text:
A Royal Bad Bank: RBS Capital Resolution (RCR)
Post categories In Banking Law, Borrower Protection, Financial Services Litigation
RBS' bad bank (Royal Bank of Scotland Capital Resolution – RCR) has been set up to take over RBS' bad loans (those with significant non-performing assets). When transferring the bad assets of RBS to the bad bank, RBS will clear its balance sheet of toxic assets. RBS will therefore take write downs in which RBS shareholders and bondholders may lose money from this solution (but not depositors). The bad bank strategy is designed to allow RBS (sans bad loans) to start to trade profitably and then move into a taxpayer exit.
Those RBS customers affected (many of whom are in this vulnerable position due to interest rate swaps mis-selling by the bank itself) are being told their loans are now 'bad RCR loans' and will be dealt with in accordance with RCR objectives as enunciated below:
Background to creation of RBS Capital Resolution (RCR) and its objectives
In June 2013 the Chancellor announced a review into the case for The Royal Bank of Scotland Group (RBS) creating a bad bank. In response to the review, RBS announced on 1 November 2013 the creation of RBS Capital Resolution (RCR). RCR is a separate business within RBS whose objective is to manage and wind down RBS' higher risk and capital intensive assets by the end of 2016.
Working in consultation with the Prudential Regulatory Authority (PRA), HM Treasury (HMT) and their advisers, RBS established RCR with effect from 1 January 2014 with a portfolio of assets totalling £29 billion. The RCR portfolio includes, but is not limited to, certain lending products and other financial instruments originated by subsidiaries of The Royal Bank of Scotland Group, including RBS PLC and Ulster Bank.
RCR was established with the following principles:
removing risk from the RBS balance sheet in an efficient, expedient and economic manner;
reducing balance sheet volatility; and
accelerating the release of capital through the management and exit of the RCR portfolio.
It is our intention to manage the RCR portfolio in accordance with these principles in order to strengthen RBS' balance sheet whilst preserving value for the Bank's shareholders. RCR has committed to reduce its portfolio of assets by 55-70% by the end of 2015 and 85% by the end of 2016.
What this means for RBS customers
We are writing to inform you that the financing arrangement listed in the Appendix to this letter (the RCR facilities) has been included in the RCR portfolio and are now being managed in line with the principles and objectives outlined above.
This means that the Bank's strategy and the options we may be able to offer in restructuring the RCR facilities are subject to the overriding commitments given to the PRA and HMT as outlined above. In particular, we will be unable to extend the term of any RCR facilities beyond 2016. We may be unable to agree additional forbearance or temporary extensions of RCR facilities in the period up to December 2016.
Our objective will be to work with you to seek a consensual repayment or refinancing of your facilities prior to December 2016, irrespective of the final maturity date and whether the facilities are currently compliant with their contractual terms. If we unable to agree a consensual exit, we will be obliged to consider alternative means of removing risk from the RBS balance sheet, which may include a sale of the loans to a third party or, in extremis, enforcement against our collateral in the case of facilities that are in default against their contractual terms and where no reasonable repayment or exit strategy can be agreed.
Within these constraints we will continue to deliver excellent customer service. We will work with you to try to agree a consensual solution that meets the bank's obligations and your business objectives.
Impact on IRHP Review
As you may be aware the Bank has agreed with the Financial Conduct Authority (FCA) to conduct a review of the sale of interest rate hedging products. We have agreed with the FCA that for the period of the review, we will not foreclose on or adversely vary existing lending facilities without giving prior notice to the customer and obtaining their prior consent unless exceptional circumstances arise. The arrangements referred to in this letter do not in any way affect the review or our undertaking to the FCA.
Appointment of new (RCR specialised) Relationship Manager
In view of this development, [a new bank employee specialised in RCR relationships] has been appointed as your new Relationship Manager to actively work with you to explore your options and to develop appropriate courses of action to enable RCR to achieve its objectives.
Dealing with RBS / RCR – Borrower Protection Lawyers
Every bank-customer relationship and lending situation is unique. For many RCR customers (that are able to) exiting from RBS/RCR may prove to be the best strategy. However, where customers cannot move to other lenders the lending bank knows that the borrower is under pressure to go along with whatever they dictate. Customers are often unable to move due to the impact of, for example, (i) a shift in property prices leading to a loan to value (LTV) covenant breach, or (ii) due to the bank citing a 'market disruption clause', or (iii) changing the interest rate in some other way, or (iv) due to previous pressure to sell assets or shares (eg via RBS Global Restructuring Group – GRG) to a bank subsidiary such as RBS West Register or (v) the huge break costs of an interest rate swap or other derivative product (that may have been mis-sold in the first place).
Many NatWest and RBS customers were mis-sold interest rate hedging products (IRHPs such as swaps, collars, cancellable swaps, structured collars) which cause significant credit limit utilisation (CLU). When selling an IRHP derivative contract to a customer, the use of the customers credit limit (over charged assets) was regularly done without the (otherwise usual) customer knowledge of credit limit applications and utilisation. We believe this practice of non-notification of CLU was designed to avoid the customer appreciating the real contingent liability risk that these complex financial instruments posed (this could be tens or hundreds of thousands or millions of pounds depending on product sold and size of notional amount and term).
The Borrower Protection team at LEXLAW has a wealth of experience and success in negotiating with banks and building societies and challenging unfair lender practices and mis-selling. As the leading UK lawyers dealing with these disputes for borrowers, members of the team regularly appear in print, TV and radio broadcast media discussing misconduct by the major banks and building societies.
Tags Banking Law, Banks, Borrower Protection, Derivatives, Financial Services, GRG, Mis-selling, RBS, RBS Capital Resolution, RCR, Swaps Mis-selling, WestRegister
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Holly-Weird?! Adam Beach to play Slipknot in Suicide Squad?
Three days ago the Winnipeg Press (Adam Beach's home town newspaper) ran an article that said Adam Beach was rumoured to play Slipknot in the new Suicide Squad film. This was after the David Ayer Cast tweet. There has been some speculation on who Adam Beach would play in the film, however, there still isn't a definite answer.
With Slipknot currently making the comic book website rounds, it is time to start buying up his first appearance, still readily available on ebay for under $5.
Slipknot first appears in Fury of the Firestorm #28 – this is the second volume of the book, published in 1984.
If this rumour proves to be true this book should see a very clear increase in demand.
Happy Speculating!
tagged with comics heating up on ebay, heating up, invest, Misc. Comic Speculation, on ebay, speculate

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One Of The Country's Major Suppliers Of Police Body Cameras Has Banned Facial Recognition. But That Isn't Enough
Vanessa Taylor
Photo Credit: LOS ANGELES, CA – FEBRUARY 18: A Los Angeles police officer wear an AXON body camera during the Immigrants Make America Great March to protest actions being taken by the Trump administration on February 18, 2017 in Los Angeles, California. Protesters are calling for an end to stepped up ICE raids and deportations, and that health care be provided for documented and undocumented people. (Photo by David McNew/Getty Images)
As awareness around facial recognition continues to grow, a primary concern has been its potential to open up new frameworks for mass surveillance. That concern grew even more pressing as people realized that facial recognition could potentially be used in body cameras, essentially creating roving, real-time surveillance systems on the chests of police.
On Thursday, Axon — the company that created the Taser and supplies 47 out of the 69 largest police agencies in the United States with body cameras and software — announced a ban on the use of facial recognition on its devices. Although this can certainly be considered a temporary victory, Axon's announcement must be carefully analyzed — both within social contexts, the words that the company used, and its own history.
Axon's decision comes from the first report of an AI and Policing Technology Ethics Board that the company originally formed in April of 2018. The board was developed to lead Axon in ethically developing products and examining their impact on communities. The board wrote:
"Face recognition technology is not currently reliable enough to ethically justify its use on body-worn cameras. At the least, face recognition technology should not be deployed until the technology performs with far greater accuracy and performs equally well across race, ethnicities, genders, and other identity groups. Whether face recognition on body-worn cameras can ever be ethically justifiable is an issue the Board has begun to discuss, and will take up again if and when these prerequisites are met."
Within the board's decision, careful attention must be paid to the phrasing "not currently reliable enough" and the continued suggestion that the technology can reach a point of reliability. Indeed, facial recognition has a multitude of problems when it comes to recognizing — or even seeing — anyone who isn't a white man. Amazon's Rekognition is an easy program to examine in order to see some of facial recognition's issues.
In July 2018, the ACLU reported that Rekognition incorrectly matched 28 members of Congress to mugshots, including six members of the Congressional Black Caucus. Early this year, researchers from M.I.T. Media Lab found that Rekognition had greater errors in trying to recognize darker-skinned women. While testifying at a House hearing where advocates were requesting a moratorium on facial recognition, one of the researchers, Joy Buolamwini — who also founded the Algorithmic Justice League — said about her research:
"In one test, Amazon recognition even failed on the face of Oprah Winfrey labeling her male. Personally, I've had to resort to literally wearing a white mask to have my face detected by some of this technology. Coding in white face is the last thing I expected to be doing at M.I.T., an American epicenter of innovation."
On its face, all of this seems to be evidence that supports the Board's conclusion. Facial recognition isn't ready to be commercialized — yet. However, the Board misses perhaps the most obvious factor of this discussion: the role of the police. Although facial recognition may be riddled with errors, even a perfect program does not belong in the hands of police departments who would then be able to turn it against already vulnerable communities. Beyond the police themselves, the society that we live in has created conditions where facial recognition simply shouldn't exist because it impacts will not be evenly distributed.
Axon may have tried to paint itself as doing some good by creating this ethics board, but the best thing the company could do is not exist. This is, after all, the developers of the Taser. Although no government agency really tracks Taser use or its impacts, a 2019 Reuters review of police records, news reports, and court documents found that at least 49 people died after being shocked by police with a Taser in 2018 alone.
There will never be a point where police use new technologies like facial recognition for the benefit of marginalized communities because police do not exist for the benefit of the oppressed. Nothing makes that more clear than body cameras themselves. Although they were often touted as solutions to police brutality, people are now rushing to ensure they can't be used to mass surveil people for simply existing on the streets.
Ultimately, Axon's decision is temporary relief disconnected from history. The company is waiting on a "maybe" that should never come. Facial recognition will never be reliable enough for police use or incorporation into body cams.


