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Savage 93R17 Long Term Review: 17HMR on a Budget
ByJames Willmus Updated on November 3, 2021
About six months ago I came across a brand new Savage 93R17 left-handed model at a discount. Being a southpaw, the pickings for guns are much more limited compared to the rest of the population, and 17HMR was a caliber I was interested in learning more about, so I put my own money down, grabbed a scope, and set about putting this rifle through it's paces.
The Savage 93R17 has proven to be an accurate, simple rifle that is best used as a small game rifle and as a training device for more expensive center fire hunting rifles.
This gun does have a couple of issues that need to be noted, but I'll get to those in a minute. For now, let me start by explaining why I needed a 17 HMR and how the Savage fulfills these needs.
Uses for the 17 HMR
This is not going to be an in depth ballistics analysis, but we do need to talk about why someone would even consider buying a 17 HMR rifle. After all, there are certainly some drawbacks for this caliber.
The 17 HMR is more expensive than the 22L yet reloading it isn't really worth it. It also drifts in wind quite a lot despite its speed, and ballistically it can't compete with the 223 Rem or the 22-250, two excellent calibers for eliminating coyotes and the occasional hog.
So what is a 17 HMR good for?
As a varmint cartridge for pest animals, the 17 HMR can't be beat. Need to eliminate some squirrels, gophers, prairie dogs, skunks, rabbits, rats, foxes etc…? The 17 HMR can kill all these at 2-3x the effective range of the 22LR and with better ballistics than the 22WMR. Also, while the 22LR will drop 30+ inches at 200 yards, the 17 HMR will drop about 8-9 inches. Holdover is child's play with a 17 HMR.
Another benefit that isn't stated as often is that varmint bullets in the 17 HMR tend to fragment upon hitting something solid. That means a stray bullet isn't going to punch through exterior walls or ricochet into the next section. A caliber that can eliminate nuisance animals without collateral damage is fantastic!
In addition to pests, I also wanted to have a rifle that I could practice with that better replicated big game hunting calibers than the 22LR. Considering hunting ammo for the 300 Win Mag is about $3.00 on average, the $0.30 per round of the 17 HMR saves a lot of practice costs while being a viable 200 yard cartridge for both hunting and target practice.
So it is for these two roles, pests and practice, that I decided to try out the Savage 93R17.
From the moment I picked this gun up off the rack at the local gun store, the Savage 93R17 felt really good in my hands. This particular model was the 'GV' which has a more traditional looking wooden stock with a raised comb. The metals were blued with a matte finish for a clean, classic look.
Like many modern rimfire rifles, the Savage uses a detachable 5 round box magazine that feeds into a simple, solid bolt action. Savage included an Accutrigger for the gun and a simple, two position safety switch located on the bolt side of the receiver. The action is mirrored on left handed versions which is nice because lefties usually have to compromise often for right-handed controls.
Despite being a rimfire, the 93R17 has a bit of heft to it. This is due to the wooden stock and the 21 inch, heavy contour barrel. The barrel specifically is a great feature because it is thick and can take a lot of shots before heating up, a perfect feature when trying to take out a field of rabbits.
There were three things I wish this gun had though. First, it would have been great if the barrel was threaded for a suppressor. While I don't own one, shooting suppressed makes it easier to take out multiple pests in quick succession.
I think it would have been better to have a picatinny rail over the receiver instead of a two-piece weaver mount. Weaver's are certainly usable, but the pic rail is the more flexible, modern option. Switching out the mounting rail isn't a big trick though, so it's not a deal breaker.
The plastic butt stock is very slippery. A rifle needs to have three contact points with the body to be accurate, the forestock, the grip, and the buttstock. If one or more of those contact points is shaped wrong or too slick, this affects accuracy. I get that the 93R17 has almost no recoil, but having a bit of rubber would eliminate slipping issues.
One other nitpick I had was the use of Phillips head screws in some areas. It doesn't detract from the utility of the gun at all, and it's more of a personal opinion, but Phillip's screw heads look ugly to me.
Still, for a gun that cost less than $300 (MSRP is now $319 for the GV models) some corners had to be cut and, if given the choice between some annoying things that are easily changed and something that is critical like accuracy or safety, I'd clearly choose to keep the latter. Here are some specs you might be interested in knowing about this rifle:
MSRP: $319 Model: 93R17 GV
Action: Bolt Caliber: 17 HMR
Finish: Wood/Blued Capacity: 5 rounds
Barrel Length: 21 inches Magazine: Detachable Box
Twist Rate: 1 in 9 Scope Mount: Weaver
Length of Pull: 13.75 inches Weight: 6 lbs
Overall Length: 37.75 inches Stock shape: Varmint
Specs for the Savage 93R17
Range Tests
After purchasing the rifle, I went out and bought a scope that I thought would compliment the rifle and serve my needs. The scope I settled on was a Vortex Crossfire II 4-12×40 AO with a BDC reticle. This scope was about $189 in the store and turned out to be a perfect match for this rifle. The "AO" stands for "Adjustable Objective", and allows the scope to focus from about 10 yards to infinity.
I did not like the rings I chose for this scope, but they worked and were also fairly cheap (about $30). There are certainly cheaper options on the market, but this turned out to be a great value.
Consistency Test
After getting the scope zeroed to 100 yards, I proceeded with the first test, consistency. I made groups at 50, 100, and 150 yards. At each test, the bullets landed very close to each other. It was important to do this in as low a wind as possible to achieve a usable result. The good news is that there was no variance on where the bullets landed except for a bit of wind drift.
Speaking of wind drift, within 100 yards it is nearly unnoticeable if the wind is less than 10mph. However, due to the design of the 17 HMR bullets, past 100 yards wind drift becomes an issue quickly. I found that by 200 yards group sizes were opening up significantly even in a light wind.
Group Size Test
After seeing the encouraging results, I then wanted to make a few groups at 100 yards and measure their spread. I made five, 5-shot groups on paper then measured and recorded each. The smallest size was 0.57 inches, 0.48 inches excluding the 'flyer'. The largest size was 1.08 inches, or 0.95 inches excluding the 'flyer'. All five groups averaged out to 0.82 inches, with 0.73 inches excluding a single 'flyer'.
100 Yards Smallest (inches) Largest (inches) Average (inches)
5/5 Group 0.57 1.08 0.82
Smallest, largest, and average group sizes at 100 yards.
Why exclude the flyers? When shooting at live targets like small game animals, hunters have to know what their probability is at different ranges. If a hunter has a 10% hit rate at 500 yards, odds are the hunter shouldn't take that shot. But if the same hunter has an 80% hit rate at 300 yards, odds are good the hunter will kill the animal on the first hit.
We can extrapolate these numbers based on range and target size. Assuming that a hunter is using the same rest with the same stability, the variance in group size will only change due to the action of wind. This test was done in a light wind that I'd estimate to be about 5 mph. Using those numbers, here are the maximum acceptable range for different creatures at different wind speeds:
Animal (cranium size) 5 mph wind 10 mph wind 15 mph wind
Red Squirrel (0.7 in) 86 yds 43 yds 29 yds
Prairie Dog (1.1 in) 108 yds 54 yds 36 yds
Grey Squirrel (1.3 in) 117 yds 59 yds 39 yds
Skunk (1.6 in) 130 yds 65 yds 43 yds
Jackrabbit (1.9 in) 141 yds 70 yds 47 yds
Raccoon (2.2 in) 152 yds 76 yds 51 yds
Red Fox (3.0 in) 178 yds 89 yds 59 yds
These are ideal theoretical ranges with 80% hit rate on largest group size.
I calculated these numbers by dividing cranium size by my largest group diameter with an 80% hit rate (0.95 inches at 100 yards). The square root of this result estimates the affect of wind at different yardages (bullets travel on an exponential path in side wind). This final result is the "range factor" that can be multiplied by my base range (100 yds), giving us the maximum usable range for each animal.
To adjust for different wind speeds, yardages are divided one half if the wind is doubled (10 mph) and one third if wind is tripled (15 mph). This table should give us a pretty good idea of what this gun is capable of in different conditions assuming a steady rest.
Assuming calm conditions, the Savage 93R17 is theoretically capable of making a brain shot on a fox at 180 yards. My test groups out to 200 yards seem to support this as I was able to make approximately a 4 inch group while guessing my hold over.
The "Quarter" Test
To test the practicality of these estimates, I decided to try one more range test. I took a U.S. quarter and stuck it to the front of a target at 100 yards. According to the table above, this should be very close to replicating the brain shot on a prairie dog (1.1 inch target at 108 yards vs 0.95 inch target at 100 yards).
The results for this were pleasing. I hit 3 quarters with five shots. My two misses were by fractions of an inch and likely still would have been lethal on a prairie dog. Normally, this would be considered a trick shot, but with the Savage 93R17, it was pretty darn easy!
This final test I think pretty much proved this gun to be a "sub-MOA" rifle both on paper and in practical use. I'm sure that with a proper bi-pod and a few modifications (like a rubber butt pad), group sizes could be cut down even further Considering this rifle is cheaper than the Savage Axis, the results are down right spectacular!
Anyways, after emptying my pockets of quarters I decided to start ringing steel. While I couldn't quite reach the 300 yard plates, I managed to hit the 200 yard plates more times than not and I didn't even have to try with the 100 yard plates.
After establishing that the 93R17 makes an excellent range rifle, especially for practicing holding for wind and elevation, the next set of tests I wanted to put the gun through would be in the field. The results for this were a bit mixed, but still positive overall. However, the varied environments showed some weaknesses to the rifle and the way I set up the rifle.
First, I went out to hunt prairie dogs, pocket gophers, and the like in a variety of weather conditions. Admittedly, I'm a piss poor rodent hunter, but the few I managed to pick off were easy kills from the accuracy of the Savage. The dusty, dry air from mid-summer in Montana did not affect the rifle one bit. In fact, I had yet to experience a jam, misfire, or other shooting-related issue.
Going out to the field several times meant I could test out how well the rifle holds up to being hauled around. In the several weekends I had the Savage in the back of my suburban getting moved around (bumped around would be more accurate), I didn't notice any significant change in zero nor did I see any scratching from the finish.
There were a couple of nick marks from me accidentally hitting the gun against some concrete, but aside from that the stock has remained remarkably clean and has proven to be durable. However, my next field test showed a flaw that, although not a deal breaker, is something to be aware of.
I went to Minnesota to help my father fix some things on his cabin. While I was there, we had a squirrel infestation that needed to be eliminated before fall turned too cold in Northern Minnesota. Nesting squirrels can cause significant damage to structures if allowed to take over an old, wooden cabin. If noting else, their nests are a fire danger and attract parasites.
So, I set about eliminating squirrels that were clearly making the underside of the cabin their home. In all I managed to get six of the critters, four of them with the 17 HMR and two with my semi-auto 22LR. I didn't have a higher body count because it wasn't necessary, I was only looking to kill the animals invading the cabin, not wipe out the woods.
These were little red squirrels, so they were fast, unpredictable, and only stood still for a few seconds at a time. At close range, the Savage was next to useless because of the scope. 4x was simply too much when the dang critters were only a few yards away. I did manage to hit them when the squirrels were foraging in the undergrowth 20-30 yards away, but even then the scope was not ideal.
Savage 93R17 Problems
This reveals an flaw inherent to the philosophy behind the Savage 93R17. It's designed to take shots from 50-200 yards against relatively slow targets. The bolt action design and lack of iron sights mean that the scope either has to be built for longer ranges or shorter ranges. A 1x variable scope would be handy in the forest, but in open country the 6 or 8 power maximum from such scopes isn't enough.
No Iron Sights
The 4-12x range is way too much for the trees, but just right on open ground. If the 93R17 had iron sights like most rimfire rifles, I could have detached the scope and used the open sights. This, to me, is one issue I wish Savage would address, even if it raises the cost of the gun a bit.
The more humid environment exposed a second issue, this one being far more serious. Rust. Understandably, the 93R17 is a cheap gun, so corners need to be cut. However, cutting corners on the metal finish can lead to issues. In my case, the wet air of Minnesota quickly facilitated the formation of rust.
After two weeks in Minnesota, the gun had rust on all the matte-finished metal parts of the gun. This included the barrel, receiver, and parts of the bolt. From what I can tell, the rust is not affecting the operation of the gun one bit, but it looks very ugly and is something that isn't unique to this particular gun.
The Savage Axis Jim reviewed a while back also developed rust. Clearly then Savage needs to address this particular problem for their cheaper guns. Both might have been cheap, but in every other area they are pretty solid performers. The only recommendation I can make is that if anyone plans to purchase a Savage 93R17, go for the stainless options. Right-handed people only need to spend an extra $50.
Weak Metal Finish
On top of that, I noticed the metal finish can get market up pretty easily. While a small dent in the stock doesn't cause any issues, every mark exposing fresh metal is one more area for rust to form. The solution to light rusting is to keep the gun well oiled and away from humidity, which is precisely what I've done. So far, that seems to have stopped the rust!
Potential Modifications
One thing that is really fun about firearms is the ability to modify guns to meet certain requirements. The Savage 93R17 does a lot of things right, but there are some things I would like to change on my gun to make it more usable. In the six months I've had the gun, I've been writing down these modifications as they come to me. Here is the running list as it currently stands:
Picatinny rail over receiver
Threaded muzzle for devices
Rubber butt pad on stock
Iron sights installed on barrel
Metal trigger guard
Larger bolt handle
Proper stock bedding
Secondary rail mount for light
Quick-detach scope mounts
Stronger, rust-proof metal finish (cerakote?)
I think these modifications would address whatever shortcomings this rifle has and would add some versatility to the gun such as being able to quickly dispatch raccoons rummaging through the garbage at night or a problem coyote going for chickens. There's also a shopping list for useful accessories once these modifications have been made:
Accessory Shopping List
Detachable bi-pods (short for prone shooting, tall for sitting)
17/22 cal suppressor
Muzzle Flash hider
Pistol Flashlight
Fortunately, this list is pretty short but I think that after getting all of these for the rifle, the Savage 93R17 will have reached its maximum potential for versatility.
So far in this review, I've gone over the reasoning for why this gun exists, it's purpose and uses, first impressions of the gun, range and field tests, issues, and modifications I'd like to make. All that's left then is to give some final words for the Savage 93R17!
Overall, I really like this gun!
This rifle represents and fantastic value. The street price is roughly $280 for this gun, and in some areas (like the metal finish) the cheapness shows. But in other areas, such as its accuracy and the fact that I haven't had a single jam or light primer strike, the verdict is clear, this is a usable, cheap rifle that is able to do a lot of precision work right out of the box.
While it's just my opinion, in the last six months I've come to like this gun enough that I'm willing to invest money into it, improving its weaknesses and making it more suitable for my needs. Finding some iron sights will be interesting, and getting a better finish will take some research, but I'm confident the end result more than justifies the cost.
However, if the 17 HMR doesn't match your needs or if you're the type who wants a gun that you can take to hell and back, then the Savage 93R17 really isn't for you. Ammo gets expensive for plinking, and there isn't enough power for animals like hogs, coyotes or deer. On top of that, humid climates will affect the finish of the metal.
I suggest anyone considering this gun to think about what uses you would have for a 17 HMR. That should tell you right away if this gun is for you, or if your money is better spent elsewhere. I can say that the Savage 93R17 meets my needs as a varmint gun and for practicing at the range, and because of that I'll be keeping this rifle for quite some time.
James Willmus
Born into a hunting family, taught the basics of shooting and hunting by old-school mentors. James is now growing from those foundations to become a western hunter with a minimalist mentality.
dwight wyers says:
i live in alabama i have had mine for one year killed many small varments and in last six weeks killed 7 coyotes plus 8 point buck with shot to ear at 100 yards so however good the shooter tells how good the gun right?
i live in alabama i own a savage r17 i have killed many varments over the last year including in the last six weeks 7 coyotes and one 8 point buck shot behind the ear at 100 yards the gun is only good as the shooter right? but your right on one thing buy a gun that fits your needs and your skill level
James Willmus says:
Indeed Dwight, the shooter is easily the biggest factor of whether a gun is appropriate or not for a particular use. Personally, I wouldn't use this sort of gun on a deer but if you're able to kill one cleanly and it's legal then by all means use what you've got. When it comes to coyotes and smaller animals I wouldn't hesitate to use an HMR or an HM2, especially at close range. Seeing as you're from Alabama though, I'm interested to know how your gun is holding up to humidity? As stated in the article, mine started having rust issues almost immediately after being in a humid climate.
My stainless HMR 17 – is rusting. Disappointed


