Geocenter Motion: A Mind-Boggling Phenomenon
Let’s face it, understanding the concept of ‘geocenter motion’ is not for the faint-hearted. On a good day it is only confusing, while on bad days we would rather avoid the subject all together. The story below is meant to clear the fog somewhat for those of you who have more bad days than good days. We additionally provide links to recently calculated geocenter motion time series, for those of you who want to get started with real data.
Editorial note: This is repost of a blog entry which originally appeared in 2011 on the website of the DFG priority program “Mass distribution and mass transport in the Earth system”. Several colleagues indicated that the website is offline as of October 2018, but that they were still looking for the post. For that reason I’m reposting the original post here. Some minor edits have been made and links to more recent geocenter motion updates are provided. But besides that it reflects the original post.
The ‘motion’ part
As far as the ‘motion’ part is considered, we pretty much all agree that motion has a direction, arising from a starting point and an end point. So let’s ask ourselves a question: what are the actual starting and endpoints of the ‘geocenter motion’? Would you dare to answer this question in a group of highly respected scientific peers? (insert uncomfortable silence here…). Although deceivingly simple, the question is not so easy to answer, but it is the key to understanding the whole concept.
The ‘geocenter’ part
Let’s speculate about the endpoint, which we denote by the so-called ‘geocenter’. By ‘center’ we mean the mass center of the ‘geo’ (Earth) system. This encompasses the solid part of the Earth, the ocean, the atmosphere, ice sheets, water in rivers and lakes, and a whole lot of other stuff we can think of. In literature it is commonly denoted as center of common mass (CM) and it is a natural choice of origin for satellite-based reference systems.
What is causing this so-called motion of the geocenter?
Imagine, we move some masses (e.g. water or ice) from point A to B over the Earth’s surface. What will happen with the above defined geocenter (CM)? Well pretty much nothing! From the conservation of linear momentum we know that the center of mass must remain stationary (see figure 1 right) in an inertial frame. On the contrary, the solid part of the Earth will move, and actually deforms a little at the same time. Since we don’t usually find ourselves sitting in an inertial frame, the CM will appear to move in our frame of choice (e.g. Figure 1 left).
Observer’s perspective
As the starting point of the geocenter motion, we have the freedom to choose a convenient origin of our reference frame. Commonly, scientists tend to bolt down their fancy measurement equipment to firm rocks, which are rigidly connected to the Earth’s crust. For global comparisons of such measurements a natural choice would be to use a reference system which has its origin in the center of figure (CF) of the Earth. Alternatively, one could use an origin which is the center of mass of the solid Earth (CE). The origins CE and CF slightly differ from each other as the masses in the Earth are not homogeneously distributed.
Measuring geocenter motion
In order to observe the relative motion of CM-CF we need data which link the solid part of the Earth to a space-based reference frame. Observed ranges between Earth-fixed permanent GPS stations and GPS satellites, may provide this link. Alternatively (or in combination), we may deduce the geocenter motion by observing the shape changes (deformations) of the solid Earth. Using an elastic model of the Earth we may derive the surface loading which actually caused such deformations. When we additionally apply the conservation of linear momentum, providing us with the link between the solid Earth and space, we can calculate the associated CM-CF shift.
The latter approach has been used in a study together with the use of GRACE gravimetry and bottom pressure variations from the Finite Ocean Sea-Ice model (FESOM) (Rietbroek et al. 2012). Alternatively, a combination of GRACE and radar altimetry also allows for the resolving of geocenter motion time series. Time series of the observed geocenter motion can be seen in Fig. 2.
At least for me, it is rather difficult to eyeball the X,Y, Z plot from Fig. 2 and determine where the geocenter offset is actually pointing to. Therefore, a more intuitive plot of the seasonal geocenter motion is shown in Fig. 3. There, we can see that the magnitude of the offset between the CM and CF varies between 2 and 5 mm. The regions which are mostly affected by the geocenter motion can be found on an approximate track crossing Central Asia, Europe, South America and Australia. At the same time the antipode will be equally affected but in the opposite direction.
Space-based gravimetry
The Gravity Recovery and Climate Experiment (GRACE) constitutes of 2 satellites which map the time-varying gravity field of the Earth from space. In the satellite frame, the changes in gravity due to geocenter motion are zero. One particularly interesting observable to extract from the GRACE data is surface loading. This is the (surface) density of a thin layer containing Ocean/Atmosphere and hydrosphere, which exerts pressure on the crust of the Earth. Surface loading is also commonly expressed in the height of an equivalent layer of water.
Now here is the problem: In order to calculate surface loading from GRACE we need to change the observer’s perspective from the space-based frame to the center of figure frame. This boils down to changing the so-called degree 1 Stokes (Potential) coefficients by external ones. Only then we are allowed to further process the potential coefficients to obtain surface loading.
In other words, ignoring the geocenter motion in GRACE (cf. observing from the space-based frame) will provide us with surface loading estimates which have no real physical meaning. In terms of loading, the impact of the geocenter motion is much larger than the actual shift of the CM and CF itself. A geocenter motion shift of 4 mm will cause changes in equivalent water height of around 2 cm.
References and Further information
- Rietbroek, R., Fritsche, M., Brunnabend, S.E., Daras, I., Kusche, J., Schröter, J., Flechtner, F., Dietrich, R., 2012. Global surface mass from a new combination of GRACE, modelled OBP and reprocessed GPS data. Journal of Geodynamics 59–60, 64–71. https://doi.org/10.1016/j.jog.2011.02.003
- Rietbroek, R., Brunnabend, S.-E., Kusche, J., Schröter, J., Dahle, C., 2016. Revisiting the Contemporary Sea Level Budget on Global and Regional Scales. Proceedings of the National Academy of Sciences 201519132. https://doi.org/10.1073/pnas.1519132113
- Sun, Y., Ditmar, P., Riva, R., 2017. Statistically optimal estimation of degree-1 and C20 coefficients based on GRACE data and an ocean bottom pressure model. Geophysical Journal International.
- Swenson, S., Chambers, D., Wahr, J., 2008. Estimating geocenter variations from a combination of GRACE and ocean model output. J. Geophys. Res. 113. https://doi.org/10.1029/2007JB005338
- Geocenter motion for separate components (update from Rietbroek et al. 2016)
- Geocenter motion estimates generated by Yu (Jade) Sun and colleagues
- Geocenter motion estimates based on the method of Swenson et al. 2008 (Tellus website)


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Class Needle
Class Needle
java.lang.Object
|
+----Needle
-
public class
Needle
-
extends Object
This maintain a Needle (or a hand).
-
Version:
-
1.0 29 November 1995
-
Author:
-
Nils Hedström
-
Needle(Color, Color, int, int, int, int, int, int, double)
-
Constructs a needle.
-
drawNeedle(Graphics)
-
Draws the needle on a graphics object.
-
setAngle(double)
-
Sets the angle of the needle.
Needle
public Needle(Color in_color,
Color in_border,
int in_type,
int in_width,
int in_centerx,
int in_centery,
int in_lengthx,
int in_lengthy,
double in_angle)
-
Constructs a needle.
-
Parameters:
-
in_color
-
The color
-
in_border
-
The color of the border (the border is 1 pixel wide)
-
in_type
-
The type (0=no Needle, 1=Triangle, 2=Rectangle, 3=Romb)
-
in_width
-
The witdh
-
in_centerx
-
The x-cord of the center
-
in_centery
-
The y-cord of the center
-
in_lengthx
-
The length in the x-axis
-
in_lengthy
-
The lenght in the y-axis
-
in_angle
-
The angle (0=To the right, pi=To the left)
setAngle
public void setAngle(double in_angle)
-
Sets the angle of the needle.
-
Parameters:
-
in_angle
-
The needle's new angle. (0=To the right, pi/2 Up, pi=Left, 3pi/2 Down)
drawNeedle
public void drawNeedle(Graphics g)
-
Draws the needle on a graphics object.
-
Parameters:
-
g
-
The graphics object which the needle will be drawn upon.


