectors topologically onto a training lattice. Input data vectors that map to the same neuron are synchronised, vectors that map to neighbouring neurons might also be synchronised. This can be identified by performing a second clustering on the SOM lattice grouping neighbouring neurons with similar codebook vectors.
Detection of synchronies between medical districts is based on an SxT data organisation. The training of the SOM (lattice size 3×4) is followed by a secondary partitioning based on hierarchical clustering (See Figure 3). However, the exact number of clusters is unknown. This is why the clustering is confirmed using the Component Planes of the temporal SOM (Figure 3, step A) with a lattice size of 7×7. Component Planes are slices of the codebook vectors that represent the status of a variable for all the neurons in the SOM lattice. The correlation among variables becomes visible via similar patterns in their component planes. Methods for using Component Planes for correlation hunting have been described previously [24, 25].
Flow diagram synchrony. Flow diagram showing the steps to identify synchrony. A- clustering, B - mapping of the dataset, C- check on the robustness of the clusters.
We can re-organise our dataset in order to construct a dataset were each data vector represents one month (Figure 2 – data organisation (C)). This data organisation is also referred to as Time in Space (TxS). We trained this SOM using a lattice size of 7×7 neurons. When using the TxS SOM, variables represent spatial locations. A component plane in this case, is a representation of all the neurons a medical district has been mapped to, including the frequency. Two spatial locations with identical or similar component planes are correlated. We compared the clustering found with the SxT SOM with the component planes of the TxS SOM to verify the number of clusters. This was done by grouping the component planes of the medical districts per cluster.
After confirmation of the clustering, both the complete dataset and the individual waves are mapped back onto the SxT SOM lattice to determine the BMU (Figure 3 step B). In SOMs, training vector and mapping vector should have an equal length. However, a single wave subset is much shorter than the complete time-series. Thus, subsets of input vectors were created by combining a Nodata matrix with subsets of the scaled input data. This is possible because SOMs allow for "missing data".
M(v) is used to compare the mapping of all the subsamples with the mapping of the complete dataset by counting the number of times the same mapping occurs in both samples and dividing this by the total number of subsets. M(v) = 1 indicates a perfect score.
SOMs can also be used to identify diffusion trajectories. For this, we followed two steps: First, we grouped epidemics based on their diffusion pattern. Next, we visualised the synoptic states and created diffusion trajectories (Figure 4).
Flow diagram spatial diffusion. Flow diagram showing the steps for the identification of diffusion. A - grouping of similar waves. B - comparison of synoptic states of the groups identified under A. C- mapping of diffusion trajectories of all waves onto the Sammon's Projection, and comparison of the trajectories of the groups identified under A.
Grouping of epidemic waves with similar characteristics is done based on the SxW SOM (Figure 2 – data organisation (B)). For the SxW SOM, codebook vectors represent a medical district during a single epidemic. This SOM maintains the epidemic curve in the purest form, and allows for a high level of topological consistency. After training (lattice size 7×7 neurons), a secondary grouping is performed using the enhanced U-matrix method, and the individual waves are mapped back onto the SOM lattice (Figure 4(A)).
A limitation of the SOM algorithm is that all input vectors should be equal in length. As this data organisation is per wave, and waves cover different time periods, the vectors are aligned at the beginning of the wave, and zero values are added to shorter outbreaks, to ensure equal vector length.
Grouping of waves is found by comparing the mapping of the individual waves on the SOM lattice.
A synoptic state is a pattern that spatially characterises a diffusion state. Each wave can be represented as an ordered sequence of synoptic states. The number of states per wave is variable. This sequence provides information on the speed and on the direction of spread. The workflow for generating trajectories of synoptic states is shown in Figure 4B.
In order to retrieve synoptic states, a SOM is trained using the TxS SOM (Figure 2 – data organisation (C)). In this case each data vector represent one month (variables represent the medical districts). After training the SOM, codebook vectors are translated into a GIS map (Figure 4 – (B)). This can be done because the variables of each codebook vector represent a sequence of medical districts. By transposing the codebook vectors (to SxT) and visualising them in a GIS, each neuron (codebook vector) of the SOM lattice can be shown as a GIS map.
The data is now mapped back onto the SOM. For each month a mapping to a codebook vector is determined. After grouping successive months with the same mapping (within the same wave), a sequence of maps is retrieved, this composes the ordered sequence of synoptic states per wave.
States differ in duration. The speed is represented as the number of states and the duration of each synoptic state (the number months mapped to the state). The direction of spread can be derived from the maps via visual comparison of the states.
Alternatively, the direction of spread can be evaluated by mapping trajectories on the Sammon's projection (Figure 4 – (C)). The method of combining the analysis of synoptic states and Sammon's trajectories has been previously used by Zurita-Milla et al. . Yet, here we applied it in combination with mapping back subsets on point data.
This trajectory is constructed on a "TxS" SOM, in which each vector in the input dataset represents an epidemic month. This is the same SOM and the same mapping as used for the synoptic states.
in which d* ij is the Euclidean distance between the vectors i and j in the input space (the codebook vectors), and d ij is the corresponding distance in the output space (the Sammon's coordinates).
Like this, each codebook vector in the SOM lattice can be projected to a 2D space. The diffusion trajectory is the vector that depicts the "sequence of movement" over the SOM lattice. Arrows connect neurons in the order in which they are mapped and the shapes of different epidemic vectors can be compared to reveal (dis)similarity between diffusion patterns of different waves.
The R script used to perform the analysis discussed here is available as Additional file 1.
Identification of spatial synchrony was performed as described in Methods - section "spatial synchrony". After training, the SOM lattice was partitioned into five clusters (Figure 5A - Lattice with clusters) identifying neuron 12 as cluster 1, neurons 8, 9 and 11 as cluster 2, neuron 10 as cluster 3, neuron 7 as cluster 4 and neurons 1–6 as cluster 5.
Results Synchrony. (A) SOM lattice showing hierarchical clusters (black lines). Numbers indicate the order of the neurons. (B) GIS mapping of the complete dataset (all waves), and the individual waves. (C) Numbers in the legend correspond to the numbers of the neurons in the SOM lattice.
The identified clusters were verified using the component planes of the TxS SOM (Figure 6). When comparing the component planes of the clusters, it can be confirmed that districts mapped to the same cluster show good correlation. In our experiment, including one extra class in the hierarchical clustering would lead to neuron 6 being identified as a separate class. When visualising the component planes of this class (Figure 6 – cluster 5), it turns out that the patterns in this group are not very prominent and the group is not very homogeneous compared to the other classes.
Component planes. Component planes of a (TxS) SOM, organized by hierarchical cluster.
