algorithmx
2.0
Installation and Usage
Installation
HTTP Server
Jupyter Widget
API
Overview
Selections
NetworkX
Examples
Basic Examples
NetworkX Examples
NetworkX Tutorial
algorithmx
»
Index
Index
A
|
C
|
D
|
E
|
F
|
H
|
J
|
L
|
M
|
N
|
O
|
P
|
Q
|
R
|
S
|
T
|
V
|
W
|
Z
A
add() (api.EdgeSelection method)
(api.ElementSelection method)
add_graph() (in module algorithmx.networkx)
align() (api.LabelSelection method)
angle() (api.LabelSelection method)
attrs() (api.ElementSelection method)
C
Canvas (class in api)
canvas() (algorithmx.server.CanvasServer method)
CanvasServer (class in algorithmx.server)
color() (api.EdgeSelection method)
(api.LabelSelection method)
(api.NodeSelection method)
curve() (api.EdgeSelection method)
D
data() (api.ElementSelection method)
directed() (api.EdgeSelection method)
dispatch() (api.Canvas method)
draggable() (api.NodeSelection method)
duration() (api.ElementSelection method)
E
ease() (api.ElementSelection method)
edge() (api.Canvas method)
edgelayout() (api.Canvas method)
edgelength() (api.Canvas method)
edges() (api.Canvas method)
EdgeSelection (class in api)
ElementArg (in module api.types)
ElementFn (in module api.types)
ElementSelection (class in api)
F
fixed() (api.NodeSelection method)
flip() (api.EdgeSelection method)
font() (api.LabelSelection method)
H
highlight() (api.ElementSelection method)
http_server() (in module algorithmx)
J
jupyter_canvas() (in module algorithmx)
L
label() (api.Canvas method)
(api.EdgeSelection method)
(api.NodeSelection method)
labels() (api.Canvas method)
(api.EdgeSelection method)
(api.NodeSelection method)
LabelSelection (class in api)
length() (api.EdgeSelection method)
M
message() (api.Canvas method)
N
node() (api.Canvas method)
nodes() (api.Canvas method)
NodeSelection (class in api)
NumAttr (in module api.types)
O
onclick() (api.NodeSelection method)
ondispatch() (api.Canvas method)
onhoverin() (api.NodeSelection method)
onhoverout() (api.NodeSelection method)
onmessage() (api.Canvas method)
onreceive() (api.Canvas method)
P
pan() (api.Canvas method)
panlimit() (api.Canvas method)
path() (api.EdgeSelection method)
pause() (api.ElementSelection method)
pos() (api.LabelSelection method)
(api.NodeSelection method)
Q
queue() (api.Canvas method)
queues() (api.Canvas method)
R
radius() (api.LabelSelection method)
receive() (api.Canvas method)
remove() (api.ElementSelection method)
(api.NodeSelection method)
rotate() (api.LabelSelection method)
S
shape() (api.NodeSelection method)
shutdown() (algorithmx.server.CanvasServer method)
size() (api.Canvas method)
(api.LabelSelection method)
(api.NodeSelection method)
start() (algorithmx.server.CanvasServer method)
svgattr() (api.ElementSelection method)
T
text() (api.LabelSelection method)
thickness() (api.EdgeSelection method)
traverse() (api.EdgeSelection method)
V
visible() (api.ElementSelection method)
W
withQ() (api.ElementSelection method)
Z
zoom() (api.Canvas method)
zoomlimit() (api.Canvas method)
zoomtoggle() (api.Canvas method)


1 / 6
2 / 6
3 / 6
4 / 6
5 / 6
6/ 6
hello syvyvyghhhhhhhhhhhhhhhhhhhhhhhh
hhh
hh
hhhhhh
Full Air Conditioned Rooms.
Stylish architectural pieces of work inside every room.
Affordable SPA which will make you relax,stressfree.
Free WiFi to all our guests.
24 hrs running water(hot water facility is also available).
A vast Swimming Pool with hot spring & Jaccuzi.
World-Class Food made by our world class chefs.
An Excellent decor dinning hall which will give you the enviornment for eating.
Fast and Impressive Room Service.
Heavy discounts offers and vouchers for our Guests.


Loops are a way to do an action over and over again, within constraints that you set.
It is a good way to alter a bunch of things at once!
We are going to learn a second type of loop that is different from a while loop... afor loop.
Use the buttons below to add body segments to the BitbyBotapillar, then use the second set
to make a change to all the body segments.
Usually a loop runs very quickly - so quickly you don't see each change individually!
Choose 'Loop Slowly" if you would like to see it run slowly.
1


A casual meetup every Wednesday nights at the Code for America Headquarters.
An open conversation online about what we're doing now and what's next. We're also on freenode IRC at #codeforamerica.
Code for America is the organization that helped inspire many of us that we can change our City.
We are a Code for America Brigade, which means we're volunteers that contribute our talents toward improving the way our local governments and community organizations use the web. Brigade is about action and you're here because you are someone who takes action. See what kind of things brigades do, or watch the inspirational TED talk given by Jennifer Pahlka, the founder of Code for America.
View our Meetups site for our next location and time. We meet every Wednesday where we hack (develop) our projects, or brainstorm on current issues and activities around San Francisco. It's actually a lot of fun, with great people and usually food/drink.
Reach out to us on the Code For San Francisco Google Group
Organizers are Mick Thompson<EMAIL_ADDRESS>or @dthompson) and Felix Sargent<EMAIL_ADDRESS>or @antagonist) and Hannah Young<EMAIL_ADDRESS>or @hannahdotyoung) and we want to hear from you!


Data scientists often have to work with a relatively small sample from an enormous population. So suppose we are drawing at random $n$ times from a population of size $N$ where $N$ is large and $n$ is small relative to $N$. Just go with the flow for now – all of this will become more precise as this section develops.
Suppose the population mean is $\mu$ and the population SD is $\sigma$. Let $S_n$ be the sample sum. Then, regardless of whether the sample is drawn with replacement or without, $$ E(S_n) = n\mu $$
The variance of the sample sum is different in the two cases.
| $~~~~~~~~~~~~~~~~~$ | sampling with replacement | sampling without replacement |
|---|---|---|
| $Var(S_n)$ | $n\sigma^2$ | $n\sigma^2\frac{N-n}{N-1}$ |
| $SD(S_n)$ | $\sqrt{n}\sigma$ | $\sqrt{n}\sigma\sqrt{\frac{N-n}{N-1}}$ |
The "without replacement" column is the same as the "with replacement" column apart from what are called correction factors. The one for the SD is called the finite population correction or fpc.
$$ \text{finite population correction} ~ = ~ \sqrt{\frac{N-n}{N-1}} $$The name arises because sampling with replacement can be thought of as sampling without replacement from an infinite population. Every time you draw, you leave the proportions in the population exactly the same as they were before you drew.
A more realistic version of that image is drawing without replacement from an enormous finite population. Every time you draw, you leave the proportions in the population almost exactly the same as they were before you drew.
We used this idea earlier when we said that sampling without replacement is almost the same as sampling with replacement provided you are drawing a relatively small sample from a very large population.
The fpc gives us a way to quantify this idea.
First note that when $N$ is even moderately large, $$ \frac{N-n}{N-1} ~ \approx ~ \frac{N-n}{N} ~ = ~ 1 - \frac{n}{N} $$
which is the fraction of the population that is left after sampling.
If $N$ is large and $n$ is small relative to $N$, then
$$ \frac{N-n}{N-1} ~ \approx ~ 1 - \frac{n}{N} ~ \approx ~ 1 $$and a fortiori $$ \sqrt{\frac{N-n}{N-1}} ~ \approx ~ 1 $$
So whether you are sampling with replacement or without, the variance of the sample sum can be taken to be $n\sigma^2$. The formula is exact in the case of sampling with replacement and an excellent approximation in the case of sampling without replacement.
The table below gives the fpc for a variety of population and sample sizes.
pop = make_array(1000, 10000, 50000, 100000, 500000, 1000000)
def fpc(pct):
samp = np.round(pop*pct/100, 0)
return np.round(((pop-samp)/(pop-1))**0.5, 6)
Table().with_columns(
'Population Size', pop,
'1% Sample', fpc(1),
'5% Sample', fpc(5),
'10% Sample', fpc(10),
'20% Sample', fpc(20)
)
The values in each column are essentially constant, because each is essentially the square root of the fraction not sampled:
sample_pct = make_array(1, 5, 10, 20)
(1 - sample_pct/100)**0.5
All of these fpc values are fairly close to 1, especially in the 1% column where they are all essentially 0.995. That is why the fpc is often dropped from variance calculations.
The SD of a simple random sample sum depends only on the sample size and the population SD, provided the population size is large enough that the fpc is close to 1.
That's clear from the formula. If the fpc is close to 1, as it often is, then
$$ SD(S_n) \approx \sqrt{n}\sigma $$which involves only the sample size $n$ and the population SD $\sigma$.
To understand this intuitively, suppose you are trying to determine the composition of a liquid based on the amount in a test tube. If the liquid is well mixed, does it matter whether the amount in the test tube was drawn from a bowl or from a bathtub? It doesn't, because both the bowl and the bathtub are so much larger than the test tube that they might as well be inifinite.


[§279G-4] Declaration of policy. It is the policy of this State to encourage commuting to and from work by means other than a motor vehicle occupied by one person. [L 1986, c 90, §2]


This widget automatically creates all the tools required to accomplish WFST editing using GeoExt. See WFSTFeatureEditing.js for the source code of this example.
Most of the components are created asynchronously using WFS requests. A GetCapabilities request is used to create the vector layers and a DescribeFeatureType one is used to get the fields and the geometry type. From there, DrawFeature, SelectFeature and many other controls and widgets are automatically created for each layer.
Only one layer can be edited at a time in one of the following mode : draw or edit.
The 'edit' mode can either use a 'filter' mode or not. This demo uses the filter mode. Enabling the 'edit' mode on a layer doesn't show the features yet. You need to draw a box on the map to get the features first, then click on the feature you wish to edit. You can also select it from the grid on the left of the window that pops up when you select a feature. If only one feature was returned by box you drawn, it's automatically selected. The "Return to selection" button clears the features that were returned by the box and reenable the box selection.
While creating vector layers with a WFS protocol, if a WMS layer having the same name present in the map, it will be automatically refreshed upon each modifications and also automatically shown when editing is enabled on its WFS counterpart. These features are shown in this demo.
The widget has an 'ignoreAttributes' property that can be set to a list of fields we don't want to be displayed in both grids. This feature is shown in this demo some user-specific fields are not shown in both grids.
The demo also available in French.
Here's the list of components that are not in GeoExt that are used by this widget.


| Service | Departure/Return Time | Rate per Angler |
|---|---|---|
| Rock Fish + Salmon Combo | 7 AM to 4 PM | $200 |
| Salmon | 7 AM to 12 PM or 1 PM TO 6PM | $100 |
| Rock Fish | 7 AM to 12 PM or 1 PM TO 6PM | $100 |
| Dungeoness Crab | Usually lasts about 3 hours. Departure Times Vary | $90 |
| Custom Trips* | ... | ... |
*The Samurai is a multi-purpose charter boat. Whatever the species you are trying to target, we can meet your needs. Captain and crew are experienced with all sport fishing practices. Call us for further details.
Our Combo Rock Fish and Salmon package gives you the most bang for your buck. Imagine coming home with a limit of fresh salmon and rock fish! There could be well over 40lbs of filleted meat in your bag!
People travel all from all over the globe to fish our waters for salmon. Come see why! Nothing is as comparable to landing a salmon (other than, perhaps putting it on the BBQ).
We do something slightly different here on the Samurai. At Hooked On Mendo, we believe that part of Sport Fishing should include, well, Sport. Therefore, we use rods and reels that let the rock fish fight. Instead of using 40lbs line for a 3-`0 lbs fish, we use something that makes it much more exciting. If you've never tried it out, it's well worth spending a half day out on the beautiful Pacific.
Although we may not be the Deadliest Catch, our crab are just as tasty. Come see how it's all done and take a mess of crab home for dinner! You may even be able to spot whales, which tend to migrate during the same season as crab.


