Does PA claim to be sound? (More standard notation: \(\square X\) for \(PA[X]\).) \[PA[PA[X]\implies X]\] No. Then PA would be inconsistent. (Why?) \[PA[PA[X]\implies X]\implies PA[X].\] (Converse is obviously true.)
Alas, this means we can’t prove PA sound with respect to any important class of statements. (?)
Lob’s sentence: L says “If a proof of L exists, then C.” \[L = \square L \implies C\] (?If we assume \(\square L \implies L\), then \(\square L \implies C\). Assuming \(\neg \square L\) doesn’t give anything.)
cf. “If this sentence is true, then Santa Claus exists.”
For a sentence to refer to itself, it must contain a self-replicating recipe - when the recipe is executed, it produces a copy of the complete sentence, including the recipe itself
Generalities about Lob.
- The difference between truth and provability. If we defined \(\square\) correctly, \(\square S \to S\) looks to always be true. However, it’s not provable, unless \(S\) is provable!
- \(\square\) seems like negation in some way. (Cf. proof by contradiction)
- We would want PA on occassion to assert its own soundness. But no, it only asserts its soundness on statements that are actually provable.
- \(\square (\square S \to S)\) only when \(S\) is provable.
- It seems bizarre!
Proof
Note:
- \(\implies\) means a deduction rule. From \(A\) you can deduce \(B\). \(A\implies B\) is not a statement within PA, it is within the meta-logic you’re defining everything in (which can be PA if you want!). (But you can consider \(A\to B\), which is a statement in PA.)
- \(\to\) is a statement in PA. \(\to\) is turned into \(\implies\) by MP.
I’m not distinguishing between \(PA \vdash X\) and \(\square X\). They are equivalent (in the meta-logic).
Use
- (A1) \(\square X \implies \square \square X\). This takes work to prove. Show that given a proof of \(X\), you can turn it into a proof that a proof of \(X\) exist! Mind-bending.
- (A2) \(\square (\square X \to \square \square X)\). (A1) is provable.
- (A3) \(\square (\square (A \to B) \to (\square A \to \square B))\). This is straightforward: if you have a proof of \(A\), and a proof of \(A\to B\), you can make a proof of \(B\) by joining the proofs together and adding a modus ponens at the end. You can furthermore prove this within PA.
- (MP) \(\square A, \square (A\to B)\implies \square B\). The non-meta version of the above.
- (B1) \(\square (A\to B), \square (B\to C), \implies \square (A\to C)\).
- (B2) \(\square (A\to B), \square (A\to (B\to C)), \implies \square (A\to C)\).
Let \(L=\square L \to C\). If \(L\) is provable, then \(C\).
- \(\square (\square L \lra \square (\square L \to C))\). Unpack the box.
- \(\square (\square C\to C)\). Lob’s hypothesis.
- \(\square (\square (\square L \to C) \to (\square(\square L)\to \square C))\) by A3.
- \(\square( \square L \to (\square (\square L)\to \square C))\). I.e., we distributed the \(\square\) in (1). If \(\square L\) and \(\square \square L\), then \(C\). But this means we can get \(C\) from just \(\square L\), because of A2!
- Use A2.
- \(\square (\square L \to \square C)\).
- \(\square (\square L \to C)\).
- \(\square\square (\square L \to C)\).
- \(\square\square L\). KEY! To prove \(L\), you just have to prove \(\square L \to C\), by definition of \(L\)! So you get \(\square\square L\) for free! (Not \(\square L\).) How did we get \(\square\square L\)? We used Lob’s hypothesis and A2.
- \(\square C\).
Questions:
- What happens if we add the axiom \(\square X \implies X\)? What might break? Is Proofs can now include the statement “\(\square X \implies X\)”?
- “if PA asserted its own soundness (in general), PA would become inconsistent.” Does this just mean we can’t add this axiom, or that it being provable leads to a contradiction? (Can you get this from “This theorem is unprovable” type things?)
- Undefinability of truth: no predicate True such that \(True(X)\text{ xor } True(\neg X)\) and \(True(X)\implies X\). Or: if you extend with primitive “True” you get a contradiction.
Intuition: If a program doesn’t have native support for meta-programming, then you can’t program in meta-programming. Here, I mean: take a data structure and splice it into the program itself. (But you could always write a compiler in the language, and apply the compiler to the string…)
This setting is different. To prove something, you have to write down the entire proof. You aren’t allowed to make a program that writes down the proof. Ex. you want to show \(\forall x, P(x)\). You are not allowed to write down a program that given \(x\), spits out a proof of \(x\).
If you try the recursive fix, it’s not clear that the language is still consistent. Perhaps now \(\square \perp\), and hence \(\perp\)!