The mapping of both the complete dataset and of the individual waves was visualised in a GIS (Figure 5B and 5C). The results of the mapping for the complete time series, revealed that cluster 1 represents the medical district in which Reykjavik (the most dominant city in the process) is located, and a group of medical districts mapped to cluster 2, surrounding Reykjavik, are highly synchronised. On the SOM lattice this cluster is adjacent to cluster 1. In the north of Iceland three more medical districts were identified (mapped to clusters 3 and 4) that are potential regional diffusion centres. They have a relatively high incidence rate but are topologically further away from Reykjavik on the SOM lattice. On further examining it is revealed, that these correspond to the areas of Ísafjörδar and Akureyri. By examining the codebook vectors of the SOM lattice, neurons 1–6 are grouped into one large cluster (cluster 5). The codebook vectors of these neurons show that these represent medical districts with lower frequencies.
When comparing the total mapping with the mapping of the individual waves (Figure 5) we see that in each of these waves more local medical districts are mapped to clusters 1–4 (indicating a role in the diffusion process). This shows that there is a group of medical districts that are important in all outbreaks, but also medical districts that play a role in the diffusion process of single epidemics. For incidental mapping to cluster 2 (highly synchronised with Reykjavik) we see several additional mappings in the northern parts in almost all waves. Incidental mapping to clusters 3 and 4 may indicate (second level) diffusion synchrony with local northern and north western centres or a different diffusion pattern. Especially wave 9 has many medical districts mapped to cluster 4 throughout the island. This can be an indication of a different direction of diffusion.
The quantization error is the average distance between the input vector and the BMU. The results are shown in Table 1. Quantization error for the complete dataset is high (77.11). After mapping the individual waves, this value improves to 42.33-73.56. The error for the complete dataset is high because it is difficult to match a vector over the total length of the time series. Values improve however (become smaller) when matching only parts of the time frame.
Quantization errors for all SOMs.
To test the robustness of mapping back of individual waves the figure of merit M(v) was calculated (Table 2). This figure expresses the number of medical districts that were mapped to the same cluster for the mapping of the complete dataset and also for the mapping of the individual waves. The average over the combined waves was 0.82 (scale 0–1), indicating robust clusters over the temporal period.
Results figure of merit for mapping of individual waves.
The grouping of epidemic waves was performed as described in Methods - section "Grouping waves". This experiment produced a SOM with a high level of topological consistency between the neurons (Figure 7A). Visually, interpretations like "early" (top right), "middle" (top left), "late" (bottom right), and "high" (edges) or "low" (middle) intensity can be given to the neurons. By enhancing the U-matrix, ten clusters were identified (Figure 7B).
Clusters SxW SOM. Enhanced U-matrix, with light background colours indicating high values, dark colours indication low values and graph lines indicating the clusters (A). Lattice with codebook vectors and cluster lines (B).
The partitioning of the SOM lattice into clusters can be translated into a heat key and the data can be visualised in a GIS. Where red colours represent "early", yellow colours "middle" and green colours represent "late". Figure 8 shows the mapping of the individual waves using this heat key. Comparison of the GIS maps revealed that waves 11 and 14 are fast (early) developing waves (primarily red coloured), waves 12 and 15 are primarily yellow, meaning their spread is of medium speed, and wave 9 is a late developing wave (green coloured). However, interpretation of these maps is "intuitive" (it depends on the human interpretation of the colour scheme). Hence, it is easier to evaluate the results by mapping directly onto the SOM lattice.
GIS mapping SxW SOM. GIS mapping using colour coding for the clusters.
This leads to the results shown in Figure 9. After mapping the complete dataset, each wave was mapped back individually. Most waves do not have mapped samples for all neurons or clusters, but the mapping is grouped to a particular area of the lattice. Similar waves should be projected to the same clusters of the lattice.
Mapping SxW SOM on SOM lattice. Mapping of the Medical Districts on the SOM lattice. Each dot representing one medical district.
Two groups of epidemics were identified. These are the fast developing (early) epidemic waves 8, 11, 13 and 14 (Group A) that have many medical districts mapped to the upper right hand of the lattice, and the slow developing (late) epidemics, waves 9, 12 and 15 (Group B) that show a mapping to the lower and left part of the SOM lattices. The quantization error for this experiment, when mapping back the complete dataset, is 6.08 (Table 1). This shows that for this experiment, the distance between the codebook vectors and the data vectors was much smaller compared to the mapping of the synchrony experiment. This was to be expected as single waves were used.
This experiment was conducted on a Time in Space (TxS) SOM as explained in Methods - section "Synoptic states". The trained lattice is shown in Figure 10A. For this type of SOM, each codebook vector represents one specific spatial pattern, so the SOM lattice can be translated into a lattice of GIS maps (see Figure 11).
Codebook vectors and Sammon's Projection TxS SOM. Lattice showing codebook vectors, with neuron 1 in the lower left corner, numbering in rows, from left to right and bottom to top, ending with neuron 49 in the top right hand corner (A) and Sammon's Projection with interpretation (B).
Lattice converted to GIS maps. Lattice showing the codebook vectors as maps of synoptic states. Numbering from left bottom corner (1) to the top right (49), in rows from left to right.
When we conducted an interpretation of these maps, it turns out that neuron 23 represents a static state of almost no disease occurrence. Around this neuron, we identified synoptic states that represent disease occurrence in certain (combinations of) compass points, with the top right of the lattice corresponding to infection in the north and south-western parts of Iceland, and the lower left of the SOM lattice corresponding to infection in the eastern and northern parts of the island.
Next, the codebook vectors of each wave were mapped as an ordered synoptic spatial time series in a GIS (Figure 12). This way, a sequence of maps per epidemic wave can be constructed to reflect the spatialtemporal patterns found in each epidemic wave. When comparing the patterns of Group A – consisting of waves 8, 11, 13 and 14 – we noticed that these patterns are all about equal in length, and are spatially very similar. This group seems to consist of fast developing waves. Group B – waves 9, 12 and 15 – shows more diversity in number of synoptic states and in diffusion pattern. Information about the number and duration of synoptic states can also be found in Table 3. Number of states for Group A ranges from 9–10, for Group B from 11–14. Duration of each state ranges from 1–6 months. Group B waves have a longer duration of the first two states.
Trajectories of synoptic states. Representation of the waves as a sequence of synoptic states.
Number and duration (in months) of synoptic states per wave.
For a further analysis of the diffusion direction, the diffusion trajectories were projected as a Sammon's projection (Figure 13). This experiment is described in section "Sammon's Trajectories" of the Methods section. The data organisation and the SOM lattice (see Figure 10A) were the same as for the previous experiment. The Sammon's projection of the lattice is shown in the same figure (Figure 10B). In this projection the distance between the vectors is explicitly mapped, but the topological relationships are not necessarily maintained. Numbers in the Sammon's projection refer to the numbers of the neurons. As can be observed, the Euclidean distance between the neurons in the top right hand of the SOM lattice (numbers 35, 42, 48, 49) is relatively large.
Trajectories on Sammon's Projection. Trajectories of spatiotemporal diffusion on the Sammon's Projection.
For each wave, the "trajectory of diffusion" was mapped onto the Sammon's projection (Figure 13) and the trajectories were compared. Waves 8 and 14 both have a trajectory starting in neuron 23, moving in a circular fashion to the right hand side of the figure (reaching neuron 35 as one of their peak stages) to return to neuron 23. Their diffusion pattern is strikingly alike. This diffusion corresponds to the sequence of "infection in the Reykjavik area" followed by "infection in the Reykjavik area and the north", and returning via infection for example infection in the east back to neuron 23.
