To cook on a canoe you will be needing some food supplies and utensils. It’s a good idea to carry everything in one box so that everything is conveniently grouped together for cooking on board and easily carriable for when you go ashore. If you are going on a long cruise, it’s also a good idea to keep most of your food supplies in different parts of the canoe. The box should have enough supplies for at least three meals. You can replenish it with the larger supplies storage when you stop for the night or at a campsite. Keeping the larger box separate from the smaller box will make the cruise more comfortable.
Box Tips – Size and Material • Your box could be made of wood, tin or galvanized iron. o Using wood can be less expensive, work well, and you can do it yourself if you want. o But a box made out of iron or tin will be much more durable. • Make sure the box is water-tight especially if made of wood) • Because canoes come in different shapes and sizes, pick a box that will fit comfortably in your canoe.1/4 inch pine wood
Heavier pine wood for the bottom (if box will be used as a seat)
The cover: 2 inches deep
The handle: Thin, wide, flat strap tacked to the cover
If your box will be stored below-deck:
You can have the flanges of the cover fall over the sides of the box with the strap attached to one end and fastened to the other end by a hook. This way, you can remove the cover and take items out of the box without having to take the box out.
If your box will be used as a seat:
You can hinge the cover on one side and have two hooks attached on that other end. For the back-rest, screw two pieces of ¾ inch pine to the sides running horizontally six or eight inches from the farthest end of the box with holes one inch apart. Pass a ¼ inch brass rod through for the back-rest to play on. This will allow it to recline by changing the rod from one set of holes to another.
Use several water-tight tins so that nothing mixes or spills inside of the box.
The Consolidated Fruit Jar Company in New York makes tin screw-tops for jars and canisters that are water-tight. You can order these tops, in assorted sizes, and have a someone make the tin cans in the sizes you want. They can also give you a pint or quart earthen jar with a water-tight screw-top. This jar can keep butter safe for a long time in hot weather and can be lowered in a net into the water without leaking.In the tin cans:
coffee
tea (or cocoa)
sugar
flour (or meal)
rice
alcohol
Store salt and pepper in small spice boxes with perforated bottoms
Eggs are safest in the tins with the flour, coffee and rice
Bread and bacon should be wrapped in macintosh and put near the top of the chest
Vinegar goes in a marked whisky flask
Canned goods can stay in their own cans
Alcohol stove and cooking utensils in the box
Small coffee pot with handle and riveted lip
Cups or plates made of tin
Fork and knife sheaths inside the box cover
Plates nesting in the frying pan
Frying pan with no handle, and fastened so as to be easily accessed
Knife and fork with a sheath
Pair of small blacksmith's pliers to serve as a handle for frying pan
A three-quart tin and two smaller ones nested in it
Waterproof bags for extra food provisions, clothing, blankets.
Using several bags instead of one or two large can save space.
A canoeist's portable oven is made of two small basins, one of which has "ears" riveted to its rim. The basins should be made of sheet-iron so they can easily be kept clean. These should not go in the provision chest, as they will dirty everything with which they come in contact.
There is no perfect canoe stove. The "flamme forcé" is good because it takes up a little more room than the folding type, and it does not give more heat but it burns for a longer time, and is not top-heavy when a heavy pot or pan is set on it.
For cooking in large utensils have three of these flamme forcé alcohol lamps, light them and place them side by side.
Folding stoves to use ashore and burn wood in will get in your way so avoid them and never carry an oil stove.
Self-raising or Hecker's prepared flour, wheat, rye, Indian or Graham, is easily made into bread and slapjacks. Pilot bread will keep an indefinite time. Indian meal is very nutritious and easily made, Canned tomatoes, corn, fruits, beans, soups, salmon, etc., are easy to prepare, and can be stored as ballast in the canoe.
Here is an example of how Mr. Hicks, of the Toronto Canoe Club, prepares certain kinds of food in cans for ballast:
"Get a number of flat square tin cans made like oyster cans, of a handy size to lie under your floor boards.  Put up fruit in square flat cans in the same way.” - American Canoeist
I haven’t tried this myself but it seems like an excellent idea. The Brunswick canned soups are cheap, easily prepared and filling. Although, not everyone likes them because of the powder that they dry to. Try adding dried beef, corned beef, lemons and sardines. Gather potatoes, onions and other vegetables along the way if needed or possible.
If you’re not sure how much of each food item to bring on a cruise, here’s a list that I used for a week-long trip, which worked out great.
1 lb. sugar (cut loaf)
1/8 lb. tea
1 lb. flour
1-1/2 lbs. crackers
1/2 lb. lard; 1/2 lb. rice
3/4 lb. coffee
1 lb. butter
1 can condensed milk
3 loaves bread
3/4 peck potatoes
1/2 peck meal
1 pint molasses
2 oz. pepper
1 bottle pickles
1 bottle yeast powder
1 qt. salt


Welcome! I am current Masters of Library and Information Science graduate student at the University of Missouri, and my goal is to one day obtain a position where I get to manage and improve internal processes of libraries and archives.
When I entered the MLIS program in 2018, my goal was to learn more about the LIS profession. I came into the program with a lot of technical experience, but little knowledge of how the field worked. I feel that lacking a sense of what being an LIS professional is hinders my growth. Upon graduation in May 2021, I will be leaving the MLIS profession with the knowledge of what the core ethics of librarianship are, and the role I play as an LIS professional. With this portfolio, it is my hope that I am able to demonstrate two things. The first, is that you can see my what my interests are and my growth as a professional. Second, is to highlight the work I have done in the MLIS program. The work selected is what I feel demonstrates my mastery of the four student learning outcomes of the program.


