President Obama's politically motivated, science-eschewing ethanol mandates ravage the environment in America's heartland and the Gulf of Mexico while enabling the well-connected to reap windfall profits.
Even the cemetery that disappeared like an apparition into a cornfield. It wasn't supposed to be this way.
In some cases, such as its decision to allow wind farms to kill eagles, the administration accepts environmental costs because they pale in comparison to the havoc it believes global warming could ultimately cause.
But it's a cost the administration is willing to accept. It believes supporting corn ethanol is the best way to encourage the development of biofuels that will someday be cleaner and greener than today's. Pulling the plug on corn ethanol, officials fear, might mean killing any hope of these next-generation fuels.
Still, corn supplies the overwhelming majority of ethanol in the United States, and the administration is loath to discuss the environmental consequences.
"It just caught us completely off guard," said Doug Davenport, a Department of Agriculture official who encourages southern Iowa farmers to use conservation practices on their land. Despite those efforts, Davenport said he was surprised at how much fragile, erodible land was turned into corn fields.
Shortly after Davenport spoke to The Associated Press, he got an email ordering him to stop talking.
"We just want to have a consistent message on the topic," an Agriculture Department spokesman in Iowa said.
That consistent message was laid out by Agriculture Secretary Tom Vilsack, who spoke to ethanol lobbyists on Capitol Hill recently and said ethanol was good for business.
The numbers behind the ethanol mandate have become so unworkable that, for the first time, the EPA is soon expected to reduce the amount of ethanol required to be added to the gasoline supply. An unusual coalition of big oil companies, environmental groups and food companies is pushing the government to go even further and reconsider the entire ethanol program.
To understand how America got to an environmental policy with such harmful environmental consequences, it's helpful to start in a field in Iowa.
Leroy Perkins, a white-haired, 66-year-old farmer in denim overalls, stands surrounded by waist-high grass and clover. He owns 91 acres like this, all hilly and erodible, that he set aside for conservation years ago.
Soon, he will have a decision to make: keep the land as it is or, like many of his neighbors, plow it down and plant corn or soybeans, the major sources of biofuel in the United States.
For decades, the government's Conservation Reserve Program has paid farmers to stop farming environmentally sensitive land. Grassy fields naturally convert carbon dioxide into oxygen, which helps combat global warming. Plus, their deep root systems prevent topsoil from washing away.
For Perkins and his farmer neighbors in Wayne County, keeping farmland in conservation wasn't just good stewardship. It made financial sense.
A decade ago, Washington paid them about $70 an acre each year to leave their farmland idle. With corn selling for about $2 per bushel (56 pounds) back then, farming the hilly, inferior soil was bad business.
Many opted into the conservation program. Others kept their grasslands for cow pastures.
Lately, though, the math has changed.
"I'm coming to the point where financially, it's not feasible," Perkins said.
The change began in 2007, when Congress passed a law requiring oil companies to blend billions of gallons of ethanol into gasoline.
Oil prices were high. Oil imports were rising quickly. The legislation had the strong backing of the presidential candidate who was the junior senator from neighboring Illinois, the nation's second-largest corn producer.
"If we're going to get serious about investing in our energy future, we must give our family farmers and local ethanol producers a fair shot at success," Obama said then.
The Democratic primary field was crowded, and if he didn't win the Iowa caucuses the road to the nomination would be difficult. His strong support for ethanol set him apart.
President Bush signed the bill that December.
It would fall on the next president to figure out how to make it work.
President Obama's team at the EPA was sour on the ethanol mandate from the start.
As a way to reduce global warming, they knew corn ethanol was a dubious proposition. Corn demands fertilizer, which is made using natural gas. What's worse, ethanol factories typically burn coal or gas, both of which release carbon dioxide.
Then there was the land conversion, the most controversial and difficult-to-predict outcome.
Digging up grassland releases greenhouse gases, so environmentalists are skeptical of any program that encourages planting more corn.
At the White House and the Department of Agriculture, though, there was plenty of enthusiasm.
One of Obama's senior advisers, Pete Rouse, had worked on ethanol issues as chief of staff to Sen. Tom Daschle of South Dakota, a major ethanol booster and now chair of the DuPont Advisory Committee on Agriculture Innovation and Productivity.
Another Obama adviser at the time, Heather Zichal, grew up in northeast Iowa — as a child, she was crowned "sweet corn princess" — and was one of the Obama campaign's leading voices on ethanol in her home state.
The administration had no greater corn ethanol advocate than Vilsack, the former Iowa governor.
Writing the regulations to implement the ethanol mandate was among the administration's first major environmental undertakings. Industry and environmental groups watched closely.
The EPA's experts determined that the mandate would increase demand for corn and encourage farmers to plow more land. Considering those factors, they said, corn ethanol was only slightly better than gasoline when it came to carbon dioxide emissions.
Sixteen percent better, to be exact. And not in the short term. Only by 2022.
By law, though, biofuels were supposed to be at least 20 percent greener than gasoline.
From a legal standpoint, the results didn't matter. Congress exempted existing coal- and gas-burning ethanol plants from meeting this standard.
But as a policy and public relations issue, it was a real problem. The biofuel-friendly Obama administration was undermining the industry's major selling point: that it was much greener than gasoline.
So the ethanol industry was livid. Lobbyists flooded the EPA with criticism, challenging the government's methods and conclusions.
The EPA's conclusion was based on a model. Plug in some assumed figures — the price of corn, the number of acres planted, how much corn would grow per acre — and the model would spit out a number.
To get past 20 percent, the EPA needed to change its assumptions.
The most important of those assumptions was called the yield, a measure of how much corn could be produced on an acre of land. The higher the yield, the easier it would be for farmers to meet the growing demand without plowing new farmland, which counted against ethanol in the greenhouse gas equation.
Corn yields have inched steadily upward over the years as farms have become more efficient. The government's first ethanol model assumed that trend would continue, rising from 150 bushels per acre to about 180 by the year 2022.
Agriculture companies like Monsanto Co. and DuPont Pioneer, which stood to make millions off an ethanol boom, told the government those numbers were too low.
They predicted that genetically modified seeds — which they produce — would send yields skyrocketing. With higher yields, farmers could produce more corn on less land, reducing the environmental effects.
Documents show the White House budget office also suggested the EPA raise its yield assumptions.
When the final rule came out, the EPA and Agriculture officials added a new "high yield case scenario" that assumed 230 bushels per acre.
The flaw in those assumptions, independent scientists knew, was that a big increase in corn prices would encourage people to farm in less hospitable areas like Wayne County, which could never produce such large yields.
But the EPA's model assumed only a tiny increase in corn prices.
"You adjust a few numbers to get it where you want it, and then you call it good," said Adam Liska, assistant professor of biological systems engineering at the University of Nebraska. He supports ethanol, even with its environmental trade-offs.
When the Obama administration finalized its first major green-energy policy, corn ethanol barely crossed the key threshold. The final score: 21 percent.
It didn't take long for reality to prove the Obama administration's predictions wrong.
The regulations took effect in July 2010. The following month, corn prices already had surpassed the EPA's long-term estimate of $3.22 a bushel. That September, corn passed $4, on its way to about $7, where it has been most of this year.
Yields, meanwhile, have held fairly steady.
But the ethanol boom was underway.
It's impossible to precisely calculate how much ethanol is responsible for the spike in corn prices and how much those prices led to the land changes in the Midwest.
Supporters of corn ethanol say extreme weather — dry one year, very wet the next — hurt farmers and raised prices.
But diminishing supply wasn't the only factor. More corn than ever was being distilled into ethanol.
Historically, the overwhelmingly majority of corn in the United States has been turned into livestock feed. But in 2010, for the first time, fuel was the No. 1 use for corn in America. That was true in 2011 and 2012. Newly released Department of Agriculture data show that, this year, 43 percent of corn went to fuel and 45 percent went to livestock feed.
The more corn that goes to ethanol, the more that needs to be planted to meet other demands.
Scientists predicted that a major ethanol push would raise prices and, in turn, encourage farmers like Leroy Perkins to plow into conservation land. But the government insisted otherwise.
In 2008, the journal Science published a study with a dire conclusion: Plowing over conservation land releases so much greenhouse gas that it takes 48 years before new plants can break even and start reducing carbon dioxide.
For an ethanol policy to work, the study said, farmers could not plow into conservation land.
The EPA, in a report to Congress on the environmental effects of ethanol, said it was "uncertain" whether farmers would plant on farmland that had been set aside for conservation.
America could meet its ethanol demand without losing a single acre of conservation land, Energy officials said.
They would soon be proven wrong.
Before the government ethanol mandate, the Conservation Reserve Program grew every year for nearly a decade. Almost overnight, farmers began leaving the program, which simultaneously fell victim to budget cuts that reduced the amount of farmland that could be set aside for conservation.
In the first year after the ethanol mandate, more than 2 million acres disappeared.
Since Obama took office, 5 million more acres have vanished.
Agriculture officials acknowledge that conservation land has been lost, but they say the trend is reversing. When the 2013 data comes out, they say it will show that as corn prices stabilized, farmers once again began setting aside land for conservation.
Losing conservation land was bad. But something even worse was happening.
Farmers broke ground on virgin land, the untouched terrain that represents, from an environmental standpoint, the country's most important asset.
The farm industry assured the government that wouldn't happen. And it would have been an easy thing for Washington to check.
But rather than insisting that farmers report whenever they plow into virgin land, the government decided on a much murkier oversight method: Washington instead monitors the total number of acres of cropland nationwide. Local trends wash away when viewed at such a distance.
"They could not have designed a better approach to not detect land conversion," said Ben Larson, an agricultural expert for the National Wildlife Federation.
Look closely at the corn boom in the northern Great Plains, however, and it's clear. Farmers are converting untouched prairie into farmland.
The Department of Agriculture began keeping figures on virgin land only in 2012 and determined that about 38,000 acres vanished that year.
But using government satellite data — the best tool available — the AP identified a conservative estimate of 1.2 million acres of virgin land in Nebraska and the Dakotas alone that have been converted to fields of corn and soybeans since 2006, the last year before the ethanol mandate was passed.
"The last five years, we've become financially solvent," said Robert Malsam, a farmer in Edmunds County, S.D., who like others in the central and eastern Dakotas has plowed into wild grassland to expand his corn crop.
The price of corn is reshaping the land across the Midwest. In Wayne County, Iowa, for example, only the dead can stop the corn.
A gravel road once cut through a grassy field leading to a hilltop cemetery. But about two years ago, the landowners plowed over the road. Now, visiting gravesites means walking a narrow path through the corn.
People have complained. It's too narrow for a hearse, too rutted for a wheelchair, too steep for the elderly. But it's legal, said Bill Alley from the board of supervisors.
"There is no question air quality, water quality is benefiting from this industry," he said.
But the administration never actually conducted the required air and water studies to determine whether that's true.
In an interview with the AP after his speech, Vilsack said he didn't mean that ethanol production was good for the air and water. He simply meant that gasoline mixed with ethanol is cleaner than gasoline alone.
In the Midwest, meanwhile, scientists and conservationists are sounding alarms.
Nitrogen fertilizer, when it seeps into the water, is toxic. Children are especially susceptible to nitrate poisoning, which causes "blue baby" syndrome and can be deadly.
Between 2005 and 2010, corn farmers increased their use of nitrogen fertilizer by more than one billion pounds. More recent data isn't available from the Agriculture Department, but because of the huge increase in corn planting, even conservative projections by the AP suggest another billion-pound fertilizer increase on corn farms since then.
Department of Agriculture officials note that the amount of fertilizer used for all crops has remained steady for a decade, suggesting the ethanol mandate hasn't caused a fertilizer boom across the board.
But in the Midwest, corn is the dominant crop, and officials say the increase in fertilizer use — driven by the increase in corn planting — is having an effect.
The Des Moines Water Works, for instance, has faced high nitrate levels for many years in the Des Moines and Raccoon Rivers, which supply drinking water to 500,000 people. Typically, when pollution is too high in one river, workers draw from the other.
"This year, unfortunately the nitrate levels in both rivers were so high that it created an impossibility for us," said Bill Stowe, the water service's general manager.
For three months this summer, workers kept huge machines running around the clock to clean the water. Officials asked customers to use less water so the utility had a chance to keep up.
Part of the problem was that last year's dry weather meant fertilizer sat atop the soil. This spring's rains flushed that nitrogen into the water along with the remnants of the fertilizer from the most recent crop.
At the same time the ethanol mandate has encouraged farmers to plant more corn, Stowe said, the government hasn't done enough to limit fertilizer use or regulate the industrial drainage systems that flush nitrates and water into rivers and streams.
With the Water Works on the brink of capacity, Stowe said he's considering suing the government to demand a solution.
In neighboring Minnesota, a government report this year found that significantly reducing the high levels of nitrates from the state's water would require huge changes in farming practices at a cost of roughly $1 billion a year.
"We're doing more to address water quality, but we are being overwhelmed by the increase in production pressure to plant more crops," said Steve Morse, executive director of the Minnesota Environmental Partnership.
The nitrates travel down rivers and into the Gulf of Mexico, where they boost the growth of enormous algae fields. When the algae die, the decomposition consumes oxygen, leaving behind a zone where aquatic life cannot survive.
This year, the dead zone covered 5,800 square miles of sea floor, about the size of Connecticut.
Larry McKinney, the executive director of the Harte Institute at Texas A&M University-Corpus Christi, says the ethanol mandate worsened the dead zone.
The dead zone is one example among many of a peculiar ethanol side effect: As one government program encourages farmers to plant more corn, other programs pay millions to clean up the mess.
Obama administration officials know the ethanol mandate hasn't lived up to its billing.
The next-generation biofuels that were supposed to wean the country off corn haven't yet materialized. Every year, the EPA predicts millions of gallons of clean fuel will be made from agricultural waste. Every year, the government is wrong.
Every day without those cleaner-burning fuels, the ethanol industry stays reliant on corn and the environmental effects mount.
The EPA could revisit its model and see whether ethanol is actually as good for the environment as officials predicted. But the agency says it doesn't have the money or the manpower.
Even under the government's optimistic projections, the ethanol mandate wasn't going to reduce greenhouse gas right away. And with the model so far off from reality, independent scientists say it's hard to make an argument for ethanol as a global warming policy.
"I'd have to think really hard to come up with a scenario where it's a net positive," said Silvia Secchi, a Southern Illinois University agriculture economist.
In June, when Obama gave a major policy speech on reducing greenhouse gas, he didn't mention ethanol. Biofuels in general received a brief, passing reference.
What was once billed as an environmental boon has morphed into a government program to help rural America survive.
Congress and the administration could change the ethanol mandate, tweak its goals or demand more safeguards. Going to Congress and rewriting the law would mean picking a fight with agricultural lobbyists, a fight that would put the administration on the side of big oil companies, which despise the ethanol requirement.
So the ethanol policy cruises on autopilot.
Bob Dinneen, president of the Renewable Fuels Association, the ethanol lobbying group, said there's no reason to change the standards. Ethanol still looks good compared to the oil industry, which increasingly relies on environmentally risky tactics like hydraulic fracturing or pulls from carbon-heavy tar sands.
Leroy Perkins, the farmer agonizing about what to do with his 91 acres, says he likes ethanol as a product and an industry. But he knows it fuels the corn prices that are transforming his county.
Investors from as far away as Maryland and Pennsylvania have bought thousands of acres in Wayne County, sending prices skyrocketing from $350 per acre a decade ago to $5,000 today.
One in every four acres of in the county is now owned by an out-of-towner.
Those who still own land often rent it to farming companies offering $300 or more per acre. Perkins could make perhaps $27,000 a year if he let somebody plant corn on his land. That's nothing to dismiss in a county where typical household income is $36,000.
But he knows what that means. He sees the black streaks in his neighbor's cornfields, knowing the topsoil washes away with every rain. He doesn't want that for his family's land.
He doesn't finish his sentence.
Dina Cappiello & Matt Apuzzo are journalists whose work was published by the Associated Press. Associated Press writers Jack Gillum in Washington and Chet Brokaw in Roscoe, S.D., contributed to this report.