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Vehicles With Automatic Hands-Free Power Liftgates
You can be lazy and really flaunt it
Wade Thiel, Senior Staff Writer
Automatic power liftgates are one of the handiest features of any of the recent automotive updates. While most advancements in automotive technology focus on connectivity and media, sometimes something fairly simple like an automatically opening door is just more useful. Apple CarPlay or Android Auto can't help you when you have your arms full of groceries, but an automatically opening power liftgate with hands-free operation can. Before this technology, you either had to put down whatever was in your hand to gain access to your vehicle or get a friend, family member, or nice stranger to help you open your rear hatch. It's true that a power liftgate isn't the end-all-be-all of automotive features, but once you get used to it, you won't want a vehicle without it.
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PROS: Power liftgate available on three trim levels, smooth and relaxed ride, good variation in engine lineup, spacious and comfortable passenger seats.
CONS: Can't get the power liftgate on base trim, cabin isn't the most attractive and uses cheap materials, no third row of seating.
The Ford Edge ($28,950 base MSRP) is an excellent crossover for the family of five. It offers a comfortable and spacious interior, Ford's SYNC 3 infotainment system, and decent driving dynamics with a smooth ride. The hands-free power liftgate isn't available on the base SE trim level, but on all subsequent trims – SEL, Platinum, and Sport – it comes standard. The Edge can also be had with all-wheel drive, navigation, parking aids and cameras, blind-spot monitoring, forward-collision warning, lane-keeping warning, and high-end leather seating surfaces.
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GMC redesigned the Acadia ($29,020 base MSRP) for the 2017 model year and the vehicle is sleeker than ever before and better to drive. It features a quality IntelliLink infotainment system with 4G LTE Wi-Fi, Apple CarPlay, Android Auto, Bluetooth, and Sirius XM Radio. Also available on the Acadia is a rear-seat entertainment system, a Bose audio system, blind-spot alerts, lane change alert, rear cross traffic alert, rear park assist, a rearview camera, and an automatic power liftgate. Unfortunately, the power liftgate is only available in the top four trim levels: SLE-2, SLT-1, SLT-2, and Denali.
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2017 Buick Envision
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If you want a crossover that will seal you off from the cold hard world, the Buick Envision ($34,065 base MSRP) is it. It's soft, comfortable, and luxurious. If you're interested in driving as an activity, then this isn't the vehicle for you, but if you just want something that is pleasant to sit in as you zoom around town, this is an excellent choice. The Envision comes with a fair amount of equipment at all trim levels, including an automatic power liftgate, dual-zone climate control, Apple CarPlay, Android Auto, and an 8-inch infotainment system. Optional equipment includes all-wheel drive, blind-spot monitoring, heated rear seats, heated steering wheel, rear cross-traffic alert, front parking sensors, and forward collision alert.
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The CR-V offers two different engines: a 2.4-liter four-cylinder that produces 184 hp and 180 lb-ft of torque and a 1.5-liter turbo four-cylinder that makes 190 hp and 179 lb-ft of torque. Both engines are fuel efficient but the 1.5-liter with front wheel drive does the best, getting 28 mpg city and 34 mpg highway.
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CONS: Hands-free power liftgate isn't standard in bottom trim level (optional), thirsty engine, only one engine offered.
The Hyundai Santa Fe ($30,800 base MSRP) is one of the best-equipped crossovers out there. The list of standard equipment is long and includes things like dual-zone climate control, power adjusting seats, Bluetooth, Blue Link Connected Car System, Sirius XM radio, Android Auto, and a 7-inch infotainment screen. The hands-free power liftgate is standard on all but the top trim level and is still offered as an option on the base trim. Speaking of options, the vehicle can be had with a heated steering wheel, navigation, Infinity premium audio system, 3rd-row USB ports, all-wheel drive, blind-spot warning, rear cross traffic alert and lane-departure warning. Pair all that equipment with the Santa Fe's attractive exterior and competent driving dynamics, and you have one heck of a CUV.
The lone engine option for the Santa Fe is a powerful 3.3-liter V6. It manages to pump out 290 hp and 252 lb-ft of torque, which makes it able to tow as much as a Honda Ridgeline pickup (5,000 lbs). However, its 18 mpg city and 25 mpg highway number, when equipped with front-wheel drive, is less than exciting.
View Our 2017 Hyundai Santa Fe Overview
Get Used Chevrolet Tahoe Deals In Your Area ►
PROS: Automatic hands-free power liftgate only available in top two trims, lots of standard technology, infotainment system easy to use, powerful V8 engine.
CONS: Bottom trim can't get the automatic hands-free power liftgate, Body-on-frame makes for a truck-like ride, thirst V8 engine, only one engine option, and cramped third row of seating.
The Chevrolet Tahoe ($48,510 base MSRP) has been the go-to for many families that need a three-row SUV that can tow a trailer with ease (up to 8,600 lbs). The Tahoe also comes with a lot of standard equipment, including automatic climate control, rearview camera, rear parking sensors, OnStar automatic crash response and safety features, 4G LTE Wi-Fi, Apple CarPlay, Android Auto, and Bluetooth. Unfortunately, the hands-free power liftgate is only standard in the top trim level. It's optional on the LT, midlevel trim level, but unavailable on the lowest, LS trim.
The Tahoe is powered by a 5.3-liter V8 engine that punches out 355 hp and 383 lb-ft of torque. This makes it possible to tow your boat or even a horse trailer should you need. The big V8 is pretty thirsty, but that's kind of the tradeoff you have to make with a V8. It still manages 16 mpg city and 23 mpg highway, which isn't terrible.
View Our 2017 Chevrolet Tahoe Overview
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redpajama_set_name: RedPajamaCommonCrawl