from pynads import Writer from pynads.funcs import multibind from itertools import repeat from functools import partial pairs = ((5, 'fizz'), (3, 'buzz')) def fizzer(n, pairs): fizzed = ''.join([buzz for fizz, buzz in pairs if not n % fizz]) or n return Writer(n+1, {n:fizzed}) res = multibind(Writer(1, {}), *repeat(partial(fizzer, pairs=pairs), 15)) print(res)
Writer(16, {1: 1, 2: 2, 3: 'buzz', 4: 4, 5: 'fizz', 6: 'buzz', 7: 7, 8: 8, 9: 'buzz', 10: 'fizz', 11: 11, 12: 'buzz', 13: 13, 14: 14, 15: 'fizzbuzz'})
Truly an 11x Developer solution to the [STRIKEOUT:world's] universe's premier code interview question!
What is "pynads"
All joking aside, I've been hacking together a collection of Haskell-esque tools for Python over the last few weeks. It started as a "Well, I'll learn Haskell better this way." and has become...well, honestly a tool for me learning Haskell still.
Check it out, I think it's nifty.
A Quick Tour of Pynads
pynads strives to be a pythonic form of Haskell. Which is buzzword for I did some stuff. There's:
- Functors
- Applicatives
- Monads
- Monoids
- Helpers
All of the base classes are implemented as Abstract Base Classes which makes inheritance easy. Well, that's a lie, the root object of pynads is a concrete class that just serves as an endpoint for __new__ and __init__.
Container
pynads.Container is the root object of every pynads class. It serves as a final endpoint for __new__ and __init__ as well as providing a consistent name for accessing the values held by objects in pynads. Some would say that it's a silly idea, but it works! Every class in pynads is also slotted for memory reasons since it's built around the idea of not manipulating a container but creating a new one.
The only important thing to know about Container is that it defines v as a property which actually delagates to the _get_val method. Meaning that's all that needs to be overriden to get multiple values out of a container.
For most subclasses of Container, the provided __init__ is fine, but it's a-ok to override it as well as the only setup that happens is setting a single attribute _v.
from pynads import Container class Person(Container): __slots__ = ('name', 'age') def __init__(self, name, age): self.name = name self.age = age def _get_val(self): return {'name': self.name, 'age': self.age} def __repr__(self): return "Person(name={!s}, age={!s})".format(self.name, self.age) print(Person(name="Alec", age=26).v)
{'age': 26, 'name': 'Alec'}
Functors
Functors are data types that can be mapped over with the fmap method. But calling methods isn't very Haskell like, so there's an operator that does the same thing: %.
from pynads import Functor class FmapPerson(Person, Functor): __slots__ = () def fmap(self, f): return self.__class__(f(self.name), self.age) print(str.upper % FmapPerson(name="Alec", age=26))
Person(name=ALEC, age=26)
Of course, you could just use FmapPerson.fmap but that's not very Haskellic. pynads also exports a funcs name space that contains functional shortcuts to some of these (though f % functor doesn't get much shorter). In this case, there's also:
from pynads import funcs print(funcs.fmap(str.upper, FmapPerson(name="Alec", age=26)))
Person(name=ALEC, age=26)
Every class in the pynads.concrete namespace is a functor except for Mempty (we'll get there!).
Applicatives
Applicative Functors are functors that hold functions and can then be applied to other Applicatives/Functors. Every class (except for Mempty) in pynads.concrete is also an Applicative!
from pynads import Just print(Just(lambda x: x+2) * Just(2))
Just 4
Yeah, like Haskell there's an infix operator. Except of <*> I just dropped the angle brackets because Python doesn't let you define custom operators (easily). It also combines nicely with % because they have the same precedence level!
print((lambda x: lambda y: x+y) % Just(4) * Just(6))
Just 10
BOOM! Mind blown! Whaaaaaat. Applicative uses the abstract method apply to determine how * operates. Just inherit from Applicative, define fmap and apply and boom, you're on your way. Well, you also need the unit method -- which is a class method for all pynad types, but that's not a requirement -- which knows how to put a value in a minimal context.
But wait, what if you have a curried function and you stuffed it into a Just and now you don't want to write out just_f * just_v1 * just_v2 .... Sure, you could think "Well, what if I used reduce(operator.mul, *justs)" But I thought of that already.
add_three_together = lambda x: lambda y: lambda z: x+y+z print(funcs.multiapply(Just(add_three_together), *[Just(x) for x in range(1,4)]))
Just 6
If you're mind isn't blown yet, it's because I haven't revealed...
MOOOOOOOOONAAAAAAAADS!!!!!
Monads get a bad rap because they get all sorts of overblown explainations. You want to know a what a monad is? It's another way to compute things. It's a glorified container with a special method. You have a value in a monad, a function that takes a regular value and returns a monad and you bind them together. That's it. Literally all there is to it.
from pynads import Nothing inc_if_odd_else_nothing = lambda x: Just(x+1) if not x&1 else Nothing print(Just(2) >> inc_if_odd_else_nothing)
Just 3
The Maybe monad (which consists of the Just and Nothing data types) is basically a glorified if expression. That's it! The bind operation will detect if you have a failure in your computation and short circuit it. It's essentially an abstraction over this:
def safe_func(x): if x is None: return None else: return x+1 print(safe_func(1), safe_func(None))
2 None
Notice how that didn't cause a nasty AttributeError, because None doesn't have attributes? That's all Maybe lets you do (this behavior is mimicked in its fmap and apply: fmap(f, Nothing) and apply(ap_f, Nothing) both return you a Nothing). Nothing is extraspecialsauce because it's a singleton. It's basically a monadic None. Actually, it is a monadic None because it represents...well, Nothing.
If you've got more binds than editor columns, then there's something for you as well!
print(multibind(Just(1), *repeat(lambda x: Just(x+1), 5)))
Just 6
Before you start thinking, "Well, monads are just glorified if expressions" becuase that's missing the point, Monads are the abstraction abstraction. They represent a way to compute something by abstracting away how it's computated.
There's the Writer monad above which is a value in a monadic context but it also keeps some extra side-output as well. Instead of us trying to track this extra information ourselves, Writer says, "Hey, I'll handle it for you!" It just wants a function that accepts a value and returns a Writer. But here's the really cool thing, I didn't have to use a dictionary. It could have a list, or a string or an integer, or a custom class! I hear, "But how!" Through the power of...
Monoids!
So monoids are pretty cool. They're a set of something, a "zero" and a binary operator that's transative. Lots of things form monoids. Numbers are monoids! There's two ways to make numbers monoids: with 0 and +, with 1 and *. However, pynads is lazy and only defines the first...sorry, hard choices were made.
Wait, "a zero value" but 1 != 0. That's missing the point, a zero value is a value that doesn't change the input when combined with the binary operator. x * 1 == x.
But Python's "primitive" types all form monads!
- list is a monoid. That's the zero value right there and it's binary operator would be list.extend if it was actually a binop.
- dict is a monoid with {} and dict.update
- bool is a monoid with False and |\or
- set and frozenset are also monoids with their empty instances and |
- str is a monoid with '' and + (boooo using + to combine strings! but whatever)
Tuple, Complex and Float are also monoids in exactly the ways you expect. There's a catch though: When combining tuples, you actually get a list back. I'll probably rectify this at a later point, but it's just something to live with right now.
pynads also defines [STRIKEOUT:two] three monoids of its own: List (the monadic form of tuple), Map (the applicative form of dict) and Mempty (which we're getting to!).
Making your own monoid is easy, and you've probably done it before (just not in this fashion). Just inherit from pynads.Monoid create a mempty attribute (it won't let you without it through __new__ hackery) and the mappend method for combining two instances of your monoid. Let's assume we want a real tuple Monoid. We'd do it like this:
from pynads import Monoid # also inherits from Container # so we get all the Container goodness for free class MTuple(Monoid): mempty = () def __init__(self, *vs): super(MTuple, self).__init__(vs) def mappend(self, other): # type checking optional if not isinstance(other, MTuple): raise TypeError("Can only mappend MTuple with MTuple") return MTuple(*(self.v + other.v)) def __repr__(self): return "MTuple{!r}".format(self.v)
print(MTuple(4,5) + MTuple(6,7))
MTuple(4, 5, 6, 7)
pynads.Monoid overrides + to be a shortcut to mappend. That's all well and good, but why other than have a unified way of combining values?! Because we get a way to reduce a list of monoids into a single value for free!
print(MTuple.mconcat(MTuple(1), MTuple(2), MTuple(3)))
MTuple(1, 2, 3)
Monoid.mconcat actually delegates to the mappend method and essentially looks like reduce(cls.mappend, monoids). That's it. That's all there is. But you can define your own mconcat to get performace bonuses if you need to.
from itertools import chain class CMTuple(MTuple): def __iter__(self): return iter(self.v) @classmethod def mconcat(cls, *MTuples): return CMTuple(*chain.from_iterable(MTuples))
print(CMTuple.mconcat(CMTuple(1), CMTuple(2), CMTuple(3)))
MTuple(1, 2, 3)
pynads.List and pynads.Map take advantage of this to create only one intermediate object rather than a bunch. pynads will also let you treat the builtin types as monoids as well through pynads.funcs.monoid namespace which has four functions we're interested in: mempty, mappend, mconcat and is_monoid. mempty returns the "zero" value for a type, mappend knows how to combine types, mconcat knows how to combine an iter of types into a single one and is_monoid knows if something is monoidal or not (generally, it doesn't declare decimal.Decimal to be a monoid but this is because I didn't want to add a special case -- special cases beget special cases).
This is done through introspection of types and abstract base classes (to make the type introspection [STRIKEOUT:more acceptable] less painful).
from pynads.funcs import monoid print(monoid.mempty(list())) print(monoid.mappend({'a':10}, {'b': 7})) print(monoid.mconcat("hello", " ", "world")) print(monoid.is_monoid(set()))
[] {'a': 10, 'b': 7} hello world True
The monoid namespace is just a nice interface to the nasty plumbing that lives in pynads.utils.monoidal. It's pretty gross and actually probably pretty fragile, but it works! IT WORKS!!!
Mempty
So here's the thing that lets Writer do its little trick by accept any monoid as a potential log. I can't know ahead of time what you're going to use to keep track of stuff with Writer -- I've drank plenty of spice water, but I've yet to develop prescient abilities.  It's not an original idea. Really, it's kind of a dumb object.
It's a singleton, so that's a strike against it (two singletons in my library, my god!). It doesn't actually do anything. It just sits around, taking up space until a real monoid comes along and scares it off. It's mempty value is actually itself! It's mappend just returns whatever its mappended with. And it's mconcat filters out any Mempty values before trying to mconcat the remaining values (you get a Mempty if mconcat an iter of Mempties). There's even an __iter__ method that yields from an empty tuple! What's going on!
In Haskell, mempty can be used a place holder and Haskell knows to do the right thing already. However, we have to teach Python how to use a placeholder and silently step it out of the way when a real value comes along.
Helpers
I won't explore this section indepth because pynads.funcs has a whole host of helpers, with more being added as needed. There's helpers for making constants, composing functions, an identity function (as in lambda x: x not id(x)), turning values into Monads. There's also pynads.utils if you need to plumb some of the internal workings like compatibility (did I mention all the tests pass 3.4 and 2.7?!) or how monoids are determined on things that aren't explicitly monoids.
That's it!
Well, not really. There's about five other monads implemented that I didn't cover (like Reader, which was a pain but also a lot of fun to implement). There's more coming as well. Not just monads, but other things. A proof of concept for do notation via abusing coroutines exists in the repo (lifted from a blog called Valued Lessons which served as partial inspiration for this project). And maybe Monad Transformers if I work the gumption up for it (probably yes). And who knows what else.
I spilled my brains, spill some of yours.


Humidity
is too high!
Temperature
is too high!
Carbon Monoxide
is too high!
Humidity
is a little too high
Temperature
is a little too high
Temperature
is a little too high
Temperature
No results
Humidity
No results
Carbon Monoxide
No results
Light
No results


Dan Alva To Show His Latest Work, ‘The Outsourced Series,’ At Art Basel Miami
by Felicity CarterContemporary artist Dan Alva takes the classics and reinvents for a modern day audience. He re-masters the masters if you will, and undoubtedly, it’s alluring work.His bold and unapologetic pieces stand proud, they’re incredibly intricate, they’re meticulous and he combines his figurative visual language with bursts of camouflage and stripes, taking in a contemporary direction. And excitingly, his latest body of work, entitled The Outsourced Series will be on show at Art Basel with the Oliver Cole Gallery this year. We talk to the artist to find out what we can expect from Basel 2019.
Felicity Carter: What was your first memory of art?
Dan Alva: I’ve been around art, in one way or another, since childhood. From working in the garage with my old man to picking up my first film camera, I’ve always created something. The garage was my bootcamp to what is now my studio practice. I can build and repair almost anything – but I don’t mess with electricity.
FC: Tell us more about this latest group of works titled The Outsourced Series?
DA: The Outsourced Series is a modern reinterpretation of old master paintings using original oil paint and methods. The series has become my very own MFA in Art History. It’s been in the works and under research for about three years, but this last year I really kicked it off. Aside from the painting itself, my favorite part has been these forced collaborations between dead masters that never would have happened back in those days. For example, Sleeping with the Fishes has Da Vinci, Rembrandt and Van Gogh all in one.
FC: How has your work evolved?
DA: I have always looked up to artists like Dan Colen and Damian Hirst that create a series of works, and then move on to the next concept. I go through phases, and I’m still searching for my "style," but The Outsourced Series has been my favorite thus far. It's a labor of love and, as I mentioned before, it's a thesis of sorts.
FC: Where do you find your inspiration?
DA: I don’t watch TV, I don’t watch movies, I really don’t even read much – but I absorb visuals. Online or in books, I just download images to my brain. Instagram has become a slight addiction because you can scroll forever. It’s a curated google search in my pocket. I once flew from Madrid to Miami and scrolled the entire 8 hours.
FC: Tell us about the process...
DA: My process in the studio is similar to any ad campaign I have ever worked on. Before even starting a series, I concept, sketch, create mockups, research the history, create, test and even think about how the works will be photographed. I need to visualize every step of the process before actually starting the process.
FC: Explain the naming concept and patterns used throughout the series.
DA: I’ve always seen these original works in thick, gold frames in either a museum or palace. I love the over the top frame job these masterworks are usually encased in - they remind me of gaudy mafia homes. I thought it only made sense to title the paintings after popular mafia phrases and slang terms. Most of my series are labeled with code words, and this one is no different. The camouflage pattern has been a recurring style I’ve used for the last 8 years or so. It creates a window-like effect that each artist can appear and disappear in and out of.
FC: What’s left for the remainder of the year?
DA: The Outsourced Series will be featured in this year's Art Basel for the very first time at CONTEXT with Oliver Cole Gallery. Besides my framer and a few confidantes, the works haven’t been seen by anyone. I’m looking forward to getting them out in the public eye and hearing the feedback.
See more from the artist at Art Basel or keep up to date via his Instagram page.
Art Basel Miami runs from December 5 - 8, 2019.