I'm seeing more news articles talking about this. Currently it only lets you report two different types of reports, one being speed traps but only while in "navigation mode."
Hopefully in the coming months they roll it out to more locations and allow for reporting while not in "navigation mode." I typically don't use "navigation mode" since I already know where I'm going.
I've been promoting the hell out of Waze in my area but if this takes off I'll move back to maps.


text:
(TWEEK!) > Document > Award of Distinguished Professor Policy
Award of Distinguished Professor Policy
Section 2 - Policy Statement
(1) To establish the principles and standards for the award of the title of Macquarie University Distinguished Professor.
(2) Macquarie University values excellence in scholarship and recognises the contributions of individual academic staff to its academic mission. The award of the title of Macquarie University Distinguished Professor enables recognition and reward for sustained academic excellence at Macquarie University.
(3) All Professors of Macquarie University may be considered for the award of the title of Macquarie University Distinguished Professor.
(4) The title Macquarie University Distinguished Professor is awarded to eminent Professors of Macquarie University who are of exceptional distinction and who have made an outstanding contribution to their field or discipline and to Macquarie University.
(5) The title is of high honour and is not routinely awarded.
(6) Candidature for the Distinguished Professor award will arise through nomination from Executive Deans, not self-nomination, although a nominee's agreement that their nomination can go forward will be required.
(7) To be eligible for consideration, a nominee must hold the title 'Professor' at Macquarie University. The expectation is that the person would be in the top one% of their field and continuing to demonstrate excellence.
(8) The title is awarded based on merit as assessed against the Award of Distinguished Professor Criteria Schedule.
(9) Nominations will be considered by the Distinguished Professor Committee. The Committee membership will be as follows:
Vice-Chancellor – Chair;
Deputy Vice-Chancellor (Academic);
Deputy Vice-Chancellor (Research);
Chair, Academic Senate; and
Director, Human Resources.
(10) The Vice-Chancellor has the authority to approve the award of the title of Distinguished Professor.
(11) The title is awarded for six years or until the retirement or resignation of the staff member, whichever is the earlier. The title may be awarded for consecutive periods. Re-nomination for a consecutive period will be treated as a new nomination.
(12) A salary supplement or research grant is offered to each person awarded the title.
(13) There is no limit to the number of Distinguished Professors, but it is expected that only a few highly exceptional candidates will be awarded the title each year.
(14) The University reserves the right not to make an award or to withdraw an award.
(15) The nomination and award process will be in accordance with the Award of Distinguished Professor Procedure.
(16) Refer to the Award of Distinguished Professor Procedure.

redpajama_set_name: RedPajamaCommonCrawl


As part of our project on biodiversity-poverty evidence, we conducted a systematic mapping exercise of the literature on biodiversity and poverty. This database includes details of the studies reviewed including: summary information, bibliographic details, details about the component of biodiversity and dimension of poverty addressed, and the mechanisms used to link them, as well as details about the research design and key issues covered.
Note this research was undertaken between 2012 - 2013, and the search has not been updated since to capture additional publications.
Search the studies by keyword or phrase.
Using more than one keyword will return more targeted results.
This is a case study for: Agrobiodiversity and Shade Coffee Smallholder Livelihoods: A review and Synthesis of Ten Years of Research in Central America.


Ohloh compiles data on open-source projects, in an effort to help people find, compare software and choose the right one.
Timid about diving into the oceanic waters of open-source projects?
A few former Microsoft employees have launched a Web site that evaluates open-source projects. The site, Ohloh, is not a reviews site, but instead a directory of open-source software, its co-founder said.
"We collect from the infrastructure the open-source community uses to develop the software," Ohloh co-founder and CEO Scott Collison told CNET News.com. "It also serves as an open-source directory. You can find open-source projects and compare them, and gradually find one that's right for you."
The site could appeal to developers who are frustrated by the number of open-source projects that lack clear explanations. Ohloh also seeks to help developers make a build vs. buy decision by offering code analysis, said Collison, who along with co-founder Jason Allen, previously worked at Microsoft.
"A developer thinks, hey, maybe I want to develop this kind of project. Our formula takes into account what it costs to write the code, collect the requirements, write the specs, write the code, test that code and deploy it," he said.
Ohloh's database, searchable by project name or keyword, results in a list of suggested software. Each project has a profile, beginning with a brief synopsis of what the software does.
In addition, the directory offers information such as when the project was started, how many developers are actively working on it, the languages it uses, links to the project's home page and a breakdown of current activities. Charts on the open-source project show how many lines of code have been removed or added and by whom. For those really unfamiliar with the open-source community, the profile even includes linked explanations of each open-source evaluation term.
Ohloh has been working on the directory for two years and plans to formally launch it next week. In the meantime, it has quietly rolled out its database in public beta. The database will remain free with an ad-based business model, but Ohloh plans to add services for companies looking for analysis and evaluation of their own in-house projects.
The company's investors include the two co-founders and former Microsoft executives Paul Maritz and Pradeep Singh. Collison said he and others at the company hit upon the idea for Ohloh while working at Microsoft.
"We worked on the communications pillar of Vista and Web services. While doing that, we met with a lot of corporate customers who were interested in open-source products and concepts but asked, 'How do I get an idea of how the software was made?'" Collison said. "We got so many questions from people who wanted more visibility into open-source that we thought it was a great place to start."


