Succession is an issue that is notoriously hard for families to resolve because there are so many unpredictable factors that affect whether the next generation member will be successful. Even a family that has meticulously planned out the succession process cannot foresee whether the second generation will be a fit leader while there have been a number of pleasant surprises where a relatively unprepared member has exceeded expectations.
We see many examples of such unpredictability in royal successions. In 1936, Edward VIII the Prince of Wales was crowned the King of England after his father passed away. Even from his teen years, he was meticulously groomed to take the role of future king. Upon taking the throne, however, he made clear his distaste of royal protocol and caused political concern by his apparent disregard for established constitutional conventions. Only months into his reign as king, he shocked the nation when he proposed to an American named Wallis Simpson, a woman in the midst of a second divorce. Faced with intense political and internal pressure, he abdicated his throne, becoming one of the shortest-reigning monarchs in British history. He was hastily succeeded by his younger brother Albert, who took the name George VI in his reign. The stammering young monarch's struggles were popularized in the film The King's Speech, and was even rumored to be suffering from kleptomania, forcing Scotland Yard to get involved a number of times. King George managed as best as he could until his daughter Elisabeth took the thrown to this day, closing out a tumultuous time for the royal family.
The succession story played out a bit differently across the Atlantic in the USA, with the iconic Kennedy family. The eldest son Joseph, who the family groomed for greatness and ultimately the president of the United States, prematurely died during the war. After recovering from the shock, Joe Kennedy Sr. then turned his attention to his second son, Jack (later known as John), for a run for the presidency. John F. Kennedy went on to be known as one of the most fondly remembered Presidents in U.S. history, which begs the question of how the better prepared Joseph would have fared in his attempt at the job.
In recent years, the massive Indian family business Tata Group faced a similar challenge. It was no secret that the Chairman J.R.D. Tata planned for his brother-in-law Colonel Sawhney to succeed him, as Tata had no children of his own. But Colonel Sawhney suddenly passed away, leaving behind a vacuum as the successor of the largest conglomerate in India. After some intense soul-searching, J.R.D. Tata identified his nephew Ratan Tata as his successor. When Ratan took over, he surprised all his detractors by taking the Tata Group to new heights. Who could have foreseen such an outcome from the offset?
There are times when the best laid out plans go awry in the world of family business. Which is why it may be good to always have a fallback plan, especially in succession. In this aspect, family businesses may have something to learn from professionally managed companies.
An example of this was seen in Kelkar Group, one of the largest family-run companies in India. When the third generation member Kedar Vaze came into the perfume manufacturing group in 1996, he took the role of CTO and COO, an unusual move for Indian successors who often automatically take CEO. Kedar realized that at the time, he was not the right person to lead the company and deferred himself to a non-family CEO. Although he had every right and power to take the top role, his priorities were first and foremost the performance of the company and the interests of the shareholders, allowing him to make such a radical decision. Kedar ultimately took on the role of Group CEO in 2014 after many years learning and growing in his previous roles.
Simply put, the success of the next generation is never guaranteed. For business families, the best that they can do is hope for the best and prepare for the worst.


We are a family owned business that provides residential real estate services in the Lake Norman and surrounding area. Our company is growing and in need of additional administrative support. The office has an energetic "coffee shop" atmosphere with open work concept. The position is challenging yet rewarding. We believe in empowering our people to discover their unique skills and abilities.


Hey everybody! I wanted to let you all know that my second single "The Others" is OUT NOW!!! This song is super upbeat and is about that special someone. It was written by Karen Harding and myself.
The music video directed and produced again by the amazing Luke O'Sullivan, Jakob and Jonas Thorhallsson is OUT NOW, a few remixes from some of my favourite producers (which are sounding awesome!) still to come.
Hope everyone will love my new song and video.


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Third Tuesday after the first Monday of each month
Note: The Planning and Zoning Board shall meet on an as needed basis at a designated time in City Hall and is subject Section 286.011, Florida Statutes, et seq. (Public Meeting Law).
The City Planning and Zoning Board consists of five (5) members who shall be residents of the City. Member appointments are by the City Council for four (4) year terms and members serve dual terms on the boards. Members shall serve without remuneration but may be reimbursed for actual expenses incurred in connection with their official duties as approved by the City Council. Financial disclosure is required annually to Commission on Ethics, State of Florida through the Okaloosa County Supervisor of Elections.
A major responsibility of the Planning Board is the management and update of the City Comprehensive Plan and Land Use Regulations that provide for the growth management needs of Crestview through the year 2020 and beyond. The Planning Board also functions in the dual role as the Zoning Board for the City and shall have the duties and responsibilities as set forth in the Land Use Regulations and the City Code. Most prominent among the Zoning Board's duties are the review and consideration of requests for zoning changes and the subdivision of property within the City. The Planning Board is also referred to as the Local Planning Agency (LPA) and Land Development Regulation Commission pursuant to the Florida Statutes.
This board does not operate under a separate budget; however, the City does provide administrative support.
The Planning Board was established by Ordinance in 1964 and the Zoning Board was established by Ordinance No. 503 on 03-27-78. The Local Planning Agency (LPA) was established pursuant to Section 163.3174, Florida Statutes while the Land Development Regulations Commission was established pursuant to Ordinance Number 700, February 11, 1991. Ordinance No. 839, February 12, 1996 revised the meeting date to occur on an as needed basis while Ordinance No. 904, October 14, 1996 expanded the board membership to seven (7) members.
Note: Term limits are automatically renewed for a like term unless an appointing official or the City Council as a whole states otherwise at the time of re-election, death, dismissal, or resignation by the appointee. Voting by the membership is promulgated by ordinance.