text:
Last edited by Mikajinn
3 edition of Provençal songs found in the catalog.
Provençal songs
George Elliott Clarke
by George Elliott Clarke
Published 1993 by Magnum Book Store in [Ottawa] .
Statement by George Elliott Clarke.
Series Magnum readings -- 20
Contributions Clarke, George Elliott, 1960- (autograph)
Pagination [17] leaves ;
Locker, F.; Any Poet to his Mistress. Full text is unavailable for this digitized archive article. Subscribers may view the full text of this article in its original form through TimesMachine. The Song of the Sibyl (Catalan: El Cant de la Sibilla [əl ˈkand də lə siˈβilːə]) is a liturgical drama and a Gregorian chant, the lyrics of which comprise a prophecy describing the Apocalypse, which has been performed in churches on Majorca (Balearic Islands, Spain) and Alghero (Sardinia, Italy), and some Catalan churches, in the Catalan language on Christmas Eve nearly.
Évariste Lévi-Provençal (–) was a French medievalist, orientalist, Arabist, and historian of Islam. The scholar who would take the name Lévi-Provençal was born 4 January in Constantine, French Algeria, as Makhlóuf Evariste Levi (Arabic: مخلوف إفاريست ليفي), his second name revealing that his North-African Jewish family was already somewhat Gallicized/5(6). Provence (/ p r ə ˈ v ɒ̃ s /, US: / p r oʊ-/; French: [pʁɔvɑ̃s]; Occitan: Provença in classical norm or Prouvènço in Mistralian norm, pronounced [pʀuˈvɛnsɔ]) is a geographical region and historical province of southeastern France, which extends from the left bank of the lower Rhône to the west to the Italian border to the east, and is bordered by the Mediterranean Sea to the.
Troubadour songs are generally referred to by their incipits, that is, their opening lines. If this is long, or after it has already been mentioned, an abbreviation of the incipit may be used for convenience. A few troubadour songs are known by "nicknames", thus D'un sirventes far by Guilhem Figueira is commonly called the Sirventes contra Roma. Too many of us walk through life feeling as if we don't measure up. We always seem to thirst for more. We think if we could only work harder or be better, we could be enough. But the truth is, we will never be enough. And thankfully, we don't have to be. In this 8-session study, Jennie Allen walks.
British paperbacks in print
New York Citys labor market, 1994-1997
American grape growers guide
Hooked on Rugs
Costs and decision making in transportation.
Catalogue of the private library of Mr. John H. Cavender late of St. Louis, Missouri, now of Dallas, Texas
Belgium and the February Revolution.
Marietta and Cincinnati Railroad, 1845-1883
Total telemarking
The sunken forest
room with a view
Old cricket and cricketers
The Complete Works Of Sir Joshua Reynold V1
Wiggle!
Provençal songs by George Elliott Clarke Download PDF EPUB FB2
Cançoner provençal (Provençal songbook) is a 14th century manuscript, written in Occitan, discovered in by the scholar Milà i Fontanals, and acquired in by a group of ten benefactors who wanted it given to the Biblioteca de Catalunya as a monument of Catalan medieval literature.
Catalan, which originated in northeastern Spain, and Occitan (also called langue d'oc and. Provençal literature, the body of writings in the Occitan, or Provençal, language of Provence and neighbouring regions in southeastern France.
Provençal literature flourished from the 11th to the 14th century, when its poetry reached rare heights of virtuosity and variety in its celebration of courtly, or chivalric, love.
Origins and development. The oldest extant piece of Provençal verse. His book has a plain cover and rather flat title - or, to give it its full name Provence, M.F.K. Fisher, Julia Child, James Beard, and the Reinvention of American Taste.
But it will be compulsive reading for anyone Provençal songs book in food and in Provence, and that must include almost everyone. Fennco Styles Maison Beaujard Provençal Design Tablecloth (Sea Green, 4 Napkin x20) out of 5 stars 3.
Amazon Music Stream millions of songs: Amazon Advertising Find, attract, Audible Listen to Books & Original Audio Performances. Our music library is one of the best in North America, boasting a collection of overvolumes andrecordings. We also provide access to an ever-expanding number of electronic journals and resources.
We are located in one of the world's most vibrant, multicultural, and arts-oriented. "Provence native Craig shows how the simple love of olive oil and garlic, infused in ingredients like Provençal songs book and tomatoes, reduces time in the kitchen to a half hour or less Color photographs of the south of France, by themselves, beckon; coupled with the plus recipes and Craig's memorable family stories this collective will rank among reader favorites."Reviews: 5.
PEDALING THROUGH PROVENCE is her personal collection of mor ethan 95 Provencal recipes, inspired by countryside travels a bicyclette. About the Author Sarah Leah Chase, who founded the Massachusetts specialty food shop and catering business Que Sera Sarah, collaborated on The Silver Palate Good Times s: 9.
PROVENCAL Jelly Roll Moda American Jane Quilting sew Fabric French farmhouse 7 watching. Watch. Waverly Provence Prints Fabric Sample Book Cotton Blend Mini Print Sewing Crafts. $ $ shipping. Watch. Henry Glass Flowers Of Provence Med Flower Toss Red Cotton Fabric By Yd. $ $ shipping.
Watch. Roosters La Provence. Franco-Provençal (also Francoprovençal, Arpitan, or Romand) is a dialect group within Gallo-Romance spoken in east-central France, western Switzerland, northwestern Italy, and in enclaves in the Province of Foggia in Apulia, Italy. Franco-Provençal has several distinct dialects and is separate from but closely related to neighboring Romance dialects (the langues d'oïl and Occitan, Rhaeto.
From Book 1: The first installment in the beloved, sumptuous mystery series set in Provence, featuring chief magistrate Antoine Verlaque and his old flame Marine Bonnet, who must team up to solve a pair of murders The newest book in the Provençal Mystery series, The Secrets of the Bastide Blanche, is available now from Penguin Books When local nobleman Étienne de Bremont falls to his death.
Longworth happily peppers her seasonal tale with lovely and cheeky references to films such as Manon des Sources and The Cook, The Thief, His Wife, and Her Lover, Kate Bush songs, and the swoon-inducing Bradley Cooper." —The Seattle Review of Books More Praise for M.
Longworth's Provençal Mystery SeriesReviews: Provencal love song is a crossword puzzle clue. Clue: Provencal love song. Provencal love song is a crossword puzzle clue that we have spotted 7 times. There are related clues (shown below). Filled with information about 34 breathtaking villages in the Provençal region, this book includes illustrations, beautiful photography, and even a map of where everything mentioned within the book is actually located- pretty and handy.
The Yellow House: Van Gogh, Gauguin, and Nine Turbulent Weeks in Provence – by Martin Gayford. Article content. Our cookbook of the week is Provençal by Alex Jackson, chef-owner of the acclaimed Sardine in London, England. To try a recipe from the book. Free shipping on orders of $35+ from Target.
Read reviews and buy The Mystery of the Lost Cezanne - (Provençal by M L Longworth (Paperback) at Target. Get it. "Music is the divine way to tell beautiful, poetic things to the heart." - Pablo Casals Music can lift our spirits when we are feeling down, rouse us if we feel unmotivated or make for a soothing listening experience while reading our favorite books.
The books in this section are all about music and the talented musicians who have created masterpieces that continue to entertain us to this day. Cobb's new book, Mistral: The Legendary Wind of Provence, is a record of the 20 years she spent hunting the wind.
(Marigold Warner British Journal of Photography) A lifelong fascination with the legendary winds of the South of France has yielded a lush and vibrant book―a work that resonates with the intensity of the forces it seeks to s: 1. Coupons & Deals Book Annex Buy 1, Get 1 50% Off: Books for All Ages Bestsellers 30% Off Hardcover New Releases from 20% Off Customer Favorites New Releases Coming Soon Boxed Sets Signed Books Books by Author Book Awards Celebrity Book Clubs & More Read Before You Stream Best Books of the Year B&N Classics B&N Collectible Editions B&N Exclusives.
Print book: EnglishView all editions and formats: Rating: (not yet rated) 0 with reviews - Be the first. Subjects: Provence-Alpes-Côte d'Azur (France) -- History. Provençal poetry -- History and criticism.
Provençal poetry. View all subjects; More like this: Similar Items. Enjoy our Provencal Reading List. Search by book type by clicking the headings below. From there, you will be directed to specific page listings by genre.
On each page, the books are listed in alphabetical order (by author's last name). To order a copy, please click on the book cover images or titles. Lulu's Provençal Table tantalizes the reader with Olney's descriptions of the regional food served as the vineyard meals at the Domaine.
Having been described as 'a gastronomic love poem to France's most exhilarating region,' this is an essential book for any serious food lover's library.Elizabeth Bard is an American journalist and author based in Provence, France, where her second book, Picnic in Provence: A Memoir With Recipes, took first book, Lunch in Paris: A Love Story with Recipes has been a New York Times and international bestseller, a Barnes & Noble "Discover Great New Writers" pick, and the recipient of the Gourmand World Cookbook Award for Best First.Death in Provence is a shiny new mystery series, bordering on cozy, by Serena Kent.
⭐ ⭐ ⭐ ⭐ Every mystery needs a sleuth, and here we have Penelope Kite. She seems to find dead bodies wherever she goes. Penelope buys Le Chant d'Eau ("The Song of Water"), a stone farmhouse in the hilly countryside of Provence, as a retirement gift for herself. She's divorced from a /5().
caistab-gabon.com - Provençal songs book © 2020