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This past weekend, we packaged up carefully all of my fine art botanic prints.... and Eric designed beautifully our display at Ravenswood Art Walk 2009. This art exhibit had over 2,000 attendees, 184 artists and 43 venues...phew! It was blocks and blocks of artistic talent all out for all to see. There were dancers, painters, sculptors, photographers, graphic artists, chefs, etc etc! We had a great time meeting and greeting the neighborhood and new friends. Thank to all who came out to support what is so close to my heart.
This group of dancers was Jeezebelly. They were awesome!
This year Ravenswood Art Walk had a special exhibit, The Blago Project. It took place in the former wire tapped offices of the Friend of Rod Blagojevich. The exhibit was rooms and hallways filled with Blagojevich as the inspiration.
This was my favorite art from "The Blago Project" by Bianca McGraw made of cardboard and synthetic hair. Gross but awesome.
Last but not least is Simply Jessie Photography's exhibit!


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Satan's Snowdrop
Pocket Books, 256pp
Guy N. Smith had established himself on the horror bookshelves in the seventies with a string of novels with easily definable themes. City folk move to the countryside but don't understand country lifestyles and rub the locals the wrong way. Conflict between man and nature manifests in deadly attacks by the latter against the former. Whether it was werewolves or crabs or bats or locusts, the approach wasn't a particular varied one, even if it grew in pessimism and vehemence as the decade ran on. He wasn't any one trick pony, given the porn digests, Disney novelisations, thrillers and a sadistic war novel that also saw release in the seventies, but this novel was a major departure for him.
It's not surprising that Pocket Books picked it up for distribution in the United States, their only title of his, because it's fundamentally an American novel. It's not that the lead characters during the first half of the book are American, because those in the second half are British, the villain is German, a Gestapo torturer, and the key location that haunts the entire novel is a plot of land just behind the Reichenbach Falls, a cable car journey up from Meiringen in Switzerland. It's that the themes are all American, with a strong focus on evil strong enough to seep into a building and remain dangerous down the years; that building being surprisingly mobile, being packed into crates and shipped initially from Switzerland to the U.S. and then again to England, under the direction of the rich and powerful who do such things on whims.
It's the title that resonated with me first. It was pitched as 'The Scavenger's Daughter', the name of an evocative torture device that's mentioned early in the novel but doesn't otherwise factor into the plot. It was written under the underwhelming title of 'Snowdrop', which was then unsurprisingly changed to 'Satan's Snowdrop' in between the generation of the proof cover and the final publication by Hamlyn, the first edition carrying its eventual title on the front cover but its previous incarnation on the spine. It ended up with the right name, I'm sure, because it's a stellar juxtaposition of darkest horror and the most innocuous of flowers.
What it recounts is a journey of inevitability, reliant on the arrogance of the beautiful people who think they can do anything. Everything that happens here, every single death and every single disaster, could easily have been prevented, had a succession of these people just seen beyond their own narcissism. It's not a flattering story to Americans, really, as it takes brutal aim at a traditional American worship of a fake class of nobility, the sort of people who are famous only for having lots of money and who flaunt it on shows like 'Lifestyles of the Rich and Famous'. If he wasn't fictional, Al Pennant would surely have an ongoing invitation to showcase his latest property and trophy wife on that show.
He's the lead for the first half of the book, even though it's named for his son, Tod, and the character of most note exudes his foulness throughout it, even though he dies during the prologue. The latter is the villain of the piece, a man named Reichenbach, who is finally caught after a twenty-year revenge quest by one of his prison camp subjects, a French Resistance operative named Pierre Lautrec. His vengeance is short-lived because the German is shrunken and cancerous, already near death even before he visits karma upon him, shredding him with a steel-tipped bullwhip just as Reichenbach had taken his testicles two decades earlier with six expert strokes while he was manacled to a ceiling. After Reichenbach dies, Lautrec does too, whirled through supernatural visions and dropped down the steps of the house to die in front of a blood-drenched snowdrop in the Swiss dirt.
Fast forward in time and Reichenbach's house is a decaying mansion up for sale for half a million bucks. La Maison des Fleurs, they call it and Al Pennant, a millionaire from New York, snaps it up for a discount with plans to have it renovated in situ, live in it with his wife and son for a few months and then ship it back to the States in the spring to sit on a plot of land in Long Island. He gets as far the renovation and a posh party to celebrate, but then the deaths begin. One guest dies in her bed of sheer terror, having suffered a torture chamber hallucination. She's not the only one to see things in the house either, with Al's trophy wife Veronica and their ten-year-old son Tod tormented by them. Of course, confident old Al ignores them both, because if he didn't, we wouldn't have a novel.
Almost everything here feels like new ground for Smith. Only two aspects feel familiar. The brutality of the gore is one, because the proclivities of a sadistic Gestapo torturer are just perfect fodder for him. The other is a blurring of time, characters in the present day being whisked back supernaturally in time, but not place, to experience something crucial to the plot. That begins in the prologue with Lautrec's journey back to the prison camp before he dies. Elizabeth Quilmer, the guest who dies at the party, has no history with Reichenbach but is transported back to his torture chamber too, as is Al Pennant's wife Veronica, prompting her to press to leave the building with her son, against her husband's wishes.
Of course, Al doesn't let her, talking her out of such ridiculous notions, and that ends as badly as you're already expecting, with Tod taking a dive out of the window of the freshly moved La Maison des Fleurs in Long Island after a memorable scene with a demonic space-hopper—I kid you not—and Veronica sent mad by her son's death, dying herself ten months later in an asylum. And so Al has to sell, because what else can he do? His prime competitor, Bruce Parlane, is offering him a good price and so this house goes back into packing crates to be shipped into the English countryside to torment him and his family, Welsh wife Anthea and their son Rusty, the second half not a million miles distant from the first.
I liked this novel more on a fresh re-read than I remember doing back in the eighties, but it doesn't feel like a Guy N. Smith novel. I don't know if he was targeting the American market, which had seen a pair of his books already reprinted by Signet: 'Killer Crabs' and 'Bats Out of Hell'. Pocket took this one, then Dell took over for eight more in the second half of the eighties. However, he didn't break that market with regards to new material until the nineties, when Zebra started to publish some originals, starting with 'Witch Spell' in 1993, another primarily psychological horror novel, albeit in a different vein to this one.
What I liked was how Smith managed to adapt his traditional themes into a new framework. There may not be a clash here between city folk and country folk, but there's a clash between the arrogant rich of the world, whether they be American or English, and the regular people, the policemen, workmen and others, who all offer the same common sense advice but are all consistently ignored. Armand Heron is the chief gendarme in Meiringen and Al Pennant wants protection from him against whoever's aiming to get him to quit his investment; but what he gets is the firm statement that M. Heron wouldn't spend a night in La Maison des Fleurs for a million francs. If only the rich man had listened to the regular man, he'd still have a wife and son, if not a haunted house.
What I didn't like was how it so obviously played to the American market. Not subscribing to the theory that people who have money are inherently better than those who don't, I didn't give a monkey's about Al Pennant or Bruce Parlane. Quite frankly, I was waiting for them to get theirs, but Reichenbach takes his time and it's the innocents who suffer first. Then again, I can't really call Veronica Pennant innocent given that she presses to leave with Tod at one point, only to give in to her husband's rationalisations as she wouldn't be able to easily find another husband worth this many millions if she does. She's sensitive in ways that he isn't, but she's just as shallow and worthless. Of course, I was hardly rooting for the Nazi torturer, but he's emphatically a monster here rather than a man, so I don't know how much ideology is applicable. He was an evil sadist and would have remained so regardless of which uniform he wore.
I wonder what Guy really thought of this novel. It's not a bad one at all and it does its job well, though I have to note that it racks up a substantial cast of characters without many of them getting more than a brief moment in the spotlight. It's primarily about two rich families of three and they're the only ones who are given any real depth, which is difficult to achieve, of course, when they're so shallow. Many of the scenes feel like the inner monologuing that Guy was so good at, especially in moments of terror, is just let loose to run amok. Mostly it feels like an experiment to break into a new market; that failed and so was discarded. I don't recall any of Guy's other novels feeling so American in approach except perhaps 'Blood Circuit' a couple of years later, which I haven't revisited yet.
Maybe it was important to him, not as a horror novel but as the opportunity to explore one of his many fascinations within the framework of one. I'm talking, of course, about Sherlock Holmes, a character he adored and adapted into a number of his own detectives. Whenever he wrote a Dixon Hawke story or a Raymond Odell story, he was really writing a Sherlock Holmes story without the ability to mention that name. Eventually, of course, he was able to write a real Holmes story, 'The Case of the Sporting Squire', based on a case that Sir Arthur Conan Doyle mentioned but never wrote. It appeared in a Mike Ashley edited anthology, 'The Mammoth Book of New Sherlock Holmes Adventures'.
Holmes, of course, famously met his supposed end, along with his arch-nemesis, Professor Moriarty, at the Reichenbach Falls, such an important location here. Guy went as far as to visit this pivotal location, in 1958, and, of course, wrote about the trip in an article called 'A Pilgrimage to the Reichenbach Falls'. "My mission," he wrote, "was simply to satisfy my curiousity, perhaps to try and absorb the atmosphere engendered by Sir Arthur Conan Doyle, and to compare the scene of 'The Final Problem' with that story which had fired my imagination." He described a deserted Meiringen, a solo ten-minute cable car ride up onto the mountain and the deafening noise of the waterfall from the platform at the end of a trail.
It's an interesting piece, full of atmosphere, that clearly highlighted how this location stayed with the author, who was visiting as such even that long ago, rather than as a regular tourist. I don't find it at all surprising that it should eventually show up in one of his horror novels, especially given the way that he chose to end it. But there lie spoilers. ~~ Hal C F Astell