redpajama_set_name: RedPajamaCommonCrawl


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Citroën Articles (General) - Citroënvie Articles - Citroënvie! - News - VIDEO
BX 4TC Evolution Rally Car & Citroën Group B Rally History
When we mentioned in Citroënvie on Oct. 16, 2021 that a BX 4TC Evolution rally car, one of just just 6 or 7 examples still in existence, was coming up for sale, we were intrigued.
We've had feedback from many of our members saying that they have pretty much over the years glossed over the BX and it's racing history. However, the Artcurial auction of the BX 4TC Evolution on Nov 4, where it sold for an astounding 417,200 €, certainly put it, and it's road-going homologation version, in the limelight!
Built from the BX saloon with a body constructed by Heuliez, the four-wheel drive 4TC Evolution had a 380 bhp 4-cylinder turbocharged engine and transmission using elements of the Peugeot 505 Turbo Production car. The gearbox came from the Citroën SM.
On Dec 20, a car enthusiast who goes by the the name of Jack, posted a video where he discusses the BX's racing image and history. He shows the auctioned BX 4TC Evolution, now in the UK and in the hands of its new owner as part of the The Saunders Collection. (Check it out on Instagram).
In the video, Jack shows that it is the same car, in part by Gary Nicholls (the new owner's engineer) cleaning the blackened boost gauge in the center dashboard console, (seen in that state in the picture below, taken for the auction).
It's a great video about this often overlooked chapter of Citroën's Group B rally car history! View it here:
Artcurial Auction BX 4TC Evolution rally car BX racing history Gary Nicholls Group B rally Jack The Saunders Collection Video
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You wouldn't immediately put Liz Cope and Sara Hale together with Andreas Neumann. The two former Orange senior brand managers are experts in strategy, marketing and branding – smart, commercially-aware and grounded. Neumann, on the other hand, is a chain-smoking, street-savvy, German-born pop music producer with designer hair, a passion for gadgets and connections with everyone from The Neptunes and Lenny Kravitz to Mica Paris.
But the trio's starkly contrasting backgrounds are certainly compatible for their joint venture, Grasshopper Music. It is a 'sound design' consultancy, aiming to work with corporate clients to inject some noise into their currently silent brands.
Clients are gradually becoming aware that audio is an important side to their brands, says Cope, who left Orange as head of brand last March, as it helps increase recognition and engage staff and consumers. It could even be argued that a 'sonic identity' – think of Direct Line's jaunty fanfare mnemonic – is more memorable than a visual logo. Grasshopper took on Orange as its first client last July to put better music into its stores, and is currently in talks with Lego, H&M, Cannon, Volkswagen and Alfa Romeo, among others.
'After tone of voice, sound is the next big thing,' she adds. 'It must be embedded in a brand and strategically thought-out.' In other words, every single sound must have a logical, commercial reason for being there. This is where Neumann, as composer and producer, comes in.
Once brand owners have developed ideas for their corporate sound with Grasshopper, Neumann works with artists to put together tracks of different sounds to build up a complete identity. 'Once it's composed, individual strands of the identity can be extracted and used across different media. A beep out of the context of the entire identity, for example, can become a ringtone,' he says.
Composing original music for a brand is preferable to hijacking existing tunes – it won't have associations in people's minds. If a brand uses, say, a contemporary pop song to advertise on TV, chances are a potential customer was dumped to it or, worse, another company has got there first.
More importantly, new music can feature a layered texture of sounds, each with its own branded message. If a brand wants to imply that it has strong environmental or social credentials, its sonic identity could sample rainforest noises or perhaps a gospel choir.
Grasshopper aims to measure the success, or otherwise, of its work in the same way that commercial radio measures its playlists – with market research that plays short 'hooks' of tunes to see how recognised they are. Unlike radio, however, it will weave brand questions into the research. This process helps the Grasshopper team to sell its work to clients, who love to see hard data.
At MetaDesign in Berlin, sound design is not a new concept. Under head of corporate sound Carl-Frank Westermann, it has developed sonic identities for several clients, including its recent work for Siemens. Its 'sound' is being implemented this month, and the team is currently developing ringtones based on the identity.
'We have created an entire "Klangraum" [soundscape] for Siemens which reflects the contrasting elements of its brand,' says Westermann. Electrical 'fizz' noises represent the technological aspect of the brand, while street and traffic sounds stand for Siemens' role as a modern communications business. The ringtones are 'an additional application [of Siemens'] sound environment', he notes.
Siemens' short identifying 'riff' or sonic identity is a four-note marque built around a rising scale and ending on a positive 'up', while its strong, syncopated rhythm further identifies it. 'Consumers are visually aware, but at present they only really notice the sound of a brand when it's either loud or bad, or both,' Westermann adds.
He agrees that non-original music can pose problems. 'How do you decide whether drum and bass or a string quartet is more appropriate to your brand? If the music is original, you have more control and can be more creative,' he says.
Where sonic logos now work best, says Dan Jackson, managing director of London-based Sonicbrand, is where they are sophisticated, global entities which lead a company's entire strategy. 'Take the McDonald's "I'm Lovin' It" riff. It's more than a sonic logo, it is part of an entire song by Justin Timberlake and is used all over the world across hundreds of McDonald's touchpoints,' he says.
And even though sonic branding may be a new concept to many companies, Jackson is seeing a demand for the next level of branding – the voice.
'There is a trend away from the sonic logo. We were totally music-oriented, but are now getting more and more calls from clients to look at their voice branding,' says Jackson. 'It started with Orange, which wanted vocal guidelines for its handsets and advertising. It's a case of mapping brand values on to voices,' he explains.
The concept works for both recorded voices and educating 'live' people, such as those working in call centres, on using certain inflexions in their voices. 'Call centres are where a company's real business is done. They are a massive opportunity,' adds Jackson.
Ever the composer, Neumann has taken this marriage of music and branding to its logical conclusion. He has released a single, Heart, with UK producers Amos and Rikard Berg, DJ Jeremy Healy and Mica Paris – collectively known as Seraphim Suite. The single takes the brand values of BMW as its inspiration, according to Neumann. The future of music may be brands, and the future of brands is almost certainly music.


Ranveer Singh – Vaani Kapoor starrer Befikre has had an average run. The box office collections of the film that released last Friday were about Rs34.36 crore.
The film is set in Paris and deals with the concept of casual relationships with no-strings-attached. It is director Aditya Chopra's directorial comeback after 8 years.


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Q: How to show that $f:[0,1]\times{\rm Sp}(n,\mathbb{R})\to {\rm Sp}(n,\mathbb{R})$ is continuous? If $f:[0,1]\times {\rm Sp}(n,\mathbb{R})\to {\rm Sp}(n,\mathbb{R})$ is defined as
$$f(t,A)=(A^{T}A)^{-t/2}$$
where, ${\rm Sp}(n,\mathbb{R})$ denotes the set of $n\times n$ real symplectic matrices. Prove that $f$ is continuous.
As far my knowledge, I know only three methods to prove a function is continuous:
(i) A function $f:X\to Y$ between topological spaces $X$ and $Y$ is continuous if and only if for every open(closed) subset $A\subset Y$, $f^{-1}(A)$ is open(Closed),
(ii) $\epsilon-\delta$ definition and
(iii) Composition of continuous function is continuous.
I tried using any of these methods but I cannot, for (i) I dont know what type of sets in ${\rm Sp}(n,\mathbb{R})$ are closed or open, for (ii) I dont know what the metric is and (iii) I'm still figuring out..

A: Note that $A^TA$ being a positive definite matrix , its logarithm is well defined, as the unique (real) matrix $B$ such that $e^B = A^TA$. You can verify this from Wikipedia,
The function $B \to \log B$ is continuous on symmetric positive definite matrices. This can be explained by the following logic : $A$ being positive definite and symmetric, one easily sees by the spectral theorem that  $B = UDU^{-1}$ (for $D$ diagonal consisting of the eigenvalues of $B$ and $U$ the matrix of eigenvectors of $B$), we have $\log B = U (\log D) U^{-1}$ where $U$ is the matrix consisting of eigenvectors of $B$ and $\log D$ is taking the logarithm of every diagonal entry of $D$.
Once this happens, note that as $B_n \to B$ in matrix norm in this space of psd matrices, the eigenvalues converge, and therefore so do the eigenvectors (only in this case, not in general, because if $B_n = U_nD_nU_n^{-1}$, $B_n$ is not too far from $B$, $D_n$ is not too far from $D$ so $U_n$ can't be too far from $U$ etc. Read more on this kind of estimation here.) in any norm of choice. It follows by continuity of the real logarithm, that the matrix logarithm is continuous.
That, composed with $A \to A^TA$ being continuous, tells you that $A \to \log A^TA$ is a continuous function.
Finally, $t\to -\frac t2$ is continuous obviously. Now, a product of continuous functions of separate variables is continuous in the joint topology, so $(t,A) \to (-\frac t2 \log(A^TA))$ is continuous.
The exponential is continuous, being a uniform limit of the polynomials $P_n(T) = \sum_{k=0}^n \frac{T^k}{k!}$. Thus, after composition , we find that $$(t,A) \to \exp\left(-\frac t2 \log(A^TA)\right)$$
is continuous.
The RHS equals $(A^TA)^{-\frac t2}$ by definition of a power of a matrix when the power is not rational. Thus, by a series of compositions (with some black boxes for you to fill) the result is a continuous function.

redpajama_set_name: RedPajamaStackExchange


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Queens senator's bill to eliminate pelvic examinations on unconscious patients passes the state Senate
Max Parrott/QNS
State Senator Jessica Ramos
By Max Parrott
The state Senate passed Senator Jessica Ramos' bill to stop the non-consensual pelvic examinations on unconscious patients in New York on Monday, culminating a push against the common practice in the medical community and activist circles.
Ramos' bill will stop medical students from being routinely instructed to practice pelvic examinations on unconscious and anesthetized patients, many of whom are unaware that this practice is happening. The law will require informed consent from patients about any training exercises performed on their bodies.
"When New Yorkers are in the care of a doctor, they must be aware of all procedures they will undergo," said Ramos in a statement.
The rationale of Ramos's version of the bill focuses on instilling medical students entering the field of gynecology with the importance of asking consent for medical procedures.
It references an article published by the American Journal of Obstetrics and Gynecology that indicated while the majority of first-year medical students were at first disturbed about performing pelvic exams on unconscious women without consent, their concerns diminished by the time they graduated. The law claims this "ethical erosion" could have implications for how a physician would treat patients down the road.
New York will be the sixth state in the nation to ban the practice. It's not clear exactly how many teaching hospitals in the city perform this practice, but it is common nationally. The bill comes after a renewed wave of public discourse on the practice over the past several years, but the practice has been a subject of contention since the 1990s.
The debate has gone beyond its ethical questions. The medical community has even questioned whether the pelvic exam is an important skill to teach medical students.
Ramos' bill does not enter into the debate of the procedure's medical value, instead establishing informed consent as a baseline for all procedures. The American Medical Association defines informed consent as a situation where a patient authorizes any specific medical intervention prior to treatment.
"Informed consent must be given by the patient for all procedures, even if just for training purposes. It is of utmost importance to instill the value of informed consent on medical students in New York," said Ramos.
New York State Legislature
pelvic examination
Queens DA recount update: Cabán and Katz teams want every ballot counted, but first ballot objection surfaces
Three rising stars: New Queens legislators in Albany and D.C. mark shifting political dynamics


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Product portals provide up to date information on plan and investment performance. Login to a variety of personal and business portals at MassMutual. Learn how to prepare for retirement with your employer-sponsored k savings account via this video from our educational partners at Society of Grownups.
Are you making these retirement plan mistakes? 68%* of people regret not doing this Learn More >. YOUR (k) PLAN YOUR GUIDE FOR THE (k) STOCK PURCHASE PLAN. You've reached an exciting milestone in your employment with the company. Participating in the Plan is a wonderful opportunity. It's a simple, convenient way to begin saving for your future. And.