Austin Rivers hilariously baits his father into getting ejected from game (Video)
by TSD StaffHoliday dinners might be a bit awkward in the Rivers household from here on out, after what happened during Thursday’s Clippers-Rockets game at Toyota Center.
Both teams traded runs, but it was the Rockets who controlled the majority of the contest and came out victorious, 102-93.
And one particular moment in the game went viral, as it wasn’t a sequence of events we normally see in the NBA. Not only that, it took place between father and son.
It took place late in the fourth quarter, with Clippers head coach Doc Rivers upset at the officials, and letting them know about it. His son, Austin, who plays for the Clippers, got in his dad’s face, and began giving him the “T” motion — indicating he deserves a technical foul. He did it as a joke, but not only did Rivers get a technical, but the referees actually ejected him from the game.
Check out Austin’s reaction to it all. This tweet was perfect.
Too funny.


WITNESS LIST Infrastructure Development and Security Committee September 17, 2003 - 1:00P SB 8 FOR: Urakalo, Ana Podiatrist (Texas Podiatric Medical Association), Austin, TX ON: Behrens, Michael Executive Director (Texas Department of Transportation), Austin, TX Jackson, Bob Deputy General Counsel (Texas Department of Transportation), Austin, TX Registering, but not testifying: For: Friese, Jan Executive Director (Texas Counseling Association), Austin, TX Gionfriddo, Pam Policy Analyst (Mental Health Association), Austin, TX Providing written testimony: For: Zimmermann, Ph.D., David R. Psychologist (Self), Austin, TX


Converting sequences¶
This module provides various functions and classes to access sequences and
buffer-style objects in different ways. It also provides conversion routines
to improve the interoperability of sequences with ctypes
data types.
Providing read-write access for sequential data¶
Two classes allow you to access sequential data in different ways. The
CTypesView
provides byte-wise access to iterable objects and allows
you to convert the object representation to matching byte-widths for
ctypes
or other modules.
Depending on the the underlying object and the chosen size of each particular
item of the object, the CTypesView
allows you to operate directly
on different representations of the object’s contents.
>>> text = bytearray("Hello, I am a simple ASCII string!")
>>> ctview = CTypesView(text, itemsize=1)
>>> ctview.view[0] = 0x61
>>> print(text)
aello, I am a simple ASCII string!"
>>> ctview.to_uint16()[3] = 0x6554
>>> print(text)
aello,Te am a simple ASCII string!"
The snippet above provides a single-byte sized view on a bytearray()
object. Afterwards, the first item of the view is changed, which causes a
change on the bytearray()
, on the first item as well, since both, the
CTypesView
and the bytearray()
provide a byte-wise access to
the contents.
By using CTypesView.to_uint16()
, we change the access representation to
a 2-byte unsigned integer ctypes
pointer and change the fourth 2-byte
value, I to something else.
>>> text = bytearray("Hello, I am a simple ASCII string!")
>>> ctview = CTypesView(text, itemsize=2)
>>> ctview.view[0] = 0x61
>>> print(text)
aello, I am a simple ASCII string!"
>>> ctview.to_uint16()[3] = 0x6554
>>> print(text) aello,Te am a simple ASCII string!"
If the encapsuled object does not provide a (writable) buffer()
interface, but is iterable, the CTypesView
will create an
internal copy of the object data using Python’s array
module and
perform all operations on that copy.
>>> mylist = [18, 52, 86, 120, 154, 188, 222, 240]
>>> ctview = CTypesView(mylist, itemsize=1, docopy=True)
>>> print(ctview.object)
array('B', [18, 52, 86, 120, 154, 188, 222, 240])
>>> ctview.view[3] = 0xFF
>>> print(mylist)
[18, 52, 86, 120, 154, 188, 222, 240]
>>> print(ctview.object)
array('B', [18, 52, 86, 255, 154, 188, 222, 240])
As for directly accessible objects, you can define your own itemsize to be used. If the iterable does not provide a direct byte access to their contents, this won’t have any effect except for resizing the item widths.
>>> mylist = [18, 52, 86, 120, 154, 188, 222, 240]
>>> ctview = CTypesView(mylist, itemsize=4, docopy=True)
>>> print(ctview.object)
array('I', [18L, 52L, 86L, 120L, 154L, 188L, 222L, 240L])
Accessing data over multiple dimensions¶
The second class, MemoryView
provides an interface to access
data over multiple dimensions. You can layout and access a simple
byte stream over e.g. two or more axes, providing a greater flexibility
for functional operations and complex data.
Let’s assume, we are reading image data from a file stream into some buffer object and want to access and manipulate the image data. Images feature two axes, one being the width, the other being the height, defining a rectangular graphics area.
When we read all data from the file, we have an one-dimensional view of the
image graphics. The MemoryView
allows us to define a
two-dimensional view over the image graphics, so that we can operate on
both, rows and columns of the image.
>>> imagedata = bytearray("some 1-byte graphics data")
>>> view = MemoryView(imagedata, 1, (5, 5))
>>> print(view)
[[s, o, m, e, ], [1, -, b, y, t], [e, , g, r, a], [p, h, i, c, s], [ , d, a, t, a]]
>>> for row in view:
... print(row)
...
[s, o, m, e, ]
[1, -, b, y, t]
[e, , g, r, a]
[p, h, i, c, s]
[ , d, a, t, a]
>>> for row in view:
... row[1] = "X"
... print row
...
[s, X, m, e, ]
[1, X, b, y, t]
[e, X, g, r, a]
[p, X, i, c, s]
[ , X, a, t, a]
>>> print(imagedata)
sXme 1XbyteXgrapXics Xata
On accessing a particular dimension of a MemoryView
, a new
MemoryView
is created, if it does not access a single
element.
>>> firstrow = view[0]
>>> type(firstrow)
<class 'sdl2.ext.array.MemoryView'>
>>> type(firstrow[0])
<type 'bytearray'>
A MemoryView
features, similar to Python’s builtin
memoryview
, dimensions and strides, accessible via the
MemoryView.ndim
and MemoryView.strides
attributes.
>>> view.ndim
2
>>> view.strides
(5, 5)
The MemoryView.strides
, which have to be passed on creating a
new MemoryView
, define the layout of the data over different
dimensions. In the example above, we created a 5x5 two-dimensional view
to the image graphics.
>>> twobytes = MemoryView(imagedata, 2, (5, 1))
>>> print(twobytes)
[[sX, me, 1, Xb, yt], [eX, gr, ap, Xi, cs]]
Array API¶
-
class
sdl2.ext.
CTypesView
(obj : iterable[, itemsize=1[, docopy=False[, objsize=None]]])¶ A proxy class for byte-wise accessible data types to be used in ctypes bindings. The CTypesView provides a read-write access to arbitrary objects that are iterable.
In case the object does not provide a
buffer()
interface for direct access, the CTypesView can copy the object’s contents into an internal buffer, from which data can be retrieved, once the necessary operations have been performed.Depending on the item type stored in the iterable object, you might need to provide a certain itemsize, which denotes the size per item in bytes. The objsize argument might be necessary of iterables, for which len() does not return the correct amount of objects or is not implemented.
-
bytesize
¶ Returns the length of the encapsuled object in bytes.
Indicates, if changes on the CTypesView data effect the encapsuled object directly. if not, this means that the object was copied internally and needs to be updated by the user code outside of the CTypesView.
-
object
¶ The encapsuled object.
-
view
¶ Provides a read-write aware view of the encapsuled object data that is suitable for usage from
ctypes
.
-
to_bytes
() → ctypes.POINTER¶ Returns a byte representation of the encapsuled object. The return value allows a direct read-write access to the object data, if it is not copied. The
ctypes.POINTER()
points to an array ofctypes.c_ubyte
.
-
to_uint16
() → ctypes.POINTER¶ Returns a 16-bit representation of the encapsuled object. The return value allows a direct read-write access to the object data, if it is not copied. The
ctypes.POINTER()
points to an array ofctypes.c_ushort
.
-
to_uint32
() → ctypes.POINTER¶ Returns a 32-bit representation of the encapsuled object. The return value allows a direct read-write access to the object data, if it is not copied. The
ctypes.POINTER()
points to an array ofctypes.c_uint
.
-
to_uint64
() → ctypes.POINTER¶ Returns a 64-bit representation of the encapsuled object. The return value allows a direct read-write access to the object data, if it is not copied. The
ctypes.POINTER()
points to an array ofctypes.c_ulonglong
.
-
-
class
sdl2.ext.
MemoryView
(source : object, itemsize : int, strides : tuple[, getfunc=None[, setfunc=None[, srcsize=None]]])¶ The
MemoryView
provides a read-write access to arbitrary data objects, which can be indexed.itemsize denotes the size of a single item. strides defines the dimensions and the length (n items * itemsize) for each dimension. getfunc and setfunc are optional parameters to provide specialised read and write access to the underlying source. srcsize can be used to provide the correct source size, if
len(source)
does not return the absolute size of the source object in all dimensions.Note
The MemoryView is a pure Python-based implementation and makes heavy use of recursion for multi-dimensional access. If you aim for speed on accessing a n-dimensional object, you want to consider using a specialised library such as numpy. If you need n-dimensional access support, where such a library is not supported, or if you need to provide access to objects, which do not fulfill the requirements of that particular libray,
MemoryView
can act as solid fallback solution.-
itemsize
¶ The size of a single item in bytes.
-
ndim
¶ The number of dimensions of the
MemoryView
.
-
size
¶ The size in bytes of the underlying source object.
-
source
¶ The underlying data source.
-
strides
¶ A tuple defining the length in bytes for accessing all elements in each dimension of the
MemoryView
.
-
-
sdl2.ext.
to_ctypes
(dataseq : iterable, dtype[, mcount=0]) → array, int¶ Converts an arbitrary sequence to a ctypes array of the specified dtype and returns the ctypes array and amount of items as two-value tuple.
Raises a
TypeError
, if one or more elements in the passed sequence do not match the passed dtype.
-
sdl2.ext.
to_list
(dataseq : iterable) → list¶ Converts a ctypes array to a list.
-
sdl2.ext.
to_tuple
(dataseq : iterable) → tuple¶ Converts a ctypes array to a tuple.
-
sdl2.ext.
create_array
(obj : object, itemsize : int) → array.array¶ Creates an
array.array
based copy of the passed object. itemsize denotes the size in bytes for a single element within obj.