| Package | Description |
|---|---|
| org.apache.drill.jdbc |
JDBC driver for Drill.
|
| org.apache.drill.jdbc.impl | |
| org.apache.drill.jdbc.test |
| Class and Description |
|---|
| AlreadyClosedSqlException
SQLException for object-already-closed conditions, e.g., calling a method
on a closed
Statement . |
| CachingConnectionFactory
A connection factory that caches connections.
|
| ConnectionFactory
A factory used to get open
Connection instances. |
| ConnectionInfo
An immutable bag of parameters that describes a
Connection . |
| DatabaseMetaDataGetColumnsTest
Test class for Drill's java.sql.DatabaseMetaData.getColumns() implementation.
|
| DatabaseMetaDataTest
Test for Drill's implementation of DatabaseMetaData's methods (other than
those tested separately, e.g.,
getColumn(...) , tested in
DatabaseMetaDataGetColumnsTest )). |
| DrillConnectionConfig
NOTE: DrillConnectionConfig will be changed from a class to an interface.
|
| ITTestShadedJar.AbstractLoaderThread |
| JdbcApiSqlException
SQLException for JDBC API calling-sequence/state problems.
|
| JdbcTestBase |
| JdbcTestBase.ModelAndSchema |
| JdbcTestBase.TestDataConnection |
| NonClosableConnection
A connection decorator that ignores
Connection.close() calls. |
| SQLConversionException |
| SQLConversionOverflowException |
| Class and Description |
|---|
| AlreadyClosedSqlException
SQLException for object-already-closed conditions, e.g., calling a method
on a closed
Statement . |
| DrillConnection
Drill-specific
Connection . |
| DrillConnectionConfig
NOTE: DrillConnectionConfig will be changed from a class to an interface.
|
| DrillDatabaseMetaData
Drill-specific
DatabaseMetaData . |
| DrillResultSet
Drill-specific
ResultSet . |
| DrillStatement
Drill-specific
Statement . |
| JdbcApiSqlException
SQLException for JDBC API calling-sequence/state problems.
|
| Class and Description |
|---|
| Driver
Main class of Apache Drill JDBC driver.
|
| JdbcTestBase |
Copyright © 2021 The Apache Software Foundation. All rights reserved.


Atari Zoo Neural Activity Visualizer
Activations to view
Conv1
Conv2
Conv3
FC
Select model to visualize
Ape-X
A2C
DQN
ES
GA
Rainbow
Run 1
Run 2
Run 3
Alien
Amidar
Assault
Asterix
Asteroids
Atlantis
BankHeist
BattleZone
BeamRider
Berzerk
Bowling
Boxing
Breakout
Centipede
ChopperCommand
CrazyClimber
DemonAttack
DoubleDunk
Enduro
FishingDerby
Freeway
Frostbite
Gopher
Gravitar
Hero
IceHockey
Jamesbond
Kangaroo
Krull
KungFuMaster
MontezumaRevenge
MsPacman
NameThisGame
Phoenix
Pitfall
Pong
PrivateEye
Qbert
Riverraid
RoadRunner
Robotank
Seaquest
Skiing
Solaris
SpaceInvaders
StarGunner
Tennis
TimePilot
Tutankham
UpNDown
Venture
VideoPinball
WizardOfWor
YarsRevenge
Zaxxon
Final
400M Frames
1B Frames
1 hour
2 hours
4 hours
6 hours
10 hours
Initial
[No canvas support]
[No canvas support]
[No canvas support]
Playback controls
Rewind
Stop
Play
SlowMo
FramePlus
FrameMinus
Advanced features
Toggle Draggable Timeline
Toggle Full Activation View
BookmarkURL
Note this webapp borrows from
this very helpful demo
.


Python Deployment Sucks
I don't want to whinge. But I think it's healthy to assess something I currently find difficult, that I might better understand where the deficiencies lie - probably in me - and how they might be addressed.
Deploying a Python program to fellow programmers is easy - just send them the script, they can install the interpreter if they haven't already, and figure it out from there. Use of PyPI helps with this.
But distributing Python as source code is only suitable when distributing to other programmers. For distributing to end-users, it is totally inappropriate. They have no idea what Python is nor whether it is installed, and they should never need to know. While it is arguably possible to lead a user through the process of installing Python so that they can run your script, it is an absolutely ghastly, terrifying experience for them, and they will never willingly use your software again. They want an icon they can double-click on which just runs, period. Considering the potential complications such as requiring side-by-side installations of multiple versions of Python, I don't see this as a workable solution for any serious software to be used by non-programmers.
Thus we have the projects to convert Python source into stand-alone executables. I've used py2exe and bbfreeze, and both do a brilliant jobs at what they do. But there are problems with the use of such tools.
With a compiled language, you can simply give the binary to someone with the right operating system, and they can double click it to run it. Better still, this process of compiling and linking the executable for an end-user is the exact same process that you perform all the time for yourself while you are developing the software. There is no additional overhead required to create an executable for end-users (presuming the simplest case of end-users on the same operating system that you are developing on.)
With Python this is not true. The whole 'create a distributable binary' step is an additional headache over and above whatever effort you've already gone to to make the program run in your own development environment. Because of this, in a very pragmatic sense, deployment of Python programs requires a whole extra set of work that deployment of compiled programs does not.
Presumably this is true for all interpreted languages. I've never worked seriously with one before. I should go read about how Ruby and more venerable languages approach and solve these problems.
This situation would not be quite so bad if there was a clear way to proceed, but for me, my first few binary distributable projects have been a real ramshackle hack through the docs of py2exe and the various projects which compete with it. Lots has to be figured out to choose a tool, and then to get it working with your project. This has to be done over for each OS you choose to deploy on. For me, it has been a real time-sink, and I'm still not really happy with the results.
The resulting executables are bulked out by including the interpreter with each one. This is not a suitable technique for including a bunch of lightweight command-line executables to augment your shell -something which, otherwise, you would think Python should be absolutely ideal for. I guess in some circumstances you could work around this somewhat by using an svn-style 'one executable, many commands' interface, although this isn't always suitable.
I'd prefer it if, instead of bundling an interpreter, py2exe looked to see if a suitable version of Python was already installed, and if not, downloaded and installed it side-by-side with any existing installations and then used it to execute your program.
I can't figure out how to tweak the output from py2exe such that my executable file isn't buried in a menagerie of various other .dll, .zip and .exe files. An end-user would have no idea what to click on. I want to make it easy for my users, by creating an executable by itself, with a subdirectory of binary dependencies (and a different subdirectory of data.) I could sidestep this by creating an installer that creates a shortcut to the relevant executable - but again, that would be yet another day of needless make-work, which has to be undertaken over again for each OS you plan to deploy on.
For large projects, these sorts of problems are surmountable - they have enough man hours to soak it up.  Writing a hundred-line script to solve your friend's particular problem and emailing him the binary is awkward to say the least. Entries to the PyWeek 'game in a week' contest are substantially impacted - producing those binaries burns up hours and hours, when you have precious few to spare, and very few of the presumably 'average developers' taking part managed to create binaries that just worked for everyone.
In Python's favour, many of the wrinkles and complications that arise during the process are due to cross-platform issues. Getting hold of binary dependencies for other operating systems, stuff like that. The only reason this seems harder in Python than it is in other languages, is that when using other languages you often don't even attempt to deploy across multiple operating systems. The apparent difficulty of doing this in Python is in actual fact just an illusion caused by being able to attempt it in the first place.
Doubtless some of my other perceived problems lie in my own misunderstandings. Nobody else seems to struggle with this as much as I do. Comments are welcome.
Brought to you from the a-crap-post-is-better-than-no-post-at-all dept.