Waves 11, 12 and 13 have trajectories with similar characteristics compared to the previous group. Their trajectories also follow a circular path from neuron 23 to the upper-right hand side of the graph indicating similar diffusion. However the lines of waves 11 and 13 show cross-overs and wave 12 shows an opposite direction. Cross-overs occur when a fast spread (or decline) to all areas takes place. Where waves 8 and 11 decline to the north, the opposite direction of wave 12 is triggered by a decline to the north and south of the island. However, these diffusion trajectories do not differ significantly from the trajectories of wave 8 and 14.
Wave 9 and 15 have the strongest deviation from the general pattern. For the interpretation of these results we mapped the compass directions onto the Sammon's projection (Figure 10B). Wave 9 and 15 show strong vertical trajectories. These correspond to a different spatiotemporal diffusion pattern. For Wave 9 this is a pattern from neuron 45 to 28 (spread starting in the north) and for Wave 15 from (1, 14, 27) from the east, via the north to the western parts of the island.
The use of SOMs, combined with Sammon's projection, has enabled us to identify synchrony between locations (medical districts), and to map diffusion trajectories of a time series of epidemic waves revealing their spatiotemporal diffusion patterns. This integrated approach was carried out using three different data organisations (in space and time). Training the SOM on the complete time series and mapping back individual waves has shown to be a simple but effective way to compare general spatiotemporal diffusion patterns for a complete time series with patterns of individual waves. Results found are consistent with results found for the same epidemics, using different methods.
The synchrony experiment revealed a number of medical districts that form the diffusion structure for all of the waves and, additional medical districts that only play a role in some waves. The medical districts that were identified as forming the stable structure all have a large number of inhabitants and the centres in the north and north west are connected to Reykjavik via domestic air travel. The identified medical districts show a great similarity to the structure found by Cliff et al. , in their quarterly lag maps of geographical spread, first and second quarter. They are also consistent with epidemiological theory (hierarchical diffusion models).
The research identified two different groups of epidemics with fast developing (group A) and slower developing waves (Group B). These groups were identified using the SxW SOM but the synoptic state experiment, based on the TxS SOM revealed the same grouping. Similar results were found by Cliff et al.  who found for Group A mean lag time in months of respectively 8.42, 9.05, 10.54 and 5.85 months and for Group B a mean lag time of 15.45, 11.29 and 13.32 months.
Experiments also showed that the fast developing waves (Group A) show considerable similarity in their spatiotemporal patterns. They all spread from Reykjavik to the north-eastern areas. Group B waves cannot be characterized by a single direction of spread; yet, the diffusion trajectories show that the spread of wave 9 and 15 does not follow the Reykjavik pattern. Findings were compared to Cliff et al.  that describe the spread of wave 9 as being confined to the northern parts of the island. For wave 15 the same source notes that it was slow moving, that it started in the south, but the difference in diffusion pattern, was not reported.
Two methods were used for the clustering of the trained SOMs: the enhanced U-matrix and hierarchical clustering combined with a validation based on component planes of a temporal SOM. Although both methods lead to reliable results, the enhanced U-matrix is advantageous because it does not require the user to determine the number of clusters. However, other enhancement methods for the U-matrix exist, for example methods including the second best matching neuron . These may be worth further exploring.
The proposed method was used on a time series of seven epidemic waves and a relatively small number of spatial locations. Spatially this method is scalable without any problems. However, there are probably a minimum number of waves needed to come to a reliable mapping of the individual outbreaks. If the diversity in the training dataset is too small this may lead to problems. The synchrony experiment was tested with shorter time series including fewer epidemics. A series of 4 outbreaks still gave a reliable result for our case study, but this may depend on the complexity of the dataset.
Iceland is a small island with only one larger city and it is clear that this city is the "motor" of the diffusion mechanism. In this regard, the selected dataset is ideal for testing new methods (also because there are seven epidemic waves available). However, it would be interesting to test the proposed method in a much more heterogeneous environment (with more large cities and a more complex diffusion pattern). For this study all medical districts were included, but some of these centres represent areas with low population. When working with larger datasets, removal of sparsely populated areas may be an option.
SOMs are relatively easy to train and combine with other visualisation methods to enhance their analytical possibilities. However, results are very sensitive to values of input parameters (size and shape of the training lattice, number of iterations, type of initialization), and deriving meaningful information can be challenging . The method as applied here, therefore focusses more on comparison then on absolute characterisation of patterns.
Besides Measles, this method is potentially useful to explore and understand spatialtemporal diffusion patterns of other infectious diseases (e.g. Influenza, Pertussis) as SOMs can deal with large datasets as well as with missing data. This understanding might lead to new paradigms of modelling and validating spatially explicit disease models based on reproducing observed diffusion patterns.
This method can also be used for real time disease mapping. As data from partial outbreaks can be mapped back, comparison of diffusion patterns with previous waves may lead to early indications of the characteristics of an epidemic and, thus, help to design intervention actions. When linked to systems for Volunteered Geographic Information, web-based monitoring networks a fully automated analysis may be an interesting option.
In this paper we proposed a SOM-based method to analyse spatiotemporal diffusion of infectious diseases. The method is based on training a SOM for a larger time-series (including multiple waves) and mapping back individual outbreaks for characterisation and comparison. Via a number of experiments we showed how this method can be applied for finding synchronies between spatial locations and for comparing spatialtemporal diffusion patterns of different epidemics.
We also demonstrated how different types of data organisation (in space and time) can help to reveal different information. Several types of secondary clustering (hierarchical, enhanced U-matrix and Component planes) were shown, that can be used to improve the SOMs performance. The integration of SOMs with other visualisation techniques, especially Sammon's Projection and GIS was used to detect, interpret and visualise spatial temporal patterns.
Results of the method are consistent with diffusion patterns found using other methods; this makes SOMs an interesting alternative, worth further exploring. For instance, by applying it to a larger dataset in a more dynamic geographic environment, by coupling it to a spatially-explicit disease model or by using it for near-real time disease monitoring.
EWA would like to thank Dr. Suresh Rao (Purdue University), for the valuable feedback given during the sabbatical in which this paper was written. We also want to thank Cliff et al.  for publishing the Measles dataset used during this research.
EWA and RZM designed the experiment together, EWA performed the data analysis, EWA and RZM analysed the results and wrote the paper together. Both authors read and approved the final manuscript.