| simpleparse.examples.transformation | index /home/mcfletch/pylive/simpleparse/examples/transformation.py |
A simple example of parsing
I have no idea for whom I originally created this code,
(which was originally written for SimpleParse 1.0) nor
why they wanted it.
Running as a script will do some timing tests, but the
tests are rather... simplistic.
The grammar is slow parsing around 5-10% of the speed I
normally expect from SimpleParse/mxTextTools parsers.
I'm guessing it gets into lots and lots of partial parses
of the "interesting" production, and that the huge number
of reported productions slows it down. For example,
making atom non-reporting gives a 15% speedup on my
machine.
| Modules | ||||||
|
| Classes | ||||||||
|
|
| Data | ||
| declaration = "\nset := (interesting/multset/plusset)+\nmul...(set/atom),'++',(set/atom),(set/atom),(set/atom)\n" parser = <simpleparse.parser.Parser instance> |


This story is about a year and a half old now, but I have just now decided to submit it for whatever reason. I am 20 years old now and I still drink, somtimes heavily. I had drank many times before the night when this all happened and I had thrown up plenty of times as well as blacked at previously, but on this night I went way overboard.
It all started off like usual myself and my now ex-girlfriend went to a party at my friends house so I got a shitload of Avalanche.  So I agreed but as I started drinking I stopped careing and I just kept taking shots, one after the other. In about 1 - 2 hours we had drank all the Avalanche. I had no idea how many shots I took because I blacked out for a while somewhere around 13, but a friend of mine and I estimate that it was about 20.
 Then I proceeded to stagger outside onto my friends pourch which is concrete. The last thing I remember is falling forward onto my face and laying face down in my own puke.  Apparently while I was unconscious in my own vomit my friends found me and were worried about me. They carried me in and wiped my face off and watched me to make sure I didnt choke or anything.
So next day I go home and have to go out to eat with my dad and have to pretend I not hungover. My dad thought I had the shits because I kept going to the bathroom to puke at Texas Roadhouse. To sum it all up, I ended up being sick and not eating hardly anything for 4 days. I lost 10 pounds and I didnt drink for about 6 months. I couldnt eat mints or chew gum that was similar to the flavor of avalanche for a while.   The whole thing didnt really scare me, but made me realize that there is a very real possibility that on any given night I could accidently drink myself to death.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Like Skiathos, this is an attractive pine covered island, famous for its citrus fruit and plums. The main ports are Limin Skopelou (the capital) on the east coast and Loutraki on the west coast, however outside the main towns there is very little concession to tourism and this island can still feel pretty isolated.
That said, the well known 2008 film "Mamma Mia!" was filmed here, so you may find that the lovely deserted bay you are anchored in is now on the Mamma Mia tour boat itinerary.
Whilst this island has become a very popular tourist destination in recent years, Skopelos town still retains its Greek charm and is a relaxing place to visit.
Port police and customs based here.
Limin Skopelou (Skopelos)
With strong NE winds, sea conditions can make this harbour dangerous to enter. Moor stern/bows-to on the north quay, taking care not to snag the harbour chain lying on the bottom about 30m from the wall. There is space for a good number of yachts here and there are some laid moorings available.
Note that boats on the first 200 meters of the north pier get sprayed with saltwater in strong northerly winds, force 5 and over.
Now that the new, high, harbour wall is complete, shelter here has improved. However, with a strong Meltemi it can get very rough in the harbour so be sure to keep some distance off the quay. It's not dangerous, but can be uncomfortable. The holding is very good in the mud.
This harbour now has automatic service poles for water and electricity on the outer quay (16 amps). Sticks are available in the harbour kiosk at the foot of the small pier and in the first restaurant from the north pier.
Fuel can be delivered by mini-tanker (Tel: 24240 34049).
Car and passenger ferries, slow and fast (Flying Dolphins and Fast Cats), run to Volos, Alonnisos, Skyros, Skiathos, Evia and the mainland Agios Konstantinos.
Loutraki
Situated on the NW corner of the island the harbour can easily be spotted by the village on the hill above it. This harbour is busy with hydrofoils and ferries. In strong southerly winds the harbour may become untenable.
There is a pontoon for yachts installed in the NE corner of the harbour for the summer months and removed in the winter. The north side of the pontoon can be shallow. The pontoon has automatic electric and water service poles. Buy a stick to activate the service from the charter office 100m from the pontoon.
Alternatively, anchor fore and aft and take a long line ashore to the wall on the SE side of the harbour. Or free swing in the south of the harbour away from the ferry turning area.
The town is good for provisioning, with plenty of tavernas. There is a bus to Skopelos town and a ferry to neighbouring Skiathos.
Klima
Just south of Loutraki, this harbour has been expanded and yachts can moor along the NW quay. There is water and electricity and fuel by mini-tanker can be arranged. Again, not a safe haven in southerly winds.
Other Harbours/Anchorages
Panormou, midway down the west coast, is a large, deep bay. On its south side is an inlet with much better shelter. Anchor fore and aft on the western side of the inlet and take a long line ashore. Quite often katabatic winds will blow down into the inlet at night, beam onto the yachts at anchor, so make sure your anchor is well set. This is a popular spot.
Last updated May 2016.


You can trigger validation of different event combinations via
$scope.$broadcast(eventNames.validateRequest, ["default", "blur", "$beforeSaveFinal"]);
eventNames is an injectable constant to avoid misspells, but you can use a string instead.
NGDynamicValdiatorModule.constant("validationEventNames", {
validateRequest: "VALIDATE_REQUEST"
});
NGDynamicValidator listens for a $scope event named "VALIDATE_REQUEST" and triggers validation for every event name that is passed as
the second parameter - you can trigger the 'default' event this way, as well as any JS events or pseudo events (starting with $).
Validation triggered by such requests is synchronous:
$scope.$broadcast(eventNames.validateRequest, ["default", "blur", "$beforeSaveFinal"]);
//validation has finished by now
if ($scope.form.$invalid) {
...
}
An array of strings with specific validationRequestGroups can be passed as a third parameter to the $broadcast / $emit. If you do - only inputs belonging to at least one of those groups will validate.
Combined validation summary for specific events will be available on the form controller as showcased by
ng-disabled="form.$invalidEventsSummary.default
|| form.$invalidEventsSummary.blur
|| form.$invalidEventsSummary.$beforeSaveDraft"
and on each input controller on that form, much in the same way as $error of vanilla angular. $error is also maintained. The difference between
the two is that $error agregates validity status from $setValidity, for which NGDynamicValidator uses names of the validator functions, and
$invalidEventsSummary agregates this information for different event names. In both cases, one function from an array returning false invalidates
the entire $setValidity key / event name.
Do note the propagation model of angular events and make sure that the right scope receives them.


Buyapowa is a social commerce business. We think social should be making you money, not costing it. Our unique e-commerce model helps you to get your social audience shopping, by giving them a genuine level of control alongside real entertainment and genuine social currency. We call the model Co-buying, and it works.


MINA is a digital Web platform of archaeological investigations in post-colonial India (currently 1953-1960). Produced as a project for the Institute on Digital Archaeology Method and Practice, the platform aims to encourage students to employ digital methods in archaeology and to promote scholarly interest in the archaeology of India and neighbouring South Asian nations.
Interact! Use the zoom buttons to explore a specific area. Click on an individual marker to learn more on each investigation.
Locations (i.e. latitude and longitude) are accurate to roughly 1 degree or 100 km


Gets SYSTEM privileges with one of two methods.
Does not need Admin
https://github.com/rapid7/meterpreter/blob/2a891a79001fc43cb25475cc43bced9449e7dc37/source/extensions/priv/server/elevate/namedpipe.c',
'https://github.com/obscuresec/shmoocon/blob/master/Invoke-TwitterBot',
'http://blog.cobaltstrike.com/2014/04/02/what-happens-when-i-type-getsystem/',
'http://clymb3r.wordpress.com/2013/11/03/powershell-and-token-impersonation/
Technique to use:
'NamedPipe' for service named pipe impersonation
or
'Token' for adjust token privs.
moduleSource = self.mainMenu.installPath + "/data/module_source/privesc/Get-System.ps1


I'd like to start by saying that I didn't initially take Ambien to trip, I used it as a sleep aid and became dependent on it.
Two years ago, I had sleeping problems. I didn't know why, and I never saw a doctor for it. I just couldn't sleep and it drove me crazy. Once I found out about Tylenol PM, I took that because it helped aid my sleep. Then once the bottle of Tylenol was empty, I found my dad's prescription Abmien. I did some quick research and decided to try it.
Let me tell you, Ambien gave me the best sleep ever. I would wake up so relaxed and felt energized the next day. I started taking reasonable doses almost every night, like between 10 ang 20mg.
It was inevitable that having taken it so much for sleep that I would eventually realize how it made me feel when I was awake and on Ambien. I smoked pot frequently at that time, and I liked the way I felt on Ambien. It made me feel light, carefree, (tired), and hazy.
So, the one night I decided that instead of just going to sleep after eating my Ambien, I would instead go to my basement and have fun while on the drug. Take advantage of the way I feel, rather than sleep it off. BIG MISTAKE.
Details here are very hazy. I took 4 10mg Ambiens that night, more than I usually had taken, but still didn't seem like too much to me. I wanted to feel good, then have the best sleep of my life.
The last thing I remember was sitting on my couch watching TV. My next memory jumps to me waking up in the ER in a scary hospital bed, and my dad walking over to me saying 'So what other drugs do you take?'
Apparently what happened was that I tried to make it upstairs to my room on the third floor sometime in the middle of the night, and I collapsed on the second staircase.  While half-conscious in the ER, I admitted to them that I had taken abunch of Ambien, though I couldn't remember exactly how many. Still being under the guidance of my parents, I was pretty fucked.
I was sent to weekly therapy after that, and my parents don't seem to look at me the same anymore. It WASN'T worth it, and I will avoid Ambien and stick to my Celestial Seasons Tea that puts me to sleep the same.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Classes
The following classes are available globally.
-
Cache is used internally for various tasks such as in-memory caching image data. Primarily used in UIImageView|NSImageView|WKInterfaceImage and Image (SwiftUI) helpers.
See moreDeclaration
Swift
public final class Cache
-
This class is used to create instances of Repository that can be used to start requests based on the given endpoint.
See moreDeclaration
Swift
public final class Client<Repository> where Repository : EndpointProtocol
-
A configuration class that can be used with Environment, Repository, Endpoint and Request. A configuration object follows the following rules:
- When a Configuration object is passed to an Environment, each Repository with its Endpoints and requests will inherit this configuration.
- When a Configuration object is passed to Repository, all its Endpoints and Requests will inherit this configuration.
Declaration
Swift
public final class Configuration
extension Configuration: NSCopying
-
Endpoint configuration class which is used in EndpointProtocol implementations.
See moreDeclaration
Swift
public final class EndpointConfiguration
-
The Environment contains information about host, port, configuration and it’s used in Request instances.
See moreDeclaration
Swift
open class Environment
-
Inherit this class as to create your transformers. You should pass FromType and ToType (generic types) in your subclass definition.
See moreDeclaration
Swift
open class Transformer<FromType, ToType> : NSObject, TransformerProtocol
-
This class is inherited by Request to control the execution of the request.
See moreDeclaration
Swift
public class Operation : Foundation.Operation
-
Use this class to create custom queues.
See moreDeclaration
Swift
public final class Queue : OperationQueue
-
Adds Reachability supportt
See moreDeclaration
Swift
open class Reachability