http://w3af.org/
https://github.com/andresriancho/w3af
w3af is a Web Application Attack and Audit Framework. The project’s goal is to create a framework to help you secure your web applications by finding and exploiting all web application vulnerabilities.
Our framework is proudly developed using Python to be easy to use and extend, and licensed under GPLv2.0
w3af - Web Application Attack and Audit Framework
w3af is an open source web application security scanner which helps developers and penetration testers identify and exploit vulnerabilities in their web applications.
The scanner is able to identify 200+ vulnerabilities, including Cross-Site Scripting, SQL injection and OS commanding.
Contributing
Pull requests are always welcome! If you're not sure where to start, please take a look at the First steps as a contributor document in our wiki. All contributions, no matter how small, are welcome.
Links and documentation
w3af's main site
Project documentation


|
CameraFrame
|
Gets a frame from the given camera in szParam2 format
|
|
CloseCamera
|
Gets a frame from the given camera in szParam2 format
|
|
CloseObject
|
Closes an object opened with LoadObject
|
|
CloseRecognize
|
Close a recognize handle opened by FaceRecognize
|
|
FaceRecognize
|
Recognizes a face and returns its index
|
|
LoadObject
|
Loads xml file describing an object
|
|
ObjectDetect
|
Detects an object and returns an array
|
|
OpenCamera
|
Opens camera number nParam1
|
|
ReadCamera
|
Gets a frame from the given nParam1 camera index after a delay of nParam3 milliseconds
|


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a
w
s
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Control the player
r
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zoko


Play as a tiny frog with a big attitude as he crosses traffic!
Play this zany game where you run away from a yeti!
This classic game will teach you the basics of building!


Represents the size of each dimension in a tensor.
More...
#include <shape_gpu.h>
|
| | ShapeGPU () |
| | Constructs a default shape. More...
|
| |
| | ShapeGPU (std::initializer_list< int > il) |
| | Constructs a shape. More...
|
| |
|
| ShapeGPU (const ShapeGPU &shape) |
| |
|
| ShapeGPU (const Shape &shape) |
| |
|
__host__ __device__ void | updateStrides () |
| |
|
__host__ __device__ void | set (int i, int dim) |
| |
| __host__ __device__ int | dim (int i) |
| | Gets a reference to the int representing the size of the i th dimension represented by this object. More...
|
| |
| __host__ __device__ int | dim (int i) const |
| | Gets the size of the i th dimension represented by this object. More...
|
| |
| __host__ __device__ int | operator[] (int i) |
| | Gets a reference to the int representing the size of the i th dimension represented by this object. More...
|
| |
| __host__ __device__ int | operator[] (int i) const |
| | Gets the size of the i th dimension represented by this object. More...
|
| |
|
__host__ __device__ int | stride (int i) const |
| |
|
__host__ __device__ int | bstride (int i) const |
| |
| __host__ __device__ size_t | size () const |
| | Gets the number of dimensions represented by this object. More...
|
| |
| __host__ __device__ int | elements () const |
| | Gets the total number of elements in a tensor of this shape. More...
|
| |
|
__host__ __device__ int | index (int *d) const |
| |
|
__host__ __device__ int | bindex (int *d) const |
| |
|
__host__ __device__ void | dims (int i, int *d) const |
| |
|
int * | begin () |
| | Gets a pointer to an int that specifies the size of the first dimension represented by this object.
|
| |
|
int * | end () |
| | Gets a pointer to an int that specifies the size of the last dimension represented by this object.
|
| |
|
const int * | begin () const |
| | Gets a const pointer to an int that specifies the size of the first dimension represented by this object.
|
| |
|
const int * | end () const |
| | Gets a const pointer to an int that specifies the size of the last dimension represented by this object.
|
| |
|
__host__ __device__ bool | operator== (const ShapeGPU &other) const |
| |
|
__host__ __device__ bool | operator!= (const ShapeGPU &other) const |
| |
Represents the size of each dimension in a tensor.
Note: this class currently is hard-coded to four dimensions.
◆ ShapeGPU() [1/2]
| marian::ShapeGPU::ShapeGPU |
( |
| ) |
|
|
inline |
Constructs a default shape.
This default shape has four dimensions. The size of each dimension is 1.
◆ ShapeGPU() [2/2]
| marian::ShapeGPU::ShapeGPU |
( |
std::initializer_list< int > |
il | ) |
|
|
inline |
Constructs a shape.
- Parameters
-
| i | A list of integers representing the size of each dimension. |
◆ dim() [1/2]
| __host__ __device__ int marian::ShapeGPU::dim |
( |
int |
i | ) |
|
|
inline |
Gets a reference to the int representing the size of the i
th dimension represented by this object.
- Returns
- a reference to the int representing the size of the
i
th dimension represented by this object
◆ dim() [2/2]
| __host__ __device__ int marian::ShapeGPU::dim |
( |
int |
i | ) |
const |
|
inline |
Gets the size of the i
th dimension represented by this object.
- Returns
- the size of the
i
th dimension represented by this object
◆ elements()
| __host__ __device__ int marian::ShapeGPU::elements |
( |
| ) |
const |
|
inline |
Gets the total number of elements in a tensor of this shape.
For example, if this shape represents a 5x100 tensor, this method would return 500.
- Returns
- the total number of elements in a tensor of this shape
◆ operator[]() [1/2]
| __host__ __device__ int marian::ShapeGPU::operator[] |
( |
int |
i | ) |
|
|
inline |
Gets a reference to the int representing the size of the i
th dimension represented by this object.
- Returns
- a reference to the int representing the size of the
i
th dimension represented by this object
◆ operator[]() [2/2]
| __host__ __device__ int marian::ShapeGPU::operator[] |
( |
int |
i | ) |
const |
|
inline |
Gets the size of the i
th dimension represented by this object.
- Returns
- the size of the
i
th dimension represented by this object
◆ size()
| __host__ __device__ size_t marian::ShapeGPU::size |
( |
| ) |
const |
|
inline |
Gets the number of dimensions represented by this object.
- Returns
- the number of dimensions represented by this object
The documentation for this struct was generated from the following file:


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Trait
Breif description of the trait and how it relates to your company/business.
Trait
Breif description of the trait and how it relates to your company/business.
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Breif description of the trait and how it relates to your company/business.
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Breif description of the trait and how it relates to your company/business.


Full Stack Web Developer
Web Developer from University of Toronto Full Stack Web Development Bootcamp. Skills in Javascript, node.js, React, MongoDB and mySQL.
I’m a practical learner who loves learning through projects and getting hands dirty with codes. My technical skills combined with my experience in collaborative, deadline-driven environments make me a strong addition to your team.


--- author: Andre Charland categories: app date: '2010-11-30 00:57:15' link: http://blogs.nitobi.com/andre/index.php/2010/11/29/phonegap-at-android-only-in-sweden/ permalink: /2010/11/30/phonegap-at-android-only-in-sweden-12/ slug: phonegap-at-android-only-in-sweden-12 status: publish title: PhoneGap at Android Only in Sweden type: post format: html ---
Check out Brian Leroux's great preso at Android Only in Sweden earlier this fall.
Android Only 2010 - PhoneGap: Not only Android from Peter Svensson on Vimeo.
We're stoked to see more interest from Europe! Brian will be back in Stockholm in February for jFokus. Let us know if you'd like to meetup!