In 1970, Dr. John Simon, our Royal Oak veterinarian, purchased Woodside Animal Clinic. From this location, he has been providing traditional and holistic veterinary treatment to pets and their owners throughout the Detroit metro area, including the community of Southfield. Over his many years serving as a veterinarian in Royal Oak, Dr. Simon has studied numerous alternative treatments, giving him the unique qualifications of one of the area's only holistic veterinarians.
Yet, even with his understanding of natural treatments, Dr. Simon is well versed in traditional care and treatment, and uses them whenever needed. With his balanced approach to veterinary care, you can ensure that your pet will receive the best possible care and treatment, with a natural approach whenever possible, when you make Woodside Animal Clinic your veterinary home.
As a veterinarian serving the greater Detroit area, Woodside Animal Clinic offers both traditional and holistic veterinary care. Traditional veterinary care for our patients include wellness exams, preventative medications, parasite prevention, vaccinations, senior pet care and dental care.
We combine this traditional care with non-invasive, drug-free treatments whenever possible. Our holistic veterinarian services include assisting our pet owners with proper nutrition, herbal therapy for common ailments, acupuncture, pet chiropractic care, detoxification services and seizure therapy. We believe this two-sided approach will provide the ideal treatment and wellness options for our patients, allowing us to diagnose and treat diseases while also supporting wellness.
In addition to this balanced approach, our small animal vet clinic has a fully equipped lab and radiology department. We also have a pharmacy on site to dispense any medications, herbs or homeopathic treatments Dr. Simon prescribes. This means we can provide everything your pet needs for health and wellness in one location.
Driving to our traditional and holistic vet clinic from Southfield is easy. We are conveniently located in Royal Oak as a veterinarian serving Southfield and the rest of the Detroit metro area. To find us, head east on 10 Mile Road toward Southfield Road, turning left at the first cross street onto Southfield Road. Follow this to West Eleven Mile Road, where you will turn right. After about three miles, turn left onto Stanford Road. Take a quick right onto Columbia Road and then a left on Woodward Avenue.
We are conveniently located at 27452 Woodward Avenue in Royal Oak, Michigan. If you are having trouble finding us, look for the Beef Carver restaurant's sign. We are located just past it. If you reach 12 Mile Road, turn around because you have missed us.
If you would like to make an appointment with our veterinarian in Royal Oak, give us a call at 248-545-6630. We are currently offering specials for new patients, and would love to show you how our holistic and traditional approach can make a difference for your pets.