Grasp Planner Example¶
In this part of the tutorial we will reference the scene that we have generated in the Workcell Builder Example
If you currently do not have a working perception system, you can still test out the package using a rosbag located in /workcell_ws/src/easy_manipulation_deployment/grasp_planner/rosbag/perception_simulator.zip
The perception rosbag is an simple box as shown below.
Unzip Perception Rosbag¶
Before starting, if you are planning to use the perception simulator to test out this package, first unzip the folder in the /grasp_planner/rosbag/ folder.
sudo apt-get install unzip
unzip ~/workcell_ws/src/easy_manipulation_deployment/grasp_planner/rosbag/perception_simulator.zip -d ~/workcell_ws/src/easy_manipulation_deployment/grasp_planner/rosbag/
Set up end effector parameters¶
The end effector we are currently using is the Robotiq 2F-85 gripper, thus we need to set the configuration files before running the grasp planner. In the configuration file /workcell_ws/src/easy_manipulation_deployment/grasp_planner/config/attributes.yaml
, replace the contents with the following:
end_effector:
type: finger
attributes:
fingers: 2
distance_between_fingers: 100
longest_gripper_dim: 25
table_height: 450
parameters:
min_zero_angle: 0.01
min_height_diff_to_grip: 5
min_gdi_diff_for_comparison: 5
note that the parameters like table_height
is specific to this current ROSbag, and should be changed accordingly for your own set up. more information about the other parameters, check out Grasp Planner Configuration
Running the grasp planner¶
For this part of the example you need 2 terminals.
In Terminal 1:
cd ~/workcell_ws
source /opt/ros/foxy/setup.bash
colcon build
source install/setup.bash
ros2 run grasp_planning grasp_planning_node
You should then see the output in terminal 1:
[easy_manipulation_deployment][Grasp Planner] Waiting for topic....
In Terminal 2:
cd ~/workcell_ws
source /opt/ros/foxy/setup.bash
colcon build
source install/setup.bash
ros2 bag play src/easy_manipulation_deployment/grasp_planner/rosbag/perception_simulator/perception_simulator/perception_simulator.db3
You should then see the output in terminal 2:
[INFO] [1610531624.795616932] [rosbag2_storage]: Opened database 'src/easy_manipulation_deployment/grasp_planner/rosbag/perception_simulator/perception_simulator/perception_simulator.db3' for READ_ONLY.
In Terminal 1, you should then see the following
[easy_manipulation_deployment][Grasp Planner] Objects Detected!
[easy_manipulation_deployment][Grasp Planner] Gripper Type Detected: finger
If a valid grasp is found, the grasp planner will also show the grasp quality of the grasp selected
[easy_manipulation_deployment][Grasp Planner] Best Grasp found! GDI Score: 2941
The grasp planner will now be publishing the grasp poses for the object to be used in the grasp_execution
stage of the manipulation pipeline.
Next we will look at how to visually check these grasp poses before running the grasp execution: Grasp Validator Example


The team was able to discuss issues and work to be done for an hour and fifteen minutes during class. Topics included setting up AWS and issues incountered with git.


I'm currently studying hard to became a computer programmer, passionate about coding with HTML, Javascript and Python
Technologies I really like to work with
In facebook or Github


- 432 Million Smartphones
- 352 million Android (81.7%)
- 77 million iOS (17.9%)
- other OS (0.3%)
* Samsung
* LG
* Sony
* Huawei
* Xiaomi, Oppo, Vivo and many more..
- Hardware
- Customizable
* Andy Rubin *
* Chris White *
* Nick Sears *
* Rich Miner *
Palo Alto, California
October 2003
Open Handset Alliance
October 10, 2007
July 2005
$50 Million
String employeeName;
int studentNumber;
boolean isLegalAge;
double salaryTax;
Camel Casing
public class Employee {
}
public class MainActivity {
}
public interface Callback {
}
Pascal Casing
private double calculateDeductions(int salary) {
double netPay;
return netPay;
}
public void showStatus {
Log.d("Status", "Single");
}
private boolean isEmpty(String name) {
return true/false;
}
Camel Casing
“It's harder to read code than to write it.”
- Joel Spolsky
- proper use of logs
|
Loloy Marquez |
|
John Frederick Marquez |
|
<EMAIL_ADDRESS>|


public class ProductionVehicleDAO extends ProductionDAO<Vehicle> implements VehicleDAO
| Constructor and Description |
|---|
ProductionVehicleDAO(org.hibernate.Session session) |
| Modifier and Type | Method and Description |
|---|---|
Filter<Vehicle> |
afterProductionDate(java.time.LocalDate productionDate)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles which are produced after the given date.
|
Filter<Vehicle> |
atLeastMileage(int mileage)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles which have at least the given mileage.
|
Filter<Vehicle> |
atProductionDate(java.time.LocalDate productionDate)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles which are produced at the given date.
|
Filter<Vehicle> |
beforeProductionDate(java.time.LocalDate productionDate)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles which are produced before the given date.
|
Filter<Vehicle> |
byBrand(java.lang.String brandName)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles which have the given brand.
|
Filter<Vehicle> |
byCustomer(Customer customer)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles in fleets owned by the given Customer.
|
Filter<Vehicle> |
byFleet(Fleet fleet)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles in the given fleet.
|
Filter<Vehicle> |
byLicensePlate(java.lang.String licensePlate)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles which have the given licensePlate.
|
Filter<Vehicle> |
byModel(java.lang.String model)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles which have the given model.
|
Filter<Vehicle> |
byType(VehicleType type)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles which are the given type.
|
Filter<Vehicle> |
maxMileage(int mileage)
Returns a Filter to use in ListFiltered in this class, which returns all Vehicles which have the given mileage or less.
|
void |
validateVehicles(java.util.Collection<Vehicle> vehicles)
Checks if every vehicle in the collection uses the right constraints
|
create, get, listFiltered, remove, update
public void validateVehicles(java.util.Collection<Vehicle> vehicles) throws ConstraintViolationException
VehicleDAO
validateVehicles
in interface VehicleDAO
ConstraintViolationException
public Filter<Vehicle> byBrand(java.lang.String brandName)
VehicleDAO
byBrand
in interface VehicleDAO
brandName
- The brand to use in the filterpublic Filter<Vehicle> byModel(java.lang.String model)
VehicleDAO
byModel
in interface VehicleDAO
model
- The model to use in the filterpublic Filter<Vehicle> byLicensePlate(java.lang.String licensePlate)
VehicleDAO
byLicensePlate
in interface VehicleDAO
licensePlate
- The licensePlate to use in the filterpublic Filter<Vehicle> atProductionDate(java.time.LocalDate productionDate)
VehicleDAO
atProductionDate
in interface VehicleDAO
productionDate
- The productionDate to use in the filterpublic Filter<Vehicle> beforeProductionDate(java.time.LocalDate productionDate)
VehicleDAO
beforeProductionDate
in interface VehicleDAO
productionDate
- The productionDate to use in the filterpublic Filter<Vehicle> afterProductionDate(java.time.LocalDate productionDate)
VehicleDAO
afterProductionDate
in interface VehicleDAO
productionDate
- The productionDate to use in the filterpublic Filter<Vehicle> atLeastMileage(int mileage)
VehicleDAO
atLeastMileage
in interface VehicleDAO
mileage
- The productionDate to use in the filterpublic Filter<Vehicle> maxMileage(int mileage)
VehicleDAO
maxMileage
in interface VehicleDAO
mileage
- The productionDate to use in the filterpublic Filter<Vehicle> byType(VehicleType type)
VehicleDAO
byType
in interface VehicleDAO
type
- The type to use in the filterpublic Filter<Vehicle> byFleet(Fleet fleet)
VehicleDAO
byFleet
in interface VehicleDAO
fleet
- The fleet to use in the filterpublic Filter<Vehicle> byCustomer(Customer customer)
VehicleDAO
byCustomer
in interface VehicleDAO
customer
- The customer to use in the filter


Welcome to my own personal site. Here you will find various info about me.
Welcome to my own personal site. Here you will find various info about me.
I am 22-years-old from Pittsburgh, PA in my senior year at Point Park University. I am majoring in Information Technology an plan to use the skills learned in the major to make my own and help make video games after I graduate. I also know the Japanese language quite well, whih will help me on my dream to work at Nintendo of Japan.
| Interests | Courses | ||||
| Video Games | Anime | Music | BMGT 221 | BMGT 417 | CMPS 262 |
| Streaming/Recording | Japanese |
Sonic Adenture DX - Part 1
| Show | Hide |
Sonic Adenture DX - Part 2
| Show | Hide |


Developed by:
Vladislav Kubyshkin
Template designed by:
Reza Shintia Dewi
PAUSED...
Hi Dribbble!
Reza Shintia Dewi
0:00
0:00


head: "blocks/multiple.html"
header: "prepend-block-multiple.html"
header: "blocks/multiple.html"
body: "prepend-block-multiple.html"
body: "blocks/multiple.html"
footer: "prepend-block-multiple.html"
footer: "blocks/multiple.html"
foot: "blocks/multiple.html"