Blockchain
Transactions
Blocks
Actions
Tokens
Dapp
RamPriceChart
Voting Progress
Account
Accounts
Block Producers
Reponse Team
BlockID
Account
TxnID
BlockHash


Learn STEM with free online courses and MOOCs from Universitat Politècnica de València, University of Illinois at Urbana-Champaign, École Polytechnique Fédérale de Lausanne, Universidad de los Andes and other top universities around the world. Read reviews to decide if a class is right for you.
Popular Subjects:
| # | Course Name | Level | Production Year |
|---|---|---|---|
| 1 | Atmospheric Chemistry | Undergraduate | 2013 |
| 2 | Techniques of artificial intelligence | Undergraduate | 2019 |
| 3 | Advanced animal behaviour | Graduate | 2000 |
| 4 | Fundamentals of Neuroscience | Undergraduate | 2020 |
| 5 | Thermodynamics and Phase Equilibria | Undergraduate | 2020 |
| 6 | AnatomyX-Muscuskeletal Cases | Undergraduate | 2020 |
| 7 | Scientific Methods and Research | Undergraduate | 2020 |
| 8 | Genetic Models for Animal Breeding | Graduate | 2019 |
| 9 | Quantum Mechanics for Scientists and Engineers | Graduate | 2016 |
| 10 | Introduction to Deep Earth Science | Undergraduate | 2018 |


I had the idea of extending the return values to support lo-dash or underscore so the developer could make use of sorting functions (among everything else these libraries support).
Though, after talking with @kadamwhite - I feel that we should make this as lightweight as possible and let the developer choose how they want to take action on the return results.
Still opening the Issue for discussion on this topic.
I do this with LayoutManager when you request a collection of child views. For instance:
// The return value from `getViews` is a wrapped Lo-Dash/Underscore instance.
var mainView = parent.getViews().find(function(view) {
return view.name === "main";
});
I had the idea of extending the return values to support lo-dash or underscore so the developer could make use of sorting functions (among everything else these libraries support).
Though, after talking with @kadamwhite - I feel that we should make this as lightweight as possible and let the developer choose how they want to take action on the return results.
Still opening the Issue for discussion on this topic.
I do this with LayoutManager when you request a collection of child views. For instance:


Javascript & AngularJs
I've working with plain Javascript since 2005.
Paralelly, I started to work with AngularJs since 2011 (v0.6),
and I've been using it in a daily basis both on a commercial an personal
perspective since then.
Testing is a must for me (unit + functional + integration).
HTML(5) & CSS(3)
I'm building HTML & CSS based webapps since 2002. On the recent years,
I've embraced the capabilities of HTML5 and CSS3 (including LESS & SASS) in order
to build solutions that
not only have to work fine, but also have to look really well.
I really like to work on UI / UX topics, and I build my apps based on the
mobile-first principle.
HTML(5) & CSS(3)
I've experience working with a different range of technologies, frameworks and tools
such as
NodeJs, Express, GruntJs, Bower and MongoDB
Talking about deployment, I've been working with TeamCity, Jenkins and Heroku.


No Food Left Behind, also known as a NFLB, is a food donation app, that doesn't want food to go to waste. The food that you have leftover, should never be wasted and should be donated for a good cause. Click the button that can classify your role in our society. If you are a volunteer that happens to be in high school, you can get credits from this!


{% extends 'marketplace/base.html' %} {% block extraheaders %} {% load static %} {% endblock %} {% block pagecontents %} {% load markdown_deux_tags %}
Solve for Good is a platform for social good organizations to post data projects they need help with, for volunteers to help scope those projects into well-defined problems, and to help solve those problems.
{{ newspiece.contents|markdown }}
{% endif %}
Projects our volunteers are working on.
Organizations working on social good.
Volunteers that make our projects a reality.


Voat chat enhancer v0.2.1
I don't know if I'll be able to figure out how to push updates so... if I don't:
check here for updates