The Tennessee Board of Regents, the state university and community college system, will meet in regular quarterly session at Pellissippi State Community College located on Hardin Valley Road in Knoxville, Tenn., on Thursday and Friday, Sept. 25 and 26.
Committee meetings will take place Thursday and begin at 1 p.m. EDT in the Clayton Performing Arts Center in the Alexander Building. Committees will meet in this order: Tennessee Colleges of Applied Technology, Academic Policies and Programs, Personnel and Compensation, Finance and Business Operations, and Audit.
Approval of the resolutions of appreciation for retired TCAT directors Don Sadler and Mark Powers.
A full agenda for the Board and committee meetings and meeting materials are available to download on the TBR website at http://www.tbr.edu/about/default.aspx?id=1390. All meetings are open to the public and the press as observers except for the non-public executive session of the Audit Committee. Any member of the public or media who plans to attend should contact Monica Greppin-Watts at monica.greppin-watts@tbr.edu or 615-366-4417 before noon CDT Wednesday, Sept. 24, so access can be arranged. The meetings are also accessible to view via live streaming video athttp://www.ustream.tv/channel/tennessee-board-of-regents using the password tbr2011.
Anyone with a disability who wishes to attend should contact Greppin-Watts to request services needed to facilitate attendance. Contact may be made in person, by writing, by e-mail, by telephone or otherwise and should be received by noon Tuesday, Sept. 23.


With the closing of the low power FM application window in November 2013, and with no indication that the FCC will open another one, we have compiled a list of alternatives to low power FM broadcasting. The following discussion presents a range of alternatives and options that offer various levels of viability. It is our judgement at Prometheus that none of these are as generally promising as fighting for a reinstatement and expansion of the original LPFM service, however one of them might be appropriate for your group to pursue.
With the first nationwide test of the Emergency Alert System (EAS) slated for 2:20pm Eastern time today, September 28th, 2016; it will shortly be time to file "form two" in the on-line ETRS system -- before midnight in fact! Here's a quick guide, assuming you have filed "form one".
Did you receive the EAS test?
Did you retransmit the EAS test?
What are translators, and what do they have to do with community radio?
Update March 2012: We won! The FCC released new rules on March 19, 2012 that implement a market-by-market analysis to ensure space for new LPFMs. See our article explaining the details. Thank you to everyone who wrote letters to the FCC on this issue! The text below, from Fall 2010, is outdated. We have preserved it here to give historical background on the translator fight at the FCC.
Two studies released by Common Frequency show that a well-intentioned FCC plan may actually gut the potential of community radio. Intended as a compromise, this plan would actually hand over the vast majority of open frequencies to companies that already own stations, keeping community radio off the dial in urban areas.


text:
Breveny.co.uk
The official home of the Breveny Chronicles
Watch out dragons... Here be Aaron!
Aaron Gray and the Dragon War Is now available as a book and ebook.
Aaron is a brat, which is understandable after everything he's been through. He also sees dragons when he hums, which isn't understandable by anyone, ever. He soon gets sucked into the dragons' dangerous war, where his only defences are his embarrassingly magical fingernails, and a fierce, ten-year-old girl.
Available everywhere across the world! Check out goodreads / amazon uk / amazon us / barnes & noble / book depository or your favourite book or ebook retailer.
Short Story: Seeger Vs Gray
Mrs Seeger took a deep breath and opened the meeting room door. This was not going to be an easy meeting. Waiting for them in the room was a middle-aged man with pale blue eyes and the faint outline of stubble around his chin. His hair was well groomed and he was dressed in a sharp blue suit. His immaculate appearance would have been quite intimidating if he hadn't been slouched in his chair, a childish, sulky look on his face.
"So you still haven't found my son," the man said, arms folded. "I'm not happy about this. How did you lose him?"
"We didn't lose him, Mr Gray. He ran away. Usually, he shows up again after a few hours, but this time…"
"Usually?" Mr Gray interrupted. "So you're saying he runs away often? And you let him?"
"Well he's quite a lot older than when you last looked after him," said Mrs Seeger, mentally kicking herself for matching his petulance. "It's not always possible to convince him to stay."
"Well, you should have locked his door."
"We don't do that I'm afraid."
"Well you don't do that, and now my son is missing." Mr Gray leant forward, glaring at Mrs Seeger.
"I know. We're doing everything we can to find him. If we hear anything at all, I'll let you know."
"You'd better. I'm already talking to my solicitors about you neglecting my child. What made him run away anyway?"
Mrs Seeger paused. She knew the reason but wasn't sure whether it was appropriate to say. In the end, she didn't have to. There must have been something in her facial expressions because Mr Gray guessed. He slumped back into the chair and sighed.
"You talked to him about seeing me, didn't you? Oh, crud."
Mrs Seeger smiled internally at Mr Gray's use of "crud", his son's favourite word.
"Well yes, but to be honest I don't think he meant to stay missing for this long. We managed to track him to a bus stop in town, but then he sort of disappeared."
"Disappeared?"
"Well, the CCTV footage on the bus seems to indicate he just vanished. Obviously, that's impossible, but we can't trace him after that point."
"Sounds like he's tricked you. Can I see the video?"
"Yes, I'll arrange it with the police. They have a copy."
"Great, any other clues?"
"Well, he was talking to a girl on the bus. The police have identified her as Julia Price. You might have seen the appeal for her on the news. They went to school together briefly but as far as we can tell there wasn't any recent contact."
"Yeah right." Mr Gray laughed half-heartedly. "He's got himself a girl and they've run off somewhere, he's pretending it's all about me and he's playing you for a mug."
"It's possible," said Mrs Seeger, "but I severely doubt it. That's not really Aaron's style and he's a bit young still for that kind of thing."
Mr Gray shrugged but made no further comment. The room filled with silence. After a few moments, Mrs Seeger hummed to herself. Perhaps it was because he was on her mind, but Mrs Seeger found herself humming the same tune that she'd heard Aaron humming so many times in the past. To her surprise Mr Gray flinched, suddenly staring at the ceiling. Mrs Seeger stopped humming and Mr Gray regained his composure.
"Mr Gray, will you wait here a moment, please? I have some pictures that Aaron drew that I'd like to show you."
Posted by David at 15:00
Labels: Aaron, Julia, Meeting, Mr Gray, Mrs Seeger, Short Story
bb cherryz 12 July 2017 at 18:18
This is really good! Love it!! It's nice to have an insight on mr Gray
David 13 July 2017 at 07:30
Thankyou! I'm also fairly sure that it's the first time we learn Julia's last name :)
Introducing Dextra
Fan Art - Naiad
Hilary Hobgoblin's Birthday
Charizard is NOT a dragon!
Stonefest Report
From Julia's World: Missing Person
Appeal: Ban Deliberate Pain as a Form of Child Res...
Stonefest This Weekend!
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Woodside Trading Ltd, a trading arm of The RSPCA Leicester Branch (Woodside Animal Centre), is a company that has developed a range of pet-friendly products using the knowledge, passion and industry expertise of some of the country's best animal care professionals. With their new product lines in place, we were approached by Woodside Trading to assist them in developing eye-catching branding, packaging and POS displays. We have now successfully developed unique identities for Kernel Kat, an eco-friendly cat litter that lasts up to 5 times longer than regular litters; Pooch, a unique range of breed-specific shampoos for dogs and Aniderm, a range of dry and wet shampoos for both cats and dogs.
Contact us today for a free hours brand advice workshop where we will advise you how we would help improve your brand image.


text:
Primary Sources: People - The European Enlightenment: Malthus, T.R.
Online Sources: T. R. (Thomas Robert) Malthus
Books by Malthus, T. R. at Project Gutenberg
T.R. Malthus at The HathiTrust Digital Library
Thomas Robert Malthus at The Online Library of Liberty
A free online collection of Malthus' works.
Book Sources: T. R. (Thomas Robert) Malthus
A letter to Samuel Whitbread, Esq. M.P. on his proposed bill for the amendment of the poor laws by T. R. Malthus
Call Number: Online - free - HathiTrust
A letter to the Rt. Hon. Lord Grenville by T. R. Malthus
occasioned by some observations of his lordship on the East India Company's establishment for the education of their civil servants.
A Reply to the Essay on Population by William Hazlitt
by the Rev. T.R. Malthus; in a series of letters. To which are added, extracts from the Essay; with notes.
An Essay on the Principle of Population by T. R. Malthus
or, A view of its past and present effects on human happiness; with an inquiry into our prospects respecting the future removal or mitigation of the evils which it occasions.
Call Number: HB861 .E7
Introduction by Michael P. Fogarty.
Letters to Mr. Malthus, on several subjects of political economy by Jean Baptiste Say
and on the cause of the general stagnation of commerce. To which is added A catechism of political economy ... Translated from the French of J.B. Say, by John Richter.
Observations on the effects of the corn laws by T. R. Malthus
and of a rise or fall in the price of corn on the agriculture and general wealth of the country.
Principles of political economy, considered with a view to their practical application by T. R. Malthus
Remarks on the Production of Wealth by Simon Gray
and the influence, which the various classes of society have, in carrying on that process : in a letter to the Rev. T.R. Malthus, occasioned by his attempt to maintain the division of classes into productive and unproductive / by S. Gray, Esq. Author of "The happiness of states". 18
The grounds of an opinion on the policy of restricting the importation of foreign corn by T. R. Malthus
Intended as an appendix to "Observations on the corn laws"
The measure of value stated and illustrated by T. R. Malthus
with an application of it to the alterations in the value of the English currency since 1790 / by the Rev. T.R. Malthus, M.A. F.R.S. Professor of history and political economy in the East India College, Hertfordshire. 20
The principles of political economy by T. R. Malthus
Thomas Robert Malthus on the Nature and Progress of Rent by T. R. Malthus
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text:
Spinal Cord Injury Zone / News Videos	/ 2016 / June / 13th
World's First Child-Sized Exoskeleton Aims To Improve Quality Of Life Of Kids With Spinal Muscular Atrophy
Published: June 13, 2016 | Source: parentherald.com | Spinal Cord Injury: Muscular Dystrophy
A research team in Spain has developed the world's first exoskeleton prototype to help disabled children learn how to walk and sustain mobility. The exoskeleton comes with long support rods that adjust to the legs and torso of a child with spinal muscular atrophy, or SMA.
The prototype, which weighs 26 pounds and cost $56,000 to make, was developed by the Superior Council for Scientific Research at the Polytechnic University of Madrid, teleSUR reported. The exoskeleton will be used as a mobility therapy in health centers all over the world for children with ages between three and 14.
Improved Quality Of Life
Elena Garcia, lead researcher at the Center for Automation and Robotics CSIC, said children with spinal muscular atrophy are diagnosed around seven to 18 months of age. Kids who have the condition are unable to walk on their own because their nerve cells located in the spinal cord don't transmit signals to their muscles, resulting to inactivity. Inactive muscles are smaller in size, a medical condition called atrophy, according to the Muscular Dystrophy Association USA.
Spinal muscular atrophy is a genetic disease. It affects muscles located close to the center of the body like the shoulders, thighs, hips and upper back. Weak back muscles can lead to spinal curvatures (scoliosis and osteoporosis).
The condition is the number one genetic cause of death for infants, Cure SMA wrote. Garcia noted that people with spinal muscular atrophy have higher risks of respiratory failure especially if the muscles that control breathing and swallowing are affected by the disease.
Hopes Placed On Exoskeleton Device
Garcia said a patient's quality of life can be improved by the exoskeleton because it can prevent or lower the risks of developing other health issues associated with spinal muscular atrophy, teleSUR further reported. The exoskeleton, which is made with aluminum and titanium and has a 5-hour battery, is equipped with a clever modifying system that adapts to each child's symptoms, according to KSLA-TV.
Researchers hope that the exoskeleton could be purchased by families as early as next year depending on the funding. The exoskeleton is currently in the pre-clinical stage, but researchers aim to move it outside of hospital settings and make it available for patients so they can use it at home.
Just recently, researchers from the University of Missouri developed a gene replacement therapy for spinal muscular atrophy patients with respiratory distress type 1 or SMARD1. The therapy targets motor neurons affected by the disease and improves its protein expression. SMARD1 also enhances a patient's muscle strength, SMA News Today reported.
By Samantha Finch, Parent Herald
Previous articleDr. Arthur Jenkins giving TED-style: spinal cord injury and proposed changes
Next articleThe Apple Watch Will Soon Track Fitness for Wheelchair Users
Intel, Brown University collaborate on "intelligent spine technology"
Women's wheelchair rugby takes over Houston