Galvanize Delivers
Order Delivery
Menu
Royale with Cheese
$8.99
Add to Order
Arugula Pie
$11.99
Add to Order
Smoked Swine
$14.99
Add to Order
Ice Cream Biscuit
$7.99
Add to Order
Order
Item
Price
Subtotal
$0.00
Tax
$0.00
Total
$0.00
Name
Phone Number
Address
Place Order


DataStoreProtocolType
@objc
public enum DataStoreProtocolType : Int
Constants that provide information regarding protocol type of data store manager.
-
The protocol type FTP.
Declaration
Swift
case ftp
-
The protocol type FTPAccount.
Declaration
Swift
case ftpAccount
-
The protocol type HTTP.
Declaration
Swift
case http
-
The protocol type IRC.
Declaration
Swift
case irc
-
The protocol type NNTP.
Declaration
Swift
case nntp
-
The protocol type POP3.
Declaration
Swift
case pop3
-
The protocol type SMTP.
Declaration
Swift
case smtp
-
The protocol type SOCKS.
Declaration
Swift
case socks
-
The protocol type IMAP.
Declaration
Swift
case imap
-
The protocol type LDAP.
Declaration
Swift
case ldap
-
The protocol type AppleTalk.
Declaration
Swift
case appleTalk
-
The protocol type AFP.
Declaration
Swift
case afp
-
The protocol type Telnet.
Declaration
Swift
case telnet
-
The protocol type SSH.
Declaration
Swift
case ssh
-
The protocol type FTPS.
Declaration
Swift
case ftps
-
The protocol type HTTPS.
Declaration
Swift
case https
-
The protocol type HTTPProxy.
Declaration
Swift
case httpProxy
-
The protocol type HTTPSProxy.
Declaration
Swift
case httpsProxy
-
The protocol type FTPProxy.
Declaration
Swift
case ftpProxy
-
The protocol type SMB.
Declaration
Swift
case smb
-
The protocol type RTSP.
Declaration
Swift
case rtsp
-
The protocol type RTSPProxy.
Declaration
Swift
case rtspProxy
-
The protocol type DAAP.
Declaration
Swift
case daap
-
The protocol type EPPC.
Declaration
Swift
case eppc
-
The protocol type IPP.
Declaration
Swift
case ipp
-
The protocol type NNTPS.
Declaration
Swift
case nntps
-
The protocol type LDAPS.
Declaration
Swift
case ldaps
-
The protocol type TelnetS.
Declaration
Swift
case telnetS
-
The protocol type IMAPS.
Declaration
Swift
case imaps
-
The protocol type IRCS.
Declaration
Swift
case ircs
-
The protocol type POP3S.
Declaration
Swift
case pop3S
-
Creates a new instance with the specified raw value.
If there is no value of the type that corresponds with the specified string value, this initializer returns nil.
Declaration
Swift
public init?(rawValue: String)
Parameters
rawValue
The raw value to use for the new instance.
-
The corresponding value of the raw type.
Declaration
Swift
public var rawValue: String { get }
-
A textual representation of this instance.
Declaration
Swift
public var description: String { get }
-
A textual representation of this instance, suitable for debugging.
Declaration
Swift
public var debugDescription: String { get }


Voice / Tone Guidelines
What's on this Page?
- Conversational Voice
- Friendly but Authoritative Tone
- Consistency
Voice - Make It Conversational
Engage your readers. Think of who they are and imagine that you are speaking directly to them, face to face. Make it conversational.
Try to put yourself in the shoes of our users. Understand their needs, ask yourself what would help them build a better application. One way to do this is to put your own spoken voice in your writing. After all, we're developers and fellow technical people, we've all read documentation, tried new APIs. Speak from your experience. Make it a conversation between you and your reader.
Tone - Friendly but Authoritative
Try to keep the tone casual and approachable, without being too informal.
Make it friendly, empathetic.
Try to write in the Active mode, not Passive. This is treated in the language page.
Use the second person, as in "Your data .." or "You should do X ..". Imply the second person, as in "do this, do that". Using the second person engages a user; it makes them feel you are writing to them - which is actually the case because we always try to write with a reader in mind.
The second person (you, your) also helps avoid the passive tone.
That said, the second person can be overused. Sometimes formality creates authority [TODO - I need some examples here. For example, write "It …", as in "It sounds more official … to say". Use "the" (as in "the data") when the noun (data) needs to stand out. Use "we", as in "we can expect"), or the impersonal (as in "a query is sent").] But these are exceptions; the rule is to personalize the text with the second person.
Tend towards succinct. When in doubt, cut content as opposed to adding. though it takles longer to write less, it's worth the extra time. Write a lot, leave it for a day, remove the next day.
Make use of code snippets and examples, but keep them to the point.
Avoid marketing or sounding too "pitchy". The primary purpose of the docs isn't to sell Algolia. Replace superlatives with concrete facts: if something is "great", be more specific: is it "effective", "fast", "comprehensive", …? This is treated in the Language page.
Incorporate our values - trust, grit, candid, candor, care - into your writing: take a look at the Algolia Voice Guide to see how to do this.
Active/Passive
- Be active. Create a feeling of action.
- Freely use "we" and "you".
- Start with a person (we or you) or a thing that performs an action ("the engine processes your request", "Algolia receives your data and processes it").
- Avoid starting with "it" or "there is/are". These terms are impersonal and passive and lead to passive and wordiness.
- But don't avoid starting with a central object; for example, "the response contains 10 items" instead of "you will receive a response with 10 items". Focus is put on response instead of process.
- "It" can effectively be used to convey the consequences of an action - it happens after you or we have performed an action. But this construct can often be avoided.
- The general idea is that nothing just happens: we and you make things happen, the engine performs. Active identifies the actor.
Future / Present
Consistent Terminology
- Consistent word choice on jargon, features, and important concepts.
- Consistent use of headers, especially on the page.
- Consistent use of examples. For every theory or statement, back it up with an example.
- Consistent use of hyperlinks
- Consistent use of acronyms
Adding Marketing Content to Technical Documentation
This section is useful for two reasons:
- To avoid putting marketing content into the docs.
- To see how we can valorize not sell our services.
Good technical writing sells itself
- Write to convey or clarify information, otherwise don't write it.
- Our quality or "goodness" should speak for itself.
- A feature is discussed as naturally flowing from detecting a problem and solving it, not because we are great for having it.
- Use straightforward, plain English, with a friendly voice and style, no fluff.
- Valorizing Algolia comes from word choice, voice and style, confidence, clarity - in other words, good writing sells Algolia.
Feature / Benefit / Value (concrete) / Value (Customer Dreams)
A feature is what something is, and a benefit is what users can do or accomplish with it, and value is how doing this adds "value" to the company. Value can also go further and make a customer dream: Algolia can give them a great reputation, make them a leader in the industry.
Here's a good example:
- Feature: "Wetness indicator."
- Benefit: "Know when your baby’s diaper needs changing."
- Value: "Have confidence your baby is comfortable and protected"
- Dream value: Be a Great Parent
Some points:
- Our documentation should always combine feature with benefit.
- We don't need to focus that much on value, but don't shy away from it either.
- However, the dream value should probably never be in our docs.
Feature/Benefit
Feature + benefit is a useful way to introduce something, it makes the feature more memorable and convincing.
Feature/Benefit examples:
- Sometimes it's ok to change "10 Gigs of space" to "Space for 1M records". Or combine them ""10 Gigs of space for 1M records".
- "With a 64 GB hard drive, you can store all your important documents"
- "Merchandise with Query Rules"
- "Algolia offers filtering to better enable your users to fine-tune their results."
Provide an example before explaining a feature.
- "You might want to know if users are getting no results, or very few. Maybe you need to track user activity to better inform UI decisions. We provide click analytics just for these purposes."
Marketing Vocabulary
Some of these ideas and words can be used but toned down. Here are some guidelines.
- Speed is a feature not a selling point
- As you type is a useful strategy not groundbreaking
- An Algolia index or solution is Exhaustive: We can actually handle any use case - describe them, otherwise and empty claim is … empty, clutter, noise.
- Settings are comprehensive, arising naturally out of the subject, to solve the problem of search
- Settings are precise and well-calibrated, again, arising naturally out of the necessities of search
- Tie-breaking algorithm is "effective.
The structure of our style guide


Nigeria system is a total joke, criminals have hijacked the country – 2Face Idibia
by Damilola IsmailNigerian legendary artiste, 2face Idibia has stated that the system in Nigeria is a total joke as he expressed his complete disappointment in the leaders of the country.
2face Idibia took to his social media page to not only say Nigeria system is a total joke, he added that it has been hijacked by criminals with no dignity, shame or honor.
Staying hopeful, he prayed that the young men taking up leadership positions in the country do their best to influence a positive change rather than the current ones who he claims dedicated their time to ensuring the country lags behind.
He wrote;
Naija system is a total joke. Criminals have hijacked naija. Men and women that have dedicated their time 2 make sure that naija will carry last.. No shame. No dignity. No honor. I pray the young men that have entered the system will do their best to influence positive change.
Follow us on Facebook – @Lailasnews; Twitter – @LailaIjeoma for updates


Example 1 (Default settings)
First Item
Second Item
Third Item
Example 2 (Left action only, no auto-close)
First Item
Second Item
Third Item
Example 3 (Wide actions, long snap duration)
First Item
Second Item
Third Item


Warehouse Keeper Sorint:
Ah, the Demon's Staff! Bring me these coins and I'll give you one.
1 Beleth's Blood Dragon
1 Silver Dragon
13 Gold Wyrms
Trade
Go back to the list of prizes


[1]
None that I can see.; Won't charge. Customer support non-existent.
[2]
this is great product; none so far


Resources
Here is a starter list of resources that may help you with finances. The resources cover crowdfunding for survivors, locating shelters, financial education, and scholarships to pursue higher education.
YouHelp
YouHelp is a crowdfunding resource for survivors. You can fundraise directly for yourself through this organization, or contribute funds to others.
domesticshelters.org
This resource allows you to search for the nearest shelter, and shows the services they provide. Shelters are great places to get housing assistance, referrals from professionals for services, and more. This can help ease the burden of the costs for relocation.
National Coalition Against Domestic Violence
The link will will take you directly to NCADV's financial education page. This resource provides financial education via a free webinar series and pdf book.
Women's Independent Scholarship Program
This program provides funding for survivors wanting to pursue higher education after relocation. The purpose is to help survivors achieve skills that will lead to a good career and financial empowerment.