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a supervisory & control system for networks of MQTT clients
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iCare
NEW ERA OF TREATMENT
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Carbon Dioxide Capture and Sequestration: Storage Safety and Security
To be a viable climate mitigation option, sequestration of carbon dioxide (CO2) must be safe and secure. Current scientific and technical knowledge, coupled with ongoing project experience, confirms that well-selected, well-designed, and well-managed geologic sequestration sites provide a safe way to permanently sequester CO2.
Proper site selection and careful monitoring are important factors in minimizing and identifying any potential impacts to drinking water, human health, and ecosystems. EPA has put into place a regulatory framework to ensure safe storage of CO2.
Answers to common questions about the safety and security of carbon capture and sequestration (CCS) are provided below.
Is carbon dioxide stored in underground caverns?
No. Carbon dioxide (CO2) is compressed into a liquid then injected into solid, but porous rock. These rock formations are located thousands of feet underneath cap rock impermeable to CO2. The CO2 becomes trapped in the pore spaces of the rock. Over time, the CO2 dissolves into the pore water or may be transformed into solid minerals.
Can injected carbon dioxide cause earthquakes?
EPA’s Underground Injection Control (UIC) regulations for Class VI wells helps ensure that CO2 injection activities will not result in significant seismic activity. Geologic sequestration sites will be located in seismically stable, non-volcanic areas. Pressure build-up resulting from CO2 injection could cause minor seismic events, though most of these would be too small to be noticed.
Can the injected carbon dioxide leak into underground sources of drinking water?
Proper management of CO2 sequestration sites will reduce the potential risks to the surrounding area and resources posed by CO2 injection and sequestration activities. This management takes into account the four major pathways through which injected fluids can migrate up into underground sources of drinking water (USDWs). The four pathways are:
- The drilled holes (“well bores”) of improperly constructed injection wells
- Improperly plugged deep wells in the injection area
- Transmissive faults or fractures in the surrounding rock formations
- Lateral and upward movement into hydraulically connected USDWs.
Requirements of the UIC Class VI Rule and Subpart RR-Geologic Sequestration of Carbon Dioxide of the Greenhouse Gas Reporting Program require proper management of these sites. EPA’s regulations also provide required actions for remediation in the event of a leak, ensuring minimal impact on human health and the environment in the surrounding area.
Could large amounts of carbon dioxide be suddenly released?
No. The locations for geological sequestration sites are selected due to their ability to trap CO2 thousands of feet below the surface, minimizing risks to groundwater or the atmosphere. Natural releases of CO2 have occurred in stratified lakes such as Lake Nyos in Cameroon. Carbon dioxide can also seep up through the soil at active volcanoes, such as Mammoth Mountain in eastern California, or through faults or fractures. Events such as these cannot occur from geologically sequestered CO2 sites.


Trees are populated by a JavaScript array supplied in the data
property of the DuxTree
component. The array consists of objects representing each node in the tree. Because the tree is specified via a
property of DuxTree
, modifying the array will modify the tree. Here is a simple two-node tree.
const treeData = [ { id: 'node1', label: 'Title For Node 1' }, { id: 'node2', label: 'Title for Node 2' } ];
The id
and label
properties are required for every node. The id
must be
unique and can be a string or number. The label
can be a string, JSX tag, or a function. The
section on customizing appearance goes into more detail on labels.
Nodes can have a children
property that contains an array of child nodes. Children can be
lazy-loaded via a callback. See the section on loading on demand for details. Here is an example of a node
with children. The defaultExpanded
property controls whether or not a node's children are shown
when the tree is rendered. The default is false
.
const treeData = [ { id: 'folder1', label: 'Node With Children', defaultExpanded: true, children: [ { id: 'child1', label: 'Child 1 Title' }, { id: 'child2', label: 'Child 2 Title' } ] } ];


Grid
Installation
Grid is available through CocoaPods. To install it, simply add the following line to your Podfile:
pod 'GridNet'
Author
Rodrigo Carvalho<EMAIL_ADDRESS>License
Grid is available under the MIT license. See the LICENSE file for more info.


Fruits
Oranges, apples, bananas, grapes, pears, peaches, watermelon.
The food we eat plays a vital role in how we look and feel. Regular exercise is important but according to research
Oranges, apples, bananas, grapes, pears, peaches, watermelon.
Bread, cereal, crackers, oatmeal, quinoa, popcorn and rice.
poultry and fish: Sirloin steak, ground beef, pork tenderloin, venison, chicken breast, salmon, tilapia and cod.
Yogurt, cottage cheese, low-fat milk and cheese.
Take a step forward with your right leg and bend your right knee as you do so,
stopping when your thigh is parallel to the ground. Ensure that your right knee doesn’t extend past your right foot.
Bend your elbows and begin to lower your body down to the floor. When your chest grazes it,
extend your elbows and return to the start. Focus on keeping your elbows close to your body during the movement.
 Be certain not to arch your back.
Let your arms hang straight down. Ensure your neck is in line with your back and your core is engaged.
It’s clear, but we’ll mention it anyway – going to the gym is good for your health and fitness! Put simply,
during exercise we increase our cardiovascular fitness through strengthening our heart
and lungs and we increase our strength through creating lean muscle.
The Australian Government Department of Health recommends five hours of moderate exercise per week,
including muscle strengthening activities at least twice per week.
Studies show that regular exercise and an increase in strength
and cardio fitness levels can help reduce the risk of health concerns and diseases.


% for (var chunk of webpack.chunks) { for (var file of chunk.files) { if (file.match(/\.(js|css)$/)) { %> <% }}} %>
It seems you do not have JavaScript enabled. This site won't be of much use without it.
Here are the instructions how to enable JavaScript in your web browser.


Before Change
lock_path = filepath + ".lock"
with lockfile.LockFile(lock_path):
if not os.path.exists(filepath):
with open(filepath, "wb") as fh:
pickle.dump(datamanager, fh, -1)
return filepath
After Change
def save_datamanager(self, datamanager):
self._make_internals_directory()
filepath = self._get_datamanager_pickle_filename()
lock_path = filepath + ".lock"
with lockfile.LockFile(lock_path):
if not os.path.exists(filepath):
with tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(
filepath), delete=False) as fh:
pickle.dump(datamanager, fh, -1)
tempname = fh.name
os.rename(tempname, filepath)
return filepath
def load_datamanager(self):