The goal of the Chang Chavkin Scholars Program is to enable public high school students from a rural environment to aspire to and attend challenging academic colleges and universities. High school juniors will apply to the program in the spring of their junior year so that the Scholars Program may provide college advising support and guidance during the college admissions process. The program will award up to $15,000 per year for four years toward the cost of attendance at an accredited US private or public college. The Colorado program is housed with the Telluride Foundation, a community foundation whose mission is to improve the quality of life in the Telluride region (which includes the five school districts supported by the Chang Chavkin Scholars Program).


text:
DESIGN ADVANTAGE
LOW WAKE
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MONOHULLS
HYBRID VESSELS
VESSEL INQUIRY
All American Marine launches 77' Research Vessel for Duke University
Home/All American Marine launches 77' Research Vessel for Duke University
All American Marine, Inc. (AAM) has completed construction and launched an aluminum research and survey vessel for Duke University. The Duke University Marine Lab (DUML) is a hydrofoil-assisted catamaran and is an exemplary arrangement from Teknicraft Design. AAM is the exclusive builder of Teknicraft Design hulls in North America.
The new 77' x 26.5' vessel was based upon two successful Teknicraft Design vessels AAM built for NOAA. Teknicraft's patented hydrofoil-assisted hull design is proven to have industry-leading low-wake wash energy and fuel economy. The vessel is powered by twin Tier 3 CAT C18 "D" ACERT engines, rated at 803 bhp/2100 rpm. The vessel has a cruising speed of 24 knots. The vessel has liveaboard accommodations for 14 persons. Driven by twin fixed pitch propellers, this research vessel can accommodate up to 30 passengers and is constructed and certified under USCG Subchapter "T" regulations.
The vessel will allow marine scientists from DUML and other institutions to conduct research along the Atlantic seaboard in a wide range of fields including marine ecology and conservation, biological oceanography, and renewable energy ocean development. The Duke Marine Lab is a year-round teaching and research campus located on Pivers Island in Beaufort, North Carolina. It offers academic programs at the doctoral, masters and undergraduate levels, and is home to the Nicholas School of the Environment Marine Science and Conservation programs. The vessel was procured as part of an $11 million gift for the construction and operation of a new state-of-the-art research vessel that will expand teaching, research and outreach capabilities at the Marine Lab. The Marine Lab has been without a large research vessel since two of its boats were retired. The 135-foot Cape Hatteras retired in 2012 and the 50-foot Susan Hudson retired in 2014.\
All American Marine's Business Development Manager, Ron Wille, stated: "All American Marine is proud to have been selected by Duke University to build this state-of-the-art education platform. The vessel is not only equipped with Teknicraft's patented hydrofoil design, which provides both lift and fuel efficiency, it also has wet and dry labs, oceanographic equipment, a galley and sleeping quarters. It will be an ideal oceangoing classroom to train undergraduate and graduate students in oceanography and marine biology at the Duke Marine Lab at Beaufort, N.C."
In addition to the university's programs, the vessel will also be used to support science outreach programs for local K-12 teachers, students, and community members. Researchers and instructors from other institutions will also be able to charter the vessel for scientific or educational purposes.
All American Marine's new shipyard has provided an expanded capacity and production capabilities for both additional and larger vessels. The launch of this vessel further exemplifies All American's position as the technological innovator in the North American marketplace.
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text:
The Critically Hated Movie That's Crushing It On Netflix
By Tom Meisfjord/June 8, 2020 4:16 pm EST
"Another insipidly sleazy, lizard-brain shoot-'em-up that through its very dullness demonstrates how rote such ghastly fare has become in our culture." "Instantly forgettable tough-guy fantasia." "Not good." These are just a few selected lines from reviews of the critically despised The Last Days of American Crime, the motion picture which, as of June 8th, 2020, was reportedly Netflix's most-watched available offering.
The Last Days of American Crime, which released directly to Netflix on June 5th, is a lot of things: an action movie, a comic book adaptation, and, to the dismay of critics, one hundred and forty nine minutes long. More than that, it's one of the few, proud members of an elite cinematic club: it has a stunning 0% rating on Rotten Tomatoes, placing it in the same company as distinguished motion pictures like Jaws: The Revenge and that Pinocchio where a middle-aged Roberto Benigni played the main character.
There's good news and bad news
The premise is simple: America, finally fed to the gills with all this "crime" malarkey, develops a broadcast-able signal which will disable its citizen's ability to knowingly break the law. You know, that old chestnut.
The Last Days of American Crime is directed by (deep sigh) Olivier Megaton, the mind behind two Taken movies, Transporter 3, and that Hitman adaptation from 2007 that almost convinced us not to care about Timothy Olyphant. Its cast is a who's-who of solid indie actors who've been recently abandoned by Hollywood like unwanted cats — Michael Pitt of Hedwig and the Angry Inch and Funny Games appears, along with Shalto Copley, the breakout star of District 9 who largely disappeared after The A-Team reboot failed to land.
At nearly two and a half hours long and a 23% audience approval rating, The Last Days of American Crime might seem like a stretch in terms of the mass-watchability necessary to put it over the top. That said, Netflix now considers any two-minute watching period as a "view," so it's not like the bar is set too high.


text:
Q: script to automate adding hosts to a docker container Does anyone perhaps know how to create a script to automatically type in /etc/hosts on a container so that it takes an IP address something like HOST=$(hostname -i) and then
echo "hello $HOST" >> /etc/hosts but unfortunately this simple script doesn't work and I don't know much how to add these hosts automatically I mean of course it should happen inside the container, I know there is something like extra_hosts but I will have a lot of these containers so I would like to automate this somehow
It is my script:
#!/bin/bash

echo "HOST=$(hostname -i)"
echo "$HOST"
echo "Hello $HOST" >> /etc/hosts

and Dockerfile
FROM tomcat:9.0.65

RUN mkdir ipscript
COPY script.sh ipscript/
WORKDIR ipscript/
CMD sh sctr.sh
CMD ["catalina.sh","run"]

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}
Robotics control relies on compact and expressive representations of sensor data, as the task objectives are often expressed in much smaller dimensions than the sensor space dimension (e.g., the position of an object versus the size of an image). These representations are usually hand crafted by human experts, but deep-learning now makes it possible to avoid this feature engineering by using end-to-end learning (e.g., learning a policy from raw pixels). However, such approach is mostly possible in simulation because it requires a huge amount of training data --usually millions of samples-- that makes it impractical in the real world. To overcome this issue, State Representation Learning (SRL)  methods~\citep{Jonschkowski2018} can be used to create an intermediate representation that should contain only useful information to control a robot and thus simplify the policy learning task.

Many different SRL approaches have been proposed (see \citep{Lesort18} for a review), but comparing their performances is challenging. A common approach to evaluate learned representations is to compare performance in a Reinforcement Learning (RL) setting. However, because of the instability of RL algorithms and their cost, this should not be the only method used to assess learned states. Moreover, this approach gives no mean to interpret a state representation, making it difficult to understand which information is encoded in this representation.

While Reinforcement Learning has well established benchmarks, SRL has no metrics nor universal criterion to compare the different approaches. With that in mind, we propose a set of environments with an increasing difficulty, designed for comparing State Representation Learning (SRL) methods for robotic control. We also introduce qualitative and quantitative metrics along with visualization tools to facilitate the development and the comparison of SRL algorithms. The proposed framework allows fast iteration and eases research of new SRL methods by making it easy to produce statistically relevant results: the simulated environments run at 250 FPS on a 8-core machine that allows to train a RL agent on 1 Million steps in only 1h (or to generate 20k samples in less than 2 min)\footnote{Environments, code and data are available at \url{https://github.com/araffin/robotics-rl-srl}}.

In this paper, we first present quickly the reinforcement learning framework and the main state representation learning approaches that are implemented, before presenting the SRL Toolbox environments and datasets, the qualitative and quantitative evaluation methods, and a set of experiments illustrating the performances of the implemented approaches.

\section{Reinforcement Learning and State Representation Learning}

In this section, we introduce Reinforcement Learning (RL), as well as the State Representation Learning (SRL) approaches we integrated in our framework.

\subsection{Reinforcement Learning}

In RL, an agent must learn to select the best action to maximize a reward it will receive. More formally, in a given state $s_t$ ($t$ denotes the current time-step), an agent performs an action $a_t$ and receives a reward $r_t$. The learned behaviour, that should maximize the long-term discounted reward by mapping states to actions, is called a policy: $a_t = \pi(s_t)$.