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Innocentin:
Here's the Map and key he left behind. Take them and find out what he was searching for.
"Tell me more about the Map and key."


Muwie is a movie based search engine. Muwie is build by the combination of different APIs. Muwie provide information about movie you search. In this Web, you will find the information of movie you search. Thats all about Muwie, so what are you waiting for, go and hit that search button.
The all information you see on the Muwie web is provided by the omdbapi.com. Muwie do not own any of the article or movie.


Made by：China
pin：21654525
Store Price：$250
Online price：$189
product：150g 80*65(MM)
1）Parents and children alike recognize the value of play.
2）Parents want their children to have a safe and secure play environment.
3）Children just want to have fun. What better place to do both than in your own backyard?


| GEOS SDK TechDocs
|
| pobjmon ...
| printRegions ...
print-cell
print-cell [row column <cfp ds:si>]
Examples:
-
"print-cell 1 1"
-
print the cell <1,1>
-
"print-cell 1 2 *ds:si"
-
print the cell <1,2> given *DS:SI
Print information about a cell
See Also: print-row,
print-row-block,
print-cell-params,
print-column-element.
print-cell-params
print-cell-params [<address>]
Examples:
-
"print-cell-params"
-
print the
CellFunctionParameters
at ds:si.
-
"print-cell-params ds:bx"
-
print the
CellFunctionParameters
at ds:bx.
Print a
CellFunctionParameters
block.
See Also: print-row,
print-column-element,
print-row-block,
print-cell.
print-clipboard-item
print-clipboard-item [-v] <vmfile> <vmblock>
print-clipboard-item [-v] <memhandle>
print-clipboard-item [-v] <addr>
Examples:
-
"print-clipboard-item bx"
-
Print out info about the transfer item whose memory handle is in the BX register.
Prints out information about a transfer item.
-
If you give the "-v" flag, this will print out the contents of the different transfer formats, rather than just an indication of their types.
-
The -v flag will not work unless the transfer item is in a VM file.
See Also: pnormal,
pquick.
print-column-element
print-column-element [<address>]
Examples:
-
"print-column-element"
-
Print the
ColumnArrayElement
at ds:si.
-
"print-column-element ds:bx" print the
ColumnArrayElement
at ds:bx
Print a single
ColumnArrayElement
at a given address.
print-db-group
print-db-group file group
Examples:
-
"print-db-group ax bx"
-
print the group at bx/ax.
Print information about a dbase group block.
See Also: print-db-item.
print-db-item
print-db-item file group item
Examples:
-
"print-db-item bx ax di"
-
print the item at bx/ax/di
Print information about a single dbase item
See Also: print-db-group.
print-eval-dep-list
print-eval-dep-list [<addr>]
Examples:
-
"print-eval-dep-list es:0"
-
Print dependency list at ES:0.
Prints a dependency list used for evaluation.
See Also: content,
pcelldeps.
printNamesInObjTrees
var printNamesInObjTrees (0|1)
Examples:
-
"var printNamesInObjTrees 1"
-
Sets "gentree," "vistree," etc. commands to print object names (where available).
Determines whether object names are printed (where available) rather than class names when using the following commands: vistree, gentree, focus, target, model, mouse, keyboard.
The default value for this variable is zero.
See Also: gentree,
vistree,
focus,
target,
model,
mouse,
keyboard.
print-obj-and-method
print-obj-and-method <handle> <chunk> <message> [<cx> [<dx>
[<bp> [<class>]]]]
Examples:
-
"print-obj-and-method [read-reg bx] [read-reg si]"
-
Prints a description of the object ^lbx:si with the value stored and a hex representation.
-
"print-obj-and-method $h $c $m [read-reg cx] [read-reg dx] [read-reg bp]"
-
Prints a description of the object ^l$h:$c and the name of the message whose number is in $c. This is followed by the three words of data in cx, dx, and bp.
Prints a nicely formatted representation of an object, with option message, register data, label, hex address, & carriage return. The class indication may also be overriden.
-
You may specify anywhere from 0 to 5 arguments after the message number. These are interpreted as the value of the message, the registers CX, DX and BP, and the symbol token of the class to print, respectively.
-
All arguments must be integers, as this is expected to be called by another procedure, not by the user, so the extra time required to call
getvalue
would normally be wasted. (The user should call
pobj
,
gup
, or other such functions for this sort of print out.)
See Also: mwatch,
map-method,
objwatch.
| GEOS SDK TechDocs
|
| pobjmon ...
| printRegions ...


The Bride and Groom
Katherine
Katherine Alwine, daughter to Carl and Linda Alwine, lives in the Upper Peninsula of Michigan. She keeps herself busy working and volunteering on various projects including filming for Northbound. She looks forward to what the future holds and hopes for a happy marriage with Joe LaFreniere.
Joe
Joe LaFreniere, son of Joe and Tracy LaFreniere, manages J&J Insulation in the Upper Peninsula of Michigan. In his free time he likes to explore the wilderness with his Fiance and experience a beauty only the Upper Peninsula can offer.
Wedding
CEREMONY
Nathan Alwine's Hill
W6046 Piers Gorge Road, Norway MI
Saturday, September 2, 2017
Time TBD
SCHEDULE OF EVENTS
Indiana Bridal Shower
Upper Round Room at Villa Macri
April 15th 11:00 AM
225 Toscana Blvd, Grander, IN
View larger map
Michigan Bridal Shower
TBD
Reception
Saturday, September 2nd at TBD
BRIDAL PARTY
Maids of Honor
Jennifer Alwine
Samantha Alwine
Bridesmaids
Callie Alwine
Clarissa Alwine
Jenny Barros
Michelle Hunt
Vanessa Radlinger
Ashley Nichole Sears
Flower Girls
Addie Alwine
Katie Geimer
Groomsmen
Mattson
Kevin
Jimmy Moroe
Allen Bedard
Nate Alwine
Ring Bearer
Lincoln Geimer
Here Comes the Bride
Gracie Alwine
Accomodations
HOTELS
Country Inn and Suites
2005 S Stephenson Ave
Iron Mountain, MI 49801
906-774-1900
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Days Inn
2001 S Stephenson Ave
Iron Mountain, MI 49801
906-774-2181
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TRANSPORTATION
Hotel Shuttle
Norway Mountain will be offering shuttle services. If you will be requiring this service please note it in the comments section of the RSVP.  Please check back for more information closer to the date!
Other means of transportation
Tri-City Cab:<PHONE_NUMBER> City Cab:<PHONE_NUMBER>
Please respond
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Registry
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See our registry with Walmart here: www.walmart.com


IPMpack takes demographic vital rates and (optionally) environmental data to build integral projection models. A number of functional forms for growth and survival can be incorporated, as well as a range of reproductive strategies. The package also includes a suite of diagnostic routines, provides classic matrix model output (e.g., lambda, elasticities, sensitivities), and produces post-hoc metrics (e.g., passage time and life expectancy).
| Version: | 2.1 |
| Depends: | Matrix, MASS, nlme |
| Suggests: | MCMCglmm, truncnorm, mvtnorm, methods, MCMCpack, fields |
| Published: | 2014-03-17 |
| Author: | CJE Metcalf, SM McMahon, R Salguero-Gomez, E Jongejans, C Merow |
| Maintainer: | Sean McMahon <ipmpack at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | IPMpack results |
| Reference manual: | IPMpack.pdf |
| Vignettes: |
IPMpack: An R Package for demographic modeling |
| Package source: | IPMpack_2.1.tar.gz |
| Windows binaries: | r-devel: IPMpack_2.1.zip, r-release: IPMpack_2.1.zip, r-oldrel: IPMpack_2.1.zip |
| OS X binaries: | r-release: IPMpack_2.1.tgz, r-oldrel: IPMpack_2.1.tgz |
| Old sources: | IPMpack archive |
Please use the canonical form https://CRAN.R-project.org/package=IPMpack to link to this page.


please UNLOCK YOUR NEBULAS WALLET AND MAKE SURE YOU ARE LOGGED IN .
MONSTER TAMER GAME REQUIRES NEBULAS BROWSER EXTENSION THAT IS AVAILABLE AS PLUG IN FOR GOOGLE CHROME.
WHAT IS
MONSTER TAMER?