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DEA Deutsche Erdoel AG headquartered in Hamburg, Germany, is an internationally operating exploration and production (E&P) company for natural gas and crude oil. The purpose of the business is the secure, environment-friendly and sustainable production of crude oil and natural gas along with its storage. In its exploration and production operations DEA applies its extensive geo-scientific know-how, state-of-the-art drilling and production technology and the pertinent experience from over 116 years of corporate history.
Ueberseering 40
www.dea-group.com
ENGAGING ENERGY
Oil & Gas Council © 2019 . All Rights Reserved.
CLARION ENERGY LIMITED (Co.Reg.No 07098632) is registered in England and Wales, Registered Office: Bedford House, 69-79 Fulham High Street, London, SW63JW, United Kingdom. VAT No. GB 843845601


text:
import {Request} from '../lib/request';
import {Response} from '../lib/response';
import {AWSError} from '../lib/error';
import {Service} from '../lib/service';
import {ServiceConfigurationOptions} from '../lib/service';
import {ConfigBase as Config} from '../lib/config-base';
interface Blob {}
declare class EMRcontainers extends Service {
  /**
   * Constructs a service object. This object has one method for each API operation.
   */
  constructor(options?: EMRcontainers.Types.ClientConfiguration)
  config: Config & EMRcontainers.Types.ClientConfiguration;
  /**
   * Cancels a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
   */
  cancelJobRun(params: EMRcontainers.Types.CancelJobRunRequest, callback?: (err: AWSError, data: EMRcontainers.Types.CancelJobRunResponse) => void): Request<EMRcontainers.Types.CancelJobRunResponse, AWSError>;
  /**
   * Cancels a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
   */
  cancelJobRun(callback?: (err: AWSError, data: EMRcontainers.Types.CancelJobRunResponse) => void): Request<EMRcontainers.Types.CancelJobRunResponse, AWSError>;
  /**
   * Creates a job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
   */
  createJobTemplate(params: EMRcontainers.Types.CreateJobTemplateRequest, callback?: (err: AWSError, data: EMRcontainers.Types.CreateJobTemplateResponse) => void): Request<EMRcontainers.Types.CreateJobTemplateResponse, AWSError>;
  /**
   * Creates a job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
   */
  createJobTemplate(callback?: (err: AWSError, data: EMRcontainers.Types.CreateJobTemplateResponse) => void): Request<EMRcontainers.Types.CreateJobTemplateResponse, AWSError>;
  /**
   * Creates a managed endpoint. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your virtual cluster.
   */
  createManagedEndpoint(params: EMRcontainers.Types.CreateManagedEndpointRequest, callback?: (err: AWSError, data: EMRcontainers.Types.CreateManagedEndpointResponse) => void): Request<EMRcontainers.Types.CreateManagedEndpointResponse, AWSError>;
  /**
   * Creates a managed endpoint. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your virtual cluster.
   */
  createManagedEndpoint(callback?: (err: AWSError, data: EMRcontainers.Types.CreateManagedEndpointResponse) => void): Request<EMRcontainers.Types.CreateManagedEndpointResponse, AWSError>;
  /**
   * Creates a virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
   */
  createVirtualCluster(params: EMRcontainers.Types.CreateVirtualClusterRequest, callback?: (err: AWSError, data: EMRcontainers.Types.CreateVirtualClusterResponse) => void): Request<EMRcontainers.Types.CreateVirtualClusterResponse, AWSError>;
  /**
   * Creates a virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
   */
  createVirtualCluster(callback?: (err: AWSError, data: EMRcontainers.Types.CreateVirtualClusterResponse) => void): Request<EMRcontainers.Types.CreateVirtualClusterResponse, AWSError>;
  /**
   * Deletes a job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
   */
  deleteJobTemplate(params: EMRcontainers.Types.DeleteJobTemplateRequest, callback?: (err: AWSError, data: EMRcontainers.Types.DeleteJobTemplateResponse) => void): Request<EMRcontainers.Types.DeleteJobTemplateResponse, AWSError>;
  /**
   * Deletes a job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
   */
  deleteJobTemplate(callback?: (err: AWSError, data: EMRcontainers.Types.DeleteJobTemplateResponse) => void): Request<EMRcontainers.Types.DeleteJobTemplateResponse, AWSError>;
  /**
   * Deletes a managed endpoint. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your virtual cluster.
   */
  deleteManagedEndpoint(params: EMRcontainers.Types.DeleteManagedEndpointRequest, callback?: (err: AWSError, data: EMRcontainers.Types.DeleteManagedEndpointResponse) => void): Request<EMRcontainers.Types.DeleteManagedEndpointResponse, AWSError>;
  /**
   * Deletes a managed endpoint. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your virtual cluster.
   */
  deleteManagedEndpoint(callback?: (err: AWSError, data: EMRcontainers.Types.DeleteManagedEndpointResponse) => void): Request<EMRcontainers.Types.DeleteManagedEndpointResponse, AWSError>;
  /**
   * Deletes a virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
   */
  deleteVirtualCluster(params: EMRcontainers.Types.DeleteVirtualClusterRequest, callback?: (err: AWSError, data: EMRcontainers.Types.DeleteVirtualClusterResponse) => void): Request<EMRcontainers.Types.DeleteVirtualClusterResponse, AWSError>;
  /**
   * Deletes a virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
   */
  deleteVirtualCluster(callback?: (err: AWSError, data: EMRcontainers.Types.DeleteVirtualClusterResponse) => void): Request<EMRcontainers.Types.DeleteVirtualClusterResponse, AWSError>;
  /**
   * Displays detailed information about a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
   */
  describeJobRun(params: EMRcontainers.Types.DescribeJobRunRequest, callback?: (err: AWSError, data: EMRcontainers.Types.DescribeJobRunResponse) => void): Request<EMRcontainers.Types.DescribeJobRunResponse, AWSError>;
  /**
   * Displays detailed information about a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
   */
  describeJobRun(callback?: (err: AWSError, data: EMRcontainers.Types.DescribeJobRunResponse) => void): Request<EMRcontainers.Types.DescribeJobRunResponse, AWSError>;
  /**
   * Displays detailed information about a specified job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
   */
  describeJobTemplate(params: EMRcontainers.Types.DescribeJobTemplateRequest, callback?: (err: AWSError, data: EMRcontainers.Types.DescribeJobTemplateResponse) => void): Request<EMRcontainers.Types.DescribeJobTemplateResponse, AWSError>;
  /**
   * Displays detailed information about a specified job template. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
   */
  describeJobTemplate(callback?: (err: AWSError, data: EMRcontainers.Types.DescribeJobTemplateResponse) => void): Request<EMRcontainers.Types.DescribeJobTemplateResponse, AWSError>;
  /**
   * Displays detailed information about a managed endpoint. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your virtual cluster.
   */
  describeManagedEndpoint(params: EMRcontainers.Types.DescribeManagedEndpointRequest, callback?: (err: AWSError, data: EMRcontainers.Types.DescribeManagedEndpointResponse) => void): Request<EMRcontainers.Types.DescribeManagedEndpointResponse, AWSError>;
  /**
   * Displays detailed information about a managed endpoint. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your virtual cluster.
   */
  describeManagedEndpoint(callback?: (err: AWSError, data: EMRcontainers.Types.DescribeManagedEndpointResponse) => void): Request<EMRcontainers.Types.DescribeManagedEndpointResponse, AWSError>;
  /**
   * Displays detailed information about a specified virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
   */
  describeVirtualCluster(params: EMRcontainers.Types.DescribeVirtualClusterRequest, callback?: (err: AWSError, data: EMRcontainers.Types.DescribeVirtualClusterResponse) => void): Request<EMRcontainers.Types.DescribeVirtualClusterResponse, AWSError>;
  /**
   * Displays detailed information about a specified virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
   */
  describeVirtualCluster(callback?: (err: AWSError, data: EMRcontainers.Types.DescribeVirtualClusterResponse) => void): Request<EMRcontainers.Types.DescribeVirtualClusterResponse, AWSError>;
  /**
   * Lists job runs based on a set of parameters. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
   */
  listJobRuns(params: EMRcontainers.Types.ListJobRunsRequest, callback?: (err: AWSError, data: EMRcontainers.Types.ListJobRunsResponse) => void): Request<EMRcontainers.Types.ListJobRunsResponse, AWSError>;
  /**
   * Lists job runs based on a set of parameters. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
   */
  listJobRuns(callback?: (err: AWSError, data: EMRcontainers.Types.ListJobRunsResponse) => void): Request<EMRcontainers.Types.ListJobRunsResponse, AWSError>;
  /**
   * Lists job templates based on a set of parameters. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
   */
  listJobTemplates(params: EMRcontainers.Types.ListJobTemplatesRequest, callback?: (err: AWSError, data: EMRcontainers.Types.ListJobTemplatesResponse) => void): Request<EMRcontainers.Types.ListJobTemplatesResponse, AWSError>;
  /**
   * Lists job templates based on a set of parameters. Job template stores values of StartJobRun API request in a template and can be used to start a job run. Job template allows two use cases: avoid repeating recurring StartJobRun API request values, enforcing certain values in StartJobRun API request.
   */
  listJobTemplates(callback?: (err: AWSError, data: EMRcontainers.Types.ListJobTemplatesResponse) => void): Request<EMRcontainers.Types.ListJobTemplatesResponse, AWSError>;
  /**
   * Lists managed endpoints based on a set of parameters. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your virtual cluster.
   */
  listManagedEndpoints(params: EMRcontainers.Types.ListManagedEndpointsRequest, callback?: (err: AWSError, data: EMRcontainers.Types.ListManagedEndpointsResponse) => void): Request<EMRcontainers.Types.ListManagedEndpointsResponse, AWSError>;
  /**
   * Lists managed endpoints based on a set of parameters. A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on EKS so that EMR Studio can communicate with your virtual cluster.
   */