MEET YOUR BLOCK ISLAND REAL ESTATE AGENTS!
After years of training with Edith Blane, Sue and Robin have taken the helm and are keeping the tradition of island resident ownership at Offshore Property. Their island experience, insight, and personal service will make your island experience a success!
Friends and neighbors for over 30 years, Robin and Sue raised their families here on Block Island and are excited to have their daughters Krista Vila and Cori Black in the business. In 2017, Sue and Robin were happy to welcome long-time friend Kerri Gaffett to the office as a Sales Associate.
Robin came to Block Island in 1978 to work at the Manisses. She fell in love with Block Island and eventually married and raised her two children, Krista and Clayton. She has worn many hats over the years, including owning a small charter airline business and managing the Manisses Restaurant. Robin started working for Edie in 2011, got her real estate license in 2012, bought the business with Sue in 2013, and became the Principal Broker in 2015.
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Sue came to Block Island in the late 1970's with college friends. She married her husband Bill and they decided to make Block Island their year-round home. They raised two children here, Henry and Cori. She has worked for several businesses on the island including the Block Island Maritime Institute and the BI Early Learning Center. Sue began working in real estate in 1986 but left the business to pursue other interests. She always kept her license and eventually came back to the business in 2007 to work for Edie at Offshore Property. When Edie decided to retire, Sue knew it was a great opportunity and bought the business with her longtime friend Robin.
Sue is available to help you with Real Estate Sales.
Edith (Edie) Littlefield Blane is a lifelong Block Island resident and former owner of Offshore Property. She brings a wealth of historical knowledge and humor to our office. Edie was born and raised on the island and raised her two sons, Chris and Peter, here. Over the years, Edie has played a vital role in the fabric of Block Island, serving as one of the first telephone operators and many years as New Shoreham Town Clerk. She is semi-retired now but still enjoys helping people find their place on Block Island.
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Richard Foreman has owned property on Block Island since 1982 and has lived here full time for the past 13 years. He co-owns and operates Eylant Antiques inside Payne's Harborview Inn. Rick's real estate experience includes working as a real estate salesperson and broker in New York City for nearly 14 years. Offshore Property has been renting his home on Beacon Hill, known as Little Beaver, for over 25 years.
Rick Foreman passed away on May 5, 2017. We will miss him terribly. Read his obituary here.
Corlies (Cori) was raised on Block Island. She attended the Block Island School and graduated in 2009. In the Spring of 2014, she graduated from the University of Rhode Island with a degree in the Gender and Women's Studies. She became a Real Estate agent in March 2015.
Cori would love to help you with Vacation Rentals and Real Estate Sales.
Kerri is a lifelong resident of Block Island. She attended the Block Island School and graduated in 1985. In 1989 she graduated from Brown University with a BA in Psychology. For 25 years she raised her family and built homes on Block Island as half owner of Spier Construction. In 2015 Kerri and her husband were divorced and she began life anew as Kerri Gaffett. She has been an active community member, serving on the Conservation Commission, School board, and as an EMT. After sailing around the world and raising her three children she returned to school and became a licensed psychotherapist. In 2013 she graduated with a MA in Counseling from Salve Regina University. In 2016 Kerri went back to school again and became a licensed Real Estate Sales Associate.
Kerri would love to help you with Real Estate Sales.
Krista was raised on Block Island. She graduated from the Block Island School in 2008. Krista splits her time between Block Island and Providence, RI. While on the island, she works for Jessie Edwards Studio as a gallery assistant and at Offshore Property as an office assistant. Krista recently became a licensed Real Estate Salesperson.
Krista is available to help you with Real Estate Sales.
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text:
L'antic Carlos Tartiere (1932-2003), estadi del Real Oviedo entre els anys 1932 i 2000;
 El nou Carlos Tartiere (2000-), estadi del Real Oviedo des de l'any 2000.

Vegeu també
 Carlos Tartiere, la persona de qui els estadis prenen el nom.

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text:
Wisbech Standard > News > Business
Anglian Water pumps £250,000 into new training centre at College of West Anglia, Wisbech, to help produce next generation of apprentices
Published: 3:20 PM June 18, 2018 Updated: 6:24 AM November 4, 2020
New training centre at the College of West Anglia in Wisbechy opened by MP Steve Barclay, following sponsorship from Anglian Water. Photo includes MP Steve Barclay and Peter Simpson, CEO Anglian Water - Credit: Archant
A new training centre at the College of West Anglia in Wisbech was officially opened last Friday by MP Steven Barclay, following sponsorship from Anglian Water.
New training centre at the College of West Anglia in Wisbechy opened by MP Steve Barclay, following sponsorship from Anglian Water. - Credit: Archant
The water company invested £250,000 in the new facility at the college, which will train both Anglian Water engineers allowing them to continue in their professional development, as well as providing new skills for the next generation of apprentices.
As part of the Wisbech 2020 Vision, the new centre will be used for technical training and skills development as well as being an official assessment centre for employees gaining industry recognised qualifications.
It will offer a wide range of opportunities for employees and apprentices to gain skills across a variety of technical areas, including New Roads and Street Works training, permit writing, low risk confined spaces qualifications and operational manual handling training. The centre has been City and Guilds accredited for external qualification assessments.
Phil Brown, head of people development at Anglian Water said: "Ensuring that Anglian Water people get the best training to develop themselves and their career is hugely important to us. This facility will enable them to gain valuable and practical skills as well as achieving nationally recognised qualifications.
"Around a third of Anglian Water's workforce will retire in the next decade so we need to attract and then train the budding engineers and technicians from the next generation and give them the right skills and experience to be successful in our business."
He said: "A classroom at the college has been refurbished with training facilities suitable to provide hands on experience on topics like sampling and water regulations. There is also an outdoor training space, where delegates can use mock equipment, learning the skills associated with metering, leakage detection, and supply restoration."
8 Ever get that sinking feeling?
10 Man suffers serious injuries after two-vehicle crash on A47
Mr Barclay said: "Increasing skill provision is critical to delivering the Wisbech 2020 Vision.
"The new centre is a brilliant example of public and private sector collaboration, and it demonstrates the role responsible business has to play in improving the prospects of young people and underpinning economic growth in and around the town."
Anglian Water will also be hosting an open day at the new training and development facility between 11am and 3pm on Friday July 6. To register a place, please email public.affairs@anglianwater.co.uk .
To find out more and apply, visit the apprenticeship page on the Anglian Water website here: www.anglianwatercareers.co.uk/student/apprentice-trainee-schemes.aspx


Yellow is my color of hope. What's yours?
Muntik ko na makalimutan may blog pala ako. Sobrang busy!
Red is love. Courage. War. But hope? Wala naman yatang red sa presidentiables ah, hehehe.
Well for me, yellow is a color of jealousy. My friends would always quote "I feel yellow" especially when there's an issue between friendships. But yellow, could also mean hope. I remember, yellow is the motif of people power during the leadership of a tyrant.
Yup, Yellow represents the color of the HOPE.


text:
\section{Introduction}
\label{sec:intro}

Hyper-parameter tuning is a crucial phase to optimize the performance of machine learning (ML) models, which is notoriously expensive given that it typically implies repeatedly training models over large data sets. State-of-the-art solutions address this issue by exploiting cheap, low-fidelity models (e.g., trained with a fraction of the available data) to extrapolate the quality of fully trained models.

HyperBand~\cite{hyperband}, henceforth referred to as HB,  is probably one of the most popular solutions in this area. HB is based upon a randomized search procedure, called Successive Halving (SH)~\cite{successiveHalving}, which operates in stages of fixed ``budget'' (e.g.,  training time or training set size): at the end of  stage $i$, the best performing 1/$\eta$ \% configurations are selected to be evaluated in stage $i+1$, where they will be allocated $\eta\times$ larger budget. By restarting the SH procedure over multiple, so called, brackets using  different initial training budgets, HB provides theoretical guarantees of convergence to the optimum, incurring negligible computational overheads and outperforming state-of-the-art optimizers (e.g., based on Bayesian Optimization (BO) \cite{boTutorial}) that do not exploit low-fidelity observations. However, the random nature of HB also inherently limits its efficiency, as shown by recent model-based multi-fidelity approaches~\cite{bohb,fabolas}.

We introduce HyperJump (HJ), a novel hyper-parameter optimization method that builds upon HB's robust search strategy and accelerates it via an innovative, model-based technique. In a nutshell, the idea at the basis of HJ is to ``jump'' (i.e., skip either partially or entirely) some of HB's stages.
To minimize the risks associated with jumps, while  maximizing the attainable gains  by favoring earlier jumps, HJ exploits, in a synergistic way, three new mechanisms:

\vspace{-3mm}
\begin{itemize}
    \item A novel modelling technique to predict the risk of jumping, which we call Expected Accuracy Reduction (EAR). The EAR exploits the model's uncertainty in predicting the quality of untested configurations as a basis to estimate the expected reduction in the accuracy between   \textbf{(i)} the best configuration included in the stage reached after a jump and  \textbf{(ii)}  the best configuration  discarded due to the jump.
    \vspace{-2mm}

    \item A criterion for selecting the configurations to include in the HB stage targeted by a jump, which aims to minimize the jump's risk. This is  a combinatorial problem\footnote{The number of candidate configuration sets for a jump to a stage with $k$ configurations from one with $n$ configurations is $\binom{n}{k}$.}, which we tackle via a lightweight  heuristic that has  logarithmic complexity with respect to the number of  configurations in the target stage of the jump.
    \vspace{-2mm}

    \item A method for prioritizing the testing of configurations that aims to promote future jumps, by favouring the sampling of configurations that are expected to yield the highest risk reduction for future jumps.
\end{itemize}

We compare HJ with a number of state-of-the-art optimizers~\cite{hyperband,fabolas,Practical_BO,bohb,asha} on a suite of hyper-parameter optimization and neural architecture search~\cite{nats} problems, on both sequential and parallel deployments. We show that HJ  provides up to over one-order of magnitude speed-ups on a variety of deep-learning and kernel-based learning problems.
We also conduct an ablation study that sheds light on the contributions of the various mechanisms of HJ on its  performance.