The International Exchange Program at IMT Dubai reflects our commitment to global education by facilitating students to spend part of their study with one of our overseas partner universities. We have signed various agreements with our partners for one or two semesters, double degrees or progression into higher degrees. The exchange program is available to both the MBA and BsBA levels.


text:
French Ambassador visits Mercer Island school
Head of School Andrée McGiffin welcomed French Ambassador Pierre Vimont to the French American School of Puget Sound (FASPS) during his visit to the area May 27.
The visit of an important dignitary at the French American School of Puget Sound (FASPS) May 27 seemed to give Mercer Island's francophone bastion of learning a bit more of an accent as teachers and schoolchildren entertained their esteemed guest.
French Ambassador to the United States Pierre Vimont visited the school during an official two-day visit to the Seattle area, stopping at a number of cultural and business institutions along the way. Even Mayor Jim Pearman cited his own family connections to France, saying a distant relative was Revolutionary War General Marquis de Lafayette's doctor. Pearman also offered the ambassador an engraved crystal paperweight as a gift, made by the locally-owned Kusak Cut Glass Works.
"We're very proud of having you here," he said. "Thank you for coming and celebrating the school."
Wearing a dark blue suit and tie, Vimont spoke of Seattle as a "great city" and spoke admiringly of the area's art museums, libraries and schools. He encouraged Mercer Island to maintain their ties to sister-city Thonon-les-Bains, a picturesque town in the French Alps where Vimont said he sometimes vacationed with his family.
"I notice you're wearing your pin," he said, motioning to the Mayor's lapel pin which commemorates the sister cities.
Vimont's stop in Mercer Island was one of two stops outside of Seattle – the other was at the Boeing plant in Everett. His visit at the school was intended to signify the commitment of the French government to supporting their language and curriculum standards.
School administrators said they learned of the visit several weeks ago. Over the past few years, French American School of Puget Sound (FASPS) Headmistress Andrée McGiffin has been hard at work expanding her school and its curriculum for over 300 students.
"In addition to his meetings with leaders of industry, the French Ambassador will include the French American School among his visits. This truly demonstrates the French priority on education," she said.
Fourth graders from Beatrice Peder and Stephane Paccaud's were ready with a choral performance of "Petite Feuille" from the musical La Forêt and joined by other children from the French Immersion School in Bellevue. When the French Ambassador comes calling, McGiffin was ready to show it all off. She personally guided Vimont through the hallways.
"I would love to spend more time here, but it's a very tight schedule," he said.
Some of the other stops on Ambassador Vimont's itinerary included an interview with KUOW radio host Steve Scher, honoring a French citizen at Amazon.com's offices in Seattle and visiting with a local chapter of the American Jewish Committee. His entourage included French Consul General Pierre-François Mourier, cultural attaché Christophe Musitelli and honorary consul Jack Cowan.
Vimont was appointed Ambassador of France to the United States by President Nicolas Sarkozy on Aug. 1, 2007. Prior to his present appointment, Vimont was chief of staff to the minister of foreign affairs, a position he had held since 2002.
Located on Mercer Island, the French American School of Puget Sound (FASPS) offers a strong program in French and English for 300 students from preschool to sixth grade. In 2010, the school will opens a new International Middle School Program offering the highly respected International Baccalaureate Program. This bilingual section is open to students from French Schools in France, North America, and worldwide.
'Bankers hours' at Recycle Center
Drive slow, please
City and MIVAL light up Island Lanterns event
The Mercer Island Police Department (MIPD) is conducting emphasis patrols on the south end of the Island in the midst…