  listManagedEndpoints(callback?: (err: AWSError, data: EMRcontainers.Types.ListManagedEndpointsResponse) => void): Request<EMRcontainers.Types.ListManagedEndpointsResponse, AWSError>;
  /**
   * Lists the tags assigned to the resources.
   */
  listTagsForResource(params: EMRcontainers.Types.ListTagsForResourceRequest, callback?: (err: AWSError, data: EMRcontainers.Types.ListTagsForResourceResponse) => void): Request<EMRcontainers.Types.ListTagsForResourceResponse, AWSError>;
  /**
   * Lists the tags assigned to the resources.
   */
  listTagsForResource(callback?: (err: AWSError, data: EMRcontainers.Types.ListTagsForResourceResponse) => void): Request<EMRcontainers.Types.ListTagsForResourceResponse, AWSError>;
  /**
   * Lists information about the specified virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
   */
  listVirtualClusters(params: EMRcontainers.Types.ListVirtualClustersRequest, callback?: (err: AWSError, data: EMRcontainers.Types.ListVirtualClustersResponse) => void): Request<EMRcontainers.Types.ListVirtualClustersResponse, AWSError>;
  /**
   * Lists information about the specified virtual cluster. Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, describe, list and delete virtual clusters. They do not consume any additional resource in your system. A single virtual cluster maps to a single Kubernetes namespace. Given this relationship, you can model virtual clusters the same way you model Kubernetes namespaces to meet your requirements.
   */
  listVirtualClusters(callback?: (err: AWSError, data: EMRcontainers.Types.ListVirtualClustersResponse) => void): Request<EMRcontainers.Types.ListVirtualClustersResponse, AWSError>;
  /**
   * Starts a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
   */
  startJobRun(params: EMRcontainers.Types.StartJobRunRequest, callback?: (err: AWSError, data: EMRcontainers.Types.StartJobRunResponse) => void): Request<EMRcontainers.Types.StartJobRunResponse, AWSError>;
  /**
   * Starts a job run. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.
   */
  startJobRun(callback?: (err: AWSError, data: EMRcontainers.Types.StartJobRunResponse) => void): Request<EMRcontainers.Types.StartJobRunResponse, AWSError>;
  /**
   * Assigns tags to resources. A tag is a label that you assign to an AWS resource. Each tag consists of a key and an optional value, both of which you define. Tags enable you to categorize your AWS resources by attributes such as purpose, owner, or environment. When you have many resources of the same type, you can quickly identify a specific resource based on the tags you've assigned to it. For example, you can define a set of tags for your Amazon EMR on EKS clusters to help you track each cluster's owner and stack level. We recommend that you devise a consistent set of tag keys for each resource type. You can then search and filter the resources based on the tags that you add.
   */
  tagResource(params: EMRcontainers.Types.TagResourceRequest, callback?: (err: AWSError, data: EMRcontainers.Types.TagResourceResponse) => void): Request<EMRcontainers.Types.TagResourceResponse, AWSError>;
  /**
   * Assigns tags to resources. A tag is a label that you assign to an AWS resource. Each tag consists of a key and an optional value, both of which you define. Tags enable you to categorize your AWS resources by attributes such as purpose, owner, or environment. When you have many resources of the same type, you can quickly identify a specific resource based on the tags you've assigned to it. For example, you can define a set of tags for your Amazon EMR on EKS clusters to help you track each cluster's owner and stack level. We recommend that you devise a consistent set of tag keys for each resource type. You can then search and filter the resources based on the tags that you add.
   */
  tagResource(callback?: (err: AWSError, data: EMRcontainers.Types.TagResourceResponse) => void): Request<EMRcontainers.Types.TagResourceResponse, AWSError>;
  /**
   * Removes tags from resources.
   */
  untagResource(params: EMRcontainers.Types.UntagResourceRequest, callback?: (err: AWSError, data: EMRcontainers.Types.UntagResourceResponse) => void): Request<EMRcontainers.Types.UntagResourceResponse, AWSError>;
  /**
   * Removes tags from resources.
   */
  untagResource(callback?: (err: AWSError, data: EMRcontainers.Types.UntagResourceResponse) => void): Request<EMRcontainers.Types.UntagResourceResponse, AWSError>;
}
declare namespace EMRcontainers {
  export type ACMCertArn = string;
  export type Base64Encoded = string;
  export interface CancelJobRunRequest {
    /**
     * The ID of the job run to cancel.
     */
    id: ResourceIdString;
    /**
     * The ID of the virtual cluster for which the job run will be canceled.
     */
    virtualClusterId: ResourceIdString;
  }
  export interface CancelJobRunResponse {
    /**
     * The output contains the ID of the cancelled job run.
     */
    id?: ResourceIdString;
    /**
     * The output contains the virtual cluster ID for which the job run is cancelled.
     */
    virtualClusterId?: ResourceIdString;
  }
  export interface Certificate {
    /**
     * The ARN of the certificate generated for managed endpoint.
     */
    certificateArn?: ACMCertArn;
    /**
     * The base64 encoded PEM certificate data generated for managed endpoint.
     */
    certificateData?: Base64Encoded;
  }
  export type ClientToken = string;
  export interface CloudWatchMonitoringConfiguration {
    /**
     * The name of the log group for log publishing.
     */
    logGroupName: LogGroupName;
    /**
     * The specified name prefix for log streams.
     */
    logStreamNamePrefix?: String256;
  }
  export type ClusterId = string;
  export interface Configuration {
    /**
     * The classification within a configuration.
     */
    classification: String1024;
    /**
     * A set of properties specified within a configuration classification.
     */
    properties?: SensitivePropertiesMap;
    /**
     * A list of additional configurations to apply within a configuration object.
     */
    configurations?: ConfigurationList;
  }
  export type ConfigurationList = Configuration[];
  export interface ConfigurationOverrides {
    /**
     * The configurations for the application running by the job run.
     */
    applicationConfiguration?: ConfigurationList;
    /**
     * The configurations for monitoring.
     */
    monitoringConfiguration?: MonitoringConfiguration;
  }
  export interface ContainerInfo {
    /**
     * The information about the EKS cluster.
     */
    eksInfo?: EksInfo;
  }
  export interface ContainerProvider {
    /**
     * The type of the container provider. EKS is the only supported type as of now.
     */
    type: ContainerProviderType;
    /**
     * The ID of the container cluster.
     */
    id: ClusterId;
    /**
     * The information about the container cluster.
     */
    info?: ContainerInfo;
  }
  export type ContainerProviderType = "EKS"|string;
  export interface CreateJobTemplateRequest {
    /**
     * The specified name of the job template.
     */
    name: ResourceNameString;
    /**
     * The client token of the job template.
     */
    clientToken: ClientToken;
    /**
     * The job template data which holds values of StartJobRun API request.
     */
    jobTemplateData: JobTemplateData;
    /**
     * The tags that are associated with the job template.
     */
    tags?: TagMap;
    /**
     * The KMS key ARN used to encrypt the job template.
     */
    kmsKeyArn?: KmsKeyArn;
  }
  export interface CreateJobTemplateResponse {
    /**
     * This output display the created job template ID.
     */
    id?: ResourceIdString;
    /**
     * This output displays the name of the created job template.
     */
    name?: ResourceNameString;
    /**
     * This output display the ARN of the created job template.
     */
    arn?: JobTemplateArn;
    /**
     * This output displays the date and time when the job template was created.
     */
    createdAt?: _Date;
  }
  export interface CreateManagedEndpointRequest {
    /**
     * The name of the managed endpoint.
     */
    name: ResourceNameString;
    /**
     * The ID of the virtual cluster for which a managed endpoint is created.
     */
    virtualClusterId: ResourceIdString;
    /**
     * The type of the managed endpoint.
     */
    type: EndpointType;
    /**
     * The Amazon EMR release version.
     */
    releaseLabel: ReleaseLabel;
    /**
     * The ARN of the execution role.
     */
    executionRoleArn: IAMRoleArn;
    /**
     * The certificate ARN provided by users for the managed endpoint. This field is under deprecation and will be removed in future releases.
     */
    certificateArn?: ACMCertArn;
    /**
     * The configuration settings that will be used to override existing configurations.
     */
    configurationOverrides?: ConfigurationOverrides;
    /**
     * The client idempotency token for this create call.
     */
    clientToken: ClientToken;
    /**
     * The tags of the managed endpoint.
     */
    tags?: TagMap;
  }
  export interface CreateManagedEndpointResponse {
    /**
     * The output contains the ID of the managed endpoint.
     */
    id?: ResourceIdString;
    /**
     * The output contains the name of the managed endpoint.
     */
    name?: ResourceNameString;
    /**
     * The output contains the ARN of the managed endpoint.
     */
    arn?: EndpointArn;
    /**
     * The output contains the ID of the virtual cluster.
     */
    virtualClusterId?: ResourceIdString;
  }
  export interface CreateVirtualClusterRequest {
    /**
     * The specified name of the virtual cluster.
     */
    name: ResourceNameString;
    /**
     * The container provider of the virtual cluster.
     */
    containerProvider: ContainerProvider;
    /**
     * The client token of the virtual cluster.
     */
    clientToken: ClientToken;
    /**
     * The tags assigned to the virtual cluster.
     */
    tags?: TagMap;
  }
  export interface CreateVirtualClusterResponse {
    /**
     * This output contains the virtual cluster ID.
     */
    id?: ResourceIdString;
    /**
     * This output contains the name of the virtual cluster.
     */
    name?: ResourceNameString;
    /**
     * This output contains the ARN of virtual cluster.
     */
    arn?: VirtualClusterArn;
  }
  export type _Date = Date;
  export interface DeleteJobTemplateRequest {
    /**