| Baking powder |
1 Tsp |
1/4 teaspoon baking soda plus ½ teaspoon cream of tartar |
| Baking soda |
1 Tsp |
4 teaspoons baking powder |
| Brown sugar |
1 Cup |
1 cup white sugar |
| Butter |
1 Cup |
1 cup margarine |
| Chocolate |
1 Cup |
18 tablespoons unsweetened cocoa plus 6 tablespoon shortening or vegetable oil |
| Cocoa Powder |
1/4 cup |
1/6 Cup Chocolate Grated |
| Cream (half and half) |
1 Cup |
⅞ cup milk plus 1 tablespoon butter |
| Cream (heavy) |
1 Cup |
1 cup evaporated milk OR 3/4 cup milk plus 1/3 cup butter |
| Cream of tartar |
1 Tsp |
2 teaspoons lemon juice or vinegar |
| Buttermilk |
1 Cup |
1 cup yogurt OR 1 tablespoon lemon juice or vinegar plus 1 cup milk |
| Egg |
1 |
½ a mashed banana with ½ teaspoon baking powder |
| Self-Rising Flour |
1 Cup |
⅞ cup all-purpose flour plus 1 ½ teaspoons baking powder and ½ teaspoon of salt |
| Honey |
1 Cup |
1 ¼ cup white sugar plus 1/3 cup water |
| Lemon juice |
1 Tsp |
½ teaspoon vinegar |
| condensed milk |
1 Cup |
3/4 cup white sugar mixed with 1/2 cup water and 1 1/8 cups dry powdered milk. Bring to a boil and cook, stirring frequently, until thickened, about 20 minutes |
| White sugar |
1 Cup |
1 cup brown sugar |


The word-spacing property can take either a positive or negative length value. The higher the value, the farther apart the words; the lower the value, the closer together the words.


{{gettext("Origin Tokens are launching soon!")}}
{{gettext("Origin Rewards")}}
{{gettext("Earn your stake in the Origin network today. Users can earn Origin Tokens (OGN) by verifying their profiles, referring friends and family to Origin, sharing news about Origin on social media, and making purchases from our featured sellers.")}} {{gettext("Not available in the United States")}}
{{gettext("Commissions")}}
{{gettext("Promote your listings across the Origin flagship app and third-party apps with Origin Commissions. Pay with OGN to get featured prominently in search results. Application developers and marketplace operators earn Origin Tokens (OGN) by helping sellers complete their sales.")}}
{{gettext("Payments")}}
{{gettext("Use the Origin Tokens (OGN) that you've earned in Origin Rewards to pay for goods and services. Sellers earn larger stakes in the network by accepting OGN as a preferred payment method.")}}
{{gettext("Research and Development")}}
{{gettext("Origin Nodes")}}
{{gettext("Stake your OGN to run an Origin Node and earn more OGN by helping to replicate and validate profile and listing data on the network.")}}
{{gettext("Origin Relayer")}}
{{gettext("Developers can pay for usage of Origin Meta Transactions Relayers with OGN. Meta Transactions reduce user friction and encourage adoption and retention.")}}
{{gettext("Governance")}}
{{gettext("Everyone has a vote on the Origin Platform. Token holders will participate in important community votes to define future initiatives.")}}


2020 Corvette Convertible Uses Hardtop Because C7 'Vert Wasn't 'Cool'
The company briefly considered using a fabric roof for the C8 but dropped the idea.
by Chris BruceThe C8 Chevrolet Corvette Convertible marks the first time for the car to get a folding hardtop. At least part of the reason for making this major shakeup was because the designers thought a fabric roof didn't fit the style of the car.
"We looked at a convertible soft top early on, but most of our competitors are doing retractable hardtops," Corvette Chief Engineer Tadge Juechter told GM Authority. "In fact, one of the reasons that the C7 Convertible didn’t sell as well is that it didn’t look as cool."
Gallery: 2020 Chevy Corvette Stingray Convertible
Despite what Juechter says about competitors opting for retractable hardtops, the opposite actually seems to be true. Spy shots indicate the next-gen BMW 4 Series convertible switches to a fabric panel. There are also rumors of the new SL-Class using a soft top. BMW made a similar decision with the latest Z4 to move away from hardtop.

The retracting hardtop mechanism has six electric motors and two composite pieces that fold together. The components reportedly only add between 80 pounds (36 kilograms) and 102 pounds (46 kilograms) to the convertible in comparison to the coupe. The rear deck of the droptop has a heat-shielded space for stowing the roof so that the nearby engine's warmth doesn't damage the top.
Drivers can open or close the roof in 16 seconds at up to 30 miles per hour (48 kilometers per hour). A power-operated panel between the seats can move into place for greater protection from the wind.
The Corvette Convertible will go into production in 2020 and should be on sale in the early part of the year.
Source: GM Authority


Welcome to the Virtual Tokamak section. A tokamak is a machine that confines and heats plasma in order to produce fusion energy. These modules put you in the control seat of a tokamak fusion reactor. You will explore some of the variables relevant to fusion research and tune a tokamak to make it run optimally.
This applet shows how magnetic fields control and contain a plasma when it is too hot to contain by any other conventional methods. To get the longest containment time, you can manually and automatically turn on/off magnetic controls.
This applet lets you plug in waveforms for all three parameters. The output is likewise shown as a graph so you can see how the plasma changes over time.
This version of the Virtual Tokamak allows you to type in the parameter values as well as use the slider bars. More control of the reactor means better performance which means an even higher score!


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Before Change
Send the command to turn the light on.
self._brightness = kwargs.get(ATTR_BRIGHTNESS, 255)
self.device.send_cmd("xdim {}".format(self._brightness))
self._controller.read_data()
self._state = True
def turn_off(self, **kwargs):
Send the command to turn the light on.
After Change
def turn_on(self, **kwargs):
Send the command to turn the light on.
self._brightness = kwargs.get(ATTR_BRIGHTNESS, 255)
with mochad.REQ_LOCK:
self.device.send_cmd("xdim {}".format(self._brightness))
self._controller.read_data()
self._state = True
def turn_off(self, **kwargs):
Send the command to turn the light on.