--- layout: post date: 2010-07-30 time: "09:00:00" authors: ["Greg Wilson"] title: A Little Bit of Javascript tags: ["Tooling"] ---
As I've mentioned before, one actionable finding in educational research is that faded examples—ones in which progressively less of the solution is shown to students as they progress—are a very effective teaching tool. I've been thinking about how to add them to this course, and have an idea I'd like to try out. It requires more Javascript than I know, though, so I'm hoping someone who reads this blog will be willing to write it for us. (And in general, if anyone wants to help produce material for this course, please get in touch: we're looking for scripts, slides, voiceovers, examples, artwork, and everything else that an open education project needs.)
My idea is to create something like a simple folding editor to progressively expand solutions in place in a controlled order. I want to put specially-formatted comments in code to mark folds:
import sys, re '''Find all duplicated words in an input file.''' # <4> Finally, define a pattern that will match duplicated words. pattern = r'(\b\w+\b)\s+\1' # </4> # <2> Process lines of text with a regular expression using the looping pattern we've seen before. def process(lines): result = set() for the_line in lines: for match in re.findall(pattern, the_line): # <3> Extract data from matches. This is specific to *this* problem, and has to sync with the pattern. word = match.split()[0] result.add(word) # </3> return result # </2> if __name__ == '__main__': # <1> Write the main body of the program first using the read/process/write pattern we've seen before. lines = open(sys.argv[1], 'r').readlines() results = process(lines) for r in results: print r # </1>
It will initially appear as:
import sys, re '''Find all duplicated words in an input file.''' ...4... ...2... if __name__ == '__main__': ...1: Write the main body of the program first using the read/process/write pattern we've seen before...
Clicking on the fold marked '1' expands it, and draws attention to fold #2 by putting its comment text inline:
import sys, re '''Find all duplicated words in an input file.''' # ...4... # ...2: Process lines of text with a regular expression using the looping pattern we've seen before... if __name__ == '__main__': # Write the main body of the program first using the read/process/write pattern we've seen before. lines = open(sys.argv[1], 'r').readlines() results = process(lines) for r in results: print r
Clicking '2' expands it to show (and draw attention to) #3, et cetera. And there would be markers of some kind to re-fold an item, which would automatically re-fold all higher-numbered items at the same time. This would let us show readers how we created a solution, not just the solution itself; the marked-up code would be a bit ugly, but pretty easy to create (at least for small examples).
So, any volunteers?


{% extends "layout.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block proposition_header %} {% set my_account="2" %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} How much have you paid the claimant? The total amount claimed is £1,500. This includes the claim fee and any interest. What do you want to do? The amount claimed Less than the amount claimed {% include 'prototypes/prototype-oct-2019/includes/contact.html' %} {% endblock %}
The total amount claimed is £1,500. This includes the claim fee and any interest.


Viewing Event Details (Month View)
How to view the full details of an event when events are displayed in a monthly calendar.
- Click the Month View button and navigate to the required date.
- If event titles are not displayed in calendar cells, place your mouse over the date that is displayed in the required date cell and then click on the date.
- If event titles are displayed in calendar cells and then click on the linked [Event Title] to view full event details. Event details may be displayed when you position your mouse over the event title as shown in the below image.
- Click the Return link to return to the previous page.


California Commission Head Open to Replacing Utility Giant With Co-op
by JON PARTON(CN) – The head of California’s Public Utilities Commission chastised Pacific Gas & Energy in a letter released Thursday, stating that she is open to replacing the utility giant blamed for costly wildfires and blackouts with a customer owned nonprofit cooperative.
CPUC President Marybel Batjer wrote about her openness to the idea in response to a Nov. 4 letter sent by several California mayors and county leaders who advocated for such a move. PG&E filed for bankruptcy in January after it was clear the company would be on the hook for about $30 billion in liability for wildfires started by its equipment.
The city and county leaders asked the commission to consider a bold alternative to a mere restructuring of the company post-bankruptcy.
“The commission must do more than approve a plan — any plan — merely so that the bankruptcy can be concluded,” the government leaders wrote.
PG&E has received criticism for its intentional blackouts this year that left millions of Californians without power. Company leaders said the blackouts were necessary to prevent additional wildfires as high winds across Central and Northern California intensified chances of spreading fires.
Despite the blackouts, PG&E equipment has been linked as possible causes to five different wildfires in October. The company is still on probation after a 2010 felony conviction stemming from an explosion of a gas pipeline.
Last month, the commission announced a formal investigation into the company’s recent blackouts and possible safety violations.
In Batjer’s letter, she called the utility’s safety performance “unequivocally unacceptable.”
“I grant that PG&E faces historically challenging and dynamic climate change conditions, but the company has failed to successfully execute even basic operations during times of crisis,” she wrote. “From website failures to communication breakdowns in [blackout] events, PG&E’s inability to execute and implement the simple stuff is deeply troubling.”
Batjer said there are “many benefits” from models such as a customer owned co-op that “warrant further consideration.”
Other possibilities for restructuring the company include state ownership, as suggested by Gov. Gavin Newsom or a buyout by Warren Buffett’s Berkshire Hathaway.
Copyright © 2019 courthousenews.com


Triangulation
Demo
Points:
1337
Triangles:
1337
Triangulation (ms):
1337
Rendering (ms):
1337
Uniform
Grid
Circle
Number of points
Click or drag to add points.
Clear
Generate