\section{Related Work}
\label{sec:rw}

Existing hyper-parameter techniques can be coarsely classified along two dimensions: \textbf{i)} whether they use model-free or model-based approaches; \textbf{ii)} whether they exploit solely high-fidelity evaluations or also multi-fidelity ones.

As already mentioned,
HB  is arguably one of the most prominent model-free approaches. Its random nature, combined with its SH-based search algorithm, makes it not only provably robust, but also efficiently parallelizable~\cite{hyperband,bohb,asha} and, overall, very competitive  and  lightweight.

As for the model-based approaches, recent literature on hyper-parameter optimization has been dominated by  Bayesian Optimization (BO)~\cite{boTutorial} methods. BO relies on modelling techniques (e.g., Gaussian Processes~\cite{GP_book}, Random Forests~\cite{Random_Forest} or Tree Parzen Estimator (TPE)~\cite{tpe}) to build a surrogate model of the function $f:\mathcal{X}\rightarrow \mathbb{R}$ to be optimized. The surrogate model is then used to guide the selection of the configurations to test via an \textit{acquisition function} that tackles the exploration-exploitation dilemma. A common acquisition function is the Expected Improvement (EI) \cite{ei}, which exploits information of the model's uncertainty on an untested configuration $c$ to estimate by how much $c$ is expected to improve over the current incumbent.

SMAC~\cite{smac} and MTBO~\cite{mtbo} were probably the first to  adapt the BO framework to take advantage of low-fidelity evaluations, obtained using  training sets of smaller dimensions. This idea was extended in Fabolas~\cite{fabolas}, whose
acquisition function factors in the trade-off between the cost of testing a configuration and the knowledge it may reveal about the optimum.
A related body of work~\cite{freeze-thawBO,loss_NN_gps,bo_stop,Vizier}
uses models (typically GPs) to predict the loss of neural networks as a function of both the hyper-parameters and the training iterations. Models are then used to extrapolate the full-training loss and cancel under-performing training runs.

From a methodological perspective, the fundamental difference between HJ and this body of model-based works lies in the type of problems that are addressed by their modelling techniques. Since HJ exploits HB's search strategy, the models employed in HJ are meant to address a different problem than the above mentioned model-based approaches, namely quantifying the risk of discarding high quality configurations among the ones currently being tested in the current HB's stage. In other words, HJ's models are used to reason on a relatively small search space, i.e.,~the configurations in the current stage. Conversely, the previously mentioned approaches, which do not take advantage of HB's search algorithm, employ models to estimate configuration's quality across the whole search space.
Further, the modelling techniques used in some of these works~\cite{fabolas} require computationally expensive implementations, which can impose significant overhead especially when the cost of evaluating configurations is relatively cheap (e.g., when using low-fidelity observations) (Sec.~\ref{sec:eval}).

By taking advantage of the (theoretically provable) robustness of HB, which HJ accelerates in a risk-aware fashion via model-driven techniques, we argue that HJ fuses the best of both worlds: it preserves HB's theoretical robustness (Sec.~\ref{sec:theo}), while accelerating it by more than one order of magnitude  via the use of lightweight, yet effective, modelling techniques (as we will show experimentally).

The works more closely related to HJ are several recent approaches, e.g.,~\cite{bohb,bohb_similar1,bohb_similar2}
that extend HB with BO to warm start it, i.e., to select (a fraction of) the configurations to  include in a new HB bracket.
This mechanism, which HJ also incorporates (Sec.~\ref{sec:warmstart}), is complementary to the key novel idea exploited by HJ to accelerate HB, i.e., short-cutting the SH process by skipping to later SH stages in low risk scenarios.
As we will show via an ablation study comparing the impact of the various mechanisms employed by HJ, ``jumping'' is clearly the one that contributes the most to HJ's efficiency. Further, when compared with BOHB~\cite{bohb}, which warm starts HB using BO, HJ provides more than one order of magnitude speed-ups.

\begin{algorithm}[t]
    \caption{Pseudo-code for a HJ bracket consisting of $S$ stages, with budget $b$ for the  initial stage.}
    \scriptsize
    \label{alg:overall}

    \begin{algorithmic}[1]

    \STATE {Set}$\langle$Config$\rangle$ $C$ = \textsc{get\_configs\_for\_bracket}() \COMMENT{Model-driven bracket warmstart (Sec.~\ref{sec:warmstart})} \alglinelabel{alg:get_conf}

    \STATE {Set}$\langle$Config$\rangle$ T = $\emptyset$; {Set}$\langle$Config$\rangle$U = $C$; \COMMENT{T and U contain the tested/untested configs, resp.}
    \FOR [s denotes the current stage] {$s \in \{0,\ldots, S-1\}$}

    \STATE \textbf{bool} jump = \textit{false};
    \WHILE [Test configs. in curr. stage, or jump to a future stage] {$U \neq \emptyset$}

    \STATE $\langle$target, $\mathcal{S}\rangle$ = \textsc{evaluate\_jump\_risk}($s$, T, U) \COMMENT{HJ risk-analysis (Sec.~\ref{sec:risk})} \alglinelabel{alg:risk}
    \IF [Jump to target stage] {target $\neq s$}
    \STATE $s$ = target; T = U = $\emptyset$; $C$ = $\mathcal{S}$ ; jump = \textit{true}; \textbf{break};
    \ELSE

    \STATE c = \textsc{next\_conf\_to\_test}(U, T, $s$); \COMMENT{Next selected config.~minimizes future risk (Sec.~\ref{sec:order})} \alglinelabel{alg:test}
    \STATE acc = \textit{evaluate\_config}(c,$b\eta^s$) \COMMENT{Measure  config. c with budget $b\eta^s$ and return its accuracy}
    \STATE T = T $\cup~\{c\}$;   U = U $\setminus$ \{c\}
    \STATE \textit{update\_model}($\langle c,b\eta^s,acc\rangle$)

    \ENDIF
    \ENDWHILE
    \IF [Use HB's policy if HJ did not trigger a jump by the end of the stage] {$\neg$jump}
    \STATE U = $C$ = topK($C$, $|C|\eta^{-1}$) \COMMENT{test top $1/|\eta|$\% configs. in next stage}
    \ENDIF
    \ENDFOR

    \end{algorithmic}
\end{algorithm}
\setlength{\textfloatsep}{4mm

\section{HyperJump}
\label{sec:hyperjump}

As already mentioned, an HB bracket is composed of up to $S_{max}$ stages, where $S_{max}=~\lfloor~log_{\eta}(R)\rfloor$. $R$ is the maximum ``budget''  allocated to the evaluation of a hyper-parameter configuration and $\eta$ is an exponential factor (typically 2 or 3) that controls the increase/decrease of the allocated budget/number of tested configurations in two consecutive stages of the same bracket.

The pseudo-code in Alg.~\ref{alg:overall} overviews  the various mechanisms employed by HJ to accelerate a single HB bracket composed of $S<S_{max}$ stages. The bracket's initial stage allocates a budget $b$, where $b=R\eta^{-(S-1)}$, as prescribed by HB.
HJ leverages two main mechanisms to accelerate the execution of a HB bracket, which are encapsulated in the functions \textsc{evaluate\_jump\_risk} and \textsc{next\_config\_to\_test}.

\textsc{evaluate\_jump\_risk} is executed within HJ's inner loop (Alg.~\ref{alg:overall}, line~\ref{alg:risk}), to decide whether to stop testing configurations in the current stage and jump to a later stage. This function takes as input the current stage, $s$, and the configurations already and still to be tested, $T$ and $U$. It returns the pair $\langle target, \mathcal{S}\rangle$ where $target$ denotes the stage to jump to (in which case $target \neq s$) and $\mathcal{S}$ the selected configurations for the target stage. We detail this function  in Sec.~\ref{sec:risk}

If the risk of jumping is deemed too high, HJ continues testing configurations in the current stage. Unlike HB, which uses a random order of exploration, HJ prioritizes the order of exploration via the \textsc{next\_conf\_to\_test} function (Alg.~\ref{alg:overall}, line~\ref{alg:test}). This function  seeks to identify a configuration whose evaluation will lead to a large reduction of the risk of jumping, so as to favour earlier jumps and enhance the  efficiency of HJ. We discuss how we implement this function in Sec.~\ref{sec:order}.
After testing configuration $c$ in budget $b\eta^s$ and measuring its accuracy $acc$, we update the models used to predict the accuracy of untested configurations (with different budgets). We discuss the type of models used by HJ in Sec.~\ref{sec:conf-model}.
Finally,  \textsc{get\_configs\_for\_bracket} (Alg.~\ref{alg:overall}, line~\ref{alg:get_conf}) encapsulates the model's logic to select the configurations to be included in a new bracket, which we detail in Sec.~\ref{sec:warmstart}. We include in the supplemental material a figure to illustrate the key differences between HJ and HB, as well as an analysis of HJ's algorithmic complexity.

\subsection{Modelling configurations' quality}
\label{sec:conf-model}

HJ's risk analysis methodology  relies on GP-based models to estimate the accuracy of a configuration $c$ in a given budget.
As in recent related works, e.g.,~\cite{fabolas,klein2020model,trimtuner}, we include in the feature space of the GP models not only the hyper-parameters' space, but also the budget (so as to enable inter-budget extrapolation). Further, analogously to, e.g.,~\cite{fabolas,trimtuner}, we employ a custom kernel that encodes the expectation that the loss function has an exponential decay with larger budgets along with a generic Matérn 5/2 kernel that is used to capture relations among the hyper-parameters.