Game theory and decision theory
Posted: 2017-02-12 , Modified: 2017-02-12
Tags: ai safety, game theory, decision theory
Posted: 2017-02-12 , Modified: 2017-02-12
Tags: ai safety, game theory, decision theory
Papers on game theory/decision theory:
But what are the available actions? And what are the counterfactual universes correspond- ing to what “would happen” if an action “were taken”?
(A deterministic agent could only have taken one action.)
To fully describe the problem faced by intelligent agents making decisions, it is necessary to provide an idealized procedure which takes a description of an environment and one of the agents within, and identifies the best action available to that agent
Evidential blackmail: AI researcher knows whether the AI will lose $150 mil from an investment (scandal). If either is true, the AI researcher sends the info and asks for $100 mil:
Conditioned on refusing, loses $150 mil.
Counterfactual blackmail: Developer has developed computer virus which would cause both to lose $150 mil. Once deployed, only way to prevent activation 1 day later is to wire $100 mil. Researcher would only deploy if quite sure agent will pay.
CDT prescribes that an agent resist certain attempts to improve its decision procedures.
Retro blackmail: AI researcher has access to original source code. Can self-modify after researcher acquires original source code but before researcher decides whether to deploy.
Self-modify to not give in to demands.
But CDT and any decision procedure to which CDT would self-modify would lose money.
CDT: Alas, the virus has been deployed. I would have preferred that the virus not be deployed, but since it has been, I must now decide whether or not to pay up. Paying up is bad, but refusing is worse, so I’ll pay up.
Policy selection: The optimal policy is to refuse to pay up upon observing that the virus has been deployed. I now observe that the virus has been deployed. Therefore, I refuse to pay.
Consider Prisoner’s dilemma when
However, CDT evaluates actions according to a physical counterfactual where the action is changed but everything causally separated from the action is held constant. It is not the physical output of the agent’s hardware which must be modified to construct a counterfactual, it is the logical output of the agent’s decision algorithm.
Cf. \(10 \E a - a\).
UDT chooses the best action according to a world-model which represents not only causal relationships in the world, but also the logical effects of algorithms upon the world.
How to encode logical relations in graph? Underspecified: constructing graph is difficult. Graph for UDT further requires some ability to identify and separate “algorithms” from the physical processes that implement them. How is UDT supposed to recognize that the agent and its opponent implement the same algorithm?
To illustrate, consider UDT identify- ing the best action available to an agent playing a Pris- oner’s Dilemma against an opponent that does exactly the same thing as the agent 80% of the time, and takes the opposite action otherwise.
(Problem seems to be identifying what something is doing logically - it might be obfuscated. Also, graph loses info from algorithm.)
Evaluate logical implications of the agent’s algorithm selecting the policy \(\pi\).
Graph is unnecessary: the environment itself is an algorithm.
evaluates policies by searching for formal proofs, using some mathematical theory such as Peano Arithmetic (PA), of how much utility is attained in the world-model if A() selects the policy \(\pi\).
But: requires halting oracle. can only identify the best policy if there exists a proof that ex- ecuting that policy leads to a good outcome
Problem in stochastic environments (? if can model prior, seems ok)
Ex. agent uses UDT, play game with human. If numbers written sum to \(\$10\), each paid, else 0.
UDT misidentifies best policy:
Human: I don’t quite know how UDT works, but I remember hearing that it’s a very powerful predictor. So if I decide to write down 9, then it will predict this, and it will decide to write 1. Therefore, I can write down 9 without fear.
But agent with superior predictive power loses to the dumber agent! Human’s lack of power to predict UDT gives an advantage!
Problem: not guaranteed to work. As soon as proof-based UDT proves that an agent will not take a certain policy, it concludes that taking that policy leads to the best possible outcome (because from a contradiction, anything follows).
(I don’t get this…) \[ A() = UDT(\ce{E()}, \ce{A()}). \]
(What is an embedding of an agent?)
Why Lob? The short answer is that this theorem illustrates the basickind of self-reference involved when an algorithm considers its own output as part of theuniverse, and it is thus germane to many kinds of research involving self-modifying agents,especially when formal verification is involved or when we want to cleanly prove things inmodel problems.
Problem: How can Deep Thought 1.0 build Deep Thought 2.0 with guarantee of good consequences?
DT1 can’t actually figure out what actions DT2 is going to take. Naively, it seems as if it should be enough for DT1 toknow that DT2 has the same goals as DT1, that DT2’s deductions are reliable, and that DT2 only takes actions that it deduces to have good consequences on balance.
If we tryand model DT1 and DT2 as proving statements in two formal systems (one stronger thanthe other), then the only way that DT1 can make such a statement about DT2’s reliability is if DT1 (and thus both) are in fact unreliable!
One (anachronistic) way of stating Godel’s key insight is that you can use computer pro-grams to search for proofs, and you can prove statements about computer programs
This makes it more intuitive that we can embed self-reference in mathematics, because we can embed self-reference in computer code!
Quines: programs can be made which perform arbitrary tasks using their own source code we can have a program G which refers to itself in this way, and searches for proofs inarithmetic related to its own source code.
G searches for a proof of “G runs forever”. G never finds a proof, but we can never prove that G runs forever.
We can add “G never halts” or “G halts” as a new axiom.
First incompleteness. If the theory of arithmetic is consistent, then there exist undecidable statements in the theory of arithmetic.
Lob’s Theorem:
Intuition: if \(L(X)\) were provable for every \(X\), you can prove consistency.
Also a finite version of Lob’s theorem, controls length of proof of \(\phi\) in terms of \(\square \phi\to \phi\).
DT1 trusts DT2: if the action is deduced to be good, then it must actually be good.
(It can’t even trust itself?)
a simple agent with a utility function will only create successors whose formal systems are strictly weaker thanits own, since only those are fully trusted by the current system.
References: Tiling Agents for Self-Modifying AI, and the Lobian Obstacle [18] and Problems of self-reference in self-improving space-time embedded intelligence [9].
algorithm and theirs get to read the opponent’s source code, calculate for as long as they like, and then play only once.
Cooperate iff source code identical: fragile.
FairBot: search through all proofs of length \(\le N\) to see if valid proofs of \(X(FairBot)=C\). If yes, output \(C\).
Intuitively, it seems like both mutual cooperation and mutual defection are stable fixed points of the situation. However, a Lobian statement breaks the deadlock in favor of cooperation!
Proof: L(“FairBot(FairBot)=C”) follows. By Lob, there must be a proof of FairBot(FairBot=C). (! This is a case where Lob actually helps prove something!)
See Program Equilibrium in the Prisoner’s Dilemma via Lob’s Theorem.
Note on model:
When agent and universe are quined, you can’t “run the universe” on \(A\) or \(A'\). You search for proofs of \(A()=x\to U()=c\). Problem: ordering matters. If you prove \(x\) is better you choose \(x\), even though it could be worse, because \(A()=\neg x\to U()=c\) is true for any \(c\).
(Why can’t extensional work? Search for proofs in universe quined with \(A'\), then choose \(A'\).)
careful ordering of which proofs it pays attention to, and that agent can be shown to make thecorrect decisions (given enough deductive power). The idea was originally due to Benja Fal-lenstein; Tsvi Benson-Tilson’s paper UDT with Known Search Order
the same theory can have many models, some of them not at all what you were thinking of when you made the axioms. Notes: to get Peano Arithmetic need
There are models of Peano arithmetic where G holds, and other models where G fails to hold. In Robinson arithmetic “Bob” might satisfy the formula G is checking. In PA nonstandard models are weirder.
The key to understanding these is that G never halts at any finite number, but we can’t actually define in our formal language what “finite” means. Thenonstandard models of Peano Arithmetic are those which have all the usual numbers butalso lots of extra numbers that are “too large” to ever be written as lots of S’s followed by an O, but which nonetheless are swept along in any inductive statement.
Remark: nonstandard analysis
Ponder for a moment the formal system which has all the axioms of PA, plus the axiom that PA is consistent, plus the axiom that “the systemwhich has all the axioms of PA, plus the axiom that PA is consistent” is inconsistent. As it turns out, this is a perfectly consistent system (What happens if you take the recursively axiomatizable “consistency\({}^n\), \(n\in \N\)?)
We might want to endorse some particular model as the “true” one (for instance, our standard model of the natural numbers, without all of those weird nonstandard numbers), and say that logical statements are true if they hold in that model and false if they don’t. This truth predicate exists outside the language, and so the logical statements can’t talk about the truth predicate, only about weaker notions like provability.
The trouble comes when we try to construct a language that contains its own truth predicate such that \(T(\phi)\lra \phi\). \(T(X)\lra T(\neg X)\).
if P isn’t allowed to make exact statements about its own values, but only arbitrarily precise approximations, then everything can work out consistently.
P can’t rule out the possibility that reciprocals of nonstandard natural numbers (infinitesimals) exist.
followup paper by Christiano on computationally tractable approxima-tions of probabilistic logic: Non-Omniscience, Probabilistic Inference, and Metamathematics
We’re interested in a particular modal logic that constitutes a reflection of Lobian phenomena in PA, etc.
GL axiom: \(\square (\square A \to A)\to \square A\).
(What about things like \(\forall x, \square P(x)\)?)
some special cases where there are efficient algorithms for deducing provability in GL.
All sorts of formulas that refer to themselves and each other by quining. Formulas \(p\lra \phi(p,q_1,\ldots, q_k)\) modalized in \(p\): every \(p\) in \(\phi\) is in scope of some \(\square\).
When \(p\) equivalent to formula modalized in \(p\), then \(p\) is equivalent to something which doesn’t use \(p\). Godel statement \(\lra\) inconsistency of arithmetic \(\square (p\lra \square \neg p) \lra \square (p\lra \square \perp)\).
formula is provable in the modal logic if and only if a corresponding property holds for every Kripke frame in that class.
Robust Cooperation in the Prisoner’s Dilemma: Program Equilibrium via Provability Logic.
One embarrassing thing about FairBot is that it doesn’t check whether its potential cooperation would actually make any difference. (ex. it cooperates against a rock.)
Ex. PrudentBot
if we assume infinite computational power (i.e. the ability to consult a halting oracle about proof searches in Peano Arithmetic), then they can be written out as simple statements in modal logic
Find what happens using efficient algorithm!
Modal agents of rank 0: \(p\lra \phi(p,q)\), modalized in both \(p\) and \(q\) (don’t run, only prove). Equivalent to something \(p\lra \phi(q)\).


Dock panel provides basic docking functionalities and it resizes its children automatically. Following example displays nested Dock Panels, you can resize browser to see how resizing works perfectly on all browsers. It also resizes correctly while orientation of mobile devices changes.
Dock properties are not bindable as of now.
Note: AtomDockPanel should only be used for top level docking, as it consumes lots of resources, using too many dockpanels will slow down the browser.


Omnia
Description
Omnia contains a huge collection of handy extensions for your everyday project. Inspired by utility and standard libraries from many other languages such as Kotlin, Ruby, Javascript, Haskell, … Omnia implements the most necessary extensions
For other sweets
- Anchors ⚓ Declarative, extensible, powerful Auto Layout
- EasyClosure 🍩 Unified communication pattern for iOS and macOS
- Smile 😄 Emoji in Swift
- Wave 🌊 Declarative chainable animations in Swift
- EasyStash 🗳Easy data persistence in Swift
Usage
Shared
Date
DateiOS, tvOS
- UIView
- UIViewController
- UITableView
- UICollectionView
- NSLayoutConstraint
- UIFont
- UIImage
- UIScrollView
- UIColor
macOS
- NSColor
- NSView
- NSButton
Installation
Omnia is available through CocoaPods. To install it, simply add the following line to your Podfile:
pod 'Omnia'
Omnia is also available through Carthage. To install just write into your Cartfile:
github "onmyway133/Omnia"
Omnia can also be installed manually. Just download and drop Sources
folders in your project.
Author
Khoa Pham<EMAIL_ADDRESS>License
Omnia is available under the MIT license. See the LICENSE file for more info.