Web-ready in a modern style space, the Union Board Room provides a professional location for retreats and meetings. This space offers a great venue for student organizations to meet and an alcove for catering and advanced technical capabilities to encourage creative expression as your meeting goes long into the night. Comfortable, leather chairs and a glass-covered board table provides ample space to spread out all the smartphones, laptops, and spreadsheets for intense communication or laid-back discussion.


Pompey won 1-0 at struggling Bradford and are now the only League One side yet to be beaten on their travels this term.
Gareth Evans converted Ronan Curtis' cut-back in the opening stages to score what proved to be the only goal of the game.
Adam Chicksen was sent off for the hosts late on, while Hope Akpan missed a great opportunity to draw them level in stoppage-time.
And subscribers are also able to view the full 90 minutes (bottom video) of a lively Valley Parade contest.
Extended highlights of Pompey's Sky Bet League One clash with Bradford City at Valley Parade.


SNJ Motors has been tuning and building race cars for over 14 years we specialise in building engines for road or race purposes including drift, circuit, drag or just a standard rebuild. we rebuild a lot of classic and vintage engines and we also do turbo or supercharger conversions and rebuild gearboxes standard or performance.
We only use the best parts and products available and if the parts needed are not available or out of production we make them, or modify the existing parts to do the job inhand.
We always get great feedback from customers, and great results with our work. We are very proud of our reputation to be able do it and to be able to do it rite.
Horsepower sells cars, but torque wins races. We have rebuilt and tuned hundreds of engines over the years and never had one failure. All of our engines are rebuilt to a very high tolerance and standard. Workmanship is second to none we are currently restructuring our business and soon to be added will be engine dyno facilities and rolling road equipment. We will be doing remaps for many different ECU versions and ECU chips. We can rebuild your engine to meet strict rules of engine tune for homoligation rules or blueprint your engine to your specs or our own if your just looking an engine refreshment i.e. (mild rebuild) or full rebuild with all new shells and seals and rings, or a full blown power house, we can build, and advise you on the best, test proven ways and products to meet your requirements for your needs of power and car set up for all road or race cars.
We can build you anything from a full car for racing, to a brake upgrade or a service. We do engine conversions, rear wheel drive conversions, motorbike engine conversions, gearbox conversions, what ever you are after we cater for all. Anything you would like to know just give us a call or contact us via email or the contact page.


0%90°60°Breezy with a high of 89 °F (31.7 °C). Winds NNW at 13 to 18 mph (20.9 to 29.0 kph).Night - Clear. Winds variable at 9 to 17 mph (14.5 to 27.4 kph). The overnight low will be 67 °F (19.4 °C).
Today - Breezy with a high of 89 °F (31.7 °C). Winds NNW at 13 to 18 mph (20.9 to 29.0 kph).
Tonight - Clear. Winds variable at 9 to 17 mph (14.5 to 27.4 kph). The overnight low will be 67 °F (19.4 °C).


text:
What Is a Pre-Determination Letter for Health Insurance?
By: Gail Sessoms
Insurers often require pre-determination for experimental or investigational procedures.
Jupiterimages/Comstock/Getty Images
1. Do Credit Cards Renew Automatically?
2. Can a Gym Membership Be Considered a Deduction for Federal Taxes?
3. What to Do If an Insurance Company Will Not Pay a Claim?
A "pre-determination letter" is part of the claims management process for health insurance providers. The letter relates to the coverage of specific medical services under a patient's policy. Insurance companies require health care providers to follow submission instructions, such as the content that must be included in the pre-determination letter. The exact definitions and procedures for these letters vary among insurers.
The American Medical Association recommends that physicians review health insurance company contracts. Doctors will want to confirm the insurer's definition of claims-management terms and the verification processes used for cost containment. Insurers often require pre-authorization for outpatient diagnostic and surgical procedures. Pre-certification is required for hospital admission and surgical procedures. Pre-authorization and pre-certification confirm medical necessity before the insurer approves or pays a claim. Pre-determination confirms that the patient's policy covers a specific service.
Pre-Determination Letter
Insurers tell providers which medical services require pre-determination letters. These include cosmetic, investigational or experimental procedures. Pre-determination letters are submitted on the provider's letterhead. The letter requests advance verification that the patient is covered for the medical service. Failure to submit a pre-certification letter usually results in denial of the claim for payment. The American Medical Association recommends that physicians also submit pre-determination letters for services and procedures that an insurer frequently denies as medically unnecessary.
Letter Content and Attachments
Insurance companies often provide a pre-determination letter form or a request form for health care providers. Insurers also help providers prepare the letters, which should include the name and contact information for the patient and the health care provider. The letters also should include a description and medical codes for the service, fees for the service, and the date it will be performed. Providers' attachments to the pre-determination letter document the patient's medical history, such as how long she has been under the physician's care; test results; medical records and photographs. Although the pre-determination letter focuses on verification of coverage, it's important to include a statement of medical necessity.
Determination Letter
The insurance company's medical staff reviews the pre-determination letter and attachments to decide whether the service is covered under the patient's health care insurance policy, and whether it's medically necessary. The insurer sends a determination letter to the provider and the patient. This letter states the insurer's decision, the maximum fee allowed and the period for completion of the service. The pre-determination letter guarantees payment. However, payment can be affected by the patient's previous use of benefits. The insurance company might deny the claim if a change in the patient's condition makes the medical service medically unnecessary.
American Medical Association: Prepare That Claim
BlueCross BlueShield of Illinois: Coverage: Predetermination of Benefits
BlueCross BlueShield of Texas: Predetermination Form
Gail Sessoms, a grant writer and nonprofit consultant, writes about nonprofit, small business and personal finance issues. She volunteers as a court-appointed child advocate, has a background in social services and writes about issues important to families. Sessoms holds a Bachelor of Arts degree in liberal studies.
Do Credit Cards Renew Automatically?
Can a Gym Membership Be Considered a Deduction for Federal Taxes?
What to Do If an Insurance Company Will Not Pay a Claim?
When Does Homeowners Insurance Pay for Legal Fees?
Are Veteran Pension Benefits Retroactive From the Date of Disability?
Proper Procedure of Homeowners Insurance Adjusters for Handling a Water Damage Claim
Do Rottweilers Raise Homeowner's Insurance?
How do I Find the Expiration Date for a BCBS PPO Health Insurance Policy?
What Is a Gatekeeper PPO?
How Does a Post Facto Insurance Audit Work?
Is Marriage Counseling Covered by Health Insurance?
What Is the Purpose of Third-Party Insurance?