dl theory
Contents
Deep learning theory is a complex emerging field - this post contains links displaying some different interesting research directions
1.6. dl theory#
1.6.1. theoretical studies#
DNNs display many surprising properties
surprising: more parameters yields better generalization
surprising: lowering training error should be harder
Many things seem to contribute to the inductive bias of DNNs: SGD, dropout, early stopping, resnets, convolution, more layers…all of these are tangled together and many things correlate with generalization error…what are the important things and how do they contribute?
some more concrete questions:
what is happening when training err stops going down but val err keeps going down (interpolation regime)?
what are good statistical markers of an effectively trained DNN?
how far apart are 2 nets?
verification - does dnn satisfy a specification (e.g \(l_p\) robustness)
convex barrier - limitation in the tightness of the bounds obtainable by the convex relaxation of the output of a neural network
<IP_ADDRESS>. functional approximation#
dnns are very hard to study in the parameter space (e.g. swapping two parameters changes things like the Hessian), easier to to study in the function space (e.g. the input-output relationship)
nonlinear approximation (e.g. sparse coding) - 2 steps
construct a dictionary function (T)
learn linear combination of the dictionary elements (g)
background
\(L^2\) function (or function space) is square integrable: \(|f|^2 = \int_X |f|^2 d\mu\), and \(|f|\) is its \(L_2\)-norm
Hilbert space - vector space w/ additional structure of inner product which allows length + angle to be measured
complete - there are enough limits in the space to allow calculus techniques (is a complete metric space)
composition allows an approximation of a function through level sets (split it up and approximate on these sets) - Zuowei Shen (talk, slides)
composition operation allows an approximation of a function f through level sets of f –
one divides up the range of f into equal intervals and approximate the functions on these sets
nonlinear approximation via compositions (shen 2019)
how do the weights in each layer help this approximation to be more effective?
here are some thoughts – If other layers are like the first layer, the weights “whiten” or make the inputs more independent or random projections – that is basically finding PC directions for low-rank inputs.
are the outputs from later layers more or less low - rank?
I wonder how this “whitening” helps level set estimation…
nonlinear approximation and (deep) relu nets - also comes with slides
<IP_ADDRESS>. inductive bias=implicit regularization: gradient descent finds good minima#
DL learns solutions that generalize even though it can find many which don’t due to its inductive bias.
early stopping is very similar to ridge regression
ridge regression: soln will lie in \(p-dim\) row-space of X (span of the rows)
when \(\lambda \to 0\), will give us min-norm soln (basically because we project onto \(col(X)\))
early stopping in least squares
if we initialize at 0, GD soln will always be in the row-space of X
GD will converge to min-norm soln (any soln not in the row-space will necessarily have larger norm)
similar to ridge (endpoints are the same) and can related their risks very closely when \(\lambda = 1/t\), where \(t\) is GD time iterate
assume gradient flow: take step-size to 0
srebro understanding over-parameterization
ex. gunasekar et al 2017: unconstrained matrix completion
grad descent on U, V yields min nuclear norm solution
-
sgd on logistic reg. gives hard margin svm
deep linear net gives the same thing - doesn’t actually changed anything
ex. gunaskar, 2018
linear convnets give smth better - minimum l1 norm in discrete fourier transform
ex. savarese 2019
infinite width relu net 1-d input
weight decay minimization minimizes derivative of TV
How do infinite width bounded norm networks look in function space? (savarese…srebro 2019)
minimal norm fit for a sample is given by a linear spline interpolation (2 layer net)
analytic theory of generalization + transfer (ganguli 19)
deep linear nets learn important structure of the data first (less noisy eigenvectors)
datasets for measuring causality
Inferring Hidden Statuses and Actions in Video by Causal Reasoning - about finding causality in the video, not interpretation
PL condition = Polyak-Lojawsiewicz condition guarantees global convergence of loca methods
\(||\nabla f(x)||^2 \geq \alpha f(x) \geq 0\)
<IP_ADDRESS>. semantic biases: what correlations will a net learn?#
imagenet models are biased towards texture (and removing texture makes them more robust)
eval w/ simulations (reviewer argued against this)
causality in dnns talk by bottou
on mnist, color vs shape will learn color
A mathematical theory of semantic development in deep neural networks
Emergence of Invariance and Disentanglement in Deep Representations (achille & soatto 2018)
information in the weights as a measure of complexity of a learned model (information complexity)
IB Lagrangian between the weights of a network and the training data, as opposed to the traditional one between the activations and the test datum
explains tradeoff between over/underfitting
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks (lengerich..caruana, 2020)
use ANOVA to meaure 1st/2nd/3rd order effects and such
approximate ANOVA decomp. using boosted trees of depth based on a particular order
they find that increasing dropout rate forces nets to emphasize lower-order effects
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
overparameterization can hurt test error on minority groups despite improving average test error when there are spurious correlations in the data
<IP_ADDRESS>. expressiveness: what can a dnn represent?#
<IP_ADDRESS>. complexity + generalization: dnns are low-rank / redundant parameters#
measuring complexity
functional decomposition (molnar 2019)
decompose function into bias + first-order effects (ALE) + interactions
3 things: number of features used, interaction strength, main effect complexity
parameters are redundant
predicting params: weight matrices are low-rank, decompose into UV by picking a U
rethinking the value of pruning: pruning and training from scratch, upto 30% size
Lottery ticket: pruning and training from initial random weights, upto 1% size (followup)
T-Net: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor
generalization
<IP_ADDRESS>. nearest neighbor comparisons#
<IP_ADDRESS>. kernels#
To understand deep learning we need to understand kernel learning - overfitted kernel classifiers can still fit the data well
original kernels (neal 1994) + (lee et al. 2018) + (matthews et al. 2018)
infinitely wide nets and only top layer is trained
corresponds to kernel \(\text{ker}(x, x') = \mathbb E_{\theta \sim W}[f(\theta, x) \cdot f(\theta, x')]\), where \(W\) is an intialization distr. over \(\theta\)
neural tangent kernel (jacot et al. 2018)
\(\text{ker}(x, x') = \mathbb E_{\theta \sim W} \left[\left < \frac{f(\theta, x)}{\partial \theta} \cdot \frac{f(\theta, x')}{\partial \theta} \right> \right]\) - evolution of weights over time follows this kernel
with very large width, this kernel is the NTK at initialization
stays stable during training (since weights don’t change much)
at initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit
evolution of an ANN during training can also be described by a kernel (kernel gradient descent)
different types of kernels impose different things on a function (e.g. want more / less low frequencies)
gradient descent in kernel space can be convex if kernel is PD (even if nonconvex in the parameter space)
understanding the neural tangent kernel (arora et al. 2019)
method to compute the kernel quickly on a gpu
Scaling description of generalization with number of parameters in deep learning (geiger et al. 2019)
number of params = N
above 0 training err, larger number of params reduces variance but doesn’t actually help
ensembling with smaller N fixes problem
the improvement of generalization performance with N in this classification task originates from reduced variance of fN when N gets large, as recently observed for mean-square regression
On the Inductive Bias of Neural Tangent Kernels (bietti & mairal 2019)
Kernel and Deep Regimes in Overparametrized Models (Woodworth…Srebro 2019)
transition between kernel and deep regimes
The HSIC Bottleneck: Deep Learning without Back-Propagation (Ma et al. 2019)
directly optimize information bottleneck (approximated by HSIC) yields pretty good results
<IP_ADDRESS>. random projections#
relation to bousquet - fitting random polynomials
hierarchical sparse coding for images (can’t just repeat sparse coding, need to include input again)
random projections in the brain….doing locality sensitive hashing (basically nearest neighbors)
<IP_ADDRESS>. implicit dl + optimization#
implicit deep learning (el ghaoui et al. 2019)
\(\hat y (u) = Cx + D u \), where \(x = \phi(Ax + Bu)\)
here, \(u\) is a new input
\(x\) is a hidden state which represents some hidden features (which depends on \(u\))
well-posedness - want x to be unique for a given u
\(A, B\) are matrices which let us compute \(x\) given \(u\)
\(C, D\) help us do the final prediction (like the final linear layer)
ex. feedforward nets
consider net with \(L\) layers
\(x_0 = u\)
\(x_{l + 1} = \phi_l (W_l x_l)\)
\(\hat y (u) = W_L x_L\)
rewriting in implicit form
\(x = (x_L, ..., x_1)\) - concatenate all the activations into one big vector
ex. \(Ax + Bu= \begin{bmatrix} W_{L-1}x_{L-1} \\ W_{L-2} x_{L-2} \\ \vdots \\ W_1x_1 \\ \mathbf 0\end{bmatrix} + \begin{bmatrix} 0 \\ 0 \\ \vdots \\ 0 \\ W_0 u \end{bmatrix}\)
lifted neural networks (askari et al. 2018)
can solve dnn \(\hat y = \phi(W_2 \phi (W_1X_0))\) by rewriting using constraints:
\(X_1 = \phi(W_1 X_0)\)
\(X_2 = \phi(W_2 X_1)\)
\(\begin{align} &\min (y - \hat y)^2\\s.t. X_1 &= \phi(WX_0)\\X_2 &= \phi(WX_1)\end{align}\)
can be written using Lagrangian multipliers: \(\min (y - \hat y)^2 + \lambda_1( X_1 - \phi(WX_0)) + \lambda_2(X_2 - \phi(WX_1))\)
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training (gu et al. 2018)
in the lifted setting above, can replace Lagrangian with simpler expression using Fenchel conjugates
robust optimization basics
immunize optimization problems against uncertainty in the data
do so by having worst-case constraints (e.g. \(a < 5, \forall a\))
local robustness - maximize radius surrounding parameter subject to all constraints (no objective to maximize)
global robustness - maximize objective subject to robustness constraint (trades off robustness with objective value)
non-probabilistic robust optimization models (e.g. Wald’s maximin model: \(\underset{x}{\max} \underset{u}{\min} f(x, u)\)
also are probabilistically robust optimization
distributional robustness - using moments in the dl work
ch 4 and ch10 of robust optimization book (bental, el ghaoui, & nemirovski 2009)
Certifying Some Distributional Robustness with Principled Adversarial Training (sinha, namkoong, & duchi 2018)
want to guarantee performance under adversarial input perturbations
considering a Lagrangian penalty formulation of perturbing the underlying data distribution in a Wasserstein ball
during training, augments model parameter updates with worst-case perturbations of training data
little extra cost and achieves guarantees for smooth losses
On Distributionally Robust Chance-Constrained Linear Programs (calafiore & el ghaoui 2006)
linear programs where data (in the constraints) is random
want to enforce the constraints up to a given prob. level
can convert the prob. constraints into convex 2nd-order cone constraints
under distrs. for the random data, can guarantee constraints
Differentiable Convex Optimization Layers (agrawal et al. 2019)
<IP_ADDRESS>. statistical physics#
Statistical Mechanics of Deep Learning (bahri et al. 2019)
what is the advantage of depth - connect to dynamical phase transitions
there are several function which require only polynomial nodes in each layer for deep nets, but exponential for shallow nets
what is the shape of the loss landscape - connect to random Gaussian processes, sping glasses, and jamming
how to pick a good parameter initialization?
bounding generalization error
often, generalization bounds take the form \(\epsilon_{test} \leq \epsilon_{train} + \frac {\mathcal C (\mathcal F)} p\), where \(\mathcal C (\mathcal F)\) is the complexity of a function class and \(p\) is the number of examples
ex. VC-dimension, Rademacher complexity
alternative framework: algorithmic stability - will generalize if map is stable wrt perturbations of the data \(\mathcal D\)
altenative: PAC bounds suggest if distr. of weights doesn’t change much during training, generalization will be succesful
deep linear networks
student learns biggest singular values of the input-output correlation matrix \(\Sigma = \sum_i y_i x_i^T\), so it learns the important stuff first and the noise last
infinite-width limit
if parameters are random, indcues a prior distribution \(P(\mathcal F)\) over the space of functions
in th limit of infinite width, this prior is Gaussian, with a specific correlation kernel
learning is similar to learning the Bayesian posterior \(P(f|data)\), but connecting this to sgd is still not clear
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics (kunin et al. 2020)
no assumptions about DNN architecture or gradientflow
instead, assumptions on symmetries embedded in a network’s architecture constrain training dynamics
similar to Noether’s thm in physics
can much better analytically describe learning dynamics
weights have a differentiable symmetry in the loss if the loss doesn’t change under a certain differentiable transformation of the weights
ex. translation symmetry - for layer before softmax, we get symmetries across weights that shift all outputs
ex. scale symmetry - inputs to batch normalization are invariant to scaling
ex. rescale symmetry - scaling up/down 2 things which multiply or add
modeling discretization
1.6.2. empirical studies#
<IP_ADDRESS>. interesting empirical papers#
modularity (“lottery ticket hypothesis”)
contemporary experience is that it is difficult to train small architectures from scratch, which would similarly improve training performance - lottery ticket hypothesis: large networks that train successfully contain subnetworks that–when trained in isolation–converge in a comparable number of iterations to comparable accuracy
Truth or backpropaganda? An empirical investigation of deep learning theory
<IP_ADDRESS>. adversarial + robustness#
robustness may be at odds with accuracy (madry 2019)
adversarial training helps w/ little data but hurts with lots of data
adversarially trained models have more meaningful gradients (and their adversarial examples actually look like other classes)
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging
Towards Robust Interpretability with Self-Explaining Neural Networks
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review (xu et al. 2019)
Adversarial examples are inputs to machine learning models that an attacker intentionally designed to cause the model to make mistakes
threat models
poisoning attack (insert fake samples into training data) vs. evasion attack (just evade at test time)
targeted attack (want specific class) vs. non-targeted attack (just change the prediction)
adversary’s knowledge
white-box - adversary knows everything
black-box - can only feed inputs and get outputs
gray-box - might have white box for limited amount of time
security evaluation
robustness - minimum norm perturbation to change class
adversarial loss - biggest change in loss within some epsilon ball
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty (hendrycks et al. 2020)
do a bunch of transformations and average images to create each training image
certifying robustness
bound gradients of f around x - solve SDP for 2-layer net (raghunathan, steinhardt, & liang 2018, iclr)
relax SDP and then applies to multilayer nets (raghunathan et al. 2018)
improved optimizer for the SDP (dathathri et al. 2020)
interval bound propagation - instead of passing point, pass an interval where it could be
works well for word substitions (jia et al. 2019)
RobEn - cluster words that are confusable + give them the same encoding when you train (jones et al. 2020)
then you are robust to confusing the words
unlabeled data + self-training helps
training robust models has higher sample complexity than training standard models
tradeoff between robustness and accuracy (raghunathann et al. 2020)
adding valid data can hurt even in well-specified, convex setting
robust self-training eliminates this tradeoff in linear regression
sample complexity can be reduced with unlabeled examples (carmon et al. 2019)
distributional robust optimization
instead of minimizing training err, minimize maximum training err over different perturbations
hard to pick the perturbation set - can easily be too pessimistic
these things possibly magnify disparities
larger models
selective classification
feature noise
removing spurious features
group DRO (sagawa et al. 2020) - maximize error for worst group
need to add regularization to keep errors from going to 0
training overparameterized models makes this problem worse
abstain from classifying based on confidence (jones et al. 2020)
makes error rates worse for worst group = selective classification can magnify disparities
adding feature noise to each group can magnify disparities
domain adaptation
standard domain adaptation: labeled (x, y) in source and unlabeled x in target
gradual domain adaptation - things change slowly over time, can use gradual self-training (kumar et al. 2020)
In-N-Out (xie et al. 2020) - if we have many features, rather than using them all as features, can use some as features and some as targets when we shift, to learn the domain shift
<IP_ADDRESS>. tools for analyzing#
dim reduction: svcca, diffusion maps
viz tools: bei wang
1d: plot loss by extrapolating between 2 points (start/end, 2 ends)
goodfellow et al. 2015, im et al. 2016
2d: plot loss on a grid in 2 directions
important to think about scale invariance (dinh et al. 2017)
want to scale direction vector to have same norm in each direction as filter
use PCA to find important directions (ex. sample w at each step, pca to find most important directions of variance)
<IP_ADDRESS>. misc theoretical areas#
deep vs. shallow rvw
information bottleneck: tishby paper + david cox follow-up
Emergence of Invariance and Disentanglement in Deep Representations
<IP_ADDRESS>. comparing representations#
<IP_ADDRESS>. simple papers#
<IP_ADDRESS>. adam vs sgd#
svd parameterization rnn paper: inderjit paper
original adam paper: kingma 15
regularization via SGD (layers are balanced: du 18)
marginal value of adaptive methods: recht 17
comparing representations: svcca
montanari 18 pde mean field view
<IP_ADDRESS>. memorization background#
“memorization” as the behavior exhibited by DNNs trained on noise, and conduct a series of experiments that contrast the learning dynamics of DNNs on real vs. noise data
look at critical samples - adversarial exists nearby
networks that generalize well have deep layers that are approximately linear with respect to batches of similar inputs
networks that memorize their training data are highly non-linear with respect to similar inputs, even in deep layers
expect that with respect to a single class, deep layers are approximately linear
memorization in overparameterized autoencoders
autoencoders don’t lean identity, but learn projection onto span of training examples = memorization of training examples
sometimes output individual training images, not just project onto space of training images
https://pdfs.semanticscholar.org/a624/6278cb5e2d0ab79fe20fe20a41c586732a11.pdf
<IP_ADDRESS>. probabilistic inference#
multilayer idea
<IP_ADDRESS>. architecture search background#
ideas: nested search (retrain each time), joint search (make arch search differentiable), one-shot search (train big net then search for subnet)
<IP_ADDRESS>. bagging and boosting#
analyzing bagging (buhlmann and yu 2002)
boosting with the L2 loss (buhlmann & yu 2003)
boosting algorithms as gradient descent (mason et al. 2000)
1.6.3. basics#
demos to gain intuition
overview / reviews
some people involved: nathan srebro, sanjeev arora, jascha sohl-dickstein, tomaso poggio, stefano soatto, ben recht, olivier bousquet, jason lee, simon shaolei du
interpretability: cynthia rudin, rich caruana, been kim, nicholas papernot, finale doshi-velez
neuro: eero simoncelli, haim sompolinsky