Barcelona's Architect Antoni Gaudí's incredible buildings bring millions ... ... [rest of main page content] ...


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SUBROUTINE SLASD2( NL, NR, SQRE, K, D, Z, ALPHA, BETA, U, LDU, VT, $ LDVT, DSIGMA, U2, LDU2, VT2, LDVT2, IDXP, IDX, $ IDXC, IDXQ, COLTYP, INFO ) * * -- LAPACK auxiliary routine (version 3.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * November 2006 * * .. Scalar Arguments .. INTEGER INFO, K, LDU, LDU2, LDVT, LDVT2, NL, NR, SQRE REAL ALPHA, BETA * .. * .. Array Arguments .. INTEGER COLTYP( * ), IDX( * ), IDXC( * ), IDXP( * ), $ IDXQ( * ) REAL D( * ), DSIGMA( * ), U( LDU, * ), $ U2( LDU2, * ), VT( LDVT, * ), VT2( LDVT2, * ), $ Z( * ) * .. * * Purpose * ======= * * SLASD2 merges the two sets of singular values together into a single * sorted set. Then it tries to deflate the size of the problem. * There are two ways in which deflation can occur: when two or more * singular values are close together or if there is a tiny entry in the * Z vector. For each such occurrence the order of the related secular * equation problem is reduced by one. * * SLASD2 is called from SLASD1. * * Arguments * ========= * * NL (input) INTEGER * The row dimension of the upper block. NL >= 1. * * NR (input) INTEGER * The row dimension of the lower block. NR >= 1. * * SQRE (input) INTEGER * = 0: the lower block is an NR-by-NR square matrix. * = 1: the lower block is an NR-by-(NR+1) rectangular matrix. * * The bidiagonal matrix has N = NL + NR + 1 rows and * M = N + SQRE >= N columns. * * K (output) INTEGER * Contains the dimension of the non-deflated matrix, * This is the order of the related secular equation. 1 <= K <=N. * * D (input/output) REAL array, dimension (N) * On entry D contains the singular values of the two submatrices * to be combined. On exit D contains the trailing (N-K) updated * singular values (those which were deflated) sorted into * increasing order. * * Z (output) REAL array, dimension (N) * On exit Z contains the updating row vector in the secular * equation. * * ALPHA (input) REAL * Contains the diagonal element associated with the added row. * * BETA (input) REAL * Contains the off-diagonal element associated with the added * row. * * U (input/output) REAL array, dimension (LDU,N) * On entry U contains the left singular vectors of two * submatrices in the two square blocks with corners at (1,1), * (NL, NL), and (NL+2, NL+2), (N,N). * On exit U contains the trailing (N-K) updated left singular * vectors (those which were deflated) in its last N-K columns. * * LDU (input) INTEGER * The leading dimension of the array U. LDU >= N. * * VT (input/output) REAL array, dimension (LDVT,M) * On entry VT' contains the right singular vectors of two * submatrices in the two square blocks with corners at (1,1), * (NL+1, NL+1), and (NL+2, NL+2), (M,M). * On exit VT' contains the trailing (N-K) updated right singular * vectors (those which were deflated) in its last N-K columns. * In case SQRE =1, the last row of VT spans the right null * space. * * LDVT (input) INTEGER * The leading dimension of the array VT. LDVT >= M. * * DSIGMA (output) REAL array, dimension (N) * Contains a copy of the diagonal elements (K-1 singular values * and one zero) in the secular equation. * * U2 (output) REAL array, dimension (LDU2,N) * Contains a copy of the first K-1 left singular vectors which * will be used by SLASD3 in a matrix multiply (SGEMM) to solve * for the new left singular vectors. U2 is arranged into four * blocks. The first block contains a column with 1 at NL+1 and * zero everywhere else; the second block contains non-zero * entries only at and above NL; the third contains non-zero * entries only below NL+1; and the fourth is dense. * * LDU2 (input) INTEGER * The leading dimension of the array U2. LDU2 >= N. * * VT2 (output) REAL array, dimension (LDVT2,N) * VT2' contains a copy of the first K right singular vectors * which will be used by SLASD3 in a matrix multiply (SGEMM) to * solve for the new right singular vectors. VT2 is arranged into * three blocks. The first block contains a row that corresponds * to the special 0 diagonal element in SIGMA; the second block * contains non-zeros only at and before NL +1; the third block * contains non-zeros only at and after NL +2. * * LDVT2 (input) INTEGER * The leading dimension of the array VT2. LDVT2 >= M. * * IDXP (workspace) INTEGER array, dimension (N) * This will contain the permutation used to place deflated * values of D