\if 0 Independently of the choice of underlying learner, we use as feature
\textcolor{red}{review} We add an additional dimension to the search space that corresponds to the budget in order to achieve better extrapolation of the configuration's accuracy over the budgets.
Similar to Fabolas, HJ uses kernels designed to capture the expected impact on loss deriving from sub-sampling (the kernel is obtained by the inner product of a Matérn 5/2 kernel and a custom kernel that encodes the expectation that the loss function presents an exponential decay with larger budgets).
When the number of explorations increases and the cost of using GPs suffers a significant increase, HJ changes the model and starts to use a bagging ensemble of 50 decision trees. We inject diversity among the learners by generating their data sets drawing with replacement from the same data-set. The predictions follow a Gaussian distribution with mean and standard deviation derived from the predictions of each learner in the ensemble.
We choose to use this hybrid approach because we verified a drop on the performance when using only DTs. When the number of observations is small and there is not enough diversity in the data, the predictions of DTs can have a poor quality.

\fi

\subsection{Deciding whether to jump}
\label{sec:risk}

We address the problem of deciding whether to adopt HB's default policy or skip some, or even all, of the future stages of the current bracket by decomposing it into three simpler sub-problems:
\textbf{1)} modelling the risk of  jumping from the current stage to the next stage while retaining an arbitrary subset $\mathcal{S}$ of the configurations $C$ in the current stage;
\textbf{2)} identifying ``good'' candidates for the subset of  configurations to  retain after a jump from stage $s$ to stage $s+1$, i.e.,  configurations that, if included in the target stage of the jump, reduce the risk of jumping;
\textbf{3)} generalizing the risk modelling to jumps that skip  an arbitrary number of stages.
Next, we discuss how we address each sub-problem.

\subsubsection{Modelling the risk of 1-hop jumps.}
\label{sec:model-1hop}

Let us start by discussing how we model the risk of jumping from a source (current) stage $s$ to a target stage $t$ that is the immediate successor of $s$ ($t\!\!=\!\!s\!+\!1$).
Consider $C$ the configurations in $s$; $T$ and $U$ the tested/untested configurations in $s$, resp., and $\mathcal{S}$ and $\mathcal{D}$ the subset of configurations in $C$ that are selected/discarded, resp., when jumping to stage $t$.
Our modelling approach is based on the observation that  short-cutting  HB's search process and jumping to the next stage exposes the risk of discarding the configuration that achieves maximum accuracy in the current stage (and that may turn out to improve the current  incumbent when tested in full-budget). This risk can be modelled as the difference between two random variables defined as the maximum accuracy
of the configurations in the set of discarded $\mathcal{D}$ and selected $\mathcal{S}$ configurations in stage $s$ and budget $b\eta^s$, resp.:
$$\mathcal{A}^\mathcal{D}_{s} =\underset{c \in \mathcal{D}}{\text{max}} ~A(c, b\eta^s), ~~~\mathcal{A}^\mathcal{S}_{s} = \underset{c \in \mathcal{S}}{\text{max}} ~A(c, b\eta^s)$$
having denoted with $A(c_i, b\eta^s)$ the accuracy of a configuration $c_i$ using budget $b\eta^s$.
From a mathematical standpoint, we  only require $\mathcal{A}$ to be finite, so that the maximum and difference operators are defined.
So, one may use arbitrary accuracy metrics as long as they  match this assumption, e.g., unbounded but finite metrics like negative log likelihood.
\if 0
defined as the maximum accuracy $A$
of the configurations $c$ in the set of discarded $\mathcal{D}$ and selected $\mathcal{S}$ configurations in stage $s$ and budget $b\eta^s$, respectively.
\fi

One can then quantify the ``absolute'' risk of a jump from stage $s$ to stage $s+1$, which we call Expected Accuracy Reduction (EAR) (Eq.~\eqref{eq:eal}), as the expected value of the difference between these two variables, restricted to the scenarios in which configurations with higher accuracy are discarded due to jumping (i.e., $\mathcal{A}^\mathcal{D}-\mathcal{A}^\mathcal{S}>0$):

\vspace{-4mm}
\begin{footnotesize}
\begin{equation}
\begin{aligned}
 EAR_{s}^{s+1}(\mathcal{D},\mathcal{S}) &\smalleq
\int_{-\infty}^{+\infty}  \!\! P \!\left(
\mathcal{A}^\mathcal{D}_{s} \!- \!\mathcal{A}^\mathcal{S}_{s}
\smalleq x \right)  \text{max}\{\mathcal{A}^\mathcal{D}_{s} \!- \! \mathcal{A}^\mathcal{S}_{s},0\} dx  \\& \smalleq
\int_{0}^{+\infty} \!\!  x P\left(
\mathcal{A}^\mathcal{D}_{s}  \!- \! \mathcal{A}^\mathcal{S}_{s}
 \smalleq x \right) dx
\end{aligned}
\label{eq:eal}
\end{equation}
\end{footnotesize}
The EAR is computed as follows. The configurations in $\mathcal{D}$ and $\mathcal{S}$ are either untested or already tested. In the former case, we model their accuracy via a Gaussian distribution (given by the GP predictors); in the latter case, we model their  accuracy either as a Dirac
function (assuming noise-free measurements, which is HJ's default policy) or via a Gaussian distribution (whose variance can be used to model  noisy measurements). In any case, the PDF and CDF of $\mathcal{A}^\mathcal{D}$ and $\mathcal{A}^\mathcal{S}$ can be computed in closed form. Yet, computing the difference between these two random variables requires solving a convolution that cannot be determined analytically.  Fortunately, both this convolution and the outer integral in Eq.~\eqref{eq:eal}  can be  computed in a few msecs using open source numerical libraries (Sec.~\ref{exp_res}). Additional details on the EAR's computation  are provided in the supplemental material.

Next, we introduce the rEAR (relative EAR), which is obtained by normalizing the EAR by the loss of the current incumbent, noted $l^{*}$:
$rEAR_{s}^{s+1}(\mathcal{D},\mathcal{S})=
{EAR_{s}^{s+1}(\mathcal{D},\mathcal{S})}/{l^*}$.
The rEAR  estimates the ``relative'' risk of a jump
and can be interpreted as the percentage of the maximum potential for improvement that is expected to be sacrificed by a jump.
In HJ, we consider a jump ``safe" if its corresponding rEAR is below a threshold $\lambda$, whose default value we set to $10\%$. As we show in the supplemental material,
in practical settings,  HJ has robust performances for a large range of (reasonable) values of $\lambda$.
One advantage of using the rEAR, instead of the EAR as risk metric is that the rEAR allows for
adapting the risk propensity of HJ's logic, making  HJ progressively less risk prone as the optimization process evolves and new incumbents are found.

\vspace{-1mm}
\subsubsection{Identifying the safest set of configurations for a jump.}\vspace{-1mm}

\label{sec:sel_select}

Determining the safest subset of configurations to include when jumping to the next stage via naive, enumerative methods would have prohibitive costs, as it would require evaluating the rEAR for all possible subsets $\mathcal{S}$ of size $|C|/\eta$ of the configurations in the current stage $s$. For instance, assuming $\eta \!=\! 3$, $|C| \!=\! 81$ and that less than half of the configurations in $C$ were tested, the  number of distinct target sets for a jump of a single stage is
 $\binom{81}{27}\approx$~2E21.

We tackle this problem by introducing an efficient, model-driven heuristic that recommends a total of 1+2$\lfloor$log$_{\eta}|\mathcal{S}|\rfloor$ candidates for $\mathcal{S}$. The first candidate set evaluated by HJ, denoted $\mathcal{K}$, is obtained by considering the top $|\mathcal{S}|$ configurations, ranked based on their actual or predicted accuracy depending on whether they have already been tested or not.

Next, using $\mathcal{K}$  as a template, we generate $\lfloor$log$_{\eta}|\mathcal{S}|\rfloor$ alternative  sets by replacing the worst $|\mathcal{S}|/\eta^i$ (1$\leq i\leq\lfloor $log$_{\eta}|\mathcal{S}| \rfloor$) configurations in $\mathcal{K}$  with the next best configurations in $C\setminus \mathcal{K}$, ranked based on their (predicted/measured) accuracy.
We further generate $\lfloor$log$_{\eta}|\mathcal{S}|\rfloor$ alternative candidate sets by  exploiting  model uncertainty as follows: we identify the worst $|\mathcal{S}|/\eta^i$ configurations in $\mathcal{K}$,  ranked according to their lower confidence bound, and replace them with the configurations in  $C\setminus \mathcal{K}$ that have the highest confidence (we use a confidence bound of 90\%).
Intuitively, this way we remove from the reference set $\mathcal{K}$ the configurations that are likely to have lower accuracy if the model overestimated their mean. These are replaced by the configurations that, although having lower (average) predicted accuracy,
are prone to achieve high accuracy, given the model's uncertainty.
The pseudo-code of this heuristic and diagrams illustrating its behavior can be found in the supplemental material.