In the most common settings, the state $s_t$ is either a low dimension representation given by an expert human, or corresponds to the raw observation (end-to-end learning). In order to differentiate these cases, we introduce the observation $o_t$ that correspond to the raw sensor data, and use the term \textit{state} only to refer to low dimensional representations that can be provided by humans, or can be learned using SRL.

In this paper, we work in the context of Markov Decision Processes (MDP), where the next state of the system only depends on the previous state and the taken action. As described above, the term \textit{observations} refers to raw sensor data (mostly images), but do not imply partial observability as assumed in Partially Observable Markov Decision Processes (POMDP).

\subsection{State Representation Learning}

\label{sec:SRL}
SRL~\citep{Lesort18} aims at learning compact representations from raw observations (e.g., learn a position $(x, y)$ directly from raw pixels) without explicit supervision. Most of the time, the goal is to use that representation to solve a task with RL. The idea is that a \textit{low-dimensional representation} should only keep the useful information and reduce the search space, thus contributing to address two main challenges of RL: sample inefficiency and instability. Moreover, a state representation learned for a particular task may be transferred to related tasks and therefore speed up learning in multiple task settings.

Using RL notations, SRL corresponds to learning a transformation\footnote{In practice, the learned transformation is a neural network} $\varphi$ from the observation space $\mathcal{O}$ to the state space $\mathcal{S}$.
Then, a policy $\pi$, that takes a state $s_t \in \mathcal{S}$ as input and outputs action $a_t$, is learned to solve the task:

\begin{equation}
o_t \xrightarrow[SRL]{\varphi} s_t \xrightarrow[RL]{\pi} a_t
\end{equation}

In the next sections, we present approaches of SRL that are implemented in our toolbox. Each method is not mutually exclusive and can be combined to create new models.

\subsubsection{Auto-encoders (AE, VAE)}
A first approach to learn a state representation is to compress the observation into a low dimensional state that is sufficient to reconstruct the observation. This approach does not take advantage of the robotic context because it ignores the possible actions, therefore it is often associated with different objectives (e.g. forward model), and provides a performance baseline.

We integrated Auto-Encoders \citep{Baldi12} and Variational Auto-encoders (VAE) \citep{Kingma13}, i.e., auto-encoders that enforce the latent variables to follow a given distribution.

\subsubsection{Robotic Priors}
\label{sec:priors}
To build a relevant representation of states, one can use prior knowledge about the dynamics or physics of the world. This knowledge can account for temporal continuity or causality principles that reflect the interactions of an agent with its environment \citep{Jonschkowski15, Jonschkowski17}. \textit{Robotic Priors} are defined as objective functions that constrain the state representation.

\subsubsection{Forward and Inverse Models}
The dynamics of the world can be integrated by learning a forward model that predicts state $s_{t+1}$ given state $s_t$ and action $a_t$. Constraints on the state representation can be added by constraining the forward model, for instance, by enforcing the system to follow linear dynamics~\citep{Watter15}.

Another approach is to learn an inverse model \citep{Shelhamer17,Pathak17}, which predicts the taken action $a_{t}$ given two successive states $s_t$ and $s_{t+1}$. This enforces the states to encode information about the dynamics, in order to recover the action needed for such transition.

\subsubsection{Combining Approaches}

Auto-encoders tend to reconstruct everything (that is salient enough in the observation), including static objects and irrelevant features for the task (distractors), whereas forward and inverse models focus on the dynamics, usually encoding the position of the controlled robot in the representation, but not a goal that is not controlled by the actions.

However, these approaches are not mutually exclusive and can be combined to create improved state representations. For instance, \citep{Pathak17} combine both inverse and forward models and \citep{Zhang2018} additionally integrate an auto-encoder.
In the same vein, \citet{Ha18} use a VAE with a recurrent forward model to learn a state representation.

\section{Datasets and Environments}
\label{sec:datasets}

In this section, we describe a set of environments with incremental difficulty, designed to assess SRL algorithms for robotic control. They all follow the interface defined by OpenAI Gym \citep{Brockman16}, which makes integration with RL algorithms easy.

\begin{figure}[h!]
\centering

  \begin{tabular}
      {ccc} \hline
      Mobile Navigation &
      Robotic Arm
      &
      Real Robot
      \\  \hline \\[0.0005pt]

\includegraphics[height=0.95in]{arxiv-images/mobile-robot-1D} \includegraphics[height=0.95in]{arxiv-images/mobile-robot-env} & \includegraphics[height=0.95in]{arxiv-images/kuka-simple-view}
& \includegraphics[height=0.95in]{arxiv-images/baxter-real-cropped} \\ \hline
  \end{tabular}
  \caption{Environments and datasets for state representation learning.\label{fig:datasets}}
\end{figure}

\subsection{Environments Details}

The settings we propose (Fig.~\ref{fig:datasets}) are variations of two  environments: a 2D environment with a mobile robot and a 3D environment with a robotic arm. In all settings, there is a controlled robot and one or more targets (that can be static, randomly initialized or moving). Each environment can either have a continuous or discrete action space, and the reward can be sparse or shaped, allowing us to cover many different situations.

\textit{Static \& random target mobile navigation}: This setting  simulates a navigation task using a small car resembling the task of \citep{Jonschkowski-14-RSS}, with either a cylinder or a horizontal band on the ground as a goal, which can be fixed or moving from episode to episode. The car can move in four directions (\textit{forward, backward, left, right}) and will get a +1 reward when reaching the target, -1 when hitting walls, and 0 otherwise.

\textit{Static \& random target robotic arm}: This setting simulates a robotic arm (Kuka), fixed on a table, with the task of pushing a button that may move or not in between episodes. The arm can be controlled either in the $x$, $y$ and $z$ position using inverse kinematics, or directly controlling the joints. The robot will get +1 reward when the arm pushes the button, -1 when it hits the table (this will end the episode), and 0 otherwise. A variant adds distractors, i.e., moving objects on the table irrelevant for solving the task.

\textit{Simulated \& real robotic arm}: We used a real Baxter robot arm (Gazebo in simulation) to perform the same button pushing task as the previous task, with the same actions and the same rewards. The goal is to test the different methods in a real world setup.

A ground truth state is defined in each scenario: the absolute robot position in static scenarios and the relative position (w.r.t. the target) in moving goal scenarios. Note that apart from providing all described environments, we also provide the corresponding datasets
used in our evaluations: images are 224x224 pixels, navigation datasets use 4 discrete actions (\textit{right, left, forward, backward}); robot arms use one more (\textit{down}) action.

In section~\ref{sec:baselines-results}, we provide baselines results (Ground Truth, Auto-Encoder, Raw Pixels) for each environment.

\subsection{Motivation of the Goal-Based Robotics Tasks}

The environments proposed have several characteristics that make them suitable for research and benchmarking.

\textit{Designed for Robotics:} The proposed environments cover basic goal-based robotics tasks: navigation for a mobile robot and reaching a desired position for a robotic arm.

\textit{Designed for State Representation Learning:} The simplicity of the environments makes the extracted features easier to interpret (correlation can be computed between learned states and position of relevant objects). It is also clear what a good state representation should encode because of the small number of important elements: there is only the controllable robot and the target.

\textit{Designed for Research:} The environments have incremental difficulty, the minimal number of variables for describing each environment (minimal state dimension for solving the task with RL) is increasing from 2 (mobile robot with static target) to 6 (robotic arm with random target).
Having simple environments of gradual difficulty is really important when developing new methods. The proposed environments are also easily customizable so that they cover all possibilities: reward can be sparse/dense, actions can be continuous/discrete. Finally, our benchmark is completely free and fast (it runs at 250 FPS on a 8-core machine with one GPU).

\section{Evaluation of Learned State Representations}
The most practical evaluation of SRL is assessing if the learned states can be used for solving the task in RL. However, algorithm development can benefit from other metrics and visually assessing the validity of the representation being learned, for faster iteration and interpretation of the state embedding space. We provide tools and metrics for this.

\subsection{Qualitative Evaluation}

\begin{figure}[h!]
\begin{center}

  \begin{tabular}
      {ccc} \hline  Real-time SRL & Interactive scatter  &  Latent visualization  \\   \hline \\[0.0005pt]
      \begin{tikzpicture}[
        every node/.style={anchor=south west,inner sep=0pt},
        x=1mm, y=1mm,
      ]
     \node (fig1) at (0,0)
       {\includegraphics[width=1.65in]{arxiv-images/ppo2_plotting}};
     \node (fig2) at (1,1)
       {\includegraphics[width=0.3in]{arxiv-images/kuka-simple-view}};
	\end{tikzpicture} &
    \includegraphics[width=1.65in]{arxiv-images/interactive_plotting.jpg} &
    \begin{tikzpicture}[
        every node/.style={anchor=south west,inner sep=0pt},
        x=1mm, y=1mm,
      ]
     \node (fig1) at (0,0)
       {\includegraphics[width=1.2in]{arxiv-images/vae-latent}};
     \node (fig2) at (1,1)
       {\includegraphics[width=0.5in]{arxiv-images/vae-sliders}};
	\end{tikzpicture}
    \\ \hline
  \end{tabular}
\end{center}
\caption{Visual tools for analysing SRL; Left: Live trajectory of the robot in the state space. Centre: 3D scatter plot of a state space; clicking on any point displays the corresponding observation. Right: reconstruction of the point in the state space defined by the sliders.
See complementary  material for videos. \label{fig:tools}}
\end{figure}

Qualitative evaluation in our case is the perceived utility of the state representation using visualization tools. The perceived utility depends on the task at hand. For example, the state representation of the robotic arm dataset is expected to have a continuous and correlated change with respect to the arm tip position. Three tools are proposed (Fig.~\ref{fig:tools}):

\textit{Real-time SRL}: This tool is used in conjunction with a graphical interface of the simulated environment. It shows the correspondence between observation and state by plotting current position of the observation in the state representation.

\textit{Interactive scatter}: A clickable plot of the state representation, with the reward defining the colour for each point. Here, we expect to visualize structure in the state representation.

\textit{Latent visualization}: It allows to navigate in the latent space by projecting the state to the observation space. This is achieved either by reconstructing the output (for AE and VAE), or by using a nearest neighbour approach for the models lacking a reconstruction.

To deal with state dimensions larger than three, PCA is used to visually assess the learned representations in a qualitative manner.

These tools allow to have better insights of the learned representation over different settings of state representation learning. This is especially useful in situations where the state dimension is greater than 3. This way, we are able to validate different configurations before running more exhaustive and time consuming methods.

\subsection{Metrics}
\label{sec:metrics}

\subsubsection{KNN-MSE}

We use an assessment of the representation's quality based on a Nearest-Neighbours approach (as in \citep{Sermanet17}).
While the nearest neighbour coherence can be assessed visually, KNN-MSE~\citep{Lesort17} derives a quantitative metric from this information.