{include file="common/meta" /}
{include file="common/header" /} {include file="common/tab_menu" /} {include file="common/menu" /}
{include file="common/setting" /}
{include file="common/script" /}


19 January 2011 - On 23 December 2010, the Minister of Health in Uganda reported 3 laboratory confirmed cases of yellow fever, detected through a special investigation following an outbreak in the country in October 2010. The cases were reported in 3 districts of Abim, Agago and Kitgum near the border with South Sudan.
In total, five cases have been laboratory confirmed for yellow fever by the US Centers for Disease Control and Prevention (3 by RT-PCR, 1 by ELISA method and 1 by histopathology). A total of 226 cases compatible with the clinical case definition including 53 deaths have been reported by 12 districts in northern Uganda.
Following field investigations by the Ministry of Health with the support of WHO, Médecins Sans Frontières (MSF), the US and others partners, a decision was made to conduct a reactive mass vaccination campaign in 5 districts (Abim, Agago, Kitgum, Lamwoo and Pader). On 31 December 2010, WHO deployed three additional experts to support risk assessment, and planning and implementation of control measures which include strengthening of the surveillance system and the vaccination campaign.
A request for one million doses of vaccine was submitted to the International Coordinating Group on Yellow Fever Vaccine Provision (YF-ICG) on 4 January 2011 and the vaccine from the GAVI funded emergency stockpile was shipped to the country on 11 January 2011. The vaccination campaign will start on 22 January 2011, targeting a population of over 905,000 people.


This tool is a Boolean operator, i.e. it works on True or False values. Grid cells for which THE FIRST input raster has a True value BUT NOT THE SECOND. raster are assigned 1 in the output raster, otherwise grid cells are assigned a value of 0. All non-zero values in the input rasters are considered to be True, whilst all zero-valued grid cells are considered to be False. Grid cells containing NoData values in either of the input rasters will be assigned a NoData value in the output raster.
An example of each of the logical operators (AND, OR, XOR, and NOT) is given below for clarification:
The following is an example of a Python script that uses this tool:
wd = pluginHost.getWorkingDirectory()
inputFile1 = wd + "input1.dep"
inputFile2 = wd + "input2.dep"
outputFile = wd + "output.dep"
args = [inputFile1, inputFile2, outputFile]
pluginHost.runPlugin("XOR", args, False)
This is a Groovy script also using this tool:
def wd = pluginHost.getWorkingDirectory()
def inputFile1 = wd + "input1.dep"
def inputFile2 = wd + "input2.dep"
def outputFile = wd + "output.dep"
String[] args = [inputFile1, inputFile2, outputFile]
pluginHost.runPlugin("XOR", args, false)


{% extends 'base.html' %} {% block content %}
Sorry, but the page you’re looking for doesn’t not exist.
It looks like this was either the result of:


The Joint Major program requires 2 upper division Materials science electives, one from each group:
Students petition the joint major after they attain sophomore standing. Those who are accepted into the program are enrolled in both the College of Engineering and College of Chemistry, with their home college being the College of Chemistry.
Unfortunately, because the number of joint Chemical Engineering/Materials Science Joint Majors is relatively small, there is no data on the trajectory of joint majors. Those who are interested in the major are encouraged to look at the 2018 Career survey for Chemical Engineering and Materials Science and Engineering, individually.


Agency
{{ launch.lsp.name}}
Rocket
{{ launch.rocket.name}}
ISO Start
{{ launch.isostart | date:'dd/MM/yyyy HH:mm'}}
ISO End
{{ launch.isoend | date:'dd/MM/yyyy HH:mm'}}
0">
Missions
{{ mission.name }}
{{ mission.description }}
Fail reason
{{ launch.failreason }}


DR. SEUSS'S HOW THE GRINCH STOLE CHRISTMAS A film review by Steve Rhodes Copyright 1996 Steve Rhodes
RATING (0 TO ****): *** 1/2
So what are the Christmas traditions at your home? At ours, for as long as I can remember, we have curled up on the sofa and watched DR. SEUSS'S HOW THE GRINCH STOLE CHRISTMAS (1966).
In the middle of a frantic Christmas season, there is no better respite from the buy-me commercialism of the holidays than this short, but poignant story by Dr. Seuss. And there are few children's authors as literate and imaginative as the great Dr. Seuss. It is a shame that more of his works have not been made into movies or at least televised shorts. His only full length movie is THE 5,000 FINGERS OF DR. T. for which he wrote the screenplay. (And the only published review I have ever seen of it is mine in the Internet Movie Data Base.)
Dr. Seuss has always been my favorite author to read to my son, and Dr. Seuss was one of my favorites which I was a child. His stories usually rhyme. ("The 500 Hats of Bartholomew Cubbins" is one of the major exceptions.) He takes a liberal view of the English language and revels in inventing words. He even has a book, "On Beyond Zebra!," where he devises new letters as well. His arguments are convincing ones. No one who has read "On Beyond Zebra!" will ever again be satisfied with the twenty-six puny little letters we have in English. As he so amply demonstrates, there are so many words that just can not be spelled with them.
The GRINCH is narrated by the great Boris Karloff. His rich and powerful voice dominates the film. His villain is mean enough to be real without being too scary, thus giving us a film which can be enjoyed by three year olds as well as eighty-three year olds. Although he speaks his lines, his voice is so melodious that it is singing to my ears.
The animation is minimalist, but heartwarming. It has all the looks of an inexpensive production for television, which it was, but do not be fooled by its low cost exterior. This is a film with a big heart that manages never to be sappy no matter how familiar the story may sound. Few writers can teach the value of creativity as can Dr. Seuss, and few have his love of life.
In the GRINCH, our protagonist is a scrooge who lives high on a mountain and detests the happy villagers in the valley below. Called Whoville, it is populated with Normal Rockwell style cartoon figures. Typical is little Cindy Lou Who, "who is no more than two," and who has a smile that would melt butter.
The Grinch hates the happy noises of the children on Christmas, and, most of all, he finds their singing an abomination. He concocts a simple scheme. All he needs to do is dress up like Santa Claus. He can then steal all of their Christmas presents, trees, and roast beast. That will certainly prevent Christmas from coming and will provide him his desired gloomy silence.
The morose Grinch has a sweet little dog. One look at the dog, and it is love at first sight for the audience. The Grinch forces him to put on heavy fake antlers and pull an overloaded sled, but the dog smiles and tries hard to please his master.
The songs by Albert Hague are as hokey as can be, but still lovable. This is an old children's show, so you have to give it a little leeway.
The absolutely predictable ending is none the less effective. When the children start singing even without the trappings of the Christmas season, the Grinch's heart "grows three sizes that day." And Boris Karloff says, "Maybe Christmas, he thought, doesn't come from a store. Maybe Christmas, he thought, means a little bit more." As the show concludes, the families in Whoville join hands and sing, "Welcome Christmas while we stand, heart to heart, and hand to hand."
DR. SEUSS'S HOW THE GRINCH STOLE CHRISTMAS runs just 26 minutes. It is not rated but would get a G. There is nothing offensive or scary in it. It is an ideal show for all ages at Christmas. My son Jeffrey, age 7 1/2, my wife, and I all recommend it to you strongly. The world would be a better place if everyone would ponder its clear message. I give the GRINCH *** 1/2.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: December 24, 1996
Opinions expressed are mine and not meant to reflect my employer's.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