Flying Penguins: They're still Penguins - just on fatter tires!
They're still Penguins - just on fatter tires!
It was a beautiful, late summer day for the 2012 Millstone Grind mountain bike race. Although it was rather warm and very different from their natural, arctic habitat, the Penguins, represented by John, Jesse, Eric & the superhuman Marc, decided to come out and test the waters. This was in spite of just wrapping up an epic weekend at the 24 Hours of Great Glen (for which the team also did a stand-up job, considering illness and newbie-ness).
Its shaping up to be a good season for the Penguins!
Pregnancy brain lapse! And, of course, Marc represented there, as well, placing second in his single-speed marathon division. What a superman!
great to see everyone out racing after a fairly quiet summer!


text:
Panthers sign Jonathan Huberdeau to 6-year, $35.4M extension
The Arizona Coyotes snagged another solid prospect in return for cap relief, taking the injured Dave Bolland and prospect Lawson Crouse from the Florida Panthers.
Joe Pack September 6, 2016, 5:09 PM
The Florida Panthers extended Jonathan Huberdeau Tuesday to a six-year, $35.4-million contract. The AAV is $5.9 million.
Jonathan Huberdeau has agreed to a $35.4M, six-year extension with #FlaPanthers. That's a $5.9M AAV.
— Chris Johnston (@reporterchris) September 6, 2016
Huberdeau, 23, has one year remaining on his previous deal worth $3.25 million.
According to General Fanager, the Panthers have over $60 million committed to their roster for the 2017-18 season.
#FLAPanthers now have $60.8M (10F/4D/2G) committed in 2017-18 and $51.9M (5F/3D/2G) committed until 2020-21.https://t.co/FrKWalEceQ
— General Fanager (@generalfanager) September 6, 2016
Huberdeau will carry a no-move clause that kicks in for the 2019-20 season.
Jonathan Huberdeau's #FlaPanthers extension includes a NMC starting in 2019-20. It becomes an eight-team NTC for the final year.
The Panthers forward played his fourth full NHL season in 2015-16, scoring 20 goals and 59 points. He has 172 points in 272 career games.
2018 - 2019 REGULAR SEASON:
2018 - 2019 REGULAR SEASON
%2Fhockey%2Fnhl%2Fpanthers-extend-jonathan-huberdeau-35-4m-deal%2F


text:
namespace Microsoft.DotNet.CommandFactory
{
    internal class ResourceAssemblyInfo
    {
        public string Culture { get; }
        public string RelativePath { get; }

        public ResourceAssemblyInfo(string culture, string relativePath)
        {
            Culture = culture;
            RelativePath = relativePath;
        }
    }
}

redpajama_set_name: RedPajamaGithub


text:
Q: hive log4j:WARN No such property [maxFileSize] in org.apache.log4j.DailyRollingFileAppender Hello how to fixe this issues? When I try to launch hive in my commande line(I'm using amazone aws instance and ambari is well started only 3 CRIT in YARN).

A: Change the code from
log4j.appender.FILE=org.apache.log4j.FileAppender

To
log4j.appender.FILE=org.apache.log4j.RollingFileAppender

in log4j.properties
DailyRollingFileAppender doesn't support MaxFileSize, RollingFileAppender does.