For a given observation $o$, we find the nearest neighbours of its associated state $s$ in the learned state space, and project them in the ground truth state space. Then we compute the average distance to its neighbours in the latter space:

\begin{equation}\label{eq:knn_mse_crit}
\textrm{KNN-MSE}(s)=\frac{1}{k}\sum_{s' \in KNN(s,k) } || \tilde{s} - \tilde{s}' ||^2
\end{equation}
where $\textrm{KNN}(s,k)$ returns the $k$ nearest neighbours of $s$ (chosen with the Euclidean distance) in the learned state space $\mathcal{S}$, $\tilde{s}$ is the ground truth state associated to $s$, and $\tilde{s}'$ is the one associated to $s'$. A low KNN-MSE means that a neighbour in the ground truth is still a neighbour in the learned representation, and thus, local coherence is preserved.

\subsubsection{Correlation}

A Pearson $\rho$ correlation coefficient's matrix is computed  for each dimension pair ($s$, $\tilde{s}$), where $\tilde{s}$ is the ground truth (GT) state, $s$ the learned state, and $\mu_{s}$ and $\sigma_{s}$ are the mean and standard deviation, respectively, of state $s$:
\begin{equation}
\rho_{s, \tilde{s}} = \frac{ \mathbb{E}[ (s -\mu_{s}) * (\tilde{s} -\mu_{\tilde{s}})  ] } {\sigma_{s} * \sigma_{\tilde{s}}}
\end{equation}

We can visualize the correlation matrix to quantitatively assess the ability of a model to encode relevant information in the states learned.
For instance, the correlation matrix in Figure \ref{fig:corr_matrix} shows degrees of correlation between the mobile robot position and the learned states.
The plot illustrates that for each dimension $i$ of the predicted states $s$, there is a correlation close to 1 (in absolute value) with at least one dimension $j$ of the agent's real position $\tilde{s}_j$.
Therefore, this gives measurable evidences that the model was able to encode the position of the mobile robot.

\begin{figure}[h!]
  \begin{center}
  \includegraphics[width=2.8in]{arxiv-images/agent_mat}
  \caption{Correlation matrix for mobile robot navigation dataset (static target), between each dimension $s_i$ of predicted states $s$ and the ground truth $\tilde{s}_j$. We consider the ground truth to be the agent's real position. The states (dimension=2) are learned by combining a forward and an inverse model.}
  \label{fig:corr_matrix}
  \end{center}

\end{figure}

This visualization tool is quite useful for low-dimensional spaces. However, for state spaces with a high number of dimensions, looking at the correlation matrix becomes impractical. Therefore, we introduce the following measure, named \textit{GTC}\xspace$\ $for Ground Truth Correlation, that allows to compare the models ability to encode relevant information:

\begin{equation}
GTC_{(i)} = \max\limits_j |\rho_{s, \tilde{s}} (i,j)| \in [0,1]
\label{eq:c-vect}
\end{equation}

with $i \in \llbracket0, |\tilde{s}|\rrbracket$, $j \in \llbracket0,|s|\rrbracket$,
$\tilde{s} = [\tilde{s}_{1};...; \tilde{s}_{n}]$, and $\tilde{s}_{k}$ being the $k^{th}$ dimension of the ground truth state vector.

For instance, in the Mobile Robot environment with random target, the ground truth state is composed of the 2D robot position and 2D target position. That is to say, the ground truth states have a dimension of 4: $|\tilde{s}| = 4$.

The vector \textit{GTC}\xspace$\ $gives for each component $i$ of the ground truth states $\tilde{s}$, the maximum absolute correlation value between $\tilde{s}_i$ and any component of the predicted states $s$.
Therefore, \textit{GTC}\xspace$\ $measures the similarity per component, between the learned states $s$ and the ground truth states $\tilde{s}$.

We also introduce a metric, which is the mean of \textit{GTC}\xspace, that allows to compare learned states using one scalar value:

\begin{equation}
GTC_{mean} =\mathbb{E}[GTC]
\label{eq:c-mean}
\end{equation}

\subsection{Quantitative Evaluation With Reinforcement Learning}
\label{sec:quant}

Comparing the performance of RL algorithms, using the learned state representations, is the most relevant approach to evaluate the SRL methods. To do so, our framework integrates 8 algorithms (A2C, ACKTR, ACER, DQN, DDPG, PPO1, PPO2, TRPO) from Stable-Baselines~\citep{stable-baselines} (a fork of OpenAI baselines~\citep{Dhariwal17}), Augmented Random Search (ARS)~\citep{Mania18}, Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES)~\citep{Hansen03} and Soft Actor Critic (SAC)~\citep{haarnoja2018}.

\section{Experiments}
We perform experiments on the proposed datasets using states learned with the approaches described in Section \ref{sec:SRL} along with ground truth (GT).
Here, we report results obtained with PPO.

\begin{table}[h!]
  \begin{tabular}
      {lll} \hline
      Ground Truth States & Learned States & RL Performance \\  \hline \\[0.0005pt]
\includegraphics[height=1.2in]{arxiv-images/GT_mobile_robot_walls} & \includegraphics[height=1.2in]{arxiv-images/SRL_fwd_inv} &
\includegraphics[height=1.5in]{arxiv-images/Learning_Curve_mobile_2M.png} \\
      \hline
  \end{tabular}
  \centering
\caption{Ground truth states (left), states learned (Inverse and Forward) (centre), and RL performance evaluation (PPO) (right) in the mobile robot environment. Colour denotes the reward, red for positive, blue for negative and grey for null reward (left and center). The full resolution plot of the RL performance evaluation can be found in appendix Fig. \ref{fig:ppo2-mobile-random-2M} (right) }
\label{tab:experiments}
\end{table}

Table \ref{tab:experiments} illustrate the qualitative evaluation of a state space learned by combining forward and inverse models on the mobile robot environment. It also shows the performance of PPO based on the states learned by several approaches.

\begin{table} [h!]
 \centering
\begin{tabular}{ lll|ll|ll}\hline
\centering Dataset &   \multicolumn{2}{c}{\textit{Mobile-robot (1)}} &  \multicolumn{2}{c}{\textit{Robotic-arm  (3)}} &  \multicolumn{2}{c}{\textit{Robotic-arm-real (4)}}  \\
\centering Static/Random Target &  Static & Random & Static & Random & Static \\\hline
\centering \textit{Ground Truth}  & 0.0099   & 0.0164   & 0.0025   & 0.0025  & 0.0105 \\
\centering \textit{AE} & 0.0168    & 0.7213     &  0.00336   &  0.0027  & 0.0179 \\
\centering \textit{VAE} & 0.0161     & 0.1295     & 0.0032    & 0.0027   & 0.0177 \\
\centering \textit{Robotic Priors} & 0.0200  & 0.0900 & 0.0029 & 0.0027 & 0.0213\\
\centering \textit{Forward} &  0.5111   &  1.1557    &  0.1425    & 0.2564   &  0.0796 \\
\centering \textit{Inverse} & 0.0191     &   0.7703   &  0.0182  & 0.2705   & 0.0521 \\
\centering \textit{Fwd+Inv} &  0.0164   &  0.7467 & 0.0176  & 0.2705   &  0.0368\\%
\hline
\end{tabular}
\caption{KNN-MSE results for each SRL method. Ground Truth (robot absolute or relative position with respect to target object), auto-encoders, forward (fwd), inverse (inv) models and combinations.}
\label{tab:knn-mse}
\end{table}

Table \ref{tab:knn-mse} shows the KNN-MSE for the different SRL approaches on the implemented environments.

The table~\ref{tab:gt-correlation} gives the $GTC_{mean}$\xspace$ $metrics for several approaches and the associated RL performance using PPO. It shows that $GTC_{mean}$\xspace$ $is a good indicator for the performance than can be obtained in RL: a good disentanglement with a good correlation with the ground truth state (i.e. a high $GTC_{mean}$\xspace) lead to a higher mean reward in RL.

During the development of \textit{S-RL Toolbox}, we learned some useful insights on SRL. We observed that auto-encoders do not reconstruct objects if they are too small. This is an issue if the object is relevant for the task. An inverse model is usually sufficient to learn a coherent state representation (in our case, the position of the controllable object). Continuity in the state space is important to perform well in RL. Using real-time SRL tools, we note discontinuities in the state space learned by AE, even if reconstruction error is low, and that hinders RL.

\begin{table} [h!]
 \centering
\begin{tabular}{ l|llll|l|l}\hline
GroundTruthCorrelation & \textit{$x_{robot}$} & \textit{$y_{robot}$} & \textit{$x_{target}$}  & \textit{$y_{target}$} & \textit{Mean} & \textit{RL} \\\hline
Robotic Priors & 0.2 & 0.2 & 0.41 & 0.66 & 0.37 & 5.4 $\pm$ 3.1 \\
Random & 0.68 & 0.65 & 0.34 & 0.31 & 0.50 & 163.4 $\pm$ 10.0 \\
Supervised & 0.69 & 0.73 & 0.70 & 0.72 & 0.71 & 213.3 $\pm$ 6.0 \\
Auto-Encoder & 0.52 & 0.51 & 0.24 & 0.23 & 0.38 & 138.5 $\pm$ 12.3 \\
GT  &  1 &  1 & 1 & 1 & 1 & 229.7 +- 2.7 \\
\hline
\end{tabular}
\caption{\textit{GTC}\xspace and $GTC_{mean}$\xspace, and mean reward performance in RL (using PPO) per episode after 2 millions steps, with standard error for each SRL method in mobile robot with random target environment.}
\label{tab:gt-correlation}
\end{table}

\section{Related Work}

In the RL literature, several classic benchmarks are used to compare algorithms. For discrete actions, Atari Games from OpenAI Gym suite~\citep{Brockman16} are often adopted, whereas for continuous actions, MuJoCo locomotion tasks \citep{Todorov2012} is favoured. OpenAI recently open-sourced robotics goal-based tasks \citep{Plappert18}. These environments are close to what we propose; however, they use a non-free physics engine, observations are not images and customization is not easy. Also, our tools were designed with SRL in mind and offer a gradual difficulty.

Regarding the SRL literature, very diverse environments are used, without common metrics or visualizations. The SRL methods are usually only compared with learning from raw observations. \cite{Jonschkowski15} assess the quality of learned representations in a slot car scenario (similar to \cite{Lange2012}) and a mobile navigation task (following \cite{Sprague2009, Boots2011}). More recently, \cite{Ha18} present their results on Vizdoom and RaceCar environments. They show interesting insights of what was learned by navigating in the latent space and projecting states back to the pixel space (similar to Fig.~\ref{fig:tools}). \cite{Zhang2018} use a subset of MuJoCo tasks, with joints as input, and a binary maze environment. They perform an insightful ablation study of their SRL model.