     * The ID of the job template that will be deleted.
     */
    id: ResourceIdString;
  }
  export interface DeleteJobTemplateResponse {
    /**
     * This output contains the ID of the job template that was deleted.
     */
    id?: ResourceIdString;
  }
  export interface DeleteManagedEndpointRequest {
    /**
     * The ID of the managed endpoint.
     */
    id: ResourceIdString;
    /**
     * The ID of the endpoint's virtual cluster.
     */
    virtualClusterId: ResourceIdString;
  }
  export interface DeleteManagedEndpointResponse {
    /**
     * The output displays the ID of the managed endpoint.
     */
    id?: ResourceIdString;
    /**
     * The output displays the ID of the endpoint's virtual cluster.
     */
    virtualClusterId?: ResourceIdString;
  }
  export interface DeleteVirtualClusterRequest {
    /**
     * The ID of the virtual cluster that will be deleted.
     */
    id: ResourceIdString;
  }
  export interface DeleteVirtualClusterResponse {
    /**
     * This output contains the ID of the virtual cluster that will be deleted.
     */
    id?: ResourceIdString;
  }
  export interface DescribeJobRunRequest {
    /**
     * The ID of the job run request.
     */
    id: ResourceIdString;
    /**
     * The ID of the virtual cluster for which the job run is submitted.
     */
    virtualClusterId: ResourceIdString;
  }
  export interface DescribeJobRunResponse {
    /**
     * The output displays information about a job run.
     */
    jobRun?: JobRun;
  }
  export interface DescribeJobTemplateRequest {
    /**
     * The ID of the job template that will be described.
     */
    id: ResourceIdString;
  }
  export interface DescribeJobTemplateResponse {
    /**
     * This output displays information about the specified job template.
     */
    jobTemplate?: JobTemplate;
  }
  export interface DescribeManagedEndpointRequest {
    /**
     * This output displays ID of the managed endpoint.
     */
    id: ResourceIdString;
    /**
     * The ID of the endpoint's virtual cluster.
     */
    virtualClusterId: ResourceIdString;
  }
  export interface DescribeManagedEndpointResponse {
    /**
     * This output displays information about a managed endpoint.
     */
    endpoint?: Endpoint;
  }
  export interface DescribeVirtualClusterRequest {
    /**
     * The ID of the virtual cluster that will be described.
     */
    id: ResourceIdString;
  }
  export interface DescribeVirtualClusterResponse {
    /**
     * This output displays information about the specified virtual cluster.
     */
    virtualCluster?: VirtualCluster;
  }
  export interface EksInfo {
    /**
     * The namespaces of the EKS cluster.
     */
    namespace?: KubernetesNamespace;
  }
  export interface Endpoint {
    /**
     * The ID of the endpoint.
     */
    id?: ResourceIdString;
    /**
     * The name of the endpoint.
     */
    name?: ResourceNameString;
    /**
     * The ARN of the endpoint.
     */
    arn?: EndpointArn;
    /**
     * The ID of the endpoint's virtual cluster.
     */
    virtualClusterId?: ResourceIdString;
    /**
     * The type of the endpoint.
     */
    type?: EndpointType;
    /**
     * The state of the endpoint.
     */
    state?: EndpointState;
    /**
     * The EMR release version to be used for the endpoint.
     */
    releaseLabel?: ReleaseLabel;
    /**
     * The execution role ARN of the endpoint.
     */
    executionRoleArn?: IAMRoleArn;
    /**
     * The certificate ARN of the endpoint. This field is under deprecation and will be removed in future.
     */
    certificateArn?: ACMCertArn;
    /**
     * The certificate generated by emr control plane on customer behalf to secure the managed endpoint.
     */
    certificateAuthority?: Certificate;
    /**
     * The configuration settings that are used to override existing configurations for endpoints.
     */
    configurationOverrides?: ConfigurationOverrides;
    /**
     * The server URL of the endpoint.
     */
    serverUrl?: UriString;
    /**
     * The date and time when the endpoint was created.
     */
    createdAt?: _Date;
    /**
     * The security group configuration of the endpoint.
     */
    securityGroup?: String256;
    /**
     * The subnet IDs of the endpoint.
     */
    subnetIds?: SubnetIds;
    /**
     *  Additional details of the endpoint state.
     */
    stateDetails?: String256;
    /**
     *  The reasons why the endpoint has failed.
     */
    failureReason?: FailureReason;
    /**
     * The tags of the endpoint.
     */
    tags?: TagMap;
  }
  export type EndpointArn = string;
  export type EndpointState = "CREATING"|"ACTIVE"|"TERMINATING"|"TERMINATED"|"TERMINATED_WITH_ERRORS"|string;
  export type EndpointStates = EndpointState[];
  export type EndpointType = string;
  export type EndpointTypes = EndpointType[];
  export type Endpoints = Endpoint[];
  export type EntryPointArgument = string;
  export type EntryPointArguments = EntryPointArgument[];
  export type EntryPointPath = string;
  export type FailureReason = "INTERNAL_ERROR"|"USER_ERROR"|"VALIDATION_ERROR"|"CLUSTER_UNAVAILABLE"|string;
  export type IAMRoleArn = string;
  export type JavaInteger = number;
  export type JobArn = string;
  export interface JobDriver {
    /**
     * The job driver parameters specified for spark submit.
     */
    sparkSubmitJobDriver?: SparkSubmitJobDriver;
    /**
     * The job driver for job type.
     */
    sparkSqlJobDriver?: SparkSqlJobDriver;
  }
  export interface JobRun {
    /**
     * The ID of the job run.
     */
    id?: ResourceIdString;
    /**
     * The name of the job run.
     */
    name?: ResourceNameString;
    /**
     * The ID of the job run's virtual cluster.
     */
    virtualClusterId?: ResourceIdString;
    /**
     * The ARN of job run.
     */
    arn?: JobArn;
    /**
     * The state of the job run.
     */
    state?: JobRunState;
    /**
     * The client token used to start a job run.
     */
    clientToken?: ClientToken;
    /**
     * The execution role ARN of the job run.
     */
    executionRoleArn?: IAMRoleArn;
    /**
     * The release version of Amazon EMR.
     */
    releaseLabel?: ReleaseLabel;
    /**
     * The configuration settings that are used to override default configuration.
     */
    configurationOverrides?: ConfigurationOverrides;
    /**
     * Parameters of job driver for the job run.
     */
    jobDriver?: JobDriver;
    /**
     * The date and time when the job run was created.
     */
    createdAt?: _Date;
    /**
     * The user who created the job run.
     */
    createdBy?: RequestIdentityUserArn;
    /**
     * The date and time when the job run has finished.
     */
    finishedAt?: _Date;
    /**
     * Additional details of the job run state.
     */
    stateDetails?: String256;
    /**
     * The reasons why the job run has failed.
     */
    failureReason?: FailureReason;
    /**
     * The assigned tags of the job run.
     */
    tags?: TagMap;
  }
  export type JobRunState = "PENDING"|"SUBMITTED"|"RUNNING"|"FAILED"|"CANCELLED"|"CANCEL_PENDING"|"COMPLETED"|string;
  export type JobRunStates = JobRunState[];
  export type JobRuns = JobRun[];
  export interface JobTemplate {
    /**
     * The name of the job template.
     */
    name?: ResourceNameString;
    /**
     * The ID of the job template.
     */
    id?: ResourceIdString;
    /**
     * The ARN of the job template.
     */
    arn?: JobTemplateArn;
    /**
     *  The date and time when the job template was created.
     */
    createdAt?: _Date;
    /**
     *  The user who created the job template.
     */
    createdBy?: RequestIdentityUserArn;
    /**
     * The tags assigned to the job template.
     */
    tags?: TagMap;
    /**
     * The job template data which holds values of StartJobRun API request.
     */
    jobTemplateData: JobTemplateData;
    /**
     *  The KMS key ARN used to encrypt the job template.
     */
    kmsKeyArn?: KmsKeyArn;
    /**
     * The error message in case the decryption of job template fails.
     */
    decryptionError?: String2048;
  }
  export type JobTemplateArn = string;
  export interface JobTemplateData {
    /**
     * The execution role ARN of the job run.
     */
    executionRoleArn: ParametricIAMRoleArn;
    /**
     *  The release version of Amazon EMR.
     */
    releaseLabel: ParametricReleaseLabel;
    /**
     *  The configuration settings that are used to override defaults configuration.
     */
    configurationOverrides?: ParametricConfigurationOverrides;
    jobDriver: JobDriver;
    /**
     * The configuration of parameters existing in the job template.
     */
    parameterConfiguration?: TemplateParameterConfigurationMap;
    /**
     * The tags assigned to jobs started using the job template.
     */
    jobTags?: TagMap;
  }
  export type JobTemplates = JobTemplate[];
  export type KmsKeyArn = string;
  export type KubernetesNamespace = string;
  export interface ListJobRunsRequest {
    /**
     * The ID of the virtual cluster for which to list the job run.
     */
    virtualClusterId: ResourceIdString;
    /**
     * The date and time before which the job runs were submitted.
     */
    createdBefore?: _Date;
    /**
     * The date and time after which the job runs were submitted.
     */
    createdAfter?: _Date;
    /**
     * The name of the job run.
     */
    name?: ResourceNameString;
    /**
     * The states of the job run.
     */
    states?: JobRunStates;
    /**
     * The maximum number of job runs that can be listed.
     */
    maxResults?: JavaInteger;
    /**
     * The token for the next set of job runs to return.
     */
    nextToken?: NextToken;
  }
  export interface ListJobRunsResponse {
    /**
     * This output lists information about the specified job runs.
     */
    jobRuns?: JobRuns;
    /**
     * This output displays the token for the next set of job runs.
     */
    nextToken?: NextToken;
  }
  export interface ListJobTemplatesRequest {
    /**
     * The date and time after which the job templates were created.
     */
    createdAfter?: _Date;
    /**
     *  The date and time before which the job templates were created.
     */
    createdBefore?: _Date;
    /**
     *  The maximum number of job templates that can be listed.
     */
    maxResults?: JavaInteger;
    /**
     *  The token for the next set of job templates to return.
     */
    nextToken?: NextToken;