In Dalecroft Properties Ltd v Underwriters  EWHC 1263 (Comm), Mr Richard Salter QC (sitting as a Deputy Judge of the High Court) confirmed the defendant insurers' right to avoid a property insurance policy following various misrepresentations relating to the state of repair of the insured property and non-disclosures relating to acts of vandalism to the property. The property in question was subsequently destroyed by fire and a claim was made by the insured. In response, the insurers purported to avoid the policy. On the facts, it was held that there were material misrepresentations and that material non-disclosures were made, such that the insurers were entitled to avoid the policy.
The insurance policies at issue were entered into prior to the coming into force of the Insurance Act 2015, such that the old law applied in this case. It is noteworthy, however, that the judge commented that even under the 2015 Act, the insured's claim would have failed, on the basis that there was no fair presentation of the risk (as that expression is now defined in the 2015 Act) and that the defendant insurers would have declined to write the risk at all had a fair presentation been made.


text:
10 Incredible Millipede Facts
Written by Jeff Young
© Piyapong pc/Shutterstock.com
What Do Millipedes Eat? 9 Surprising Meals
The 5 Largest Millipedes in the World
Are Millipedes Poisonous or Dangerous?
Meet the 8-Foot Giant Millipede That Once…
Giant African Millipede: The Pros and Cons…
How Many Legs Does a Millipede Have?
Millipedes are fascinating creatures that have inspired curiosity and wonder in people for centuries. These many-legged invertebrates are interesting to look at, and they can be found in a variety of habitats all over the world. Here are 10 incredible millipede facts that will make you see them in a whole new light.
1. "Millipede" Means "Thousand Legs," But They Rarely Have That Many
Millipedes typically have between 80-400 legs.
©Travel Telly/Shutterstock.com
The word "millipede" comes from the Latin words mille, meaning "thousand," and ped, meaning "foot." However, most millipedes don't actually have 1,000 legs. The majority of millipede species only have between 80-400 legs.
2. The First Millipede To Have More Than 1000 Legs Was Found In 2020
Until recently, the highest number of legs ever counted on a millipede was around 750. In 2020, a team of researchers performing an environmental impact survey for a new mine in western Australia discovered a record-breaking species of millipede—Eumillipes persephone.
Adults of this species have 330 body segments and 1,306 legs. Because they live so deep underground (found more than 190 feet deep!), they have also evolved to be without eyes and pigmentation.
3. Millipedes Are Born Without Legs
Female millipedes can lay dozens or even hundreds of eggs at a time. They lay them in soil, and some species will even guard their eggs until they hatch. A few species have even been observed crafting little protective casings out of chewed-up leaves to protect each individual egg.
When millipedes are born, they emerge from their eggs as legless larvae. They soon molt for the first time to reveal 6 body segments and 3 pairs of legs.
4. Millipedes Grow Additional Body Segments and Leg Pairs With Each Molt
Molting is a process that all arthropods (arachnids, crustaceans, insects, including millipedes) undergo in order to grow. It involves shedding the exoskeleton—the hard, outer shell that protects their bodies—so that a new, larger one can form in its place.
As millipedes molt, they will grow additional body segments and leg pairs until they reach adulthood. Each new body segment that is added will have 2 pairs of legs (4 total) attached to it.
5. Many Species Eat Their Own Waste Pellets
Millipedes eat decomposing plant material, waste, and other debris.
©Piyapong pc/Shutterstock.com
Millipedes eat decomposing plants and leaf litter. Eating this way classifies them as detritivores—eaters of detritus, waste, and debris. Some species will also eat fungi and dead animals.
This type of diet means that millipedes play an important ecological role in the decomposition of dead plants and animals and the recycling of nutrients back into the soil.
As they munch on their food, they excrete little pellets of waste. Some millipede species have been observed eating these pellets that they have defecated. One reasonable explanation for this seemingly odd behavior is that they are actually feeding on the funguses that grow in their waste material, which then provide additional nutritional compounds that they otherwise wouldn't have access to.
6. Millipedes Defend Themselves With Chemicals
Most millipedes curl up into a ball to protect themselves.
©Rocky Sun / Flickr
Millipedes don't bite or sting. They don't have sharp claws to fight with, and despite their numerous legs, they're not fast enough to outrun their predators. So, how do they defend themselves?
When threatened, most millipede species will curl up into a ball. This position allows them to use their hard exoskeleton as a 360-degree shield protecting their vulnerable underbelly.
Many millipede species take defense a step further and will also exude poisonous or foul-smelling chemicals from their bodies to ward off predators that might try to eat them. The two primary ingredients in this toxin are hydrochloric acid, which corrodes skin and eyes, and hydrogen cyanide, which can cause asphyxiation.
7. Lemurs Eat Millipedes To Get Intoxicated
Lemurs in Madagascar will sometimes eat toxic millipedes.
Scientists have observed lemurs in Madagascar eating millipedes. Since the millipedes they eat release harmful chemicals as a defense mechanism, this likely causes them to become intoxicated.
This might not be about "getting high" recreationally. The chemical compounds in millipedes could potentially be used to help fight bacterial or parasitic infections. So if a lemur is feeling unwell or losing weight (possibly due to a parasite), the lemur can self-medicate by chewing on some millipedes.
8. Lemurs Also Use Millipedes As A Bug Repellent
In addition to eating millipedes, lemurs in Madagascar (and sometimes primates in other places as well) will rub millipedes deep into their fur. It's thought that they do this to help ward off biting insects, as well as to protect themselves from bacteria and parasites who may try to infect any open wounds.
9. Millipedes Were The First Known Land Animal Over 430 Million Years Ago
Fossil records indicate that millipedes were the first animal to live on land.
©Sawangkaew/Shutterstock.com
Biological life originated in the oceanic waters of Earth, probably around 3.8 billion years ago. For most of that time, all living organisms were aquatic.
According to fossil records, millipedes appear to be the first animal to breathe atmospheric oxygen and transition from living in water to living on land. The first known millipede fossil dates back to the Silurian period, approximately 430 million years ago.
10. Prehistoric Millipedes Could Grow Over 8 Feet Long!
The largest extant (still living) millipede species is the giant African millipede (Archispirostreptus gigas), which grows to be about one foot long. But millipede fossils that have been found indicate that some prehistoric species were much, much larger.
Around 300 million years ago, there lived a millipede species we now identify as Arthropleura armata. Fossils demonstrate that this species of millipede grew to be 8 feet long and nearly 2 feet wide! They are estimated to have weighed over 100 pounds and probably had few, if any, predators. Not only were these shockingly large millipedes the largest land invertebrate of their time period, they are the largest known land invertebrate of all time!
What Do Millipedes Eat?
Incredible Earthworm Facts
Meet the 8-Foot Giant Millipede That Once Roamed the Earth
Giant African Millipede: The Pros and Cons of this Exotic Pet


text:
\section{Related Work}
\label{sec:related_work}

\subsection{Traditional methods}

Many traditional methods for denoising such as BM3D \cite{dabov2006image}, non-local means \cite{buades2011non}, and weighted non-nuclear norm minimizaiton \cite{gu2014weighted} perform denoising by comparing the neighborhood of a pixel to other similar regions in the image.  The advantage of learning-based methods is that they can also take advantage of examples from other images in the dataset beyond the input image to be denoised. Other methods such as total-variation denoising \cite{chambolle2004algorithm} enforce smoothness priors on the image which tend to lead to highly quantized results.

While most previous methods for denoising are designed for additive Gaussian noise; in the case of Poisson-Gaussian noise, a variance stabilizing transform \cite{makitalo2012optimal} is applied to approximately transform the noise to be Gaussian.  However, these methods are designed explicitly for Poisson-Gaussian noise \cite{luisier2010image}.

\subsection{Deep learning methods}

At present, supervised deep learning methods for denoising \cite{zhang2017beyond,weigert2018content} typically far outperform traditional and self-supervised methods in terms of peak signal-to-noise ratio (PSNR).  Most supervised methods apply a fully convolutional neural network \cite{long2015fully,ronneberger2015u} and simply regress to the clean image.

Recently, several approaches to self-supervised denoising have been developed.
Some methods \cite{NIPS2018_7587} use as a loss function Stein's Unbiased Risk Estimate (SURE) \cite{stein1981estimation,ramani2008monte}, which estimates the mean squared error (MSE) between a denoised image and the clean image without actually having access to the clean image.  An analogous estimator for Poisson-Gaussian noise has been developed \cite{le2014unbiased}.  However, these methods require \emph{a priori} knowledge of the noise level which is unrealistic in a practical setting.  Our approach supports blind denoising and adaptively estimates the noise level at test time.

Lehtinen et al.~\cite{lehtinen2018noise2noise} introduced a highly successful approach to self-supervised denoising called Noise2Noise.  In this approach, the network learns to transform one noisy instantiation of a clean image into another; under the MSE loss function, the network learns to output the expected value of the data which corresponds to the clean image.  While this method can achieve results very close to a supervised method, it requires multiple, corresponding noisy images and thus is similarly limited in application in the live cell microscopy context.

An alternate approach to self-supervised denoising which does not require multiple noise instantiations of the same clean image is to learn a filter which predicts the center pixel of the receptive field based on the surrounding neighborhood of noisy pixels.  By training such a filter to minimize the MSE to the noisy input, the resulting filter will theoretically output the clean value \cite{batson2019noise2self,krull2019noise2void}.  Laine et al.~\cite{laine2019high} refer to a neural network built around this concept as a ``blindspot neural network.''  They improved upon the blindspot concept by extending it to a Bayesian context and introduced loss functions for pure Gaussian or Poisson noise, showing results very close to the supervised result when trained on synthetically noised data.  However, their method requires a regularization term in the loss function which can't practically be tuned in the self-supervised setting; in our evaluation we found that the regularization strength indeed needs to be tuned for best results on different datasets.  Our method avoids the need for regularization and outperforms the regularized version in our experiments.