\if 0
- Decompose in 2 sub-problems

 2- identify the longest jump (jump that can make us save the largest number of configs) that has a risk, computed as 1, lower than $\lambda$.
    - combinatorial problem that prevent naive enumerative  (example, eta=3, 5 stages, 81 conofigs in total, and evaluting the risk when we have explored, say, 41 configs, would imply enumerating approx. 2e23 differents sets). So we develop an efficient heuristic that have log complexity
        1- replace the worst $|SEL|/eta^i$ configs with best in |UNSEL|, so to yield 1+  floor ($log_\eta$ |SEL|)
        2. We further consider tjhe permutation: replace the configs in SEL with lowest LCB (in sel && may suck the most) with
the ones in UNSEL that have highest UCB (in unsel && may kick ass). Requires extra floor ($log_\eta$ |SEL|) risk evaluations
\fi

\vspace{-2mm}
\subsubsection{Generalizing to multi-hop jumps.}
\label{sec:multihop}

Alg.~\ref{alg:evalRisk} shows how to compute the rEAR of a jump that skips $j\!>\!1$ stages from the current stage $s$. This is done in an iterative fashion by computing the rEAR for jumps from stage $s+i$ to stage $s+i+1$ ($i\in[1,j-1]$) and accumulating the corresponding rEARs to yield the rEAR of the jump.

At each iteration, the candidate sets for the set $\mathcal{S}$ of configurations to be retained after the jump are obtained via the \textsc{get\_candidates\_for\_$\mathcal{S}$} function (which encapsulates the logic described in Sec.~\ref{sec:sel_select}).
Among these 1+2$\lfloor$log$_{\eta}|\mathcal{S}|\rfloor$  candidate sets, the one with minimum risk is identified. The process is repeated replacing $C$ with the candidate set that minimizes the risk of the current jump (line~\ref{alg:repl}), until $\lambda$ is exceeded, thus seeking to maximize the ``jump length'', i.e., the number of stages that can be safely skipped.
As such, the computation of the risk of a jump from a stage with $|C|$ configurations and that skips $j\!>\!1$ stages requires $\mathcal{O}(j(1+log_{\eta}|C|/~\eta)$ rEAR evaluations. This ensures the scalability of HJ's risk analysis methodology even when considering jumps that can skip a large number of stages.

\begin{algorithm}[t]
    \caption{Pseudo-code for the \textsc{Evaluate\_Jump\_Risk} function.}
    \label{alg:evalRisk}
    \scriptsize

    \begin{algorithmic}[1]

    \STATE $\langle$int s, Set$\langle$Config$\rangle\mathcal{S}\rangle$ \textsc{\textsc{Evaluate\_Jump\_Risk}}(int s, Set$\langle$Config$\rangle$ Tested, Set$\langle$Config$\rangle$ Untested) \vspace{1mm}

    \STATE rEAR = 0; $\mathcal{S}$ = $\emptyset$; $C$ = Tested $\cup$ Untested.

    \WHILE [$S$: maximum number of stages] {s $<$ S}
    \STATE Set$\langle$Set$\langle$Config$\rangle\rangle$ candidates = \textsc{\textsc{get\_candidates\_for\_$\mathcal{S}$}}(Tested, Untested)
    \STATE minRisk = min$_{\mathcal{X}\in \text{candidates}}$ rEAR$_{s}^{s+1}(\mathcal{X}, C\setminus \mathcal{X})$
    \STATE $\mathcal{S}=\text{argmin}_{\mathcal{X}\in \text{candidates}}$ rEAR$_{s}^{s+1}(\mathcal{X}, C\setminus \mathcal{X})$ \COMMENT{$\mathcal{S}$: Set that minimizes the risk}

    \IF {rEAR + minRisk $ > \lambda$}
        \STATE \textbf{return} $\langle$ s , $\mathcal{S}\rangle$ \COMMENT{Return the target stage and the set with selected configs. }
    \ELSE [Try to extend the jump by one hop]
        \STATE rEAR += minRisk;  s++
        \STATE $C$ = Untested = $\mathcal{S}$; Tested = $\emptyset$ \alglinelabel{alg:repl}
      \ENDIF
     \ENDWHILE

      \STATE  \textbf{return} $\langle$ s, $\emptyset \rangle$ \COMMENT{Jump all stages in the current bracket and start a new bracket}

    \end{algorithmic}
\end{algorithm}
\setlength{\textfloatsep}{2mm

\if 0
\begin{equation}
    P\left(x^* \notin \mathcal{S} \right) = P\left( \underset{c_i \in \mathcal{D}}{\text{max}} \textbf{ } A(c_i, b) > \underset{c_k \in \mathcal{S}}{\text{max}} \textbf{ } A(c_k, b) \right

\end{equation}

\begin{equation}
\begin{aligned}
EAL =&
\int_{0}^{+\infty}  x \cdot P\left( \underset{c_i \in \mathcal{D}}{\text{max}} \textbf{ }A(c_i, b) - \underset{c_k \in \mathcal{S}}{\text{max}} \textbf{ }A(c_k, b) = x \right) \hspace{1mm} dx
\end{aligned}
\end{equation}
\fi

\if 0
 1- given a target stage and corresponding sets of configs (sel/unsel), what is the risk...
    - average of difference of two rand. variables defined as the max accuracy of sel and unsel, resp.
        a) pdf of max of gaussians (all untested case first): known in closed form.
            a.1) then discuss how the pdf is modified if some configs in sel/unsel are tested
        b) to compute the difference between these two rand. vars we need to use numerical methods.... PEDRO writes here
                scipy.integrate.nquad
                -> C library, any specific tuning of parameters that would be worth mentioning,
\fi

\subsection{Reducing the risk of jumping by prioritizing the evaluation order of configurations}
\label{sec:order}
\vspace{-1mm}
\if 0
- ideal version of it: simulate testing, re-training with new data and re-evaluation of risk to jump.
- this could be simulated, briefly mention how. But this is overly expensive, so we approximate it by:
    1. avoiding to retrain the model with its own prediction. Only effect would be uncertainty reduction on simulated config.
    2. assume that the sets sel/unsel won't change after the sampling, but only the corresponding risk may, since the uncertainty on the accuracy of the simulated config would be nullified, but its avg value won't change.
- The key justification of this approximation is that, if one self-feeds a model with its own predicitons, it's unlikely that its state will differ significantly. Analogously, if an untested config, modelled via a gaussian with avg val eta, is replaced by a test config with a dirac centered also in eta, the risk change will not affect significantly the ranking of sels (this is totally true for the heuristic based on avg value, the one based on ucb/lcb is more likely to yield different sets)?
\fi

HJ also uses a model-based approach to determine in which order to evaluate the configurations of a stage. This mechanism aims to enhance HJ's efficiency by prioritizing the evaluation of  configurations that are expected to yield the largest reduction of risk for future jumps.

The literature on look-ahead non-myopic BO~\cite{yue2020nonmyopic,lynceus,lookahed_bo_wilcox,Lam:2017} has already investigated several techniques aimed at predicting the impact of future exploration steps on model-driven optimizers. These approaches often impose  large computational overheads, due to the need to perform expensive ``simulations'', i.e., retraining the models to simulate alternative evaluation outcomes, and to their ambition to maximize long-term rewards (in contrast to the greedy nature of typical BO approaches).

Motivated by our design goal of keeping HJ lightweight and scalable, we  depart from these modelling approaches and employ a  simpler, greedy heuristic that allows for estimating the impact of evaluating an untested configuration without requiring expensive model retrains. More in detail, we simulate the evaluation of an untested configuration $c$ by querying the model  (without retraining it) and including $c$ in the set of tested configurations (using the model predicted accuracy for $c$ as its ground truth). Next, we execute the \textsc{Evaluate\_Jump\_Risk} function to obtain the updated risk of jumping and the corresponding target stage. We repeat this procedure for all the untested configurations in the stage, and select, as the next  to test, the one that enables the longest safest jump (determined via the \textsc{Evaluate\_Jump\_Risk} function). The pseudo-code of this mechanism can be found in the supplemental material

\vspace{-2mm}
\subsection{Selecting the configurations for a new bracket}
\vspace{-1mm}
\label{sec:warmstart}
\if 0
-differences with respect to BOHB:
 1- single model with budget as a feature, better extrapolation over budgets:
    - we can use it predict the configs that will be best at full budget, BOHB. In the first iteration of HB, with the default value of eta, one can start predicting at full budget only in the last stage.
 2- TPE for efficiency reasons cause they want to drop GPs. We avoid the scalabililty problems of GPs using a hybrid solution, as we discuss in the next section.
    - The use of TPE is a bad idea: u can use DT/GP, replacing TPE in BOHB (i.e., no jumps) and get fast performa
    TODO: We can easily show the difference between BOHB and a version of BOHB that has 1 & 2 enabled jointly.. If we have time, we could show  BOHB + EI over HyGP/DT with the original budget modelling
\fi

To further accelerate HB, HJ leverages BO to determine which configurations to include when a new bracket is started --- an idea already exploited in prior work, e.g.,
~\cite{bohb,bohb_similar1,bohb_similar2} and also referred to as bracket warm starting. In more detail, HJ uses its models to identify which set of configurations  maximizes the EI when deployed using \textit{full budget} --- recall that, as in HB, our aim is to maximize accuracy using the full budget. This allows HJ to leverage prior knowledge, unlike HB, at no considerable extra cost, since no additional models are trained nor maintained.

\if 0
A first important difference between HJ and BOHB is the way in which HJ's models capture variations of the budget. As discussed in Sec.~\ref{sec:conf-model}, in HJ, we include the budget as one of the model's features; further, we use a custom kernel for GPs that encodes the expectation of exponential decay of the loss function with larger budgets. Conversely, previous works, like BOHB, e.g., train a model per budget and query the model associated with the largest budget that has a sufficient number of observations ($d+1$ configurations, where $d$ is the number of hyper-parameters to optimize). This prevents the models from extrapolating information across budgets, since the models are unaware of how a configuration's quality is affected by the budget setting. Further, it also delays the time after which one can query the model to obtain predictions related to the use of full budget, since HB's successive halving logic  tests a relatively small number of configurations using full budget. For instance, using HB's default parameters ($\eta=3$, $S_{max}=5$) and assuming $d=6$, BOHB can only start using full budget model during its 5-th bracket, i.e., after selecting more than 128 configurations (the ones included in the first 4 brackets) using models trained with low/intermediate budgets. HJ, analogously to BOHB, also starts using the model after  $d+1$ observations; however, unlike BOHB, in the above considered settings, HJ can use full-budget predictions after testing just 7 configurations.