  }
  export interface ListJobTemplatesResponse {
    /**
     * This output lists information about the specified job templates.
     */
    templates?: JobTemplates;
    /**
     *  This output displays the token for the next set of job templates.
     */
    nextToken?: NextToken;
  }
  export interface ListManagedEndpointsRequest {
    /**
     * The ID of the virtual cluster.
     */
    virtualClusterId: ResourceIdString;
    /**
     * The date and time before which the endpoints are created.
     */
    createdBefore?: _Date;
    /**
     *  The date and time after which the endpoints are created.
     */
    createdAfter?: _Date;
    /**
     * The types of the managed endpoints.
     */
    types?: EndpointTypes;
    /**
     * The states of the managed endpoints.
     */
    states?: EndpointStates;
    /**
     * The maximum number of managed endpoints that can be listed.
     */
    maxResults?: JavaInteger;
    /**
     *  The token for the next set of managed endpoints to return.
     */
    nextToken?: NextToken;
  }
  export interface ListManagedEndpointsResponse {
    /**
     * The managed endpoints to be listed.
     */
    endpoints?: Endpoints;
    /**
     *  The token for the next set of endpoints to return.
     */
    nextToken?: NextToken;
  }
  export interface ListTagsForResourceRequest {
    /**
     * The ARN of tagged resources.
     */
    resourceArn: RsiArn;
  }
  export interface ListTagsForResourceResponse {
    /**
     * The tags assigned to resources.
     */
    tags?: TagMap;
  }
  export interface ListVirtualClustersRequest {
    /**
     * The container provider ID of the virtual cluster.
     */
    containerProviderId?: String1024;
    /**
     * The container provider type of the virtual cluster. EKS is the only supported type as of now.
     */
    containerProviderType?: ContainerProviderType;
    /**
     * The date and time after which the virtual clusters are created.
     */
    createdAfter?: _Date;
    /**
     * The date and time before which the virtual clusters are created.
     */
    createdBefore?: _Date;
    /**
     * The states of the requested virtual clusters.
     */
    states?: VirtualClusterStates;
    /**
     * The maximum number of virtual clusters that can be listed.
     */
    maxResults?: JavaInteger;
    /**
     * The token for the next set of virtual clusters to return.
     */
    nextToken?: NextToken;
  }
  export interface ListVirtualClustersResponse {
    /**
     * This output lists the specified virtual clusters.
     */
    virtualClusters?: VirtualClusters;
    /**
     * This output displays the token for the next set of virtual clusters.
     */
    nextToken?: NextToken;
  }
  export type LogGroupName = string;
  export interface MonitoringConfiguration {
    /**
     * Monitoring configurations for the persistent application UI.
     */
    persistentAppUI?: PersistentAppUI;
    /**
     * Monitoring configurations for CloudWatch.
     */
    cloudWatchMonitoringConfiguration?: CloudWatchMonitoringConfiguration;
    /**
     * Amazon S3 configuration for monitoring log publishing.
     */
    s3MonitoringConfiguration?: S3MonitoringConfiguration;
  }
  export type NextToken = string;
  export interface ParametricCloudWatchMonitoringConfiguration {
    /**
     *  The name of the log group for log publishing.
     */
    logGroupName?: TemplateParameter;
    /**
     *  The specified name prefix for log streams.
     */
    logStreamNamePrefix?: String256;
  }
  export interface ParametricConfigurationOverrides {
    /**
     *  The configurations for the application running by the job run.
     */
    applicationConfiguration?: ConfigurationList;
    /**
     *  The configurations for monitoring.
     */
    monitoringConfiguration?: ParametricMonitoringConfiguration;
  }
  export type ParametricIAMRoleArn = string;
  export interface ParametricMonitoringConfiguration {
    /**
     *  Monitoring configurations for the persistent application UI.
     */
    persistentAppUI?: TemplateParameter;
    /**
     *  Monitoring configurations for CloudWatch.
     */
    cloudWatchMonitoringConfiguration?: ParametricCloudWatchMonitoringConfiguration;
    /**
     *  Amazon S3 configuration for monitoring log publishing.
     */
    s3MonitoringConfiguration?: ParametricS3MonitoringConfiguration;
  }
  export type ParametricReleaseLabel = string;
  export interface ParametricS3MonitoringConfiguration {
    /**
     * Amazon S3 destination URI for log publishing.
     */
    logUri?: UriString;
  }
  export type PersistentAppUI = "ENABLED"|"DISABLED"|string;
  export type ReleaseLabel = string;
  export type RequestIdentityUserArn = string;
  export type ResourceIdString = string;
  export type ResourceNameString = string;
  export type RsiArn = string;
  export interface S3MonitoringConfiguration {
    /**
     * Amazon S3 destination URI for log publishing.
     */
    logUri: UriString;
  }
  export type SensitivePropertiesMap = {[key: string]: String1024};
  export interface SparkSqlJobDriver {
    /**
     * The SQL file to be executed.
     */
    entryPoint?: EntryPointPath;
    /**
     * The Spark parameters to be included in the Spark SQL command.
     */
    sparkSqlParameters?: SparkSqlParameters;
  }
  export type SparkSqlParameters = string;
  export interface SparkSubmitJobDriver {
    /**
     * The entry point of job application.
     */
    entryPoint: EntryPointPath;
    /**
     * The arguments for job application.
     */
    entryPointArguments?: EntryPointArguments;
    /**
     * The Spark submit parameters that are used for job runs.
     */
    sparkSubmitParameters?: SparkSubmitParameters;
  }
  export type SparkSubmitParameters = string;
  export interface StartJobRunRequest {
    /**
     * The name of the job run.
     */
    name?: ResourceNameString;
    /**
     * The virtual cluster ID for which the job run request is submitted.
     */
    virtualClusterId: ResourceIdString;
    /**
     * The client idempotency token of the job run request.
     */
    clientToken: ClientToken;
    /**
     * The execution role ARN for the job run.
     */
    executionRoleArn?: IAMRoleArn;
    /**
     * The Amazon EMR release version to use for the job run.
     */
    releaseLabel?: ReleaseLabel;
    /**
     * The job driver for the job run.
     */
    jobDriver?: JobDriver;
    /**
     * The configuration overrides for the job run.
     */
    configurationOverrides?: ConfigurationOverrides;
    /**
     * The tags assigned to job runs.
     */
    tags?: TagMap;
    /**
     * The job template ID to be used to start the job run.
     */
    jobTemplateId?: ResourceIdString;
    /**
     * The values of job template parameters to start a job run.
     */
    jobTemplateParameters?: TemplateParameterInputMap;
  }
  export interface StartJobRunResponse {
    /**
     * This output displays the started job run ID.
     */
    id?: ResourceIdString;
    /**
     * This output displays the name of the started job run.
     */
    name?: ResourceNameString;
    /**
     * This output lists the ARN of job run.
     */
    arn?: JobArn;
    /**
     * This output displays the virtual cluster ID for which the job run was submitted.
     */
    virtualClusterId?: ResourceIdString;
  }
  export type String1024 = string;
  export type String128 = string;
  export type String2048 = string;
  export type String256 = string;
  export type StringEmpty256 = string;
  export type SubnetIds = String256[];
  export type TagKeyList = String128[];
  export type TagMap = {[key: string]: StringEmpty256};
  export interface TagResourceRequest {
    /**
     * The ARN of resources.
     */
    resourceArn: RsiArn;
    /**
     * The tags assigned to resources.
     */
    tags: TagMap;
  }
  export interface TagResourceResponse {
  }
  export type TemplateParameter = string;
  export interface TemplateParameterConfiguration {
    /**
     * The type of the job template parameter. Allowed values are: 'String', 'Number'.
     */
    type?: TemplateParameterDataType;
    /**
     * The default value for the job template parameter.
     */
    defaultValue?: String1024;
  }
  export type TemplateParameterConfigurationMap = {[key: string]: TemplateParameterConfiguration};
  export type TemplateParameterDataType = "NUMBER"|"STRING"|string;
  export type TemplateParameterInputMap = {[key: string]: String1024};
  export type TemplateParameterName = string;
  export interface UntagResourceRequest {
    /**
     * The ARN of resources.
     */
    resourceArn: RsiArn;
    /**
     * The tag keys of the resources.
     */
    tagKeys: TagKeyList;
  }
  export interface UntagResourceResponse {
  }
  export type UriString = string;
  export interface VirtualCluster {
    /**
     * The ID of the virtual cluster.
     */
    id?: ResourceIdString;
    /**
     * The name of the virtual cluster.
     */
    name?: ResourceNameString;
    /**
     * The ARN of the virtual cluster.
     */
    arn?: VirtualClusterArn;
    /**
     * The state of the virtual cluster.
     */
    state?: VirtualClusterState;
    /**
     * The container provider of the virtual cluster.
     */
    containerProvider?: ContainerProvider;
    /**
     * The date and time when the virtual cluster is created.
     */
    createdAt?: _Date;
    /**
     * The assigned tags of the virtual cluster.
     */
    tags?: TagMap;
  }
  export type VirtualClusterArn = string;
  export type VirtualClusterState = "RUNNING"|"TERMINATING"|"TERMINATED"|"ARRESTED"|string;
  export type VirtualClusterStates = VirtualClusterState[];
  export type VirtualClusters = VirtualCluster[];
  /**
   * A string in YYYY-MM-DD format that represents the latest possible API version that can be used in this service. Specify 'latest' to use the latest possible version.
   */
  export type apiVersion = "2020-10-01"|"latest"|string;
  export interface ClientApiVersions {
    /**
     * A string in YYYY-MM-DD format that represents the latest possible API version that can be used in this service. Specify 'latest' to use the latest possible version.
     */
    apiVersion?: apiVersion;
  }
  export type ClientConfiguration = ServiceConfigurationOptions & ClientApiVersions;
  /**
   * Contains interfaces for use with the EMRcontainers client.
   */
  export import Types = EMRcontainers;
}
export = EMRcontainers;