Krull et al.~\cite{krull2019probabilistic} introduced Probabilistic Noise2Void (PN2V) which takes a non-parametric approach to modeling both the noise distribution and the network output; however, their approach requires paired clean and noisy images in order to calibrate the noise model.  A recent follow-on work called PPN2V \cite{prakash2019fully} estimates the noise model using a Gaussian Mixture Model (GMM) in a fully unsupervised manner.  Again, this approach involves several hyperparameters controlling the complexity of the noise model which need to be tuned, while ours does not.  Additionally, in our experiments, we show that our approach outperforms PPN2V on several datasets.

\section{Self-supervised learning of denoising}
\label{sec:self_supervised}

The goal of denoising is to predict the values of a ``clean'' image $\mathbf{x} = (x_1,\ldots,x_n)$ given a ``noisy'' image $\mathbf{y}=(y_1,\ldots,y_n)$.
Let $\Omega_{y_i}$ denote the neighborhood of pixel $y_i$, which does not include $y_i$ itself.  We make two assumptions that are critical to the setup of Noise2Void \cite{krull2019noise2void} and follow-on works: that the noise at each pixel is sampled independently, i.e. $p(y_i|x_1,\ldots,x_n)=p(y_i|x_i)$; and that each clean pixel is dependent on its neighborhood, a common assumption about natural images.  The consequence of these assumptions is that $\Omega_{y_i}$ only gives information about $x_i$, not $y_i$.  Therefore a network trained to predict $y_i$ given $\Omega_{y_i}$ using a mean squared error loss will in fact learn to predict $x_i$ \cite{krull2019noise2void,batson2019noise2self}.

In this work, we take a probabilistic approach rather than trying to regress to a single value.  Following Laine et al.~\cite{laine2019high} and Krull et al.~\cite{krull2019probabilistic}, we can connect $y_i$ to its neighborhood $\Omega_{y_i}$ by marginalizing out the unknown clean value $x_i$:
\begin{equation}
    \underbrace{p(y_i|\Omega_{y_i})}_{\text{Noisy observation}} = \int \underbrace{p(y_i|x_i)}_{\text{Noise model}}\underbrace{p(x_i|\Omega_{y_i})}_{\text{Clean prior}} dx_i.
\end{equation}
Since we only have access to observations of $y_i$ for training, this formulation allows us to fit a model for the clean data by minimizing the negative log likelihood of the noisy data, i.e. minimizing a loss function defined as
\begin{equation}
\label{eq:loss}
\mathcal{L}^{\text{marginal}} = \sum_i -\log p(y_i|\Omega_{y_i}).
\end{equation}
In the following we will drop the $\Omega_{y_i}$ to save space.

\subsection{Poisson-Gaussian noise}

In the case of Poisson-Gaussian noise, the noisy observation $y_i$ is sampled by first applying Poisson corruption to $x_i$ and then adding Gaussian noise which is independent of $x_i$. We have
\begin{equation}
\label{eq:poisson_gaussian_noise}
y_i = aP(x_i/a) + N(0,b)
\end{equation}
where $a>0$ is a scaling factor (related to the gain of the camera) and $b$ is the variance of the Gaussian noise component, which models other sources of noise such as electric and thermal noise \cite{foi2008practical}.

We apply the common approximation of the Poisson distribution as a Gaussian with equal mean and variance:
\begin{align}
\label{eq:pg_noise}
y_i &\approx aN(x_i/a,x_i/a) + N(0,b) \\
&= N(x_i,a x_i + b).
\end{align}
The noise model is then simply a Gaussian noise model whose variance is an affine transformation of the clean value.  Note that in practice we allow $b$ to be negative; this models the effect of an offset or ``pedestal'' value in the imaging system \cite{foi2008practical}.  This general formulation encompasses both pure Gaussian ($a=0$) and Poisson noise ($b=0$).

\subsection{Choice of prior}

In order to implement our loss function (Equation \ref{eq:loss}) we need to choose a form for the prior $p(x_i|\Omega_{y_i})$ that makes the integral tractable.  One approach is to use the conjugate prior of the noise model $p(y_i|x_i)$, so that the integral can be computed analytically.  For example, Laine et al.~\cite{laine2019high} model the prior $p(x_i|\Omega_{y_i})$ as a Gaussian, so that the marginal is also a Gaussian.  Alternatively, Krull et al.~\cite{krull2019probabilistic} take a non-parametric approach and sample the prior.

In this work, similar to Laine et al.~\cite{laine2019high} we model the prior as a Gaussian with mean $\mu_i$ and variance $\sigma^2_i$.  We replace the $ax$ term in Equation \ref{eq:pg_noise} with $a\mu$ to make the integral in Equation \ref{eq:loss} tractable; this approximation should be accurate as long as $\sigma^2_i$ is small.  The marginal distribution of $y_i$ is then
\begin{equation}
p(y_i)=
\frac{1}{\sqrt{2\pi(a\mu_i+b+\sigma_i^2)}}\exp \left( -\frac{(y_i-\mu_i)^2}{2(a\mu_i+b+\sigma_i^2)}\right)
\end{equation}
and the corresponding loss function is
\begin{equation}
\mathcal{L}^{\text{marginal}} = \sum_i \left( \frac{(y_i-\mu_i)^2}{a\mu_i+b+\sigma_i^2}+\log(a\mu_i+b+\sigma_i^2) \right)
\end{equation}

\subsection{Posterior mean estimate}

At test time, $\mu_i$ is an estimate of the clean value $x_i$ based on $\Omega_{y_i}$, the neighborhood of noisy pixels around $y_i$.  However, this estimate does not take into account the actual value of $y_i$ which potentially provides useful information about $x_i$.

Laine et al.~\cite{laine2019high} and Krull et al.~\cite{krull2019probabilistic} suggest to instead use the expected value of the posterior to maximize the PSNR of the resulting denoised image.  In our case we have
\begin{equation}
\label{eq:pme}
\hat{x}_i =
\mathbb{E}[p(x_i|y_i)] =
\frac{y_i \sigma_i^2 + (a\mu_i+b) \mu_i}{a\mu_i + b + \sigma_i^2}.
\end{equation}
Intuitively, when the prior uncertainty is large relative to the noise estimate, the formula approaches the noisy value $y_i$; when the prior uncertainty is small relative to the noise estimate, the formula approaches the prior mean $\mu_i$.

\subsection{Blindspot neural network}
In our approach, $\mu_i$ and $\sigma_i^2$ are the outputs of a blindspot neural network \cite{laine2019high} and $a$ and $b$ are global parameters learned along with the network parameters.

The ``blind-spot neural network'' is constructed in such a way that the network cannot see input $y_i$ when outputting the parameters for $p(x_i)$.  The blindspot effect can be achieved in multiple ways.  Noise2Void ~\cite{krull2019noise2void} and Noise2Self \cite{batson2019noise2self} replace a random subset of pixels in each batch and mask out those pixels in the loss computation.  Laine et al.~\cite{laine2019high} instead construct a fully convolutional neural network in such a way that the center of the receptive field is hidden from the neural network input.  In our experiments we use the same blindspot neural network architecture as Laine et al.~\cite{laine2019high}.

\subsection{Regularization}

\input{table-loss}

\input{table-synthetic-params}

In a practical setting, the parameters $a$ and $b$ of the noise model are not known \emph{a priori}; instead, we need to estimate them from the data.  However, an important issue arises when attempting to learn the noise parameters along with the network parameters: the network's prior uncertainty and noise estimate are essentially interchangeable without any effect on the loss function.  In other words, the optimizer is free to increase $a$ and $b$ and decrease $\sigma_i^2$, or vice-versa, without any penalty.  To combat this, we add a regularization term to the per-pixel loss which encourages the prior uncertainty to be small:
\begin{equation}
\mathcal{L}^{\text{regularized}} = \mathcal{L}^{\text{marginal}} + \lambda \sum_i |\sigma_i|.
\end{equation}

We found in our experiments that the choice of $\lambda$ strongly affects the results.   When $\lambda$ is too high, the prior uncertainty is too small, and the results are blurry.  When $\lambda$ is too low, the prior uncertainty is too high, and the network does not denoise at all.  Unfortunately, in the self-supervised setting, it is not possible to determine the appropriate setting of $\lambda$ using a validation set, because we do not have ground truth ``clean'' images with which to evaluate a particular setting of $\lambda$.

\section{Learning an uncalibrated model}
\label{sec:uncalibrated}

This realization led us to adopt a different training strategy which defers the learning of the noise parameter models to test time.

In our uncalibrated model, we do not separate out the parameters of the noise model from the parameters of the prior.  Instead, we learn a single variance value $\hat{\sigma_i}^2$ representing the total uncertainty of the network.  Our uncalibrated loss function is then
\begin{equation}
\label{eq:uncalib_loss}
\mathcal{L}^{\text{uncalibrated}}=\sum_i
\left(
\frac{(y_i-\mu_i)^2}{\hat{\sigma_i}^2}+\log(\hat{\sigma_i}^2)
\right)
\end{equation}

At test time, however, we need to know the noise parameters $a$ and $b$ in order to compute $\sigma_i^2=\hat{\sigma_i}^2-a\mu_i-b$ and ultimately compute our posterior mean estimate $\hat{x}_i$.