Further, solutions like BOHB  avoid the scalability limitations of GPs by resorting to faster modelling techniques, namely TPE~\cite{tpe}. As we show experimentally in the supplemental material, albeit more efficient than GPs, these methods introduce approximations that can degrade the model's predictive quality and  hinder the efficiency of the optimization process.

\fi

\vspace{-2mm}
\subsection{Optimizations}
\label{sec:optimizations}
\vspace{-1mm}

\if 0
\textbf{Hybrid GPs/DTs}:
GPs represent the standard modelling technique used in BO due to their analytical tractability and flexibility~\cite{fabolas,boTutorial}. However, they can have a colossal cost to train as the number of points used to train increases (GPs scale cubically with the number of observations).
In order to attenuate this drawback, HJ exploits two different modeling techniques. At the beginning of the optimization process when the number of observations is small, HJ uses GPs to model the objective function. Similar to Fabolas, it uses kernels
designed to capture the expected impact on loss deriving from sub-sampling (the kernel is obtained by the inner product of a Matérn 5/2 kernel and a custom kernel that encodes the expectation that loss  exponential decay with larger budgets).
When the number of explorations increases and the cost of using GPs suffer a significant increase, HJ change the model and starts to use a bagging ensemble of decision trees, which is much faster to train.
We choose to use this hybrid approach because we verified an drop on the performance when using only DTs. When the number of observations is very small and there is not enough diversity in the data, the predictions of DTs can have a poor quality.
\fi

HJ adopts two optimizations that aim, resp., at reducing the cost of evaluating configurations that were previously tested with lower budgets ---
\textit{pause-resume training}
--- and increasing, at no extra cost, the information that can be fed to the model ---
\textit{opportunistic evaluation}.

\vspace{-1mm}
\noindent \textbf{Pause-resume training.}
In a HB's bracket, configurations are tested multiple times with increasing budget values. HJ takes advantage of this observation by saving the model obtained after evaluating a configuration $c$ with budget $b^{\dagger}$, noted $\mathcal{M}({c},b^{\dagger})$. If later on (in the same or in a different HJ bracket), $c$ is tested again with budget $b^*>b^{\dagger}$, HJ reloads $\mathcal{M}({c},b^{\dagger})$ and resumes the training
(thus reducing the ``cost'' of training by $b^{\dagger}$).
Note that this optimization is not new in the context of hyper-parameter optimization, e.g.,~\cite{freeze-thawBO,Vizier,raytune}

\vspace{-1mm}
\noindent \textbf{Opportunistic evaluation.}
In modern ML frameworks, (e.g.,  NNs trained using the parameter server approach~\cite{parameter-serverr}), the model's validation error is typically monitored (and made available) throughout the training process and not only upon its conclusion. We take advantage of this as follows. Assume that a configuration $c$, so far not tested with budget $b^{\dagger}$, is requested to be tested with budget $b^*>b^{\dagger}$ (e.g., when HJ jumps from the first to the last stage).
In such a case, during the evaluation of $c$ with budget $b^*$, when the training process reaches budget $b^{\dagger}$ we measure the model's current accuracy, noted $acc(c,b^{\dagger})$ and extend the model's dataset with the observation $\langle c, b^{\dagger}, acc(c,b^{\dagger}) \rangle$, enriching its knowledge base in an \textit{opportunistic} fashion, i.e.,~at no additional cost.

\if 0
\textbf{Snapshotting}: \textcolor{red}{ or Check-pointing}
In order to increase the efficiency of the optimization process, when evaluating a configuration $c_i$ on a budget $b_i$, HJ can leverage additional information to update the model via a single training instance by taking a snapshot of the accuracy whenever a budget  $b_j$ ($b_j<b_i$) is achieved.
This allows for updating the model with information on how configuration $c_i$ performs when using lower budget than $b_i$ at zero additional cost.

\textcolor{red}{maybe this part is not needed}Clearly, loading/storing the model also imposes additional latency, which is affected by, e.g, the size/architecture of the model, the underlying storage platform, etc. Thus, this mechanism  is only beneficial if the time spent training the model is actually higher then the time spent storing and loading the model.
$$T_{train}(c_i, b_i) > T_{save}+T_{load}$$

\fi

\vspace{-2mm}
\subsection{Preserving HyperBand's theoretical guarantees}
\vspace{-1mm}
\label{sec:theo}

An appealing theoretical property of HB is that its exploration policy is guaranteed to be at most a constant factor slower than random search. In order to preserve this property, HJ exploits two  mechanisms: \textbf{i)} with probability $p_{NJ}$ HJ is forced not to jump and abide by the original SH/HB logic; \textbf{ii)} when selecting the configurations to include in a bracket,
a fraction $p_U$ is selected uniformly at random (as in HB) and not using the model.
The former mechanism ensures that, regardless of how model mispredictions affect HJ's policy, there exists a non-null probability that HJ will not deviate
from HB's policy (by jumping) in any of the brackets that it executes. The latter mechanism, originally proposed in BOHB~\cite{bohb}, ensures that, independently of the model's behavior, every configuration has a non-null probability of being included in a bracket. We adopt as default value for $p_U$ the same used by BOHB, i.e., 0.3, and use the same value also for $p_{NJ}$.

\vspace{-2mm}
\subsection{Parallelizing HyperJump}
\vspace{-1mm}

Existing approaches for parallelizing HB can be classified as synchronous or asynchronous. In synchronous approaches~\cite{hyperband,bohb}, the size of the stages abides by HB's rules and parallelization can be achieved  at the level of a HB's stage, bracket, and iteration. Conversely, asynchronous methods (ASHA~\cite{asha} or Ray Tune~\cite{raytune}) consider a single logical bracket that promotes a configuration $c$ to the next stage iff $c$ is in the top $\eta^{-1}$ configurations tested in the current stage.

HJ can be straightforwardly parallelized using synchronous strategies. If parallelization is pursued at the level of brackets or iterations, each worker simply runs an independent instance of HJ that shares the same model and training set (so to share the knowledge acquired by the parallel HJ instances).
When parallelizing the testing of the configurations in the same stage, as soon as a worker completes the testing of a configuration, the model can be updated and the risk of jumping  computed. If a jump is performed, the workers that are still evaluating configurations in the current stage can either be immediately interrupted  or allowed to complete their current evaluation. In our implementation, we opted for the former option, which  has the advantage of maximizing the
workers that are immediately available for testing configurations in the stage targeted by the jump.

In more detail, our implementation adopts the same parallelization strategy of BOHB, i.e., parallelizing by stage and activating a new parallel bracket in the presence of idle workers, with one exception. We prevent starting a new parallel bracket if there are  idle workers during the first bracket. This, in fact, would reduce the available computational resources to complete the first bracket and, consequently, lead to a likely increase of the latency to recommend the first incumbent (i.e., to test a full budget configuration).

Investigating how to employ HJ in combination with asynchronous  versions of HB is out of the scope of this paper. Yet,  we argue that the key ideas at the basis of HJ could still be applied in this context, opening up an interesting line of future research. For instance, in ASHA, one could use a model-based approach, similar to the one employed by HJ, to: \textbf{(i)} promote by more than one position/budget the testing of a configuration that the model predicts to be promising; or \textbf{(ii)} use the model to determine whether it is actually beneficial to promote a given configuration, e.g., avoid promoting less promising configurations to save computational resources that may be soon required to evaluate more promising configurations.

\section{Evaluation}
\label{sec:eval}
\input{2evaluation}

\vspace{-2mm}
\section{Conclusions}
\label{sec:concl}

This paper introduced HyperJump, a new approach that complements HyperBand's robust search strategy and accelerates it by skipping low risk evaluations. HJ's efficiency hinges on the synergistic use of several innovative risk modelling techniques and of a number of pragmatic optimizations. We show that HJ provides over one-order of magnitude speed-ups on a variety of deep-learning and kernel-based learning problems when compared to HB as well as to a number of  state of the art optimizers.

redpajama_set_name: RedPajamaArXiv


The former England captain, whose wife Victoria is expecting the arrival fourth child imminently, made a surprise 20-minute cameo appearance having been sidelined for two weeks with the back problem.
But the 36-year-old ex-Manchester United and Real Madrid star responded well to treatment to make an unexpected return.
'The epidural and cortisone injections last week seemed to react well and took the pain away,' he said.
Beckham was left bemused, however, when the referee ordered him to reposition himself for a free-kick by marking the area with spray paint.
But it is common practice for officials in the States to mark the pitch and ref's carry spray tins to illustrate 10-yard distances.