redpajama_set_name: RedPajamaGithub


February 5, 2019 Posted in 5k, feet, Fun training, gait, half marathon, holistic, injuries, knee pain, low intensity training, marathon, mind, Mobility, motivation, myths, Nutrition, posture, recovery, running, safe exercise, self improvement, sleep, Training, training methods, Uncategorized, walking gait, weight loss.
At Roads to Freedom we know that for the vast majority of runners winning races and becoming more competitive is not the main goal. For many, running is about stress relief, getting away from all their worldly cares – busy schedules, family duties etc. They appreciate the sense of calm and relaxation they feel as they wind down after a run in the fresh air. It takes them out of themselves and the sheer simplicity and ease of convenience of running makes it a great way to get in touch with themselves again.
Some run for general health and fitness, many for weight management reasons, others do like to challenge themselves. Most do like to get better at running and most would like to get a little faster as a measure of their increased fitness, but not necessarily to compete or compare themselves with others. In short it's their mental and physical health they value for the most part, although some will no doubt say that it lets them eat more cake or something similar.
A large number of these modern day runners are female and many have children that they wish to be fit to play with or share the running experience with too. When I started running around 35 years ago female runners were very rare indeed. They made up a tiny fraction of most running fields and they tended to be very serious about running. Now, in many races, the number of females outnumbers male runners.
What problems do these new runners face? Many are overweight and generally out of shape and restricted in their movements. They are very busy and often have to fit in shorter runs in between other duties, like the school run. Many cannot run every day and often rely on others being around for child care.
THESE CLIENTS REQUIRE A WHOLE NEW APPROACH TO RUNNING THAT MAKES TRADITIONAL MODELS OF TRAINING AND COACHING REDUNDANT.
They don't want to spend hours in a gym, nor do they want a traditional running coach. They need an holistic approach that covers lifestyle, but also incorporates running specific knowledge.
Roads to Freedom evolved out of wanting to bring the joys and benefits of running predominantly to these people. As such, the focus is very much on adopting a holistic approach to running, as opposed to traditional running coaching which tends to be geared to making already very good runners better. This means that our approach is different, because running is seen as part of a fit and healthy sustainable lifestyle, rather than as a goal in itself.
So we address issues like weight management with nutritional recommendations, we take non-runners and get them started with appropriate programmes to bridge the gap to eventually running continuously for 30 minutes or more, then to improving at their own pace. Everything is done with encouragement, support and sensible advice. We just don't believe in judging people or 'no pain, no gain' or 'go hard or go home', which is the kind of nonsense you hear so often in the fitness industry. Everything is focused on you and there are no expectations other than those which you bring. The goal is not just to make you a better runner, but to help you become a happier, fitter, healthier person so you can enjoy life more.
We want to know where you are at now – not just your running, but your lifestyle habits too, because these will massively affect your ability to run.
Injuries past and present and any relevant medical information, to keep you safe.
We want to see how you stand and how you move and identify any problem areas which could lead to injury or under-performance if not addressed.
We want to know your goals and we will show you the best way to get there, whatever they are.
I am very passionate about running and human movement generally. Running saved my life and I've seen it do the same for others. I have experienced more benefits from running, both psychological and physical, than probably anything else in life and I still get as much pleasure from it now in my sixties as I did 35 years ago when I began. It has taught me so much about myself and life. The distances have got longer as the pace has got slower, but the sense of freedom remains.
Why not join me and discover the Road to Freedom?
Call or text David on 07504439555 or email me at davidperry57@sky.com to find out more.
Previous: Previous post: 'Armless' walking and running limits performance and causes injury.
Next: Next post: Kneed a different approach? Tim's story.