at the end of the array. On output IDXP(2:K) * points to the nondeflated D-values and IDXP(K+1:N) * points to the deflated singular values. * * IDX (workspace) INTEGER array, dimension (N) * This will contain the permutation used to sort the contents of * D into ascending order. * * IDXC (output) INTEGER array, dimension (N) * This will contain the permutation used to arrange the columns * of the deflated U matrix into three groups: the first group * contains non-zero entries only at and above NL, the second * contains non-zero entries only below NL+2, and the third is * dense. * * IDXQ (input/output) INTEGER array, dimension (N) * This contains the permutation which separately sorts the two * sub-problems in D into ascending order. Note that entries in * the first hlaf of this permutation must first be moved one * position backward; and entries in the second half * must first have NL+1 added to their values. * * COLTYP (workspace/output) INTEGER array, dimension (N) * As workspace, this will contain a label which will indicate * which of the following types a column in the U2 matrix or a * row in the VT2 matrix is: * 1 : non-zero in the upper half only * 2 : non-zero in the lower half only * 3 : dense * 4 : deflated * * On exit, it is an array of dimension 4, with COLTYP(I) being * the dimension of the I-th type columns. * * INFO (output) INTEGER * = 0: successful exit. * < 0: if INFO = -i, the i-th argument had an illegal value. * * Further Details * =============== * * Based on contributions by * Ming Gu and Huan Ren, Computer Science Division, University of * California at Berkeley, USA * * ===================================================================== * * .. Parameters .. REAL ZERO, ONE, TWO, EIGHT PARAMETER ( ZERO = 0.0E+0, ONE = 1.0E+0, TWO = 2.0E+0, $ EIGHT = 8.0E+0 ) * .. * .. Local Arrays .. INTEGER CTOT( 4 ), PSM( 4 ) * .. * .. Local Scalars .. INTEGER CT, I, IDXI, IDXJ, IDXJP, J, JP, JPREV, K2, M, $ N, NLP1, NLP2 REAL C, EPS, HLFTOL, S, TAU, TOL, Z1 * .. * .. External Functions .. REAL SLAMCH, SLAPY2 EXTERNAL SLAMCH, SLAPY2 * .. * .. External Subroutines .. EXTERNAL SCOPY, SLACPY, SLAMRG, SLASET, SROT, XERBLA * .. * .. Intrinsic Functions .. INTRINSIC ABS, MAX * .. * .. Executable Statements .. * * Test the input parameters. * INFO = 0 * IF( NL.LT.1 ) THEN INFO = -1 ELSE IF( NR.LT.1 ) THEN INFO = -2 ELSE IF( ( SQRE.NE.1 ) .AND. ( SQRE.NE.0 ) ) THEN INFO = -3 END IF * N = NL + NR + 1 M = N + SQRE * IF( LDU.LT.N ) THEN INFO = -10 ELSE IF( LDVT.LT.M ) THEN INFO = -12 ELSE IF( LDU2.LT.N ) THEN INFO = -15 ELSE IF( LDVT2.LT.M ) THEN INFO = -17 END IF IF( INFO.NE.0 ) THEN CALL XERBLA( 'SLASD2', -INFO ) RETURN END IF * NLP1 = NL + 1 NLP2 = NL + 2 * * Generate the first part of the vector Z; and move the singular * values in the first part of D one position backward. * Z1 = ALPHA*VT( NLP1, NLP1 ) Z( 1 ) = Z1 DO 10 I = NL, 1, -1 Z( I+1 ) = ALPHA*VT( I, NLP1 ) D( I+1 ) = D( I ) IDXQ( I+1 ) = IDXQ( I ) + 1 10 CONTINUE * * Generate the second part of the vector Z. * DO 20 I = NLP2, M Z( I ) = BETA*VT( I, NLP2 ) 20 CONTINUE * * Initialize some reference arrays. * DO 30 I = 2, NLP1 COLTYP( I ) = 1 30 CONTINUE DO 40 I = NLP2, N COLTYP( I ) = 2 40 CONTINUE * * Sort the singular values into increasing order * DO 50 I = NLP2, N IDXQ( I ) = IDXQ( I ) + NLP1 50 CONTINUE * * DSIGMA, IDXC, IDXC, and the first column of U2 * are used as storage space. * DO 60 I = 2, N DSIGMA( I ) = D( IDXQ( I ) ) U2( I, 1 ) = Z( IDXQ( I ) ) IDXC( I ) = COLTYP( IDXQ( I ) ) 60 CONTINUE * CALL SLAMRG( NL, NR, DSIGMA( 2 ), 1, 1, IDX( 2 ) ) * DO 70 I = 2, N IDXI = 1 + IDX( I ) D( I ) = DSIGMA( IDXI ) Z( I ) = U2( IDXI, 1 ) COLTYP( I ) = IDXC( IDXI ) 70 CONTINUE * * Calculate the allowable deflation tolerance * EPS = SLAMCH( 'Epsilon' ) TOL = MAX( ABS( ALPHA ), ABS( BETA ) ) TOL = EIGHT*EPS*MAX( ABS( D( N ) ), TOL ) * * There are 2 kinds of deflation -- first a value in the z-vector * is small, second two (or more) singular values are very close * together (their difference is small). * * If the value in the z-vector is small, we simply permute the * array so that the corresponding singular value is moved to the * end. * * If two values in the D-vector are close, we perform a two-sided * rotation designed to make one of the corresponding z-vector * entries zero, and then permute the array so that the deflated * singular value is moved to the end. * * If there are multiple singular values then the problem deflates. * Here the number of equal singular