Our contribution is two-fold: we provide a framework integrated with RL algorithms, environments and tools for SRL, along with implementations of the main SRL methods.

\section{Discussion and Conclusions}
This paper presents a set of environments on which to perform SRL benchmarks of incremental difficulty to solve tasks in RL, specifically, in robotics control. Our proposed toolbox facilitates fast iterations, interpretability and reproducibility, with a set of qualitative and quantitative metrics and  interactive visualization tools. We believe such a framework is needed to have fair comparisons, focused on robotics control, among SRL methods.

\acks{This work is supported by the DREAM project\footnote{\url{http://www.robotsthatdream.eu}} through the European Union Horizon 2020 FET research and innovation program under grant agreement No 640891.}

\newpage

redpajama_set_name: RedPajamaArXiv


Iain Renicks is a resident of Thornhill and was educated at Wallace Hall Academy. Iain is Bakery Manager with the family firm and was crowned World Scotch Pie Champion in 2012.
Iain has been with Alive since July 2009 and now boasts of having the longest continuous show on the station.
Iain was one of the founder members of Thornhill Music Festival and has promoted live music in Nithsdale as well as managing and representing bands also Iain's other hobbies include being a Controller with Dumfries and Galloway Blood Bikes. Iain was also invited to be a judge for the 2018 Community Radio Awards.
Musically his tastes are so widespread and varied it would be easier to mention what he doesn't like, that's what makes The Last Resort so interesting.


text:
Rearview: Providing a forum for 'The Voice'
James Jaillet
Overdrive's Rearview series is a new recurring monthly feature taking a historical look at the trucking industry through the lens of archived editions of Overdrive.
Of note from the Our Readers Write section in the February 1983 issue: Calls for 'someone who understands the trucking industry' to run the DOT; a letter from one trucker to his peers asking them to move their trucks from the fuel island when not fueling ('time is money, and nothing is more aggravating than being held up, especially by a trucker'); and a note titled 'Starve them,' in which Steve Davis of Florida urged independents to initiate state-by-state trucker strikes to 'starve these greedy people so they'll realize the need for the trucking industry.' Déjà vu all over again?
Overdrive's founding purpose was primarily fighting the "Bigs" looking to squash the independent trucker – Big Labor, Big Government and Big Rail, to name a few – but it didn't fight alone. Upon its 1961 launch, Overdrive also provided the first real forum for owner-operators to make their opinions known on the Bigs or any other issue that ailed them.
From its first issue, September 1961, Overdrive included the Our Readers Write section, in which truckers wrote letters to the editor, detailed on-highway happenings to their peers and responded to articles in Overdrive and in other national publications.
Though the section has changed names and formats in the 55 years since, Overdrive continues to build its reputation on championing reader representation and the principles they care about. The magazine cover still proudly uses the tagline "The Voice of the American Trucker."
In addition to the truckers cited throughout the magazine, each issue today leads off with six pages of the Voices section (as well as a regular online presence), devoted to letting readers comment on topics such as burdensome regulations, shady brokers and the etiquette of fellow truck drivers. They also speak out on non-trucking issues such as gun control and health care.
The feedback section of the August 1962 edition had letters from tax collectors perturbed by Overdrivereporting, readers wanting an expansion of the magazine's distribution and an intriguing note from owner-operator Bob Tappan of New York saying he wasn't sure his single-truck operation could survive looming tax changes. Overdrive's online presence and social media channels have expanded the forum for readers. Topical discussion threads tied to OverdriveOnline.com stories, such as these responses to a story on lawyers exploiting truck-related accidents, and posts on Overdrive's Facebook page offer new avenues for lively expression. Modern Overdrive's feature about six pages each month dedicated to giving a voice to today's owner-operators.
Rearview: Tool of the trade
Building personal payroll -- a step toward tax savings, better finances