B.DUEL
GROWING YOUR CRYPTO MONSTER VP BY ATTACK OPPONENTS AND TAKE THE LEADERSHIP IN MONSTER PARK DECENTRALIZED FOOD CHAIN.
4.MORE EXCITING WAYS TO PLAY
3V3 TEAM BATTLE
BUILD AN ARMY OF MONSTERS, THREE MONSTERS CAN FIGHT AMONG A PARTY, THE WINNER WILL ACHIEVE TEAM REPUTATION AND INTEGRATION.
BOOSTER MARKET
THERE ARE FIVE KINDS OF BOOSTERS ON THE GLOBAL OPEN MARKET, PLAYERS COULD CHOOSE [POWER], [SPEED], [AGILITY],[DEFENSE] BOOSTERS TO IMPROVE RELATED PARAMETERS, THE RESURRECTION PROP COULD REANIMATE YOUR MONSTER.


WITNESS LIST Electric Utility Restructuring (Interim) Committee March 25, 1998-9:30A Electric Cooperative Issues On: Hargett, Edd (Houston County Electric), Crockett Linebarger, Libby (Pedernales Electric), Manchaca Moore, Michael (C H. Guernsey & Co.), Oklahoma City Robinson, Debbie (Wood County Electric), Quitman Williams, Mike (TX Electric Cooperative), Austin Electric Utility Restructuring For: Flurry, David (self), Blanket Goodson, Dessie (self), Denton Hart, Kristi (self), Murchison Tirk, Kerrie (self), Colleyville On: Baker, Roy O. (AARP), Greenville Beck, Paula C. (West TX Opportunities), Lamesa Bray, Debra A. (self), Burleson Ebner, Calvin (self), Deweyville Foutz, Kim (City of Midlothian), Midlothian Goodson, Dessie (self), Denton Harting, Sue Ann (Mayor, Greenville), Greenville Little, Frances (Hill Country Community), San Saba Osborn, Maurice (Mayor, Midlothian), Midlothian Ouchley, Don (PUB), Brownsville Payne, Emmett (Central TX Opp.), Coleman Pridgeon, Marcus W. (Guadalupe Valley Elec.), Gonzales Ratliff, James (Mayor, Garland), Garland Smith, Tom "Smitty" (Public Citizen), Austin Swenson, Karen (CAP Agencies), Nacogdoches Whiteside, Charles H. (self), Kilgore Wood, John (Mayor Pro Tem, Brownsvl), Brownsville Registering, but not testifying: For: Marz, Michael J. (self), Dallas Against: Parks, Francis "Frank" (City of Weimar), Weimar On: Postell, Donald R. (City of Grand Prairie), Grand Prairie Singletary, Grady C. (self), Rusk Municipal Utility Issues On: Cooke, Alex "Ty" (Mayor Pro Tem, Lubbock), Lubbock Rose, Mark (LCRA), Austin Taylor, Bill (Kerrville PUB), Kerrville Wilkerson, Dan (Elec. Utility Services), Bryan Zion, Mark (TX Public Power Assoc.), Austin


Missing Publicity Materials We're missing publicity materials from the show. {% include boxes/phrase_if_you_can_help.html %}


The local Database for JavaScript Applications
- Realtime Queries
- Realtime Replication
- Works Offline
- Supports all JavaScript runtimes
- Great Performance
From the results of a query, to a single field of a document, with RxDB you can observe everything. This enables you to build realtime applications fast and reliable. Whenever your data changes, your UI reflects the new state.
RxDB supports replication with a CouchDB server or any custom GraphQL endpoint which smoothly integrates with your existing infrastructure. Also you can use the replication primitives plugin to create custom replications over any protocol like REST, Websocket, p2p or Firestore.
RxDB follows the Offline First paradigm where an application must work as well offline as it does online. This is done by persisting data on the client side and replicating it in the background. RxDB can even be used solely on the client side, with no backend at all.
RxDB is based on a storage interface that enables you to swap out the underlying storage engine. This increases code reuse because the same database code can be used in any JavaScript runtime by just switching out the storage settings.


Isaaq's Portfolio
My name is Isaaq and I am a second year at the University of Chicago studying Computer Science and Psychology! I first explored the wonders of Computer Science during my senior year in high school. I took dual enrollment courses at Wright College where I began my passion for coding. Since then, I have taken courses such as Introduction to Software Development (CMSC 22000) and Programming Langauges (CMSC 22100).
More About Me!
I play video games and like to tinker with my PC! PCPartPicker Part List


Floater to open right to left
-
anonymous
Hey. I have my floater in the screen position where I'm less likely to use stuff, and that's in the top-right. However, the floater is designed for a left-alignment, so the album cover appears, stays stationary and swooshes to reveal the text. In my right-alignment, the album cover is what moves, which is a bit distracting. Is it possible to have a preference to change which way it opens in the next release?
Cheers Chris
-
Greg Hurrell
This is an excellent point... I plan to add more control over floater transitions in the future, although most likely this will end up in Synergy Advance first, and potentially be "back ported" to Synergy afterwards.
-
anonymous
Cool Keep up the good work, Synergy really does rock my socks. And at less than three English pounds, I spend more each day on lunch!
Chris
Reply
This topic is now closed.


I'm AJ (@rerel on github). You can also find me on Twitter, on AngelList or check out my resume.
I like doing mobile programming everyday. Here I'm going to put some interesting findings about iOS or general Computer Science subjects.
Talk to me on Twitter or send me a mail lemesle.aurelien@gmail.com


fitness_center
RoseBank Gym
New Gym is comming in your area
Call us +917897865432
Our Gym provides fitness,strength,regular excercises and yoga


Thai translations for Saturday and Sunday were deliberately left out to illustrate fallback to default English language for missing translations.