If we had access to corresponding clean and noisy observations ($x_i$ and $y_i$, respectively) then we could fit a Poisson-Gaussian noise model to the data in order to learn $a$ and $b$.  In other words, we would find
\begin{equation}
\label{eq:pg_fit}
a,b = {\arg \min}_{a,b} \sum_i \left( \frac{(y_i-x_i)^2}{ax_i+b}+\log(ax_i+b)\right).
\end{equation}

As we are in a self-supervised setting, however, we do not have access to clean data.  Instead, we propose to use the prior mean $\mu_i$ as a stand-in for the actual clean value $x_i$.  This bootstrapping approach is similar to that proposed by Prakash et al.~\cite{prakash2019fully}; however, they fit a general parametric noise model to the training data where as we propose to fit a Poisson-Gaussian model to each image in the test set.

Our approach is summarized in the following steps:
\begin{enumerate}
    \item Train a blindspot neural network to model the noisy data by outputting a mean and variance value at each pixel, using the uncalibrated loss (Equation \ref{eq:uncalib_loss}).
    \item For each test image:
    \begin{enumerate}[label=\roman*.]
        \item Run the blindspot neural network with the noisy image as input to obtain mean $\mu_i$ and total variance $\hat{\sigma_i}^2$ estimate at each pixel.
        \item Determine the optimal noise parameters $a,b$ by fitting a Poisson-Gaussian distribution to the noisy and psuedo-clean images given by the mean values of the network output (Equation \ref{eq:pg_fit}).
        \item Calculate the prior uncertainty at each pixel as $\sigma_i^2 = \max(0.0001,\hat{\sigma_i}^2-a\mu_i-b)$.
        \item Use the noise parameters $a,b$ and the calculated prior uncertainties $\sigma_i^2$ to compute the denoised image as the posterior mean estimate (Equation \ref{eq:pme}).
    \end{enumerate}
\end{enumerate}

We believe our approach has two theoretical advantages over the bootstrap method proposed by Prakash et al. \cite{prakash2019fully}.  We can achieve a better fit to the data by training our system end-to-end, whereas Prakash et al. \cite{prakash2019fully} impose a fixed noise model during training by first estimating the noise parameters and then training the network. Second, we estimate noise parameters for each image separately at test time, whereas Prakash et al. \cite{prakash2019fully} estimate common noise parameters for all images first and fixes those parameters during training.  Our approach allows for slight deviations in the noise parameters for each image, which might be more realistic for an actual microscope imaging system where the camera configuration slightly fluctuates between images.

\input{fig-hist-fmd}

\section{Experiments and Results}
\label{sec:experiments}

\subsection{Implementation details}

Our implementation uses the Keras library with Tensorflow backend.  We use the same blindspot neural network architecture as Laine et al.~\cite{laine2019high}.  We use the Adam optimizer \cite{kingma2014adam} with a learning rate of 0.0003 over 300 epochs, halving the learning rate when the validation loss plateaued.  Each epoch consists of 50 batches of $128\times128$ crops from random images from the training set.  For data augmentation we apply random rotation (in multiples of 90 degrees) and horizontal/vertical flipping.

To fit the Poisson-Gaussian noise parameters at test time, we apply Nelder-Mead optimization \cite{nelder1965simplex} with $(a=0.01,b=0)$ as the initialization point.  We cut off data in the bottom 2\% and top 3\% of the noisy image's dynamic range before estimating the noise parameters.

\subsection{Datasets}

\subsubsection{Synthetic Data}

We generate a synthetic dataset using the ground truth images of the Confocal MICE dataset from the FMD benchmark \cite{zhang2019poisson} (described below). For training, we use the ground truth images from 19 of the 20 views and generate 50 noisy examples of each view by synthetically adding Poisson-Gaussian noise to the ground truth images using equation \ref{eq:poisson_gaussian_noise} where $a=1/\lambda$ and $b=(\sigma/255)^2$. For testing, we use the ground truth image from the 20th view and generate 50 noisy examples by synthetically adding Poisson-Gaussian noise in the same manner as during training. To ensure our method works for a wide range of noise levels, we train/test our method on all combinations $(\lambda,\sigma) \in \{0,10,20,30,40,50\}\times\{0,10,20,30,40,50\}$.

\input{table-synthetic-psnr}

\subsubsection{Real Data}

We evaluated our method on two datasets consisting of real microscope images captured with various imaging setups and types of samples.  Testing on real data gives us a more accurate evaluation of our method's performance in contrast to training and testing on synthetically noised data, since real data is not guaranteed to follow the theoretical noise model.

The fluoresence microscopy denoising (FMD) benchmark \cite{zhang2019poisson} consists of a total of 12 datasets of images captured using either a confocal, two-photon, or widefield microscope.  We used the same subset of datasets (Confocal Mice, Confocal Fish, and Two-Photon Mice) used to evaluate PN2V \cite{krull2019probabilistic} so that we could compare our results.  Each dataset consists of 20 views of the sample with 50 noisy images per view.  The 19th-view is withheld for testing, and the ground truth images are created by averaging the noisy images in each view.  We trained a denoising model on the raw noisy images in each dataset separately.

Prakash et al.~\cite{prakash2019fully} evaluated PPN2V on three sequences from a confocal microscope, imaging Convallaria, Mouse Nuclei, and Mouse Actin.  Each sequence consists of 100 noisy images and again the clean image is computed as the average of the noisy images.  Whereas the FMD dataset provides 8-bit images clipped at 255, these images are 16-bit and thus are not clipped.  Following their evaluation procedure, each method is trained on all 100 images and then tested on a crop of the same 100 images; this methodology is allowable in the self-supervised context since no label data is used during training.

\input{table-reg}

\subsection{Experiments}

In the following we will refer to the competing methods under consideration as
\begin{itemize}
    \item \textbf{Regularized (Ours)}: Blindspot neural network trained using the regularized Poisson-Gaussian loss function (Equation \ref{eq:loss}) with regularization strength $\lambda$.
    \item \textbf{Uncalibrated (Ours)}: Blindspot neural network trained using the uncalibrated loss function (Equation \ref{eq:uncalib_loss}) with noise parameter estimation done adaptively at test time (Section \ref{sec:uncalibrated}).
    \item \textbf{N2V}: Noise2Void which uses the MSE loss function and random masking to create the blindspot effect \cite{krull2019noise2void}.
    \item \textbf{PN2V}: Probabilistic Noise2Void -- same setup as N2V but uses a histogram noise model created from the ground truth data and a non-parametric prior \cite{krull2019probabilistic}.
    \item \textbf{Bootstrap GMM} and \textbf{Bootstrap Histogram}: PPN2V training -- same setup as PN2V but models the noise distribution using either a GMM or histogram fit to the Noise2Void output \cite{prakash2019fully}.
    \item \textbf{U-Net}: U-Net \cite{ronneberger2015u} trained for denoising in a supervised manner using MSE loss \cite{weigert2018content}.
    \item \textbf{N2N}: Noise2Noise training using MSE loss \cite{lehtinen2018noise2noise}.
\end{itemize}

\input{table-results}
\input{fig-images-fmd-revised}
\input{fig-images}

\subsubsection{Noise parameter estimation}

We first evaluate whether our bootstrap approach to estimating the Poisson-Gaussian noise parameters is accurate in comparison to estimating the noise parameters using the actual ground truth clean values.

To evaluate our bootstrapping method, we compare the ground truth and estimated Poisson-Gaussian noise models fit for a test image in each dataset in the FMD benchmark \cite{zhang2019poisson}. Figure \ref{fig:hist-fmd} shows that the Poisson-Gaussian pdfs generated using our bootstrapping technique closely match that of the Poisson-Gaussian pdfs generated from the ground truth images.

We further evaluate our approach by comparing the loss and estimated Poisson-Gaussian noise parameters obtained when using actual ground truth data or the pseudo-clean data generated in our bootstrap method. Table \ref{table:loss} shows that bootstrapping can provide an accurate estimation of noise parameters and result in a loss similar to that obtained from using ground truth clean data.  Here the loss value is
\begin{equation}
\label{eq:pg_fit}
\frac{1}{N} \sum_i \left( \frac{(y_i-x_i)^2}{ax_i+b}+\log(ax_i+b)\right)
\end{equation}
where $y_i$ is a pixel from the noisy image and $x_i$ is a corresponding pixel from either the ground truth clean image or the pseudo-clean image.

We perform a similar evaluation on our synthetic dataset where instead of having to estimate the true noise parameters from fitting a Poisson-Gaussian noise model with the ground truth clean image we readily have available the true noise parameters that correspond to the level of synthetically added Poisson-Gaussian noise. Table \ref{table:synthetic-params} shows the true noise parameters as well as the ones obtained using our uncalibrated method and the method described by Foi et al. in \cite{foi2008practical}. Our method tends to overestimate the $a$ parameter, whereas the estimate of the $b$ parameter is consistently accurate. This is probably because a majority of the pixels in the Confocal MICE images are dark and thus there are not many good samples for fitting the level of Poisson noise, whereas every pixel can be effectively used to estimate the Gaussian noise no matter the underlying brightness. Unlike the method of Foi et al.~\cite{foi2008practical} which obtains poor noise estimates most likely because of this, our method is still able to obtain a good estimate of the parameters by leveraging information from both the noisy image and our pseudo-clean image.