text:
Considerations in Fitting Hearing Aids with Extended Bandwidths
Oct 2, 2009 | Practice Management | 0 |
Francis Kuk, PhD, is director of audiology at the Widex Office of Research in Clinical Amplification (ORCA), Lisle, Ill, and Lars Baekgaard, MS, is a research engineer at Widex A/S, Vaerloese, Denmark.
One of the key ingredients to having a hearing aid with high-fidelity sound is to have as broad a frequency output response as possible. While many present-day hearing aids have bandwidths that reach 8000 Hz, some newer hearing aids have a high-frequency response that extends beyond 10,000 Hz. For example, the Widex Passion hearing aid, a receiver-in-the-canal (RIC) style aid, has a bandwidth that extends to more than 10,000 Hz (as measured in an ear simulator). In addition, the use of dual-receiver (or two-way) technology in a micro-size BTE (Widex mind440 m4-m-CB) provides a moderately high output, as well as a broad bandwidth (>10,000 Hz).
While these types of developments mark a technological breakthrough for hearing aid and component manufacturers, it may cause confusion to some dispensing professionals regarding verification and validation procedures. This paper provides background on extending hearing aid bandwidths and considers some of the related issues.
Factors Affecting the Bandwidth of a Hearing Aid
The bandwidth (or frequency response) reflects the range of output signals offered by a hearing aid. In a previous paper,1 we indicated that three stages—the input stage, the processing stage, and the output stage—affect the output bandwidth of the processed signal and the naturalness of sounds.
FIGURE 1. Real-ear aided response (REAR) of the mind440 m4-m-CB hearing aid when connected to a foam earmold with a Libby horn (red, 4 mm internal diameter), a #13 tube (blue, 1.9 mm), and a thin tube (green, 0.9 mm) for the BTE plumbing.
Input stage variables. The input stage refers to the microphone and the sampling rate of the analog-to-digital converter (ADC). To have a broad bandwidth, the microphone must have a broad flat response, and the sampling rate of the ADC must be as high as possible.
Many microphones today are sensitive to a broad range of frequencies and are not the limiting factor of the output frequency range. The only exception may be the step microphone (or filtered microphone), which purposely filters off the low frequencies to avoid potential saturation from overdrive.2 Another possibility is filtering off the high frequencies to avoid anti-aliasing artifacts due to insufficient sampling3 or to minimize interference caused by ultrasonic signals that are increasingly used in commercial sensors and security systems.
Of more relevance is the sampling rate. The Nyquist theorem requires that the sampling frequency must be at least twice as high as the highest frequency in the input in order to correctly capture the nuances of the input signal. Thus, a sampling rate of 20 kHz is required to capture inputs as high as 10 kHz. If the input has energy at 16 kHz, the information between 10 kHz and 16 kHz would not be captured correctly. This means that the usable signal from the hearing aid would not exceed 10 kHz even though the bandwidth may be stated as much higher. That is, a bandwidth of 12 kHz only makes sense if the input stage of the hearing aid is able to handle frequencies up to 12 kHz.
Output receivers and coupling. The output stage of a hearing aid is primarily affected by the receivers used. Receivers transduce the electrical output of the hearing aid into an acoustic form. The types of receivers (size, materials, etc) determine its electrical output characteristics (eg, maximum output levels, resonance peaks, and bandwidths).
However, the acoustic spectrum is further shaped by the presence of any earhook/tubing connected to the receivers. All receivers have some lengths of tubing connected; however, the length and the diameter of the tubing connected to a receiver vary depending on whether it is a BTE or an ITE product:
BTEs. A short tubing is used in a BTE to direct the acoustic output from the receiver to the earhook. A longer piece of tubing from the earmold connects the earhook to the earmold, and directs the acoustic output to the wearer's ear canal. These tubings have natural resonances that further shape the receiver frequency response.
A typical BTE tubing system acts like a tube that is closed on one end (receiver end) but open on the other (eardrum end). This gives the opportunity for ¼-wavelength resonances. That is, frequencies that have a wavelength 4 times the length of the tube would produce resonance peaks at odd multiples of themselves. For a #13 tube (internal diameter of about 1.9 mm) with a typical length of 75 mm (or 3 inches), the tubing resonance occurs at 1000 Hz, 3000 Hz, and 5000 Hz. The "bumps" around 2000 Hz and 4000 Hz are, respectively, the mechanical resonance of the receiver and the Helmholtz resonance formed by the air within the tube system and the air inside the receiver.4
Today, thin tubes of internal diameters around 0.9 mm have gained popularity. The use of this type of tubing shifts the tubing resonance downwards in frequencies, as well as reduces high-frequency output by 5 dB to 10 dB when compared to standard #13 tube fittings.5 Thus, the use of thin-tube fittings narrows hearing aid bandwidth (relative to #13 tubing).
Looking at it another way, tubes with a larger internal diameter (than #13 tubing) enhance the magnitude of the high-frequency output. An example is the use of Libby horn tubing, which gradually increases its internal diameter from 1.9 mm to 3 or 4 mm at its medial end. Figure 1 shows the in situ output of the mind440 m-CB hearing aid connected to a Libby horn, a #13 tube, and a thin tube. Despite the fact that the receiver used in this example is one with the extended bandwidth (to 10,000 Hz), the use of the Libby horn has further increased the output in the high frequencies by 4-5 dB. In contrast, the use of thin tubing decreased the output above 1000 Hz by as much as 10 dB at some frequencies.
While some may question the need for acoustic plumbing in today's digital hearing aids that offer flexibility in frequency shaping, it can still be very useful in cases where the degree of hearing loss pushes the output toward the maximum power output (MPO) of the hearing aid.
ITEs. For an ITE/ITC/CIC, the tube connecting the receiver to the sound outlet is typically not long enough to provide significant peaks. Thus, the typical peaks around 2000 Hz and 3000 Hz are the mechanical resonance of the receivers. It is typically higher in frequency than the mechanical resonance of a BTE receiver because of its smaller size and lighter, stiffer diaphragm.
Receiver dimensions. The output SPL of a receiver and its frequency response is dependent on the physical dimensions of the receiver. In general, the larger the receiver, the greater the air volume for the diaphragm to vibrate. This results in a higher output before peak clipping saturation occurs.
The bandwidth of a receiver is also limited by the size of the receiver. As indicated earlier, a receiver works by moving the diaphragm in synchrony with the frequency of the changing electrical field. The higher the frequency (or the broader the bandwidth), the more rapidly the diaphragm needs to move. This is possible with a smaller receiver (thus a smaller and stiffer diaphragm) that needs to drive a small volume of air. It becomes more difficult in the higher frequencies with a larger receiver without compromising the low-frequency sensitivity of the receiver and increasing the current drain of the hearing aid dramatically.
Thus, the bandwidth of most hearing aid receivers is a compromise of current drain, size, and the desired frequency region where special attention is needed. That is, if one needs to have an extended bandwidth in the high frequencies, one may need to sacrifice the low-frequency sensitivity of the hearing aid and vice versa. Figure 2 shows the physical dimensions of several receivers used in the Widex hearing aids and the associated frequency response and MPOs. The trend of a higher MPO, but narrower bandwidth, as the receiver gets larger is clear.
One solution to achieve an extended high-frequency bandwidth without too much compromise is the use of two-way or dual receivers. In simple terms, this is a two-receiver system where one is optimized for low frequencies and the other is optimized for high frequencies. The output from both receivers is summed before reaching the eardrum. The advantage of such an approach is that an extended high-frequency bandwidth can be achieved in a moderately high output receiver with little compromise on the low-frequency response. Furthermore, it economizes on the current drain and minimizes on the potential of saturation distortion. The micro-sized, Widex m4-m-CB (ClearBand) model uses such a receiver system to reach an in situ bandwidth that extends from 100 Hz to 10,000 Hz.
TABLE 1. Comparison chart showing the direct correspondence between the physical dimensions of the receivers and the frequency response and maximum power output of hearing aids.
Broader Bandwidth: Who Benefits?
The literature on the benefits of an extended bandwidth appears mixed, depending on the hearing status of the subjects. The majority of studies conducted in normal-hearing listeners and those under headphones concluded that a broader bandwidth was associated with better sound quality and better speech intelligibility. The benefit was reported in quiet as well as in noise for both normal-hearing and hearing-impaired subjects with less than a moderate degree of hearing loss.6-9
In studies with hearing-impaired children, it has become generally accepted that an extended bandwidth is necessary for optimal speech and language development. For example, Stelmachowicz et al10,11 showed significant improvement in identification of /s/ with increasing bandwidth. Some studies also suggested that high frequency is necessary for optimal localization in the vertical plane.12
Despite these endorsements for a broad (or extended) bandwidth, several studies have questioned the limited benefit of extended bandwidth for people with a sloping high-frequency hearing losses exceeding 55 dBHL.13,14 Indeed, these studies showed a decrease in speech recognition with increasing bandwidth. Additionally, the concept of "dead" regions15 raises additional questions on the need for, and the appropriateness of, amplification for high-frequency hearing loss.
Recently, Hornsby16 reported on the ability of 63 people to identify sentences in noise in a wideband (178 Hz to 9000 Hz) and a low-pass (3534 Hz) condition. While all subjects showed some benefits with the broadened bandwidth, less benefit was seen with the subjects with more severe losses. In the same presentation, Ricketts17 showed that subjects who preferred a wider bandwidth exhibited audiogram slopes of 7.5 dB/octave or less, whereas those who showed a consistent preference for a narrower bandwidth exhibited audiogram slopes of 12.5/octave or more. The presenters concluded that those who had a steeper audiogram slope (ie, more severe high-frequency loss) preferred a narrower bandwidth.
FIGURE 2. Comparison of coupler gain of the mind4-m-CB hearing aid on a 711 coupler (blue) and a HA-2 coupler (red). FIGURE 3. REAR of the mind440-m-ClearBand hearing aid (above 3000 Hz) measured at three distances from the eardrum. Note the similarity in output below 4000 Hz with the three distances.
So, How Should I Look at Extended Bandwidth, in Practice?
To summarize, the available studies seem to suggest that normal-hearing people and people with a mild hearing loss would most likely realize the sound quality and speech intelligibility benefit of the extended bandwidth. As hearing loss increases, it is likely that the noted benefits decrease.
Given today's transducer technology, it is unlikely that hearing aids with an extended bandwidth will benefit those with a severe-to-profound degree of hearing loss in the high frequencies. There are at least two reasons for this.
Current hearing aids with an extended bandwidth have only a moderate MPO (peak <125 dBSPL). This means that, even though the bandwidth may be broader, there is still insufficient high-frequency output to meet the demands of a severe-to-profound hearing loss. Indeed, as Figure 2 shows, hearing aids intended for a severe-to-profound loss typically have a bandwidth that is significantly narrower than those for a milder degree of hearing loss. In current hearing aids, bandwidth behaves in the opposite manner as MPO.
The likelihood of a dead region increases as the degree of hearing loss increases.15 This is especially the case for a precipitous hearing loss. The implication is that, even though sufficient output may be available, the wearer with a severe-to-profound loss in the high frequencies may not be able to utilize the available high-frequency output. This possibility is seen in the studies, which showed a lack of benefit from the extended bandwidth in patients with even a moderately severe hearing loss. These patients may be better served with the use of frequency transposition.18
Verification of Extended Bandwidth
The reported bandwidth of a hearing aid is affected by the coupler that is used for the measurements. Typically, the output measured on standard 2cc couplers (HA-1 and HA-2) only reflects the hearing aid output for the specific coupler. It does not reflect the actual output in the ear canal. Thus, standard 2cc coupler responses can be used only for quality assurance purposes.
A more accurate way to examine the in situ output of the hearing aid is to perform real-ear measurements. Alternatively, if one is interested in the hearing aid output in the average adult ear, one may use a 711-coupler or a Zwislocki coupler in which the impedance of the coupler matches more closely that of the average adult ear. That is why some manufacturers also report hearing aid output-responses on the 711-coupler. In general, the output from a 711-coupler is higher in magnitude and broader in bandwidth than that measured with a standard 2cc coupler. The difference increases as the frequency increases.
Figure 2 shows the output difference between a 711-coupler and a 2cc HA-2 coupler. One can see differences as much as 15 dB in the high frequencies. While the 711-coupler is more reflective of real-ear output, it is more costly and is not readily available for clinical use.
One would need to be even more careful when verifying the real-ear output of hearing aids with an extended bandwidth. The closeness of the probe-tube microphone to the eardrum could affect the accuracy of the measurement in the high frequencies. The closer the probe-mic is to the eardrum, the lower the risk of the probe-mic encountering standing wave minima and yielding an inaccurate measurement. On the other hand, a probe-mic placed close to the eardrum causes discomfort to the patient. As a compromise, it has often been recommended that the probe tube be placed around 5 mm beyond the tip of the earmold or within 6 mm from the eardrum. This would allow one to reliably measure the high-frequency output of the hearing aid to within 2 dB through 6000 Hz.19
However, the insertion depth that may be appropriate for measuring output at 4000 Hz may not be adequate for measuring output above that frequency. Figure 3 shows the real-ear output of the mind m4-m-CB hearing aid (with the extended high-frequency output) measured almost at the eardrum (12 mm from earmold tip), and at 3 mm (or 9 mm from earmold tip) and 6 mm from the eardrum (or 6 mm from earmold tip). The difference in output at 4000 Hz was within 1 dB among the three insertion depths. However, up to a 2 dB difference was noted between the 0 mm and 3 mm distance, and 15 dB at the 6 mm distance at 6000 Hz.
This suggests that, when validating the additional high-frequency output of a hearing aid with an extended bandwidth, it is important to ensure that the probe-mic is within 3 mm of the eardrum in order to accurately measure the high-frequency output above 4000 Hz. Otherwise, one may reach a false conclusion on the high-frequency output of the device. In his book, Dillon4 provides a summary chart (p 93) on the required distance of the probe-mic from the eardrum to ensure acceptable measurement errors.
In summary, the achievement of a bandwidth that extends beyond 10,000 Hz makes it possible for more people with a mild-to-moderate degree of hearing loss to enjoy even higher fidelity sounds than what is currently available. However, as a clinician, one needs to be realistic about the types of candidates for this technology, and use additional care when documenting the output of these hearing aids.
Citation for this article:
Kuk F, Baekgaard L. Considerations in fitting hearing aids with extended bandwidths. Hearing Review. 2009:16(11):32-38.
Kuk F, Jessen A, Baekgaard L. Ensuring high fidelity in hearing aid sound processing. Hearing Review. 2009;16(3):34-43.
Chasin M, Schmidt M. The use of a high frequency emphasis microphone for musicians. Hearing Review. 2009;16(2): 32-37.
Kuk F. The effects of distortion in hearing aids. In: Valente M, ed. Hearing Aids: Regulations, Options, and Limitations. New York: Thieme Medical Publishing; 1996:327-367.
Dillon H. Hearing Aids. Sydney: Boomerang Press; 2001.
Kuk F, Baekgaard L. Hearing aid selection and BTE: choosing among various "open-ear" and "receiver-in-canal" options. Hearing Review. 2008;15(3):22-36.
Hornsby B, Ricketts T. The effects of hearing loss on the contribution of high- and low-frequency speech information to speech understanding. J Acoust Soc Am. 2003;113:1706-1717.
Hornsby B, Ricketts T. The effects of hearing loss on the contribution of high- and low-frequency speech information to speech understanding. II. Sloping hearing loss. J Acoust Soc Am. 2006;119(3):1752-63.
Ricketts T, Dittberner A, Johnson E. High-frequency amplification and sound quality in listeners with normal through moderate hearing loss. J Sp Lang Hear Res. 2008;51:160-172.
Stelmachowicz P, Pittman A, Hoover B, Lewis D. Effect of stimulus bandwidth on the perception of /s/ in normal- and hearing-impaired children and adults. J Acoust Soc Am. 2001;110(4): 2183-90.
Stelmachowicz P, Pittman A, Hoover B, Lewis D. Aided perception of /s/ and /z/ by hearing- impaired children. Ear Hear. 2002;23:316-324.
Byrne D, Noble W. Optimizing sound location with hearing aids. Trends Amplif. 1998;3(2):51-73.
Ching T, Dillon H, Byrne D. Speech recognition of hearing-impaired listeners: predictions from audibility and the limited role of high-frequency amplification. J Acoust Soc Am. 1998;103:1128-1140.
Hogan C, Turner C. High-frequency audibility: benefits for hearing-impaired listeners. J Acoust Soc Am. 1998;104:432-441.
Moore B. Dead regions in the cochlea: diagnosis, perceptual consequences, and implications for the fitting of hearing aids. Trends Amplif. 2001;5(1):1-34.
Hornsby B. Hearing aid bandwidth for speech understanding. Paper presented at: American Academy of Audiology convention; April 2007; Denver.
Ricketts T. High frequency extension and sound quality. Paper presented at: American Academy of Audiology convention; April 2007; Denver.
Kuk F, Keenan D, Peeters H, Korhonen P, Auriemmo J. Twelve lessons learned about linear frequency transposition. Hearing Review. 2008;15(11):32-41.
Revit L. Real-ear measures. In: Valente M, ed. Strategies for Selecting and Verifying Hearing Aid Fittings. 2nd ed. New York: Thieme; 2002:66-124.
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