Change is the natural watchword of The SRI Conference and Community, because it is only through meaningful and sustained change that we will see a better world emerge. In that spirit, we've refreshed our logo and our website, we've created a more inclusive and engaging conference agenda, and we are actively working to broaden the SRI movement far beyond just our annual or regional events.
Look for an informative, engaging, and fun agenda this year with something for everyone, including special social and networking events throughout the conference. You might also want to consider adding to your conference experience with the Fundamentals of Sustainable and Responsible Investment Course, and the Advisor Summit Workshop.
The SRI Conference is the premier annual gathering of sustainability professionals, financial advisors, investment managers, members of mission-driven organizations, researchers, and private- and public- sector professionals who share a common goal of deploying capital as a change agent.
To provide an inspiring, engaging, and quality conference experience for all, The SRI Conference features a wide variety of breakout sessions in addition to our plenary sessions. They are organized along five separate tracks identified by icons throughout. Although not guaranteed, many sessions typically qualify for continuing education (CE) credits for CFP, CFA, and CIMA designees.
Professional Education – Sessions in this track are designed to help SRI professionals sharpen their skills and deepen their SRI knowledge base around practice building, portfolio management strategies, and other trending topics. Although not guaranteed, these sessions often qualify for continuing education (CE) credits for CFP, CFA, and CIMA designees.
ESG integration – Fixed income and equity portfolio managers will share their frameworks for the application of environmental, social, and governance (ESG) metrics in their investment analysis, providing important insights and data for asset managers, advisors, and corporate sustainability professionals.
Impact – Participants will gain a thorough understanding of direct impact investing in mission-driven organizations, its additive role in diversifying investment portfolios, as well as best practices for impact investment issuers.
Advocacy – Asset managers will discuss challenges, successes, and trending topics in shareowner advocacy initiatives. Participants will gain a better understanding of the overall process, what it means for publicly traded corporations, and how advisors can become more active shareholders on behalf of their clients.
Introduction to SRI/ESG/Impact – Participants will learn the fundamentals of SRI/ESG/Impact investing including its history and evolution, the role shareowner advocacy plays in advancing the movement, various SRI/ESG screening methods, and tips and tools for establishing and growing an SRI/ESG/Impact advisory practice. This track, along with all others, is relevant for advisors, investment managers, and professionals in the corporate space who are new to SRI and desire a better understanding how to communicate and incorporate SRI into their professional roles.
Learn more about the agenda and special conference tracks, check out the conference app, and enjoy refreshments.
Alison Taylor, Managing Director, B.S.R.
Sanjay Arya, Head of Indexes, Morningstar, Inc.
Aaron Seybert, Vice President, Enterprise Community Partners, Inc.
Elections, the SEC and Congress: What's Ahead on the Policy Front for Sustainable Investors?
Select conference sessions may be eligible for CE credit for CFP, CFA, and CIMA designees.
The SRI Conference agenda is subject to change without notice.
Each year, The Center for Sustainable Investment Education at the US SIF Foundation presents this in-depth course on SRI fundamentals.
The US SIF Foundation's Center for Sustainable Investment Education offers Fundamentals of Sustainable and Impact Investment as an introductory resource for investment advisors, financial planners, and other financial professionals who want to learn the basics of sustainable, responsible, and impact investment.
Participants receive a certificate of completion, and the course also typically qualifies for continuing education credit for various industry designations. There is an additional charge for this session. Register here.
Join us for SRI • 29!
The Advisor Summit Workshop is an extension of The SRI Conference and is presented by First Affirmative Financial Network. The workshop is designed to provide financial planners and investment advisors with the tools they need to successfully integrate SRI into their practices for the long term.
Attendees also have the opportunity to network with, and learn from experienced SRI advisors in a more intimate setting after the close of the main conference.
The Advisor Summit Workshop is typically approved for between four and six hours of continuing education credit for CFP designees, and one hour of continuing education credit for CIMA designees.
DON'T FORGET: SET YOUR CLOCKS BACK ONE HOUR TONIGHT!
The DOL Rule is Dead: Long Live the SEC Rule?
Explore the many fine dining venues The Broadmoor has to offer!