text:
Home Sports US Open 2017: Sharapova & Muguruza Out, Williams & Kvitova into Quaters.
US Open 2017: Sharapova & Muguruza Out, Williams & Kvitova into Quaters.
Maria Sharapova of Russia returns the ball to Garbine Muguruza of Spain during their women's quarter-final match at the French Open tennis tournament at the Roland Garros stadium in Paris June 3, 2014. REUTERS/Gonzalo Fuentes (FRANCE - Tags: SPORT TENNIS)
Maria Sharapova's quest for a second US Open title came to an end with a fourth round defeat in the hands of 16th seed Anastasija Sevastova.
Sevastova lost the first set, but bounced back in style for a 5-7 6-4 6-2 win to reach the US Open quarter-finals for a second straight year.
The 27-year-old will play Sloane Stephens next after the American beat Julia Goerges 6-3 3-6 6-1
"I did my best. I can be proud of that.", said Sharapova, who only made it through to the US Open main draw due to a wild card.
Sharapova had already knocked out world number two Simona Halep on the first day of the tournament, and then beat Timea Babos and Sofia Kenin on her way to the last 16.
The 30 year-old is expected to return to the top 100 when the next rankings is released.
Elsewhere, favourite Garbine Muguruza was stunned by Czech 13th seed Petra Kvitova.
Kvitova, 27, fought back from 1-4 down in the first set to win 7-6 (7-3) 6-3.
She goes on to face Venus Williams in the last eight on Tuesday.
Other result:
Venus Williams beat Carla Suarez Navarro 6-3 3-6 6-1
Previous articleIan Wright Hit Back At Ozil, Says "Sign A New Contract" If You Feel So Strongly About The Club
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text:
IBM, UK STFC Hartree Centre, NVIDIA and Mellanox Launch High Performance Computing POWER Acceleration and Design Center for Business
New Center Designed to Optimize Performance of OpenPOWER Systems for Modelling & Simulation and Big Data Analytics
Daresbury, UK	- 22 Oct 2015: IBM (NYSE: IBM), in collaboration with NVIDIA and Mellanox, and the Science and Technology Facilities Council (STFC) Hartree Centre today announced plans for the UK's first POWER Acceleration and Design Center (PADC) designed to help UK businesses exploit high performance computing on OpenPOWER systems for Modelling & Simulation and Big Data Analytics.
Today, IBM and the UK government's Science and Technology Facilities Council deepened their relationship with the opening of a POWER Acceleration and Design Center, an application design center for UK businesses looking gain actionable insight from their data. Business will have access to IBM POWER-based high performance computing systems infused with innovations developed by members of the OpenPOWER Foundation. (Credit: STFC)
The new facility marks a running start to the Government's recently announced investment and expansion of the Hartree Centre at Daresbury, which is designed to boost Modelling & Simulation, Big Data Analytics and Cognitive Computing research in the UK in order to deliver economic growth on a national scale.
Increasing application performance while minimizing energy consumption are challenges businesses face in exploiting conventional high performance computing technologies and the resulting race towards exascale computing. Optimizing existing applications on POWER-based systems can offer businesses significant competitive advantages.
Dr. Peter Allan, acting Director of the Hartree Centre, said: "The POWER Acceleration and Design Center will help industry and academia take advantage of IBM and NVIDIA's technological leadership in supercomputing and the Hartree Centre's expertise and experience in delivering solutions to real-world problems. It will also provide industry and academia with access to the Hartree Centre's research capabilities and network of global partners. The PADC will help the Government's ambition to provide world-leading facilities to enable UK businesses use Modelling & Simulation and Big Data Analytics to develop better products and services that will boost productivity, drive growth and create jobs."
A new POWER Acceleration and Design Center at the Hartree Centre in Daresbury represents a deeper partnership between IBM and the UK government's Science and Technology Facilities Council. With the help of technology innovations developed by IBM and fellow OpenPOWER Foundation members including NVIDIA and Mellanox, UK businesses will be able to gain competitive differentiation utilizing advanced computing systems to develop applications for modelling and simulation, cognitive computing and Big Data research. (Credit: STFC)
The POWER Acceleration and Design Center will be uniquely placed in the UK to provide both POWER-based infrastructure and consulting. The Hartree Centre, working with IBM, already has a history of collaborating on research and development with industry and academic institutions, and in providing access to its supercomputing facilities. These services are being extended to help organizations define application design and optimization projects and will assist in bringing together an ecosystem of computational scientists and engineers with expertise in specific domains such as chemistry, fluid dynamics, structures, and acoustics.
"This new POWER Acceleration and Design Center demonstrates IBM's commitment to expanding the software ecosystem around OpenPOWER. The center extends and complements the Acceleration and Design Centers in Jülich, Germany, and Montpellier, France, to a new class of industrial and commercial organizations," said Dave Turek, IBM's Vice President of High Performance Computing. "To that end, establishing it at the Hartree Center is in recognition of its successful engagement with industry and its record in commercializing technological developments."
Staff from IBM Research, NVIDIA and Hartree Centre will provide expertise and consultation to help improve performance for the GPU-accelerated OpenPOWER systems. To achieve the highest levels of performance it is important to exploit all components of a POWER-based system: architecture, memory, storage, interconnects and integration. Consequently, the Center will also be supported by the expertise of other OpenPOWER partners, including Mellanox, and will host a POWER-based system with the Tesla Accelerated Computing Platform, consisting of energy-efficient, high-performance NVIDIA Tesla GPU accelerators and enabling software.
"Building upon NVIDIA's long-standing collaboration with STFC, we look forward to working with IBM, Mellanox and the Hartree Centre to help UK science and industry achieve major new milestones using Tesla GPU-accelerated OpenPOWER systems," said Jeremy Purches, Director of High Performance Computing UK&I at NVIDIA. "The Tesla platform was designed from the ground up to deliver unmatched energy-efficient performance for today's most difficult computational challenges, and will ultimately play an important role in enabling the industry to achieve exascale computing levels."
"As founding members of the OpenPOWER Foundation, IBM, NVIDIA, and Mellanox share a common vision to bring a new class of systems with advanced interconnect technology to market faster to tackle today's data challenges," said Gilad Shainer, vice president of marketing, Mellanox Technologies. "Our joint collaboration with STFC Hartree Centre will enable businesses of all sizes to take advantage of scalable, higher application performance for optimal return-on-investment."
About the OpenPOWER Foundation
The OpenPOWER Foundation is an open technical community based on the POWER architecture, enabling collaborative development and opportunity for member differentiation and industry growth. The goal of the Foundation is to create an open ecosystem, using the POWER architecture to share expertise, investment, and server-class intellectual property to serve the evolving needs of customers and industry. Founded in December 2013, the organization now has more than 150 members worldwide.
IBM: www.ibm.com
STFC Hartree Centre: www.stfc.ac.uk/hartree
NVIDIA: www.nvidia.com
Mellanox: www.mellanox.com
OpenPOWER Foundation: http://openpowerfoundation.org
IBM and UK Government Open Center to Advance Application Development for High Performance Computing
IBM and STFC Open Center for Businesses to Design High Performance Computing Applications
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Cardinal Financial Company is not ranked because we do not have enough data. See how are ranks calculated.
Of the 132 complaints again Cardinal Financial, there have been 21 in the passed 18 months. Of those 21 complaints, 88.6% have been resolved in the customers favor, 4.6% have been disputed by the customer, and 88.6% have been resolved in a timely manner. The CFPB allows 60 days to resolve complaints before considering them "untimely".
Below is the list of the top 7 complaint reasons against Cardinal Financial Company. Click here to see all complaints against Cardinal Financial.