omics
Contents
1.3. omics#
some papers involving proteins and ml, especially predicting protein structure from dna/rna
1.3.1. overview#
<IP_ADDRESS>. vocabulary#
oligonucleotide = oligo = short single strands of synthetic DNA or RNA
<IP_ADDRESS>. data#
nucleic acid database (NDB)
protein databank (PDB)
rna
~~RNABase: an annotated database of RNA structures~~ (2003) - no longer maintained
Accurate SHAPE-directed RNA structure determination | PNAS (deigan et al. 2009) - chemical probing
<IP_ADDRESS>. code#
rna
Galaxy RNA workbench - many tools including alignment, annotation, interaction
viennaRNA - incorporating constraints into predictions
neural nets
1.3.2. rna structure prediction#
<IP_ADDRESS>. rna basics#
rna does many things
certain structures of RNA lend themselves to catalytic activities
tRNA/mRNA convert DNA into proteins
RNA also serves as the information storage and replication agent in some viruses
rna components
4 bases: adenine-uracil (instead of dna’s thymine), cytosine-guanine
ribose sugar in RNA is more flexible than DNA
RNA World Hypothesis (Walter Gilbert, 1986) - suggests RNA was precursor to modern life
later dna stepped in for info storage (more stable) and proteins took over for catalyzing reactions
rna structure
-
primary - sequence in which the bases are aligned - relatively simple, comes from sequencing
secondary - 2d analysis of hydrogen bonds between rna parts (double-strands, hairpins, loops) - most of stabilizing free energy comes from here (unlike proteins, where tertiary is most important)
most work on “RNA folding” predicts secondary structure from primary structure
a lot of this comes from hydrogen bonds between pairs (watson-crick edge)
other parts of the pairs (e.g. the Hoogsteen- / CH-edge and the sugar edge) can also form bonds
tertiary - complete 3d structure (e.g. bends, twists)
-
RNA-Seq - Wikipedia - RNA-Seq uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment, analyzing the continuously changing cellular transcriptome
<IP_ADDRESS>. algorithms#
computational bio book ch 10 (kellis, 2016)
2 main approaches to rna folding (i.e. predicting rna structure):
(1) thermodynamic stability of the molecule
(2) probabilistic models
note: can use evolutionary (i.e. phylogenetic) data to improve either of these
some RNA changes still result in similar structures
consistent mutations - something mutates but structure doesn’t change (e.g. AU -> G)
compensatory mutations - two mutations but structure doesn’t change (e.g. AU -> CU -> CG)
incorporate similarities to other RNAs along with something like zuker algorithm
thermodynamic stability - uses more domain knowledge
dynamic programming approach: given energy value for each pair, minimize total energy by pairing up appropriate base pairs
assumption: no crossings - given a subsequence \([i,j]\), there is either no edge connecting to the ith base (meaning it is unpaired) or there is some edge connecting the ith base to the kth base where \(i < k \leq j\) (meaning the ith base is paired to the kth base)
this induces a tree-structure to theh problem
nussinov algorithm (1978)
ignores stacking interactions between neighboring pairs (i.e. assumes there are no pseudo-knots)
zuker algorithm (1981) - includes stacking energies
probabilistic approach - uses more statistical likelihood
stochastic context-free grammer (SCFG) is like an extension of HMM that incorporates some RNA constraints (e.g. bonds must happen between pairs)
Recent advances in RNA folding - ScienceDirect (fallmann et al. 2017)
algorithmic advances
algorithmic constraints
locality - restrict maximal span of base pairs
add in bonus energies as hard or soft constraints
rna-rna and rna-protein interactions - there are different algorithms for telling how 2 things will interact
newer algorithms take into account some aspects of tertiary structure to predict secondary structure (e.g. motifs, nonstandard base pairs, pseudo-knots)
representation
...((((((((...)). .)). .((.((...)). )))))).
“dot-parenthesis” notation - opening and closing represent bonded pairs
evaluation
computing alignments is non-obvious in these representations
centroids - structures with a minimum distance to all other structures in the ensemble of possible structures
consensus structures - given a good alignment of a collection of related RNA structures, can compute their consensus structure, (i.e., a set of base pairs at corresponding alignment positions)
Folding and Finding RNA Secondary Structure (matthews et al. 2010)
new work drops the assumption of no knots (e.g. crossings)
project to topology (e.g. low/high genus) - e.g. projecting to a torus can remove crossings but still allow us to use dynamic programming
RNA secondary structure prediction using deep learning with thermodynamic integration (sato et al. 2021)
RNA-folding scores learnt using a DNN are integrated together with Turner’s nearest-neighbor free energy parameters
DNN predicts scores that are fed into zuker-style dynamic programming
<IP_ADDRESS>. 3d rna structure prediction#
Geometric deep learning of RNA structure (townshend, …, dror 2021 - atomic ai + stanford, science)
uses neural network as scoring mode (called ARES = Atomic Rotationally Equivariant Scorer)
this gives energy function for (primary sequence, structure) pair
architecture
captures rotational + translational symmetries
startup atomic ai works on this
trained with only 18 known RNA structures
uses only atomic coordinates as inputs and incorporates no RNA-specific information
Review: RNA 3D Structure Prediction Using Coarse-Grained Model (li & chen, 2021)
all-atom - more precise, each nucleotide models ~20 heavy atoms + ~10 hydrogen atoms - relatively rare
coarse-grained - less precise
steps
input - RNA sequence + optinal secondary structure + more (e.g. distance between some atom pairs)
sampling - includes discriminator (=scoring function = (potential) energy function = force field) and generator
generator proposes new structures (e.g. MCMC)
discriminator decides which is the most stable - 3 types of energy to consider
output - sometimes pick lowest energy structure, somestimes cluster low-energy clusters and pick centroid
all-atom structure reconstruction - the fragment matching algorithm (Jonikas et al., 2009a) is often used, followed by structure refinement to remove steric clashes and chain breaks
12 papers that differ slightly in these steps
ex. graph-based: 2d structure mapped to a graph
three open-source papers that yield all-atom structure
IsRNA1: De Novo Prediction and Blind Screening of RNA 3D Structures (zhang et al. 2021)
origina IsRNA (zhang & chen, 2018)
iFoldRNA: three-dimensional RNA structure prediction and folding (sharma et al. 2008 )
SimRNA (boiecki et al. 2016)
1.3.3. protein structure prediction (dna)#
<IP_ADDRESS>. protein basics#
the standard way to obtain the 3D structure of a protein is X-ray crystallography
takes ~1 year & $120k to obtain the structure of a single protein through X-ray crystallography (source)
alternative: nuclear magnetic resonance (NMR) spectroscopy
on average, a protein is composed of 300 amino acids (residues)
21 amino acid types
the first residue is fixed
proteins evolved via mutations (e.g. additions, substitutions)
fitness selects the best one
<IP_ADDRESS>. algorithm basics#
multiple-sequence-alignment (MSA) - alignment of 3 or more amino acid (or nucleic acid) sequences, which show conserved regions within a protein family which are of structural and functional importance
matrix is (n_proteins x n_amino_acids)
independent model (PSSM) - model likelihood for each column independently
pairwise sites (potts model) - model likelihood for pairs of positions
prediction problems
contact prediction - binary map for physical contacts in the final protein
common to predict this, evaluation uses binary metrics
protein folding - predict 3d positions of all amino acids in the output structure
Evaluating Protein Transfer Learning with TAPE - given embeddings, 5 tasks to measure downstream performance (rao et al. 2019)
tasks: structure (SS + contact), evolutionary (homology), engineering (fluorescence + stability)
future: interactions between molecules (e.g. protein-protein, environment, highly designed proteins)
<IP_ADDRESS>. deep-learning papers#
De novo protein design by deep network hallucination | Nature (anishchenko…baker, 2021)
deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins
Highly accurate protein structure prediction with AlphaFold | Nature (jumper, …, hassabis, 2021)
model overview
preprocessing
MSA (\(N_{seq} \times N_{res}\))
uses evolutionary information
pair representation (\(N_{res} \times N_{res}\))
finding templates - find similar proteins to model “pairs of residues” - which residues are likely to interact with each other
residue is the same as an amino acid
evoformer
uses attention on graph network
iterative
structure model - converts msa/pair representations into set of (x,y,z) coordinates
“invariant point attention” - invariance to translations and rotations
predicts a global frame (residue gas)
MSA Transformer (rao et al. 2021) - predicts contact prediction
train via masking
output (contact prediction) is just linear combination of attention heads finetuned on relatively little data
some architecture tricks
attention is axial - applied to rows / columns rather than entire input
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
<IP_ADDRESS>. covid#
The Coronavirus Nucleocapsid Is a Multifunctional Protein (mcbride et al. 2014)
coronavirus nucleocapsid = N protein
1.3.4. TIPs - using viruses for treatment#
videos
2 issues
mutation - viruses mutate, our drugs don’t
transmission - adherence / deployment - hard to give certain the drugs
solution: use modified versions of viruses as treatment
therapeutic interfering particles or TIPs are engineered deletion mutants designed to piggyback on a virus and deprive the virus of replication material
TIP is like a parasite of the virus - it clips off some of the virus DNA (via genetic engineering)
it doesn’t contain the code for replication, just for getting into the cell
since it’s shorter, it’s made more efficiently - thus it outcompetes the virus
how do TIPs spread?
mutations happen because the “copy machine” within a cell makes a particular mutation
when a new virus comes along, it makes some of the mutated parts
TIPs can’t replicate, so they take some of the mutated parts made by the new virus
then, the TIP gets copied with the same mutation as the virus and this now spreads
effect
viral load will immediately be lower
superspreaders can spread treatment to others
can take before exposure as well (although harder to get approval for this)
note: HIV mutates very fast so a TIP in a single person sees a lot of variants
HIP also enters the genome (so corresponding TIP does this as well)
Identification of a therapeutic interfering particle—A single-dose SARS-CoV-2 antiviral intervention with a high barrier to resistance (chaturvedi…weinberger, 2021)
DIP = defective interfering particle = wild-type virus
single administration of TIP RNA inhibits SARS-CoV-2 sustainably in continuous cultures
in hamsters, both prophylactic and therapeutic intranasal administration of lipid-nanoparticle TIPs durably suppressed SARS-CoV-2 by 100-fold in the lungs, reduced pro-inflammatory cytokine expression, and prevented severe pulmonary edema
TIP consists 1k - 2k bases
hard to actually look at structure here (requires cryoEM)