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Home Digital Forum: Food without Farmers speakers Bob Quinn
Bob Quinn
President and Founder
Kamut International
Bob Quinn was raised on wheat and cattle ranch in Montana (USA) which he still manages today. He has a Bachelor of Science degree and a Master of Science degree in plant pathology from Montana State University in Bozeman. He received a PhD in plant biochemistry at the University of California.
In 1983 Bob started Montana Flour & Grains, Inc. (MFG). In 1986 he introduced an ancient khorasan grain similar to durum wheat to the natural food industry. This grain was marketed under his own brand name, Kamut. The Kamut trademark guaranteed customers that that grain had been grown organically and not mixed with any other grain. Today over 4500 different Kamut brand products are being marketed throughout the world providing a new crop for over 250 organic farmers in Montana, Alberta and Saskatchewan and new opportunities for many more people to enjoy organic food.
Bob is a member of the Montana Grain Growers and the Montana Farm Bureau (FB). He is a past president of the Chouteau County FB and has served on the American FB's Wheat Committee and Grain Quality Committee. He is also an active member of Alternative Energy Resource Organization (AERO) in Montana and received AERO's Sustainable Ag Award in 1988. In 1993, he was named as one of Montana State University's 100 outstanding alumni from their first hundred years. Bob has been active in promoting regenerative organic agriculture throughout the state, the nation and the world. He helped form Montana's first Organic Crop Improvement Association (OCIA) chapter in 1987 and served as its first president. He was the chair of OCIA International's Internal Review Committee in 1989 and serve as the Board of Directors of OCIA International as a national officer form 1989-91. In 1991, he received OCIA International's outstanding member award. He served as a charter member on the first USDA National Organic Standards Board from 1992 to 1995. He has also served on a USDA agricultural research advisory committee as well as two of his congressman's ag advisory committees. He served on the Montana Department of Agriculture's first organic certification advisory board. In 2007 he received a lifetime of service award from the Montana Organic Association. He has also been a member of the Organic Trade Association (OTA) since 1987 and is also a member of OTA's farm advisory committee (FAC). In 2010, Bob received OTA's National Organic Leadership award. He has served on the board of directors for OTA's research foundation, The Organic Center, and was the chair of their science committee for a time. In the fall of 2013, he received the national Organic Pioneer Award from the Rodale Institute in Pennsylvania. In 2016, he received New Hope's Hall of Legends award. He has been a long-time member of IFOAM-Organics International and helped to form the IFOAM North American Regional Body in 2015-16. Bob was elected to IFOAM-NA's first Broad of Directors.
Across borders and generations: an intergenerational conversation on organic agriculture


text:
Michael Cerularius
Michael Cerularius, (b. Constantinople c. 1000 - d. 1059), also known as Michael Keroularios or Partriarch Michael I, was the Patriarch of Constantinople from 1043 to 1059. In 1054 he quarrelled with legates sent by Pope Leo IX over church practises which had been differing from the Roman Church for centuries, especially the use of unleavened bread in the Eucharist. The legates excommunicated him, and he likewise excommunicated them, starting the Great Schism. This schism led to the end of the alliance between the Emperor and the Papacy, and caused later Popes to ally with the Normans against the Empire. In 1965, those excommunications were rescinded by Pope Paul VI and Patriarch Athenagoras when they met in the Second Vatican Council. This was a significant step towards restoring communion between Rome and Constantinople.
Michael also quarrelled with Emperor Isaac I Comnenus over confiscation of church property. Isaac planned to depose Michael when Michael suddenly died in 1059, though there was no suspicion that he was murdered.
Alexius I List of Constantinople patriarchs Succeeded by:
Constantine III


text:
Marion B. _____[1]
Name Marion B. _____
Born 22 Oct 1906 Illinois, USA [1, 2, 3, 4]
Residence 1935 Chicago, Cook County, Illinois, USA [3]
2424 Lawndale
Residence Feb 1942 Chicago, Cook County, Illinois, USA [5]
2424 North Lawndale Avenue
Buried Jan 1999 Acacia Park Cemetery, Norwood Township, Cook County, Illinois, USA [2]
Plot 10, Row 2, Poplar Section
Died 11 Jan 1999 Port Charlotte, Charlotte County, Florida, USA [2, 4]
Last Modified 10 May 2020
Family Theodore Charles Thon
b. 20 Aug 1902, Chicago, Cook County, Illinois, USA
d. 16 Jan 1989, Port Charlotte, Charlotte County, Florida, USA (Age 86 years)
Married Abt 1927 [1]
Family lived at 2424 Lawndale Ave, Chicago in 1930, along with the family of Theodore's brother Harold. [1, 6]
Born - 22 Oct 1906 - Illinois, USA
Residence - 1935 - Chicago, Cook County, Illinois, USA
Residence - Feb 1942 - Chicago, Cook County, Illinois, USA
Buried - Jan 1999 - Acacia Park Cemetery, Norwood Township, Cook County, Illinois, USA
Died - 11 Jan 1999 - Port Charlotte, Charlotte County, Florida, USA
[S547] 1930 US Census - Household of Theodore C. Thon.
[S2629] Find A Grave memorial for Marion B. Thon, #88041958, Bob Chamberlin.
[S2970] 1940 US Census - Household of Theodore Thon.
[S6] U.S., Social Security Death Index.
[S6692] WWII Draft Registration Card of Theodore Charles Thon, Ancestry.com (National Archives & Records Administration).
[S546] 1930 US Census - Household of Harold Thon.


Extracted from flowers grown in England, Chamomile (Roman) is a light, sunny and essential oil with a sweet fruity and calming aroma. Ideal in skin care preparations to help soothe the skin and the senses. Blends well with Lavender, Orange Blossom (Neroli) and Rosewood-Tomar essential oils.
https://www.planetorganic.com/tisserand-chamomile-essential-oil-9ml/21609/ 21609 Tisserand Chamomile Essential Oil (9ml) https://www.planetorganic.com/images/products/medium/21609.jpg 35.45 35.45 GBP Tisserand InStock /essential-oils/ A light, sunny and calming essential oil with a sweet fruity aroma.