Before Change
self.is_train = is_train
def __iter__(self):
paths = cycle(self._paths) if self.is_train else self._paths
for path in paths:
cur_dataset = torch.load(path)
logger.info("Loading dataset from %s, number of examples: %d" %
(path, len(cur_dataset)))
cur_dataset.fields = self.fields
cur_iter = OrderedIterator(
dataset=cur_dataset,
batch_size=self.batch_size,
batch_size_fn=self.batch_size_fn,
device=self.device,
train=self.is_train,
sort=False,
sort_within_batch=True,
repeat=False
)
for batch in cur_iter:
yield batch
cur_dataset.examples = None
gc.collect()
del cur_dataset
gc.collect()
def max_tok_len(new, count, sofar):
In token batching scheme, the number of sequences is limited
such that the total number of src/tgt tokens (including padding)
After Change
for batch in self._iter_dataset(path):
yield batch
num_batches += 1
if self.is_train and not self.repeat and \
num_batches % self.num_batches_multiple != 0:
// When the dataset is not repeated, we might need to ensure that
// the number of returned batches is the multiple of a given value.
// This is important for multi GPU training to ensure that all
// workers have the same number of batches to process.
for path in paths:
for batch in self._iter_dataset(path):
yield batch
num_batches += 1
if num_batches % self.num_batches_multiple == 0:
return
def max_tok_len(new, count, sofar):
In token batching scheme, the number of sequences is limited
such that the total number of src/tgt tokens (including padding)


Scrum Beers
We meet discuss how to improve the agile process in our different organizations over a pint of beer.
Berlin
Germany
Gothenburg
Sweden
Halifax
Canada
Örebro
Sweden


WWWoffle bookmarklets
- Delete
- Delete document from cache.
- Refresh
- Refresh document in cache.
- Refresh options
- Show document refresh options.
- Monitor
- Monitor document.
- Index
- Show cache index for this URL.
- Fetch
- Fetch selected URL or prompt for a URL
if none is selected.
- Show cache info
- Show cache info for this document.
Requires a replacement AddCacheInfo.html
in WWWOFFLE message directory.


LONDON: Uber Loses Its Licence To Operate
by Peace FM OnlineUber has been stripped of its licence to operate in London after failing to stop unlicensed and uninsured drivers picking up thousands of passengers.
Transport for London (TfL) found that at least 14,000 trips were made with drivers who were different to the ones shown on the app.
This was due to a system change allowing unauthorised drivers to upload their photographs to legitimate Uber driver accounts, the transport body said.
Featured Video


| Quote | Author |
|---|---|
| 'I'm not going to school just for the academics - I wanted to share ideas, to be around people who are passionate about learning.' | Emma Watson |
| 'Remember there's no such thing as a small act of kindness. Every act creates a ripple with no logical end.' | Scott Adams |
| 'Intelligence plus character-that is the goal of true education.' | Martin Luther King, Jr. |


obviousx.io.weka
Classes
ObviousWekaInstance
ObviousWekaInstances
ObviousWekaInstances.ObviousWekaAttStats
ObviousWekaLoader
ObviousWekaUtils


e
Academy
1
materia
2
3
4
Materia
¿What subject do you want to teach?
Select just one material for any subject you want to teach.
Type in the search bar
Or use our list to find it:
AQUI VA LA LISTA
Level
Which levels do you teach?
Primary
Secondary
GCSE
AS Level
A Level
BTEC
Adult education
Undergraduate
Masters
Diploma
SDoctorate
Continue


Remount with noatime option during backup?
When this option is enabled, the filesystem being backed up will be remounted with the noatime option before the backup starts. This prevents the file reads done by the backup process from updating the last-accessed time of each file, and thus reduces disk activity.
When the backup is complete, the original filesystem mount options will be restored.


PropertyPro
Home
Listing
Sign up
Sign in
Search Properties
Filter by deals
For Sale
For Rent
Filtered by type
2 Bedroom
3 Bedroom
«
1
2
3
4
5
6
»


This component models a spring damper combination that can lift off. It can be connected between a sliding mass and the housing (model Fixed), to describe the contact of a sliding mass with the housing.
As long as s_rel > s_rel0, no force is exerted (s_rel = flange_b.s - flange_a.s). If s_rel ≤ s_rel0, the contact force is basically computed with a linear spring/damper characteristic. With parameter n≥1 (exponent of spring force), a nonlinear spring force can be modeled:
desiredContactForce = c*|s_rel - s_rel0|^n + d*der(s_rel)
Note, Hertzian contact is described by:
The above force law leads to the following difficulties:
In the literature there are several proposals to fix problem (2). Especially, often the following model is used (see, e.g., Lankarani, Nikravesh: Continuous Contact Force Models for Impact Analysis in Multibody Systems, Nonlinear Dynamics 5, pp. 193-207, 1994, pdf-download):
f = c*s_rel^n + (d*s_rel^n)*der(s_rel)
However, this and other models proposed in literature violate issue (1), i.e., unphysical pulling forces can occur (if d*der(s_rel) becomes large enough). Note, if the force law is of the form "f = f_c + f_d", then a necessary condition is that |f_d| ≤ |f_c|, otherwise (1) and (2) are violated. For this reason, the most simplest approach is used in the ElastoGap model to fix both problems by using this necessary condition in the force law directly. If s_rel0 = 0, the equations are:
if s_rel ≥ 0 then f = 0; // contact force else f_c = -c*|s_rel|^n; // contact spring force (Hertzian contact force) f_d2 = d*der(s_rel); // linear contact damper force f_d = if f_d2 < f_c then f_c else if f_d2 > -f_c then -f_c else f_d2; // bounded damper force f = f_c + f_d; // contact force end if;
Note, since |f_d| ≤ |f_c|, pulling forces cannot occur and the contact force is always continuous, especially around the start of the penetration at s_rel = s_rel0.
In the next figure, a typical simulation with the ElastoGap model is shown (Examples.ElastoGap) where the different effects are visualized:


Name:
Type:
CHINESE
MEXICAN
ITALIAN
Status:
OPEN
CLOSE
Location width:
Location height:
Address:
Number:
City:
Post code:
Select menager:
Add new Manager
Grade:
Slika:
Save restaurant
Back
Add new manager:
Name:
Lastname:
Gender:
Male
Female
Birthday:
Username:
Password:
Password repeat:
Cancel


About Gracenote Data Objects (GDOs)
The primary goal of any GNSDK application is to recognize media elements and access their metadata. When an application performs a query, Gracenote returns metadata about the target query element, such as the title and genre of an album element. In addition, information about the query operation is returned, such as its timestamp, the start and end range of the query results, and the number of additional results available from Gracenote.
GNSDK stores the information returned by a query within containers known as Gracenote Data Objects (GDOs). The contents of a GDO depends on:
- The kind of query and the search criteria, such as a CD TOC lookup, a fingerprint lookup, text lookup, and so on
- The query target element
- Information about the target element available from Gracenote
A GDO can contain values and/or other, related GDOs. GDOs have two purposes: Containers to access metadata returned from Gracenote, and as input to queries to retrieve additional metadata and other GDOs.
GDOs facilitate a key feature of GNSDK – interoperability between all of the Gracenote products and services. Results from one Gracenote query can be used as an input for another. For example, a MusicID result can immediately be used as an input for the Link module without the need for any application intervention. This interoperability is possible for nearly all combinations of Gracenote Services.
GDO Types
Every GDO has a type. For example, when an application performs a query to identify an Album, Gracenote returns a GDO of type Album. Therefore, for most applications, you can infer a GDO’s type based on the target element of the query and knowing the underlying data model for the element.
If needed, your application can get the type of a GDO. For example, your application might request a GDO’s type to confirm it matches the intended type.
Another use case is analyzing the results of a general text lookup. This kind of query can return multiple GDOs of different types. The application needs to process the results to determine which GDO is the best response to the query.
Response GDOs and Child GDOs
It is important to note that every identification query returns a response GDO. A response GDO is a higher-level GDO typically containing these fields (from album response GDO):
- Match references (child GDOs)
- Needs decision flag (see Matches That Require Decisions)
- Matches range start
- Matches range end
- Total number of matches
As noted, a response GDO contains references to 0-n matches encapsulated in child GDOs. A child GDO is just like any other GDO once it is retrieved. It is not dependent on its parent GDO and has the same behaviors and features of other GDOs. The fact that it was once a child of another GDO does not matter.
For example, an Album response GDO can contain child GDOs of type Track, or Artist, other Albums, and so on. A child GDO can contain its own child GDOs, such as Tracks, Artists, or Contributors, and so on.
A GDO’s child objects are enumerated with an ordinal index, starting from 1 (not 0) for the first child object. Queries for child objects take this index as input.
Child Keys and Value Keys
To extract metadata from a GDO, or get a child GDO, your application must use defined keys. There are two kinds of keys: Value and Child.
- Value Key—Used to extract a specific piece of metadata from a GDO, for example GNSDK_GDO_VALUE_ALBUM_LABEL.
- Child Key—Used to get a child GDO, for example, GNSDK_GDO_CHILD_TRACK.
Full and Partial Metadata Results
A response GDO from a query can return 0-n matches (child GDOs). These child GDOs can contain either full or partial metadata results. A partial result is a subset of the full metadata available, but enough to perform additional processing. One common use case is to present a subset of the partial results (for example, album titles) to the end user to make a selection. Once a selection is made, you can then do a secondary query to get the full results (if desired).
Note that, in many cases, the data contained in a partial result is more than enough for most users and applications. For a list of values returned in a partial result, see the Data Model in the GNSDK C API Reference.
Online and Local Database Queries
Applications that have an online connection can query Gracenote for information. Applications without an online connection, such as embedded applications used for stereo head units in cars, can instead query a Gracenote local database.
In general, local database queries return full GDO results, even when the response contains multiple matches.
Some online queries return partial GDO results containing just enough information to identify the matches. Using this information, the application can perform additional queries to obtain more information. In general, your application should always test GDO results to determine they are full or partial.
Matches That Require Decisions
When your application makes an identification query, Gracenote returns a response GDO with one of the following:
- No GDO match
- Single GDO match
- Multiple GDO matches
In all cases, Gracenote returns high-confidence results based on the identification criteria. However, even high-confidence results could require additional decisions from a user.
In such cases, GNSDK sets NEEDS_DECISION to true, indicating that the end user should select the the final result. The application should present users with the returned match(es) and allow them to select (or reject) the match.
In general, GDO responses that need a decision by a user are:
- Any multiple match response
- Any single match response that Gracenote determines needs a decision by a user. Even though a single match might be good match candidate, Gracenote might determine that it is not quite perfect, based on the quality of the match and/or the criteria used to identify the match.
About the Text Match Score
GNSDK provides a score (0-100) for text matches (accessible using the GNSDK_GDO_VALUE_TEXT_MATCH_SCORE value key for an Album GDO) based on comparing the input text to the corresponding field in the match text. This score is not an indicator of text match quality.
For example, the result text could be substantially different from the input text because the input text contained a lot of incorrect information. Such a response indicates that the results have an amount of ambiguity that the application must resolve.
About Video Product TOC Matches
VideoID uses several methods to perform TOC matches. This combination of matching methods enables client applications to accurately recognize media in a variety of situations.
| Match Type | Description |
Decision Needed? |
|---|---|---|
|
Exact Match |
Only one Product matches the DVD or Blu-ray TOC. |
No |
|
Multiple-Exact Match |
Multiple Product matches exist for the DVD or Blu-ray TOC. |
Yes |
|
An exact TOC match is not found, but one or more Product titles match. |
Yes |
|
|
Fuzzy Match |
Match for media that has slight, known, and acceptable variations from well-recognized media. |
Yes |