--- title: "Statistical Inference for Network Models" excerpt: "A Networks 2021 Satellite Symposium" layout: single permalink: / sidebar: title: "Key Links" nav: sidebar-main header: overlay_color: "#000" overlay_filter: "0.0" overlay_image: /assets/images/netsci2021.jpg ---
This workshop will address the intersection of two trends in network science. On the one hand, real-world networks are increasingly annotated with rich metadata, including vertex or edge attributes or temporal information. Making sense of such data requires moving beyond simple models of network structure. On the other, hypotheses about network structure and the processes that create those patterns are increasingly sophisticated. The tools of statistical inference for network models offer a principled and effective approach for both understanding richly annotated network data and testing interesting network hypotheses.
In particular, probabilistic models are a quantitative approach that allow researchers both to infer complicated hidden structural patterns in existing data and generate synthetic data sets whose structure is statistically similar to real data. These models facilitate handling many of the challenges of understanding real data, including accounting for noise and missing values, and they connect theory with data by providing interpretable results. Statistical inference is thus a powerful and useful tool for modeling and understanding networks.
The development of new tools and their application to understand real systems is now a major community effort in network science. Despite their power and utility, however, these techniques are not as easy or approachable as simpler tools, like degree distributions, centrality scores, and clustering coefficients. Increasingly, new applications and richer data sets offer new opportunities for developing and applying the principled techniques of statistical inference to networks.
This satellite symposium will build on seven successful previous satellites at NetSci2014, NetSci2015, NetSci2016, NetSci2017, NetSci2018, NetSci2019 and NetSci2020 by uniting theoretical and applied researchers, bringing together approaches from across network science, including machine learning, statistics, and physics. This broad cross-section of disciplines shares problems and even approaches, but each discipline brings a different perspective, emphasis and vocabulary. The purpose of this symposium is to provide a platform for cross-pollination of ideas and promote interdisciplinary interactions which leverage the diversity of approaches to a common set of problems.
There will be no published proceedings for this satellite.


Class: Aws::Rekognition::Types::DescribeStreamProcessorResponse
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb |
Constant Summary
Instance Attribute Summary
-
#creation_timestamp ⇒ Time
rw
Date and time the stream processor was created.
-
#data_sharing_preference ⇒ Types::StreamProcessorDataSharingPreference
rw
Shows whether you are sharing data with
::Aws::Rekognition
to improve model performance. -
#input ⇒ Types::StreamProcessorInput
rw
::Aws::Kinesis
video stream that provides the source streaming video. -
#kms_key_id ⇒ String
rw
The identifier for your AWS Key Management Service key (AWS
::Aws::KMS
key). -
#last_update_timestamp ⇒ Time
rw
The time, in Unix format, the stream processor was last updated.
-
#name ⇒ String
rw
Name of the stream processor.
-
#notification_channel ⇒ Types::StreamProcessorNotificationChannel
rw
The Amazon Simple Notification Service topic to which Amazon Rekognition publishes the object detection results and completion status of a video analysis operation.
-
#output ⇒ Types::StreamProcessorOutput
rw
::Aws::Kinesis
data stream to which Amazon RekognitionVideo
puts the analysis results. -
#regions_of_interest ⇒ Array<Types::RegionOfInterest>
rw
Specifies locations in the frames where Amazon Rekognition checks for objects or people.
-
#role_arn ⇒ String
rw
::Aws::ARN
of the::Aws::IAM
role that allows access to the stream processor. -
#settings ⇒ Types::StreamProcessorSettings
rw
Input parameters used in a streaming video analyzed by a stream processor.
-
#status ⇒ String
rw
Current status of the stream processor.
-
#status_message ⇒ String
rw
Detailed status message about the stream processor.
-
#stream_processor_arn ⇒ String
rw
::Aws::ARN
of the stream processor.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#creation_timestamp ⇒ Time
(rw)
Date and time the stream processor was created
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#data_sharing_preference ⇒ Types::StreamProcessorDataSharingPreference (rw)
Shows whether you are sharing data with ::Aws::Rekognition
to improve model
performance. You can choose this option at the account level or on a
per-stream basis. Note that if you opt out at the account level this
setting is ignored on individual streams.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#input ⇒ Types::StreamProcessorInput (rw)
::Aws::Kinesis
video stream that provides the source streaming video.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#kms_key_id ⇒ String
(rw)
The identifier for your AWS Key Management Service key (AWS ::Aws::KMS
key). This is an optional parameter for label detection stream
processors.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#last_update_timestamp ⇒ Time
(rw)
The time, in Unix format, the stream processor was last updated. For example, when the stream processor moves from a running state to a failed state, or when the user starts or stops the stream processor.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#name ⇒ String
(rw)
Name of the stream processor.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#notification_channel ⇒ Types::StreamProcessorNotificationChannel (rw)
The Amazon Simple Notification Service topic to which Amazon
::Aws::Rekognition
publishes the object detection results and completion
status of a video analysis operation.
Amazon Rekognition publishes a notification the first time an object of interest or a person is detected in the video stream. For example, if Amazon Rekognition detects a person at second 2, a pet at second 4, and a person again at second 5, Amazon Rekognition sends 2 object class detected notifications, one for a person at second 2 and one for a pet at second 4.
Amazon Rekognition also publishes an an end-of-session notification with a summary when the stream processing session is complete.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#output ⇒ Types::StreamProcessorOutput (rw)
::Aws::Kinesis
data stream to which Amazon Rekognition Video
puts the
analysis results.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#regions_of_interest ⇒ Array
<Types::RegionOfInterest> (rw)
Specifies locations in the frames where Amazon Rekognition checks for objects or people. This is an optional parameter for label detection stream processors.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#role_arn ⇒ String
(rw)
::Aws::ARN
of the ::Aws::IAM
role that allows access to the stream processor.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#settings ⇒ Types::StreamProcessorSettings (rw)
Input parameters used in a streaming video analyzed by a stream
processor. You can use FaceSearch
to recognize faces in a
streaming video, or you can use ConnectedHome
to detect labels.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#status ⇒ String
(rw)
Current status of the stream processor.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#status_message ⇒ String
(rw)
Detailed status message about the stream processor.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end
#stream_processor_arn ⇒ String
(rw)
::Aws::ARN
of the stream processor.
# File 'gems/aws-sdk-rekognition/lib/aws-sdk-rekognition/types.rb', line 1871
class DescribeStreamProcessorResponse < Struct.new( :name, :stream_processor_arn, :status, :, :, :, :input, :output, :role_arn, :settings, :notification_channel, :kms_key_id, :regions_of_interest, :data_sharing_preference) SENSITIVE = [] include Aws::Structure end