The effectiveness of fitting a Poisson-Gaussian noise model at test time is further evaluated in Table \ref{table:synthetic-psnr} which provides a comparison of peak signal-to-noise ratio (PSNR) on a subset of our synthetic dataset. Our method of estimating the noise parameters with our bootstrapping technique consistently improves the denoised results of the pseudo-clean image, but is ultimately bounded by the result obtained from using the pseudo-clean image along with the true noise parameters.  Results for all noise parameter combinations are given in the supplemental material.

\subsubsection{Effect of regularization}

To highlight the difficulties of hyperparameter tuning in the self-supervised context, we trained our uncalibrated model and several regularized models on the FMD datasets.  We tested a regularization strength of $\lambda=0.1,1,$ and $10$.

The results are shown in Table \ref{table:reg}.  The test set PSNR of the regularized model varies greatly depending on the setting of $\lambda$, and indeed a different setting of $\lambda$ is optimal for each dataset.  This indicates that hyperparameter tuning is critical for the regularized approach, but it is not actually possible in a self-supervised context.

In contrast, our uncalibrated method outperforms the regularized method at any setting of $\lambda$, and does not require any hyperparameters.

\subsubsection{Comparison to state-of-the-art}
Next we present the results of our performance evaluation on the FMD and PPN2V benchmark datasets.   Table \ref{table:results} shows a comparison between our uncalibrated method and various competing methods, including self-supervised and supervised methods.

Between the fully unsupervised methods that do not require paired noisy images (our Uncalibrated method, N2V, Bootstrap GMM, and Bootstrap Histogram), our method outperforms the others on four out of six datasets. A comparison of denoising results on both benchmark datasets are shown in Figures \ref{fig:images-fmd} and \ref{fig:images}

\section{Conclusions and Future Work}
\label{sec:conclusions}

Noise is an unavoidable artifact of imaging systems, and for some applications such as live cell microscopy, denoising is a critical processing step to support quantitative and qualitative analysis.  In this work, we have introduced a powerful new scheme for self-supervised learning of denoising which is appropriate for processing of low-light images.  In contrast to the state-of-the-art, our model handles Poisson-Gaussian noise which is the standard noise model for most imaging systems including digital microscopes.  In addition, we eliminate the need for loss function regularization in our method, thus making self-supervised denoising more practically applicable.  Our evaluation on real datasets show that our method outperforms competing methods in terms of the standard PSNR metric on many datasets tested.

Our work opens up new avenues in live-cell imaging such as extreme low-light imaging over long periods of time.  Future work lies in extending our model to other noise models appropriate to other imaging modalities, and exploring whether our uncalibrated method could be combined with a non-parametric prior \cite{krull2019probabilistic}.

\paragraph{Acknowledgments} This work was supported in part by NSF \#1659788, NIH \#1R15GM128166-01 and the UCCS Biofrontiers Center.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
Ontario Supporting Tourism, Job Creation in Thunder Bay Region
Province Investing in Northern Economy
April 3, 2019 10:00 A.M.
Ministry of Energy, Northern Development and Mines
Ontario's government is working for the people by supporting tourism in the Thunder Bay region with an investment in six projects, creating 18 new full- and part-time jobs.
Today, Greg Rickford, Minister of Energy, Northern Development and Mines, announced an investment of more than $1 million. Projects include:
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"Tourism is important to many communities across the north," said Minister Rickford. "With this investment, our government is strengthening the Thunder Bay region's economy, supporting the tourism sector, creating jobs and sending a strong signal that Northwestern Ontario is open for business."
Since June 2018, the Northern Ontario Heritage Fund Corporation has invested more than $59 million in 349 projects, creating or retaining over 621 jobs.
Sydney Stonier
Sydney.Stonier@ontario.ca
Leah Wong
Media Advisor, Communications Branch
leah.wong@ontario.ca
Arts and Culture Business and Economy Jobs and Employment Rural and North Travel and Recreation


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Once the new campaign hit the market, results were immediate — and compelling. Carded revenue, live race wagering, and on track handle all increased significantly.


text:
Kinsell, Carolyn. "Investigating Assistive Technologies using Computers to Simulate Basic Curriculum for Individuals with Cognitive Impairments." Handbook of Research on Human Cognition and Assistive Technology: Design, Accessibility and Transdisciplinary Perspectives. IGI Global, 2010. 61-75. Web. 18 Jul. 2019. doi:10.4018/978-1-61520-817-3.ch004
Kinsell, C. (2010). Investigating Assistive Technologies using Computers to Simulate Basic Curriculum for Individuals with Cognitive Impairments. In S. Seok, E. Meyen, & B. DaCosta (Eds.), Handbook of Research on Human Cognition and Assistive Technology: Design, Accessibility and Transdisciplinary Perspectives (pp. 61-75). Hershey, PA: IGI Global. doi:10.4018/978-1-61520-817-3.ch004
Kinsell, Carolyn. "Investigating Assistive Technologies using Computers to Simulate Basic Curriculum for Individuals with Cognitive Impairments." In Handbook of Research on Human Cognition and Assistive Technology: Design, Accessibility and Transdisciplinary Perspectives, ed. Soonhwa Seok, Edward L. Meyen and Boaventura DaCosta, 61-75 (2010), accessed July 18, 2019. doi:10.4018/978-1-61520-817-3.ch004
Technology in Healthcare
Investigating Assistive Technologies using Computers to Simulate Basic Curriculum for Individuals with Cognitive Impairments
Carolyn Kinsell (Solers Research Group, USA)
Source Title: Handbook of Research on Human Cognition and Assistive Technology: Design, Accessibility and Transdisciplinary Perspectives
Providing assistive technologies to cognitively impaired students, in the form of computer-based simulations, may improve the transfer of learning at a greater rate than other training media. The underlying premise for using computer-based simulations is that the cognitively impaired student is no longer the passive learner normally found in traditional classrooms. Instead, the cognitively impaired student becomes an active participant with the simulation and learning. In addition, this type of assistive technology provides the student with an opportunity for repeated exposure and practice at a speed in which the student feels comfortable. This chapter discusses the benefits of using computer-based simulations, defines the theoretical foundations that support the transfer of learning, and presents the processes that facilitate individual acquisition and refinement of knowledge and skills. It concludes with a review of the cognitive elements in the creation of mental models and schema.
Let me set the stage for this chapter—Thomas is a middle-school student who has been labeled as a slow learner, not only by his teachers, but by his classmates. Thomas is not slow at all of his school subjects, but reading is the hardest for him to comprehend. He gets picked on in class and is tired of it! Thomas has been heard saying, "I just can't keep up"! Unfortunately, Thomas is not alone. Based upon my readings, many individuals are not aware they have a learning disability and many are never diagnosed. In 1997, the Individuals with Disabilities Education Act (IDEA) helped to broaden the definition of the use of assistive technologies (AT) in the educational system to include special education "services" (Rapp, 2005). According to Families and Advocates Partner (FAPE, 2001), AT includes "any service that directly assists a child with a disability in the selection, acquisition, or use of an assistive technology device." This act helped to open many doors for students who are considered cognitively impaired. As noted in most AT literature, AT devices are widely used in the educational system but have mainly been provided for those who are physically challenged, such as computer screen readers for the visually impaired. However, there is an entire student body with learning disabilities at the cognitive level, such as those with attention deficit disorders (Harty, Miller, Newcorn, & Halperin, 2008), that are not being targeted as candidates for AT devices (Bausch & Hasselbring, 2004).
Addressing the concern of Thomas for "just not getting it" can be supported by the longitudinal study conducted by Juel (1988) which indicated that over eighty-eight percent of first graders who were noted to be poor readers were still considered poor readers as fourth graders. To further complicate the situation, you have the cycle of a student who is unlikely to fit in with their peers (especially if there are feelings of low self-esteem, like Thomas) as they continue to lag behind. Stanovick (1986) supports Juel's (1988) findings by calling this continual lag as the "Matthew Effect"—those who excel at reading continue to do so, while those who lag behind continue to do just that.
It was noted in a report generated by the National Institute of Child Health (2005) that although assistive devices for those with mental retardation and development disabilities exists, it is not always easy for the Mental Retardation and Development Disabilities (MRDD) individual or for those involved in their health care or as care-givers to gain access or to know these devices exist. Hasselbring and Bausch (2005) also indicated that it is not the lack of availability of AT services and devices that have caused this gap but the lack of knowledge by teachers about AT and how and when to implement. Many teachers, it appears, rely on specialists in the area of AT to implement the program, thus eliminating the immediate connection the teacher may have in the classroom to identify AT services and devices for their students. However, this gap is another topic of discussion. This chapter addresses a narrow part of AT devices, classified as computer-based simulations, that can provide the cognitively impaired a method for learning in their own context—a style of learning that could benefit a student like Thomas. Context is referring to a preferred method of learning on subjects in which the cognitively impaired individual is weak; subjects such as math, science, and reading. These contexts can use a single piece of media or hybrid approach to include animation, graphics, audio and text as a way to impart information that is in alignment with the person's cognitive impairment.
Key Terms in this Chapter
Theoretical Framework: A progression of learning from a cognitive, to associative, to an autonomous phase using knowledge- and skills-based on exposure and practice to learning situations over time.
Transfer of Learning: The ability to relate prior schema learned in the classroom to new situations outside the learning environment.
Schema: Mental models which are developed over time due to exposure to various learning situations help to make connections between new knowledge and existing prior knowledge stored in long-term memory.
Computer-based Simulations: A multimedia, interactive method for learning using a desktop or personal computer that combines two-dimensional or three-dimensional images with animation, audio, voice recognition tools, and/or video.
Cognitively Impaired: Processing and generation of new information is hindered, but not at obvious or observable levels.