{{ serviceName }}
{{ releaseVersion | log }}
Mid-ESA application |
|
Consent managementExploring a management service showing user consents for sharing data across Govt departments. |
|
Version 5Simple flow offering an "ongoing consent" question at the end allowing us to test users reactions to and understanding of this. |
|
Version 4First attempt at a NHS-centric experience. Allowing users to see all their sick notes and allowing them to share them with DWP individually. |
|
Version 3Opposite to V1. Asking for a lot of extra information to test what people's reaction is to having to give this as part of the process. The Yes/No questions around NHS number and NI number in particular are focussed on gathering users reactions rather than representing a realistic experience. |
|
Version 2Introduces Verify as an identity step and shows the user their sick note for them to confirm it's correct before "sending". |
|
Version 1A deliberately unrealistically simple version to test our assumption that users expect us to share data with almost no interaction or oversight from them. |


BEMOSS Applications
Illuminance based lighting control (IBLC)
Illuminance based lighting control is a close loop control, aiming at intelligently adjust the lights' brightness according to the ambiant light level while maintaining a constant illuminance. A lighting device and a sensor is needed to use this application.
Fault Detection
This app will look at temperature history of a thermostat, as well as outdoor temperature history, and if it detects that the current temperature profile is anamolous it will generate a notification to alert the user about the abnormal behavior.
{% if available_thermostats %}{% else %} There are no thermostat available for fault detection. {% endif %}
Existing Applications (Click app ID to see detail.)
| App ID | Type | Status | Description | Started Time | Delete |
|---|---|---|---|---|---|
| {{ app.app_agent_id }} | {{ app.app_type.app_name }} | {{ app.status }} | {% if app.app_type.app_name == 'iblc' %} {% if 'target' not in app.app_data %} Target illuminance not set. {% else %} Target illuminance set to {{ app.app_data|get_item_in_dict:'target' }} Lux. {% endif %} {% else %} {{ app.app_data.description }} {% endif %} | {{ app.start_time | as_local_time}} | X |


Email<EMAIL_ADDRESS>Trans Lunar Designs Incorporated 564 Worthington Road Chester Springs, PA 19425-2411


Scroll down ▾
Text about building on Ann Street. Text about building on Ann Street. Text about building on Ann Street. Text about building on Ann Street.
Text about building on Hopkins Street.
Text about building on Hopkins Street.
Text about building on Hopkins Street.
Text about building on Hopkins Street.
Source credit to come
Text about building on Hopkins Street.
Text about building on Hopkins Street.
Text about building on Hopkins Street.
Text about building on Hopkins Street.
Source credit to come
Text about building on Forest Street.
Text about building on Forest Street.
Text about building on Forest Street.
Text about building on Forest Street.
Text about building on Forest Street.
Source credit to come


Macros reference¶
Variables and macros can be used in templates (see the Jinja Templating section)
The following come for free out of the box with Airflow.
Additional custom macros can be added globally through ORM Extensions, or at a DAG level through the DAG.user_defined_macros
argument.
Default Variables¶
The Airflow engine passes a few variables by default that are accessible in all templates
Variable |
Description |
|---|---|
|
the execution date as |
|
the execution date as |
|
the previous execution date as |
|
the previous execution date as |
|
the next execution date as |
|
the next execution date as |
|
the day before the execution date as |
|
the day before the execution date as |
|
the day after the execution date as |
|
the day after the execution date as |
|
same as |
|
same as |
|
same as |
|
the execution_date (pendulum.Pendulum) |
|
the previous execution date (if available) (pendulum.Pendulum) |
|
execution date from prior succesful dag run (if available) (pendulum.Pendulum) |
|
start date from prior successful dag run (if available) (pendulum.Pendulum) |
|
the next execution date (pendulum.Pendulum) |
|
the DAG object |
|
the Task object |
|
a reference to the macros package, described below |
|
the task_instance object |
|
same as |
|
same as |
|
same as |
|
a reference to the user-defined params dictionary which can be overridden by
the dictionary passed through |
|
global defined variables represented as a dictionary |
|
global defined variables represented as a dictionary with deserialized JSON object, append the path to the key within the JSON object |
|
a unique, human-readable key to the task instance
formatted |
|
the full configuration object located at
|
|
the |
|
a reference to the DagRun object |
|
whether the task instance was called using the CLI’s test subcommand |
Note that you can access the object’s attributes and methods with simple
dot notation. Here are some examples of what is possible:
{{ task.owner }}
, {{ task.task_id }}
, {{ ti.hostname }}
, …
Refer to the models documentation for more information on the objects’
attributes and methods.
The var
template variable allows you to access variables defined in Airflow’s
UI. You can access them as either plain-text or JSON. If you use JSON, you are
also able to walk nested structures, such as dictionaries like:
{{ var.json.my_dict_var.key1 }}
Macros¶
Macros are a way to expose objects to your templates and live under the
macros
namespace in your templates.
A few commonly used libraries and methods are made available.
Variable |
Description |
|---|---|
|
The standard lib’s |
|
The standard lib’s |
|
A reference to the |
|
The standard lib’s |
|
The standard lib’s |
|
The standard lib’s |
Some airflow specific macros are also defined:
-
airflow.macros.
datetime_diff_for_humans
(dt, since=None)[source]¶ Return a human-readable/approximate difference between two datetimes, or one and now.
-
airflow.macros.
ds_add
(ds, days)[source]¶ Add or subtract days from a YYYY-MM-DD
- Parameters
>>> ds_add('2015-01-01', 5) '2015-01-06' >>> ds_add('2015-01-06', -5) '2015-01-01'
-
airflow.macros.
ds_format
(ds, input_format, output_format)[source]¶ Takes an input string and outputs another string as specified in the output format
- Parameters
>>> ds_format('2015-01-01', "%Y-%m-%d", "%m-%d-%y") '01-01-15' >>> ds_format('1/5/2015', "%m/%d/%Y", "%Y-%m-%d") '2015-01-05'
-
airflow.macros.
random
() → x in the interval [0, 1).¶
-
airflow.macros.hive.
closest_ds_partition
(table, ds, before=True, schema='default', metastore_conn_id='metastore_default')[source]¶ This function finds the date in a list closest to the target date. An optional parameter can be given to get the closest before or after.
- Parameters
table (str) – A hive table name
ds (list[datetime.date]) – A datestamp
%Y-%m-%d
e.g.yyyy-mm-dd
before (bool or None) – closest before (True), after (False) or either side of ds
- Returns
The closest date
- Return type
>>> tbl = 'airflow.static_babynames_partitioned' >>> closest_ds_partition(tbl, '2015-01-02') '2015-01-01'
-
airflow.macros.hive.
max_partition
(table, schema='default', field=None, filter_map=None, metastore_conn_id='metastore_default')[source]¶ Gets the max partition for a table.
- Parameters
schema (str) – The hive schema the table lives in
table (str) – The hive table you are interested in, supports the dot notation as in “my_database.my_table”, if a dot is found, the schema param is disregarded
metastore_conn_id (str) – The hive connection you are interested in. If your default is set you don’t need to use this parameter.
filter_map (map) – partition_key:partition_value map used for partition filtering, e.g. {‘key1’: ‘value1’, ‘key2’: ‘value2’}. Only partitions matching all partition_key:partition_value pairs will be considered as candidates of max partition.
field (str) – the field to get the max value from. If there’s only one partition field, this will be inferred
>>> max_partition('airflow.static_babynames_partitioned') '2015-01-01'


{% extends "base.html" %} {% block body %} {% if form.errors %}
Your username and password didn't match. Please try again.
Don't have an account? Register now.


{{template "header" .}}
I used to use CI in PHP and tornado in Python, there are both lightweight, so they has following advantages:
beego uses Go HTTP server underlying to serve each request in its own goroutine and logic processes without affecting others. Low pay of goroutines gives you ability to build high-concurrency web applications, like RESTful API services.
beego provides session support in memory, files, Redis or MySQL and cache support in memory, Redis or Memcache. For simple usage, Beego also has thread-safe map for storing temporary data during the runtime.
beego gives developers freedom to replace or integrate default modules with customized implementations. Beego's modular design allows you only use features that your apps need, no binding.
beego is an open-source, high-performance app framework for the Go programming language. This web framework has been already used for building web servers and tools in SNDA's CDN system.
Edit, save, and refresh. Bee tool compiles your code and templates when you're coding. Developers should put more focus on how to make things work not wait.
Including intelligent routing, parameters filtering, templating, caching, validation, session, logging, hot upgrade, and safe map. All of these just need few lines of code.
beego builds on top of the Go HTTP server, which was recently benchmarked to serve three to ten times as many requests as Rails across a variety of loads.
我以前经常用 PHP 的 CI 框架和 python 的 tornado 框架，这些框架都是非常轻量级的，轻量级就有利于我们：
beego 底层使用 Go HTTP 服务器 来为每个请求创建一个 goroutine 与相应的逻辑处理程序，从而使得每个请求都各自独立、互不影响。这也同时为您创造了构建高并发 Web 应用程序的条件，例如：RESTful API 服务.
beego 支持将 Session 存储在内存、文件、Redis 或 MySQL 中，也支持将 缓存 存储在内存、Redis 或 Memcache 内。对于一些简单的数据存储，beego 同样提供线程安全的 Map 在运行时存放您的临时数据。
beego 给予了开发者们使用自定义实现的模块来替换或集成默认模块的自由。beego 的模块化设计允许您仅使用您的应用程序需要的功能，抛弃多余的部分。
beego 是一个开源的、高性能的 Go 语言 应用框架。该框架已经被实际应用于构建盛大 CDN 系统的 Web 服务器。


SCIM-enabled Identity Providers (IdPs) can automate provisioning of GitHub organization membership.
Custom media type: cloud-9-preview
cloud-9-preview


Press left or right to scroll through the weeks. Much of this information has been collated from various news articles and sources. The accuracy should be taken with a slight pinch of salt. For feedback on data improvements try github or tweet me @simon_b_johnson.


Helping Libraries Transform
To enrich lives and communities through universal access to knowledge lifelong learning, and literacy.
To develop strong library branches that are vital community destinations for knowledge, inspiration, innovation, and renewal.
Leadership and Innovation: Provide excellence in library services through open communication, team work and cooperation, respect and recognition, education and learning, and by incorporating innovative solutions in a quality library environment.
Information and Literacy: We believe in the importance of informed and literate communities, and we provide services and programs to support lifelong learning.
Tradition and Technology: We value the tradition of the library and the legacy of the printed word, and embrace the benefits that modern technology provides.