Make a backup of your Kubernetes cluster using the following etcd endpoint.
Use this as default if no etcd endpoint is provided.
Use this to restrict the objects you want to back up. If left empty, all Kubernetes objects will be backed up.


I travel
I'm a Internation student from China, came to the U.S. 7 years ago alone, attended high school in San Marcos, Texas.
In the past 7 years, I have traveled Australia, Japan, Ireland, United Kingdom and doezen of cities in the United State and China.
(Gallery coming soon ...)


FORCES OF NATURE Reviewed by Jamie Peck
Rating: **1/2 (out of ****) DreamWorks SKG / 1:42 / 1999 / PG-13 (language, thematic material) Cast: Sandra Bullock; Ben Affleck; Maura Tierney; Steve Zahn; Blythe Danner; Ronny Cox; Meredith Scott Lynn; David Strickland Director: Bronwen Hughes Screenplay: Marc Lawrence; Ian Bryce; Donna Roth
Sandra Bullock's girl-next-door sunniness is appealing in the same way that Ben Affleck's swarthy-but-affable ease is appealing, so it's reasonable to presume that a movie featuring their respective appeals will be, um, appealing as well. "Forces of Nature," one such romantic comedy, lives up to that precursor despite saddling these actors with roles that might seem to contradict their proven likability: She's a wild child with hefty emotional baggage while he's a harried, straightforward bundle of nerves. The casting goes completely against type, but Bullock and Affleck's stars shine brightly even when the film gets dangerously dim - which is also, unfortunately, how it ends up.
The story is basically composed of a string of natural and man-made disasters that lead this nice-looking but evidently mismatched pair to profess their mutual affection for each other. So far, so fun, as this much of Forces delivers largely entertaining fluff. What happens in the dire aftermath, however, is best described as forced. It's not fair for a movie to be amusing within the confines of several standard cinematic conventions - the Meet Cute, the Opposites Attract and the Road Movie among them - and then bungle the moment of truth and subsequent climax and finale.
Thank goodness for Bullock and Affleck, then, who click even in the most contrived of circumstances. They first share screen time on a New York-to-Georgia flight that, due to a nasty accident involving a seagull and a wing engine, never gets off the ground. Affleck's Ben (no stretch there) already has the jitters because he's en route to his opulent wedding in Savannah, and said mishap doesn't exactly set his mind at ease - especially after grandpa keeled over at the bachelor party only to confide on his hospital bed that marriage is nothing but constricting chaos.
Despite these two seemingly isolated incidents of ill will, Ben hooks up with Bullock's fellow passenger and free spirit Sarah (what, they didn't want to call her Sandra?) for alternate travel strategies - she's headed his way, so he figures he'll share the joys of outrageous fortune. But one bit of bad luck turns into another, and soon, Ben and Sarah have gotten arrested, robbed and quite wet from the torrential downpour of rain and hail that follows them almost about everywhere - shot as beautiful visual compositions by "Out of Sight" cinematographer Elliot Davis - and these are merely a few of the crises they endure while trying to go south.
Such lively misadventures, which comprise a bulk of "Forces of Nature," take a cue from "Planes, Trains & Automobiles" in their growing absurdity and vehicular setting - aside from the aforementioned airliner tragedy, Ben and Sarah inadvertently run into railroad trouble and hitch a ride from a friendly hophead, though not in that order. Meanwhile, "Forces of Nature" cuts back and forth between their plight and Ben's bride, waiting by the phone and expecting the worst. This fairly thankless part is filled by Maura Tierney, who was also given the thankless part of Jim Carrey's wife in "Liar Liar," and she's further hampered by an equally thankless subplot in which a successful ex tempts her.
Of course, that flummox would be forgivable had Affleck and Tierney's guaranteed nuptial-day reunion and its aftermath rang true, but for once, an unpredictable, anti-Hollywood send-off flops. A powerful hurricane hits the area just in time for this conclusion, and tosses the colorful decor about in its mighty winds like rainbow confetti. It's a pretty sight, but it makes one wonder if anybody in front of the camera pays attention to the Weather Channel, or if anybody behind it couldn't think of a better way to cap off an otherwise enjoyable movie.
© 1999 Jamie Peck E-mail<EMAIL_ADDRESS>Visit The Reel Deal Online: http://www.gl.umbc.edu/~jpeck1/ "Remember that old college boy joke about how, after sex, the ideal women turns into a pizza and a six-pack? In this movie, the ideal man turns into a vibrator and Ted Koppel." -Roger Ebert on "Let's Talk About Sex"
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Kernel Configuration¶
For advanced users we here provide a description of the parameters that may be changed without running the risk to spoil the kernel. Nevertheless, you have to do this at your own risk (best effort support only ;)
Finding the Kernel¶
First of all, we have to find the location where the kernel has been installed:
In the running kernel (notebook or console):
)python os.path.abspath(__file__)
or, similarly
)python kernel_file_path
Example:
Edit the Kernel File¶
Open the file path (from above) into your favourite editor and look for
the BEGIN user configuration options
.
# ********************************
# BEGIN user configuration options
# ********************************
Foreign commands¶
The following commands are not part of FriCAS, however, we use the same
prefix (namely )
) as the system commands (the %
as the ipython magics,
might be confusing). So, for instance, the prefix for Python commands could
be changed to %python
or whatever.
pycmd = ')python'
shcmd = '!'
shutd = ')shutdown'
gplot = ')gnuplot'
Start options¶
The start options cotrol how the fricas webserver will be started:
fricas_start_options = '-noht' ### -nox blocks if draw is used (others?)
fricas_terminal = [] ### E.g. ['xterm','-e'] for 'xterm'
The FriCAS start options excactly correspond the ones from
$ fricas -h
fricas
[-nosman] use plain command line interface (disables other options)
[-ht |-noht] whether to use HyperDoc
[-gr |-nogr] whether to use Graphics
[-clef |-noclef] whether to use Clef
[-iw |-noiw] start in interpreter window
[-ihere|-noihere] start an interpreter buffer in the original window.
[-nox] don't use X Windows
[-go |-nogo] whether to start system
[-ws wsname] use named workspace
[-list] list workspaces only
[-clefprog fname] use named program for Clef
[-h] show usage
[-eval code] evaluate specified code at start
[-texmacs] setup FriCAS for communication in TeXmacs protocol
[-emacs] setup for emacs frontend
However, let us emphasize that not many of them will actually work as
expected when set in fricas_start_options
. The options -nosman
and -nox
, for example, might block in case some commands are used which
require the X11
system. We have not tested much of the possible
combinations, but en/dis-abling Clef and Hyperdoc for example, is certainly
no problem.
Shell commands¶
These variables control the external shell commands:
shell_timeout = 15 # Timeout for shell commands in secs.
shell_result = None # store last sh result in python
shell_result_fricas = '__system_result:="{0}"' # store sh result in Fricas
The shell_timeout
may be adjusted to suite you needs (in case your shell
jobs might run more than 15 seconds).
The shell_result
must not be changed (just initializing).
The shell_result_fricas
variable holds the name of the FriCAS variable
which stores the last shell output as string. If you wanted another name
than __system_result
, then you would change it here (note that the
part :="{0}"
is mandatory).
HTML prefix¶
The HTML prefix defines which strings (Type: String) returned from FricAS will be interpreted as HTML code.
html_prefix = '$HTML$'
You are free to change this, provided that Python can recognize it (technically: str.startswith).
Gnuplot¶
For the built-in Gnuplot feature:
# gnuplot javascript files location
gpjsf = '/static/gpjs'
# gnuplot canvas template (html5)
gptpl =r"""
<script src="{0}/canvastext.js"></script>
<script src="{0}/gnuplot_common.js"></script>
<canvas id="{1}" width=600 height=400></canvas>
<script>{2}</script>
<script>{3}();</script>
"""
The variable gpjsf
holds the name of the location from where
the Gnuplot Javascript files are served, i.e. correspondingly
localhost:8888/static/gpjs
Note: there must be a symbolic link from the Gnuplot installation to this directory.
The gptpl
is a template for the HTML5 canvas that will be injected to
the Jupyter notebook.
End of user config¶
If you make changes beyond this point, then you will be on your own:
# ***************
# END user config
# ***************
Extending the kernel¶
The jfricas
kernel is essentially a simple Python wrapper kernel that
can be extended according to the corresponding Messaging in Jupyter
documentation.