MD - Medicine, MD - Dermatology, MD - Dermatology
With over 12 years of practice experience, Dim-Jamora Dermatology aspires to bring the highest quality of dermatologic care to individuals from all walks of life in the Philippines. Located at The Medical City Ortigas and Makati Medical Center, the Clinic is at the hub of a vibrant medical community. At the helm of Dim-Jamora Dermatology is Dr. Krisinda Dim-Jamora, the first Philippine member of the American College of Mohs Surgery and a Philippine Board-Certified Dermatologist.
Unit 1610 (16th floor)
The Medical City Ortigas
Ortigas Avenue, Pasig
(Near Meralco and Rockwell Business)
Room 133
#2 Amorsolo St
Legaspi Village, Makati
(Near RCBC Plaza Ayala Avenue)
Unit 1610 (16th floor)
The Medical City Ortigas
Ortigas Avenue, Pasig
(Near Meralco and Rockwell Business)


Fortnite Chapter 2: Where to find the second letter T
by Lauren AitkenThere’s another letter to find in Fortnite and this time, you’re looking for the second letter T.
By now, you’ll have gathered enough letters over the last few weeks to spell out F-O-R-T-N-I, so here’s where you can find the second letter T.
As usual, this area will get quite busy so you might want to use Party Assist and team up with a buddy, otherwise, grab a weapon and build a few walls around you once you’ve found it.
Where to find the second letter T
To get started, head to Weeping Woods. You’re looking for a wooden bridge to the north of the area over the river. On a platform below, you’ll find the letter T spinning around. Grab it to complete the challenge.
Season 1 has been extended to February 2020, so you’ve got plenty of time to find these letters.
Where to find the Fortnite letters
- Where to find the letter F
- Where to find the letter O
- Where to find the letter R
- Where to find the letter T
- Where to find the letter N
You can also check out our other Fortnite Chapter 2 guides such as where to find E.G.O. Outposts, how to upgrade your equipment and where to find the boat launch, coral cave and flopper pond.
Games featured in this article


Editorial: Toughen rules for stray animals | Honolulu Star-Advertiser
Because the Hawaiian Humane Society holds the city’s animal services contract, it tends to an array of pet welfare matters including stray rescue and pickup, sheltering and medical care as well as adoptions. Read more
Mahalo for reading the Honolulu Star-Advertiser!
You're reading a premium story. Read the full story with our Print & Digital Subscription.
Already a subscriber? Log in now to continue reading this story.
Print subscriber but without online access? Activate your Digital Account now.


A Swift wrapper for Apple's CommonCrypto
library.
IDZSwiftCommonCrypto
Since CommonCrypto
is not a standalone module, you need to generate a fake module map to convince Xcode into allowing you to import CommonCrypto
. The GenerateCommonCryptoModule
script provides two methods for doing this. Which method you choose depends on whether you want to able to use CommonCrypto
and, by extension, IDZSwiftCommonCrypto
in playgrounds.
To make CommonCrypto
available to frameworks and playground use the command:
./GenerateCommonCryptoModule iphonesimulator8.0
This command creates a CommonCrypto.framework
in the SDK system library directory. You should now be able to use either CommonCrypto
or IDZSwiftCommonCrypto
in a playground simply importing them or in your own app project by dragging the IDZSwiftCommonCrypto.xcodeproj
into your project.
If you do not want to add any files to your SDK you can use the command
./GenerateCommonCryptoModule iphonesimulator8.0 .
This method creates a CommonCrypto
directory within the IDZSwiftCommonCrypto
source tree, so the SDK directories are not altered, but the module is not available in playgrounds. To use the framework in your own project drag the IDZSwiftCommonCrypto.xcodeproj
into your project and set the Module Import Path to the directory containing the CommonCrypto
directory created by the script. For more about this, see my blog post Using CommonCrypto in Swift


--- title: legendre0 sidebar: function_reference_sidebar permalink: legendre0.html folder: function_reference toc: false ---
Evaluate all Legendre polynomials up to degree N in x and returns a matrix of the function values 1st dimension -> degree 2nd dimension -> x Use the recurence formula (n+1)*P_{n+1} = (2n+1)*x*P_n - n*P_{n-1}
| N | degree |
| x | input nodes |
| l | function evaluations |


Should we vote?
 Etsy irony small batch locavore Blue Bottle literally, swag twee meditation


--- layout: post permalink: /en/content/coffee-2-sugar/ title: Coffee. 2 Sugar. language: en --- {% include JB/setup %}
My dad owns a coffee shop.
My dad is silent as-well, and that’s the second thing I can say about
him.
When customers enter his coffee shop, he hardly greets them.  He pours it into old yet clean glass cups, places the kettle on
the
table, takes the sugar jar that was hidden in his hand, pulls out a
silver
spoon from his off-color buttoned shirt and stares into the client’s
eyes.
Then, he puts the spoon in the jar, stacks sugar, pours into the coffee
and
mixes.
For three decades now, people murmur there, "My god, that’s exactly -
I mean exactly - how I like my coffee."
The third thing about my dad is that he’s still in love with my mom,
even though it’s been ages since they are together, and almost all their
friends are either divorced or hate each other.
My mom also loves him.
When he comes back from work, he kisses her and goes to make her coffee.
She puts the sugar by herself. She’s the type of people who prefers
making it on their own.
After that, she falls asleep on the couch, and he hugs her. Hugs and
quiets.
Me - I have a small manufacturing house of garments. I wish it was bigger, but so far it’s not very successful, mostly because people don’t understand the idea. Or maybe because my marketing is wrong. I don’t know exactly what, but something is just not working properly there. "No, it’s not a regular mass-production," I explain. "No, these are not one-off’s, neither. It’s not something in between! It’s simply something else. Its mass-customization."

There is this chick that shows interest lately. Behind her there’s a big client - something that can promote my business quite significantly. She has charming eyes. That’s the first thing I said about her. She also laughs from my jokes, and it is so rare to the extent, that this is the second and third things I say about her. When we just met, she also didn’t understand what I was talking about. So I invited her to my place, the next day at 5 am - "Brush your teeth, but don’t drink any coffee", I demanded.
She arrived at five fifteen with swollen eyes. "I’m a night person," she apologized. I sat her on the couch and went to the kitchen to make her coffee. "Two sugar," she yawned. "Two, one - what’s the difference. It’s all the same." I could have kept speaking, I could have clarified my intentions, I could have explained the analogy. But I didn’t. I don’t know why. When I came back to the living room, she was already asleep on the couch. I placed the coffee on the table, lay down next to her, and hugged her. Silently.
I think I start to resemble my father.


Getting Started
Fantasy League football is the worlds biggest fantasy league. Each week millions of fans around the world compete against their league rivals in a bid to win great prizes the league title. We are here to help you get started. From picking your team to daily and weekly tactics EPL fan zone will help crown you champions. Ever wanted to manage a football team, but never had the opportunity? Draft Fantasy Football is the perfect opportunity to show off your managerial skills. In Draft Fantasy Football, every manager in your league picks a unique team of players in what is known as "The Draft". The Draft progresses in turns with each manager picking a player on his turn until everyone has a full squad.
Leagues
We offer both classic and head to head leagues, as well as private and public leagues.
- Classic Leagues:
- In classic leagues teams are ranked based on the total number of points scored by their players throughout the season.
- Head to Head Leagues:
- In head to head leagues each team plays against another opponent each week of the season. Just like the Premier League, a win is worth 3 points, a draw is worth 1 point, and a loss is worth 0 points.
- Private Leagues
- Private leagues work the same way as public leagues except they require a password to join them. Private leagues also allow the league creator to manually start the draft with the click of a button. Public leagues start automatically at a given date. Also, settings for private leagues can be changed before the draft starts, whereas the settings in public leagues can't be changed.
Picking a Well Balanced Team
This refers to both price and teams.
- Teams:
- Selecting your FPL team from a range of teams ensures you are well covered in the event that one side does particularly well or badly in any given weekend.
- Price:
- Ensure that the selection of 1-3 high price players is not at the expense of the rest of your team. It is important that not only 1-3 players are relied upon to score all of your points. If there is an injury, or if the players form drops, it can hurt your points in a big way.


The ScriptEnginePlugin provides an easy to use and easy to implement framework that makes scripting.dev.java.net ScriptEngines usable inside of jEdit.
It provides a ScriptEngineService which allows this plugin to pick up any ScriptEngines (installed separately) that are in other plugins.
It also provides 2 convenient methods for executing the current buffer or executing the currently selected text. Both of these methods provide the standard macro variables in the ScriptContext making them available to any scripts executed.