Boomers and even a lot of Gen X'ers in most of the US might never give living in a glass cube a second thought but when those cube houses are built in and intended for the West Coast market, then all bets are off.
When two new companies, one started by Architects and the other by an Amazon executive, began putting ideas on their iPads and their iMacs for glass dominant glass houses, the only place they would reasonably have any chance of succeeding was in California, Oregon and Washington.
Going from the design stage to actually building and having a modular factory build a couple of them for demonstrations and investors is a long way from ramping up production, starting a factory, meeting strict CA building codes, establishing a sales and marketing network and actually making a profit.
Blu Homes learned this lesson as they burned through millions of dollars of investor's money only to watch their company grow to the point of opening a factory and trying to go national before closing their factory and only marketing in Northern California.
Cover, a new Los Angeles-based startup is getting into the business of prefabricated homes in a unique way by combining technology, efficiency, and a one-stop-shop approach. Cover was founded by architects Alexis Rivas and Jemuel Joseph in 2014, with an eye towards building precision-designed homes that aren't dependent on an army of architects, engineers, and general contractors.
The company's work comes in at a time of abundance for prefab home builds, ranging from stackable shipping container homes to igloos. But the architects at Cover are convinced their approach is different from any other prefab, design-build, or engineering model that has ever existed.
A unique aspect to Cover is its target market; instead of focusing on the custom designs and constructions that generic home builders offer, the company is focused on accessory dwelling units — detached living space intended for use as a home office, guest bedroom or in-law suite.
Cover's factory is designed to have the capacity to produce 100 accessory dwelling units annually, but the company is growing quickly. The four-person team has already produced one structure, and has seven projects in the design phase, ranging from a $90,000 backyard office to a $300,000 one-bedroom home. Yet the Cover team's goal is to consistently make their products more affordable by reinvesting profits in research and development to produce a more efficient design and manufacturing process.
The other company that has caught my attention is Blokable. Aaron Holm, former Amazon product manager and burgeoning entrepreneur, wants to transform the housing market by scaling up manufactured housing all along the West Coast. His new venture is called Blokable, and with it Holm wants to offer shippable housing units that are fully integrated with his company's smart home technology hub.
The venture originated with Holm's fascination with shipping container architecture. After visiting several shipping container communities in Detroit in 2015, he became convinced that the only way to free up the housing market was by making housing that you can order and configure as easily as buying a car.
Blokable is entering a market with lots of competition, but Holm believes his company has the advantage over other modular architecture by integrating modern technology into each Blok. The company will outfit each Blok unit with its "BlokSense" smart-home control platform, which will provide a way for homeowners to control smart electric, water, temperature, lighting and security systems. The smart-home platform also allows occupants to set the system to different modes including Home, Away, Sleep, or Vacation.
Both of these companies share a vision. That housing has to begin to evolve and where else but the West Coast could it start.
Once you realize that houses really haven't changed that much over past 100 years, it's easy to see why they want to be disrupters.
Growing up in Elmira, NY and now living and working in California gives me an old vs new perspective on things. Food, transportation, politics and housing.
You are so right about housing in the West. We are not afraid to try something new and different. Whether we embrace it is another thing entirely. Personally I find these new homes something I could live in for a short period of time but they will never catch on as permanent housing in place of what we have now.
Every Sunday my newspaper has article after article about a new prefab builder. One would think that CA is building only modern design homes on rolling green hills overlooking the Ocean. Sadly it isn't. It's more likely you will live in a condo or a home built in a community where every house looks the same.
I can't even imagine anyone in Elmira wanting one of these homes except possibly as a hunting camp where you could sit in your recliner drinking beer and watching for deer through the wall of glass. Now that would be awesome.
They will have a HARD road ahead of them! The energy code here continues to get more and more difficult to build homes with a lot of glass. We're seeing homes that were designed and built just last year with an energy score exceeding minimums by 20% now failing to meet the minimum requirements by as much as 30%. That's a 50% swing in performance measure for this counting. We're being forced to increase insulation R values for walls and roof, go to 92-98% efficient w/h's and mini-splits instead of conventional furnaces.
There have been many projects similar to this, either at the conceptual level or even ones that have gotten to actual construction that have been based on "pluggable" components. They all seem to find niche markets but haven't been able to jump into the mainstream. While home construction is still basically the same as it has been since the 1830's, home design is still basically the same as it has been since the 1500's. It seems like efforts to create paradigm changes in construction processes and methods have combined that with modern/contemporary design dooming them both to limited marketability to the mainstream masses.


text:
« The Plight of Homeless Ex-Servicemen: Please read this carefully - or not at all | Main | Army Redundancies: Will it be the brightest and best who volunteer? »
Armed Forces Day: May we ask your support?
Tomorrow (Saturday 30 June) is Armed Forces Day (AF Day). When it began as Veterans' Day in 2006, I was uneasy about the idea, especially as a new veteran (I wish there were a better word, but…); it didn't seem very British.
But three years later the name and focus changed to Armed Forces Day, with the serving soldier/sailor/airman centre-stage. And this is as it should be.
The idea of the day, says the MoD's AF Day agency (at www.armedforcesday.org.uk), is to raise public awareness of the contribution made to our country by those who serve and have served in Her Majesty's Armed Forces; and secondly to give the nation an opportunity to show its support for the men and women who make up the Armed Forces community (from currently serving troops to Service families and from veterans to cadets).
The National Event will this year be held in Plymouth (see www.visitplymouth.co.uk); details of other events throughout the country are on the AF Day website.
'Your support for the Armed Forces provides a much valued morale boost for the troops and their families', says the MoD.
Just to emphasize the point, I would venture to say that those who have no direct or indirect experience of servicemen and –women could easily underestimate the power of moral support. Not sympathy – support. Anyone who's been on a homecoming parade can testify to it.
If you have no opportunity to get to one of the events, you might instead spare a few minutes to click on each of these links, which will achieve something of the first part of the aim – to raise awareness of what the Forces do. Find out more about operations the Forces are involved in around the world, or visit the official sites of the Royal Navy, British Army and Royal Air Force.
And if you're free on Saturday or Sunday and would like a memorable family day out, seeing a particular part of the Army and its 20th and 21st Century history, as well as something of today's approach to operations, may I suggest that you visit the tenth annual 'Tankfest' at the Tank Museum in Dorset. The climax of the arena displays will be a mock battle re-creating a World War One attack – with infantry, tanks (including one from the film 'Warhorse'), and for the first time, bi-planes.
The Museum is world-class – indeed, the finest collection of armoured vehicles in the world. www.tankmuseum.org
June 29, 2012 Comments (0) | Permalink
Allan Mallinson's blog
ALLAN MALLINSON
Allan Mallinson began training for the Anglican priesthood at seventeen, took a three-year break to join the army but stayed for thirty-five, serving with both the infantry and cavalry worldwide, extensively in the MoD and latterly with the FCO. While serving he wrote a history of the regiment he commanded (Light Dragoons, The Making of a Regiment), and began the series of novels based on the adventures of Matthew Hervey, an officer in the fictional 6th Light Dragoons in the Duke of Wellington's day, which Allan Massie described in the Spectator as having "done for the British army what C.S. Forester and Patrick O'Brian did for the Royal Navy". After leaving the army to write full time, Mallinson was recruited by Simon Heffer to write defence column for the Telegraph. Antony Beevor, reviewing Mallinson's 2009 book The Making of the British Army (recently updated and published in paperback) described him as "one of the most acute commentators on the problems [the Army] faces today."
Books by Allan Mallinson
The Syrian Uprising is an Opportunity for the Israeli Air Force
"One in eight service personnel has attacked someone in a rage after returning from the battlefield." Why I'm not surprised; just sickened.
The Army cuts: Think again, Mr Cameron, on your flight back from Helmand
Why didn't we think of troops for Olympic security before?
The Flak Towers of Leytonstone: The Spirit of the Blitz could be even more fun than the Olympics themselves
Hammond's New Model Army: A gamble of huge proportions; He must not be allowed to quit before he sees it through
Army Redundancies: Will it be the brightest and best who volunteer?
The Plight of Homeless Ex-Servicemen: Please read this carefully - or not at all
Is it true that homeless ex-Servicemen cannot get into shelters because these are full of homeless immigrants?


Somewhere in the wilds of rural Manitoba stands this cluster of ruined ceramic objects. Are they evidence that this region was once inhabited by an advanced culture that worshipped trilobites and ammonoids? Is this a monument to those lost, long-extinct creatures?
Now THAT'S cool! And the trilobites are very well rendered … clear evidence of a palaeontologically-advanced society, now mysteriously vanished (rather like trilobites and ammonoids themselves).
Yes, vanished with little trace, but the worship of marine creatures suggests that it may have been a Paleozoic to Mesozoic branch of the Atlantean civilization. Perhaps further inquiry will lead to the unearthing of additional pieces?
Too cool! The Paleozoic geek in me wants to suggest that the culture that produced the wonderful art are some long lost brethren… Cambrian ptychopariids, Devonian phacopids, and I suggest for the ammonoids, goniatites.
Wonderful! What is it about …potters and paleontology?!! Yet again Graham, another example of people working with clay and having a yen for ancient creatures. Perhaps some of that clay is composed of the remnants of these extinct creatures…and it magically rubs off on them…? The odd pathways of art…sort of mystical. Just my silly musing.


In this set of tutorials we'll use the reinforcement tools in Revit Structure to create a rebar model and cost schedule. Software required: Revit 2015.
In this set of tutorials we'll use the reinforcement tools in Revit Structure to create a rebar model and cost schedule. We'll begin from the ground up and start modeling the reinforcement for the footings. We'll learn about placing rebar into hosted structural elements, as well as working with various rebar properties like type, deformation, length, shape, and also visibility. Once we become comfortable with these basic concepts, we'll then learn how to use the various other reinforcement tools like path, cover settings, area, and fabric area. Once our model is in place we'll then generate a rebar schedule that displays type, length, assemblies, and even cost. By end of this training you'll know how to model rebar for concrete structures, gain an understanding of the various ways to apply reinforcement, and also generate a material cost estimation report for your design. Software required: Revit 2015.
Hi there. I'm Pierre with Digital-Tutors, an Autodesk authorized publisher. In this set of tutorials, we'll use the reinforcement tools in Revit Structure to create a rebar model, and even a cost schedule. We'll begin from the ground up and we'll start modeling the reinforcement for our footings. We'll learn about placing rebar into hosted structural elements, as well as working with various rebar properties like type, deformation, length, shape, and even visibility. Once we become comfortable with these basic concepts, we'll then learn how to use the various other reinforcement tools like the Path button, cover settings, area, and even fabric area to generate rebar for the rest of our model. Once our model is in place, we'll then generate a rebar schedule that displays type, length, assemblies, and even cost. By the end of this course, you'll know how to model rebar for concrete structures, you'll gain an understanding of the various ways to apply reinforcement, and you'll also learn how to generate a material cost estimation report for your preliminary design. With that, I'm ready to get started, and I'll meet you in the next lesson.