My Hobbies
I love playing the instruments such as bass guitar, electric guitar and also acoustic guitar and in which I can play or create a song
I also love coding and to play football


Welcome to HeadWay! The ultimate multi platform application, that helps people find a new look look or create one. We are dedicated to keeping this application simple and free. HeadWay is completely free of charge and has a (WePay)payment system which Hair Stylist, or Hair Dressers may use process payments. More details on the payment system can be found at www.wepay.com.
In order to register for HeadWay, just go hit enter or any register button that is available. Once you have interact with any of those buttons you will be given, two options.
In order to leave comments and make appointments with barbers, you must register and confirm your account via email
Once you chose this option, you can create a hair dresser account. This allows you to add services, add a payment system, and get and confirm appointments. You can also upload 1 photo that best represents you


Some Flask App
Start address
End address
Date and Time
Query
From: {{ start }}
To: {{ end }}
At: {{ date }}
Start Location (Lat/Long lookup)
Date and time - DONE
Search radius
Optional Destination (Lat/Long lookup)


Package : 7 nights 8 days MV Mahabaahu Downstream Cruise
Vessel : M V Mahabaahu
Neamati (7) to Guwahati (1)
7 Nights/8 Days
Sibsagar: Ahom kingdom, a meal with a tea planter family - Majauli: Vaishwanite monastery, two cultural dances by monks and folk dancers - Mishing tribal village - Boat safari - Biswanath Ghat: Silk weaver's village, Tea plantation - Silghat: Tea factory with cultural dances - Kaziranga National Park: jeep and elephant safaris, Agrarian Bangladeshi immigrant village, Umananda Temple, Kamakhya Temple


{% extends "base.html" %} {% block title %}About{% endblock %} {% block content %} About A File Integrity Monitoring system based on OSSEC Feel free to contact us at<EMAIL_ADDRESS>{% endblock content %}
Feel free to contact us at nguyenvanthong.uit@gmail.com


##Propositional Logic
Branch of logic that studies the ways of joining entire propositions, statements or sentences to form more complicated propositions, statements or sentences.
The simplest statements are indivisible units and hence propositional logic does not study these logical properties and relations that depends on these statements.
Symbolized reasoning in which each sentence or statement is broken into a subject and a predictate. The predicate modifies or defines the properties of the subject. In FOL, a predicate only refers to a single subject. FOL is known as first order predicte calculus.
There exist quantifiers to help define first order logic statements, as well as operators.


Ratings: 'The Voice' Reclaims Monday's Demo With No New '9-1-1' as Competition
CBS comedies “The Neighborhood” and “Bob Hearts Abishola” hit season highs in viewers
by Jennifer MaasNBC’s “The Voice” is back on top as the king of Monday primetime thanks to Fox’s decision to air back-to-back reruns of “9-1-1” and “Prodigal Son” last night, instead of new episodes.
Meanwhile, CBS’ freshman comedy “Bob Hearts Abishola” and sophomore sitcom “The Neighborhood” both hit season highs in total viewers, while the Max Greenfield-led series also scored its highest rating for Season 2 in the key adults 18-49 range.
NBC was first in ratings with a 1.0/4 in the advertiser-coveted 18-49 demographic and second in total viewers with an average of 6.3 million, according to preliminary numbers. At 8 p.m., “The Voice” Top 13 performances episode scored a 1.2/5 and 7.7 million viewers. “Bluff City Law” at 10 settled for a 0.5/3 and 3.6 million viewers.
Also Read:
ABC was second in ratings with a 0.9/4 and first in viewers with 6.8 million. At 8, “Dancing With the Stars” posted a 0.9/4 and 7.1 million viewers. At 10, “The Good Doctor” held that 0.9/4 and received 6.1 million viewers.
CBS was third in ratings with a 0.7/3 and in viewers with 5.8 million. At 8, “The Neighborhood” put up a 1.0/5 and 6.6 million viewers. At 8:30, “Bob Hearts Abishola” took a 0.8/4 and 6.1 million viewers. At 9, “All Rise” managed a 0.6/2 and 5.3 million viewers. “Bull” at 10 had a 0.6/3 and 5.8 million viewers.
Fox and Telemundo tied for fourth in ratings, each with a 0.5/2. Fox was fourth in viewers with 2.3 million, airing all reruns. Telemundo was fifth with 1.4 million.
Also Read:
Univision was sixth in ratings with a 0.4/2 and in viewers with 1.3 million.
The CW was seventh in ratings with a 0.2/1 and in viewers with 655,000. At 8, “All American” got a 0.2/1 and 706,000 viewers. At 9, “Black Lightning” had a 0.2/1 and 604,000 viewers.


Yes, but only when you are initially creating it.
The id
and text
properties are required on each object, and these are the properties that Select2 uses for the internal data objects. Any additional paramters passed in with data objects will be included on the data objects that Select2 exposes.
Because Select2 falls back to an <optgroup>
when creating nested options, only a single level of nesting can be supported.
<option>
tags being created?
The data
option is a shortcut that Select2 provides which allows you to load options into your select
from a data array.
id
for their unique identifiers, what can I do?
You can re-map your identifier before passing it to Select2.
text
for the text that needs to be displayed
These can also be re-mapped.


This hack was brought to you with the help of the Yeoman Angular generator.
Saving us the effort of making it look respectable
Compiled stylesheets to make your life easier
It's like Javascript, but groovier...
AngularJS is a toolset for building the framework most suited to your application development.
Spectacular Test Runner for JavaScript.
{{thing}}


Read our April 11 update on the COVID-19 Pandemic.
Learn more.
DOVETAIL
INT.
MEET THE TEAM
ALLISON
CEO / NATURE TRAVELLER
FAVE SPOT:
THAILAND
STEVEN
CFO / DIVING ENTHUSIAST
FAVE SPOT:
BALI
JESS
CMO / FOODIE TRAVELLER
FAVE SPOT:
BORA BORA
TOM
CXO / THRILL SEEKER
FAVE SPOT:
DUBAI


Ploror688.github.io
Answers - The Most Trusted Place for Answering Life's. Lost in NYC: A Subway Adventure: A TOON Graphic (TOON Graphics) Nadja Spiegelman, Sergio García Sánchez on Amazon.com. FREE shipping on qualifying offers. WATCH GUARD 24/7 has strong roots in law enforcement and provides security and private investigation services throughout NY, NJ, and CT. With an attention to detail. Training Topics : Audiences : Capacity and Outreach: Languages: NYC Training Collaborative on Early Intervention workshop about the NYC EI program
Home Page - PS184 (01M184) - Shuang Wen School 雙文學校.
Homework Help and Textbook Solutions bartleby. NYC's top Tutors, Summer Camps, Preschool, Gifted Talented and Test Prep programs for children ages 2-14. Brooklyn, Staten Island, Queens, Manhattan.
Learn Data Science in 12 weeks in New York City! Read reviews of NYC Data Science Academy and do your research. July 16, 2015. NEW YORK-Mayor Bill de Blasio and Counsel to the Mayor Maya Wiley announced today a first-of-its kind investment by the administration to bring. College homework help - High School College University. If you’re in desperate need of some motivation, we’re going to fix that problem, right here, right now. Prepare yourself. WARNING: I’m going to swear. Welcome to Education Resources : Trainings Available. PSAT and CUNY SAT REQUIREMENTS. My College QuickStart is an online college and career planning kit available free of charge to all students who take the PSAT/NMSQT. My sidewalk chalk blog. There are lots of reports about locations for new public or charter school programs. Online homework help for college students. Professional academic assignments writing editing proofreading. We provide best academic assistance on a huge variety.
NYC Parking Ticket Required Elements Redux. NYC Data Science Academy Reviews Course Report. Welcome to THE HUB for Yoga Higher Ed! . 200HR TT NYC MAR 8-MAY
The UFT sponsors a homework help program for elementary and middle school students called Dial-A-Teacher. Classroom teachers answer homework questions at<PHONE_NUMBER>. On-line math problem solver that will solve and explain your math Thesis Statement Writing Help step-by-step. Forget about the following troublesome questions:
Required elements are NYC parking ticket gold. Here's how a misdescribed required element resulted in a dismissal of a parking ticket. PS184 (01M184) - Shuang Wen School 雙文學校 A Chinese English Dual Language New York City Public School, Pre-K to 8th Grade 紐約市公立中英雙語學校. Mayor de Blasio Announces up to $10 Million Investment. Free step-by-step solutions to Algebra 1 Common Core<PHONE_NUMBER>485) - Slader.
Food Bloggers Instagram NYC Foodie Accounts To Follow.
The Course to Better Grades. With a little help from experts, you’ll be on your way up, pronto. Here’s how it works. Search We scour the world for brainiac.
FTKny.com - Tutors, STEAM Preschool Summer Dial-A-Teacher United Federation of Teachers. Free step-by-step solutions to Stewart Calculus: Early Transcendentals<PHONE_NUMBER>909) - Slader. Whether you're shopping for a classic Japanese sleeve or a small, tongue-in-cheek symbol, New York affords you the chance to get inked WATCH GUARD 24/7 Security Guards, Private Investigators. Algebra 1 Common Core<PHONE_NUMBER>485) - Slader. Need Some Motivation Right Now? Read This IMMEDIATELY. In a city of never-ending menus and food trends, it can be hard to figure out the next best brunch spot, lunchtime haunt, or recipe to master. However You guys. I’m tired. First of all, the job is going great. Having to go to work everyday is a gigantic life change, and trying to juggle it with school is making. Cascades High School – (01M650) New York City Department. The 11 Best Tattoo Shops In NYC: Gothamist.
Answers.com is the place to go to get the answers you need and to ask the questions
Stewart Calculus - Homework Help and Answers :: Slader.
Phonics Homework Helper provides children in first grade with extra help in learning important phonics skills. Packed full of fun-to-do activities and appealing.
Thesis Statement Writing Help Essays Book Notes.


MenuOptions - input masks and multi-column autocomplete combined
Example of a simple menu (positioned at bottom)
Menu


HW02 Plus
• Nordic52832 with BT4.2 BLE
• 0.42” Blue OLED HQ Screen
• Support Android4.4+ or iOS 8.0+
• PAI (Personal Activity Intelligence)
• 24hrs Heart Rate Tracking
• High-End Double Color Design
• Premium Metallic Brand Buckle
HW02
• Nordic52832 with BT4.2 BLE
• 0.42” Blue OLED HQ Screen
• Support Android4.4+ or iOS 8.0+
• 24hrs Heart Rate Tracking
• High-End Double Color Design
• Hidden Display Screen
• Soft and Durable TPU Strap
• Premium Metallic Brand Buckle
HW01 Plus
• Nordic52832 with BT4.2 BLE
• 0.87” OLED HQ Screen
• Support Android4.4+ or iOS 8.0+
• PAI (Personal Activity Intelligence)
• 24hrs Heart Rate Tracking
• Detachable TPU Strap Design
• Convenient USB Direct Plug
• Scratch Proof IML Display
HW01 Pro
• Nordic52832 with BT4.2 BLE
• 0.87” OLED HQ Screen
• Support Android4.4+ or iOS 8.0+
• 24hrs HeartRate monitor
• Detachable TPU Strap Design
• Convenient USB Direct Plug
• Scratch Proof IML Display
HW01
• Nordic52832 with BT4.2 BLE
• 0.91” OLED HQ Screen
• Support Android4.4+ or iOS 8.0+
• 24hrs HeartRate monitor
• Sport & Auto Sleep Detection


First page
Back
Continue Last page
Overview
Image
WASM – ports
Projects
https://github.com/kripken/sql.js
SQLite compiled to JavaScript through Emscripten
https://github.com/jvail/spatiasql.js
https://github.com/kripken/llvm.js
LLVM compiled to WASM using Emscripten
https://github.com/Kagami/ffmpeg.js
https://github.com/KnicKnic/WASM-ImageMagick
https://github.com/richardassar/zpipe
https://github.com/libgit2/libgit2/pull/4400
Git inside the browser
Doom 3 (The web IS Doom!)
http://www.continuation-labs.com/projects/d3wasm/
https://github.com/torch2424/wasmBoy
Gameboy Emulator Library written in Web Assembly using AssemblyScript.
6.