Specification:
(a and b and c and d and e and f and g and h) then (u or v or w or x or y or z) then (u or v or w or x or y or z) then (u or v or w or x or y or z)
AST:


 The problem is that Beautiful can't figure out whether it wants to be a coming-of-age drama or a scathing comedy about the absurdity of pageants. Instead of choosing one, the film tries to be both, and the net result is an uneven picture that runs about thirty minutes too long.
Beautiful opens in 1986, where thirteen-year-old Naperville, Illinois resident Mona Hibbard is shown getting braces. She's paying for the corrective procedure with her own money (in monthly installments) and is thrilled when the dentist tells her that they'll be off in time for the Junior Miss Pageant.
 Instead of pouring her time and energy into a expensive and time-consuming drug and/or alcohol habit (like most American kids in similar situations), Mona instead devotes her life to one simple idea – becoming a beauty queen. Her only friendship is one made out of convenience – mousy grammar school seamstress Ruby can help Mona create different outfits for her run at various pageants.
Flash to 1999, where Mona (Minnie Driver, Return to Me) has just won the Illinois state pageant, earning a trip to the prestigious Miss American Miss competition in Long Beach, California. The next two cinematic months unfold over an ungodly ninety-plus minutes. We learn that Mona has sabotaged her rivals on several occasions, and more shockingly, had a baby that she pawned off on Ruby (Joey Lauren Adams, Big Daddy) to raise as her own. Why? Because Miss American Miss contestants can't be mothers.
To make matters worse, Mona can't stand the sight of her own daughter, Vanessa (Hallie Kate Eisenberg , the kid from the Pepsi commercials).  But after a oddly grim mix-up at Ruby's employer, Mona has to look after Vanessa, bringing out the motherly instinct that she had never shown previously.
Beautiful's script was written (and co-produced) by Jon Bernstein, whose only previous writing credit was the appalling Jerry Springer film Ringmaster. It's difficult to say whether the film had a weak, choppy script, or if Fields insisted on cramming too much into the final product. In addition to blowing an abnormally large chunk of time on Mona's childhood, Beautiful spends way too much time on the final pageant. Do we really need to see the entire talent portion of each of the competition's five finalists? Tom make matters worse, the film has an ending that will make you cringe, unless you enjoyed the last scene of Rudy.
The best thing about Beautiful is the casting. Driver and Eisenberg look like they could be related, but is that really enough to keep an entire two-hour movie afloat? The film's moral is that it's okay to lie and hurt other people, so long as you eventually end up on top. And, unlike other films about single-minded egomaniacs (like Wall Street), Mona walks away from her experience as a winner, a hero and a role model. Beautiful sure isn't pretty. In fact, it's downright ugly.
On the plus side, the film could (hopefully) be the final nail in the coffin of Destination Films – the studio that has given us the impressively bad lineup of Whipped, Bats, Thomas and the Magic Railroad and Eye of the Beholder.
1:52 – PG-13 for adult language and thematic elements
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Pennywall is a micro paywall / donation-wall / tip-jar for sharing your content on social networks. You can generate as many pennywalls as you like, one for each link you want to share.
[ See demo ] [ Another demo ]
Users can choose to tip you with a tiny payment, or simply skip past the wall to the target link.
$ cat pennywall.json
{
"apiKey": "YOUR_API_KEY",
"destination": {
"url": "https://kevsveganblog.com/post_how_to_eat_bananas"
},
"theme": {
"name": "heart",
"title": "Kev's Vegan Blog"
}
}
$ cat pennywall.json | bash <(curl -s https://pennywall.io/build)
{"success": true, "path": "https://give.pennywall.io/v1myg6q"}
A themeable donation-wall generator for sharing social content.
Fork it on GitHub.


Question - {{ question.language }} - {{ question.timestamp }}
(assigned to: {% if question.translator %}{{ question.translator}}{% else %}unassigned{% endif %})
Original Question
English Translation
{{ question.translation }}
{% if not question.doctor %}
This question has not been assigned a doctor. Would you like to claim it?
{% else %}
Medical Answer
{% if not question.is_answered %}


Cardi B Shows Fans Inside Her ‘Twerk Shop’ In New Holiday-Themed Pepsi Commercial
by Aynslee DarmonPepsi is in full Christmas mode thanks to Cardi B.
In a brand new commercial for the brand’s Holiday campaign, the “Bodak Yellow” rapper stars in “A Cardi Carol”, a hip-hop reimagining of the classic tale.
The clip starts off with sharing a story from Cardi’s childhood with her sister, but when she ends up on the naughty list Cardi decides to make her own rules and deliver money to everyone.
“I want some of that Pepsi money. I need that!” she says. “Time to make it rain… deer.”
Pepsi shared the new ad to Twitter, writing, “Cardi B doesn’t care if you’re naughty or nice… everyone has a chance to get cash this holiday.”
Cardi B famously starred alongside Steve Carell and Lil Jon in last year’s Pepsi SuperBowl ad.


Physical NZ street addresses only.
Any NZ address; including PO Boxes or Private Bag numbers.
Same as shipping


We believe in providing the best work possible for our customer. View our portfolio for completed projects!
We believe in safety. Our company has all required insurances and required documentation involving our trade.
Colorado based remodeling business and family owned company.
The first thing we build is trust! We take great pride in our reputation of high
quality performance and outstanding customer service. As a family owned and operated business, we will
continue these trends for decades to come. Our team thrives on designing and building new spaces to meet the
needs of businesses and families. Eagle Remodeling Corp is hands down the most polite, on time, and
professional Colorado Company!
We go the extra mile to fully understand your project thus creating the highest level of customer
satisfaction. Our accurate, detailed bids and scheduling processes are produced with your personal or
business needs in mind to keep disruption to a minimum. With nearly two decades of industry experience under
our belt, you can be assured that our system of planning and executing projects will be worry and hassle
free. Leaving no detail left unturned, our in house drafting team will work to make sure your plans are as
perfect as possible from the start.
For more information please give us a call or email.


{% extends "templates/base.html" %} {% block content %}
{%trans%}Use our service to anonymously and securely store an online distributed proof of existence for any document.{%endtrans%} {%trans%} Your documents are NOT stored in our database or in the bitcoin blockchain, so you don't have to worry about your data being accessed by others. {%endtrans%}
{%trans%} All we store is a cryptographic digest of the file, linked to the time in which you submitted the document. In this way, you can later certify that the data existed at that time. This is the first online service allowing you to publicly prove that you have certain information without revealing the data or yourself, with a decentralized certification based on the bitcoin network. {%endtrans%}
{%trans%} The key advantages are anonymity, privacy, and getting a decentralized proof which can't be erased or modified by anyone (third parties or governments). Your document's existence is permanently validated by the blockchain even if this site is compromised or down, so you don't depend or need to trust any central authority. All previous data timestamping solutions lack this freedom. {%endtrans%}
{%trans%} The document is certified via embedding its SHA256 digest in the bitcoin blockchain. This is done by generating a special bitcoin transaction that encodes/contains the hash via an OP_RETURN script. This is a bitcoin scripting opcode that marks the transaction output as provably unspendable and allows a small amount of data to be inserted, which in our case is the document's hash, plus a marker to identify all of our transactions. {%endtrans%}
{%trans%}Once the transaction is confirmed, the document is permanently certified and proven to exist at least as early as the time the transaction was confirmed. If the document hadn't existed at the time the transaction entered the blockchain, it would have been impossible to embed its digest in the transaction (This is because of the hash function's property of being second pre-image resistant). Embedding some hash and then adapting a future document to match the hash is also impossible (due to the pre-image resistance of hash functions). This is why once the bitcoin blockchain confirms the transaction generated for the document, its existence is proven, permanently, with no trust required.{%endtrans%}
{%trans%}If anyone wants to manually confirm the document's existence at the timestamped time, they should just follow these steps:{%endtrans%}


--- layout: default page_title: Timeoff Company Registration Is Complete # permalink: timeoff-register-ok.html ---
A timeoff-management window opens, with a confirmation "Registration is complete":
timeoff-management


Loading... (If this takes too long it means there was a serious error!)
{{modInfo.name}} {{VERSION.withoutName}}
Congratulations! You have reached the end and beaten this game, but for now...
Please check the Discord to see there are new content updates!
It took you {{formatTime(player.timePlayed)}} to beat the game.

Play Again Keep Going
The Modding Tree Discord
Main Prestige Tree Discord
If you would like to speedrun this, press Play Again and record your attempt, then submit on the Discord Server in the channel #speedrun-submissions.
Oh, you are still reading this?
←
v1.3.5
The system now handles convertToDecimal for everything automatically!
Created a tutorial for getting started with TMT and Github.
Branches can be defined without a color id. But they can also use hex values for color ids!
Page title is now automatically taken from mod name.
v1.3.4
Added "midsection" feature to add things to a tab's layout while still keeping the standard layout.
Fix for being able to buy more buyables than you should.
v1.3.3
Fix for the "order of operations" issue in temp.
v1.3.1
Added custom CSS and tooltips for Layer Nodes.
Added custom CSS for upgrades, buyables, milestones, and challenges, both individually and layer-wide.
You can now use HTML in most display text!
You can now make milestones unlockable and not display immediately.
Fixed importing saves, and issue with upgrades not appearing, and probably more.
Optional "name" layer feature, used in confirmation messages.
v1.3: Tabception... ception!
Added subtabs! And also a Micro-tab component to let you make smaller subtab-esque areas anywhere.
Added a "custom" prestige formula type, and a number of features to support it.
Added points/sec display (can be disabled, automatically disabled if getPointGen is 0).
Added h-line, v-line and image-display components, plus components for individual upgrades, challenges, and milestones.
Added upgEffect, buyableEffect, and challEffect functions.
Added "hide completed challenges" setting.
Moved old changelogs to a separate place.
Fixed hasMilestone and incr_order.
Static layers now show the currency amount needed for the next one if you can buy max.
Full history
←
{{modInfo.name}}
{{VERSION.withName}}
The Modding Tree by Acamaeda
The Prestige Tree made by Jacorb and Aarex
Original idea by papyrus (on discord)
Changelog
The Modding Tree Discord
Main Prestige Tree server
Note by Jacorb: If anyone wishes to make a mod of this game, that is perfectly fine with me, just make sure to name it something different (ex: Prestige Tree NG+) and to let me know on
my discord .
Time Played: {{ formatTime(player.timePlayed) }}
Hotkeys
{{key.desc}}
←
Save
Autosave: {{ player.autosave?"ON":"OFF" }}
HARD RESET
Export to clipboard
Import
Offline Prod: {{ player.offlineProd?"ON":"OFF" }}
Theme: {{ getThemeName() }}
Show Milestones: {{ player.msDisplay.toUpperCase() }}
High-Quality Tree: {{ player.hqTree?"ON":"OFF" }}
Completed Challenges: {{ player.hideChalls?"HIDDEN":"SHOWN" }}
{{VERSION.withoutName}}
i
Dev Speed: {{format(player.devSpeed)}}x
Offline Time: {{formatTime(player.offTime.remain)}}
Reach {{formatWhole(ENDGAME)}} to beat the game!
You have
{{format(player.points)}}
{{modInfo.pointsName}}
({{format(getPointGen())}}/sec)
←
You have {{formatWhole(tmp.layerAmt[layer])}} {{layers[layer].baseResource}}
Your best {{layers[layer].resource}} is {{formatWhole(player[layer].best)}}
You have made a total of {{formatWhole(player[layer].total)}} {{layers[layer].resource}}


GitHub
Pick a Size
Pick a Pizza
Select Crust
Select Sauce
Select Cheese Intensity!
Customize Your Toppings!
Your
is done!
Your
pizza has
,
, and
.
Once you are finished with that slice...
be sure to checkout the source on
GitHub!
« Prev
»