AP Top Business News at 1:10 a.m. EST
by EndPlayCalifornia crisis of fires, blackouts decades in the making
Fed's Powell sees steady growth, signals pause in rate cuts
Nike cuts ties with Amazon, but shoes won't vanish from site
Ex-government health chief joins warnings about EPA proposal
US budget deficit surges to $134.5 billion in October
Economy in mind, Bolsonaro changes tack and cozies up to Xi
Japan's economy slows, logging 0.2% annual expansion in 3Q
Flying this Thanksgiving? Expect record crowds at airports
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Navajo company confident about Wyoming, Montana coal bonding


Class: Aws::EC2::Types::ScheduledInstance
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | (unknown) |
Overview
Describes a Scheduled Instance.
Instance Attribute Summary
-
#availability_zone ⇒ String
rw
The Availability Zone.
. -
#create_date ⇒ Time
rw
The date when the Scheduled Instance was purchased.
. -
#hourly_price ⇒ String
rw
The hourly price for a single instance.
. -
#instance_count ⇒ Integer
rw
The number of instances.
. -
#instance_type ⇒ String
rw
The instance type.
. -
#network_platform ⇒ String
rw
The network platform (
.EC2-Classic
orEC2-VPC
). -
#next_slot_start_time ⇒ Time
rw
The time for the next schedule to start.
. -
#platform ⇒ String
rw
The platform (
.Linux/UNIX
orWindows
). -
#previous_slot_end_time ⇒ Time
rw
The time that the previous schedule ended or will end.
. -
#recurrence ⇒ Types::ScheduledInstanceRecurrence
rw
The schedule recurrence.
. -
#scheduled_instance_id ⇒ String
rw
The Scheduled Instance ID.
. -
#slot_duration_in_hours ⇒ Integer
rw
The number of hours in the schedule.
. -
#term_end_date ⇒ Time
rw
The end date for the Scheduled Instance.
. -
#term_start_date ⇒ Time
rw
The start date for the Scheduled Instance.
. -
#total_scheduled_instance_hours ⇒ Integer
rw
The total number of hours for a single instance for the entire term.
.
Instance Attribute Details
#availability_zone ⇒ String
(rw)
The Availability Zone.
#create_date ⇒ Time
(rw)
The date when the Scheduled Instance was purchased.
#hourly_price ⇒ String
(rw)
The hourly price for a single instance.
#instance_count ⇒ Integer
(rw)
The number of instances.
#instance_type ⇒ String
(rw)
The instance type.
#network_platform ⇒ String
(rw)
The network platform (EC2-Classic
or EC2-VPC
).
#next_slot_start_time ⇒ Time
(rw)
The time for the next schedule to start.
#platform ⇒ String
(rw)
The platform (Linux/UNIX
or Windows
).
#previous_slot_end_time ⇒ Time
(rw)
The time that the previous schedule ended or will end.
#recurrence ⇒ Types::ScheduledInstanceRecurrence (rw)
The schedule recurrence.
#scheduled_instance_id ⇒ String
(rw)
The Scheduled Instance ID.
#slot_duration_in_hours ⇒ Integer
(rw)
The number of hours in the schedule.
#term_end_date ⇒ Time
(rw)
The end date for the Scheduled Instance.
#term_start_date ⇒ Time
(rw)
The start date for the Scheduled Instance.
#total_scheduled_instance_hours ⇒ Integer
(rw)
The total number of hours for a single instance for the entire term.


Prototype Jungle depends on HTML5 canvas and JavaScript features which are lacking in old versions of Internet Explorer. Please use a modern browser: IE 10, Firefox, Chrome, or Safari


Hangman v0.9.6 Hangman.Simple.Registry
Module implements a simple stash registry keeping track of active pids, active ids, and their values e.g. Player.t or Game.t
Registry key is of type {String.t, pid}
Host GenServers typically setup monitors for the pids under registry, to trigger registry removal
Summary
Functions
Adds a new key to the active registry list
Helper to retrieve the active key given pid or id
Creates a new registry
Removes the key from either the :value state or the :active list Returns the updated registry
Update the value state given the initial partial id key or full tuple key. Returns the updated registry
Helper to retrieve the value state given the active key
Types
Functions
Helper to retrieve the active key given pid or id
Removes the key from either the :value state or the :active list Returns the updated registry
Update the value state given the initial partial id key or full tuple key. Returns the updated registry


Cards for Humanity is a fast-paced online version of the popular card game, Cards Against Humanity, that gives you the opportunity to donate to children in need - all while remaining as despicable and awkward as you naturally are.
Would you like to play as a guest ? Click here
To start a new game Click here


form
.form-group
form.form-inline
form.form-horizontal
select.form-control[multiple]
p.form-control-static
Validation styles on form controls


RT @twostraws: In other news, http://www.fakeiphonetext.com - join the meme-in-the-making!
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About us
The Curbmap project was begun in late 2016, at the ongoing civic hack nights as a part of Hack For L.A.
Our current workforce includes, but is not limited to:
- Isabelle Americano, Product Management and Design
- Ben Coppersmith, Database Consultant and Data Science Lead
- Joshua Herman, Project Director
- Tommy Kelly, React Native Project Lead
- Shashin Patel, UX & Design
- Eli Selkin, Database, API, Maps, this landing page, etc.
- Kuhan Muniam, Android Developer
Focus
Currently curbmap is focused on the City of Los Angeles, with plans to expand to other areas as we optimize our models.
Los Angeles is a large metropolitan expanse with a large variety of restrictions to parking. We anticipate that lessons
learned here will generalize.


{% extends '_base.html' %} {% block content %}
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