values are found. As each equal * singular value is found, an elementary reflector is computed to * rotate the corresponding singular subspace so that the * corresponding components of Z are zero in this new basis. * K = 1 K2 = N + 1 DO 80 J = 2, N IF( ABS( Z( J ) ).LE.TOL ) THEN * * Deflate due to small z component. * K2 = K2 - 1 IDXP( K2 ) = J COLTYP( J ) = 4 IF( J.EQ.N ) $ GO TO 120 ELSE JPREV = J GO TO 90 END IF 80 CONTINUE 90 CONTINUE J = JPREV 100 CONTINUE J = J + 1 IF( J.GT.N ) $ GO TO 110 IF( ABS( Z( J ) ).LE.TOL ) THEN * * Deflate due to small z component. * K2 = K2 - 1 IDXP( K2 ) = J COLTYP( J ) = 4 ELSE * * Check if singular values are close enough to allow deflation. * IF( ABS( D( J )-D( JPREV ) ).LE.TOL ) THEN * * Deflation is possible. * S = Z( JPREV ) C = Z( J ) * * Find sqrt(a**2+b**2) without overflow or * destructive underflow. * TAU = SLAPY2( C, S ) C = C / TAU S = -S / TAU Z( J ) = TAU Z( JPREV ) = ZERO * * Apply back the Givens rotation to the left and right * singular vector matrices. * IDXJP = IDXQ( IDX( JPREV )+1 ) IDXJ = IDXQ( IDX( J )+1 ) IF( IDXJP.LE.NLP1 ) THEN IDXJP = IDXJP - 1 END IF IF( IDXJ.LE.NLP1 ) THEN IDXJ = IDXJ - 1 END IF CALL SROT( N, U( 1, IDXJP ), 1, U( 1, IDXJ ), 1, C, S ) CALL SROT( M, VT( IDXJP, 1 ), LDVT, VT( IDXJ, 1 ), LDVT, C, $ S ) IF( COLTYP( J ).NE.COLTYP( JPREV ) ) THEN COLTYP( J ) = 3 END IF COLTYP( JPREV ) = 4 K2 = K2 - 1 IDXP( K2 ) = JPREV JPREV = J ELSE K = K + 1 U2( K, 1 ) = Z( JPREV ) DSIGMA( K ) = D( JPREV ) IDXP( K ) = JPREV JPREV = J END IF END IF GO TO 100 110 CONTINUE * * Record the last singular value. * K = K + 1 U2( K, 1 ) = Z( JPREV ) DSIGMA( K ) = D( JPREV ) IDXP( K ) = JPREV * 120 CONTINUE * * Count up the total number of the various types of columns, then * form a permutation which positions the four column types into * four groups of uniform structure (although one or more of these * groups may be empty). * DO 130 J = 1, 4 CTOT( J ) = 0 130 CONTINUE DO 140 J = 2, N CT = COLTYP( J ) CTOT( CT ) = CTOT( CT ) + 1 140 CONTINUE * * PSM(*) = Position in SubMatrix (of types 1 through 4) * PSM( 1 ) = 2 PSM( 2 ) = 2 + CTOT( 1 ) PSM( 3 ) = PSM( 2 ) + CTOT( 2 ) PSM( 4 ) = PSM( 3 ) + CTOT( 3 ) * * Fill out the IDXC array so that the permutation which it induces * will place all type-1 columns first, all type-2 columns next, * then all type-3's, and finally all type-4's, starting from the * second column. This applies similarly to the rows of VT. * DO 150 J = 2, N JP = IDXP( J ) CT = COLTYP( JP ) IDXC( PSM( CT ) ) = J PSM( CT ) = PSM( CT ) + 1 150 CONTINUE * * Sort the singular values and corresponding singular vectors into * DSIGMA, U2, and VT2 respectively. The singular values/vectors * which were not deflated go into the first K slots of DSIGMA, U2, * and VT2 respectively, while those which were deflated go into the * last N - K slots, except that the first column/row will be treated * separately. * DO 160 J = 2, N JP = IDXP( J ) DSIGMA( J ) = D( JP ) IDXJ = IDXQ( IDX( IDXP( IDXC( J ) ) )+1 ) IF( IDXJ.LE.NLP1 ) THEN IDXJ = IDXJ - 1 END IF CALL SCOPY( N, U( 1, IDXJ ), 1, U2( 1, J ), 1 ) CALL SCOPY( M, VT( IDXJ, 1 ), LDVT, VT2( J, 1 ), LDVT2 ) 160 CONTINUE * * Determine DSIGMA(1), DSIGMA(2) and Z(1) * DSIGMA( 1 ) = ZERO HLFTOL = TOL / TWO IF( ABS( DSIGMA( 2 ) ).LE.HLFTOL ) $ DSIGMA( 2 ) = HLFTOL IF( M.GT.N ) THEN Z( 1 ) = SLAPY2( Z1, Z( M ) ) IF( Z( 1 ).LE.TOL ) THEN C = ONE S = ZERO Z( 1 ) = TOL ELSE C = Z1 / Z( 1 ) S = Z( M ) / Z( 1 ) END IF ELSE IF( ABS( Z1 ).LE.TOL ) THEN Z( 1 ) = TOL ELSE Z( 1 ) = Z1 END IF END IF * * Move the rest of the updating row to Z. * CALL SCOPY( K-1, U2( 2, 1 ), 1, Z( 2 ), 1 ) * * Determine the first column of U2, the first row of VT2 and the * last row of VT. * CALL SLASET( 'A', N, 1, ZERO, ZERO, U2, LDU2 ) U2( NLP1, 1 ) = ONE IF( M.GT.N ) THEN DO 170 I = 1, NLP1 VT( M, I ) = -S*VT( NLP1, I ) VT2( 1, I ) = C*VT( NLP1, I ) 170 CONTINUE DO 180 I = NLP2, M VT2( 1, I ) = S*VT( M, I ) VT( M, I ) = C*VT( M, I ) 180 CONTINUE ELSE CALL SCOPY( M, VT( NLP1, 1 ), LDVT, VT2( 1, 1 ), LDVT2 ) END IF IF( M.GT.N ) THEN CALL SCOPY( M, VT( M, 1 ), LDVT, VT2( M, 1 ), LDVT2 ) END IF * * The deflated singular values and their corresponding vectors go * into the back of D, U, and V respectively. * IF( N.GT.K ) THEN CALL SCOPY( N-K, DSIGMA( K+1 ), 1, D( K+1 ), 1 ) CALL SLACPY( 'A', N, N-K, U2( 1, K+1 ), LDU2, U( 1, K+1 ), $ LDU ) CALL SLACPY( 'A', N-K, M, VT2( K+1, 1 ), LDVT2, VT( K+1, 1 ), $ LDVT ) END IF * * Copy CTOT into COLTYP for referencing in SLASD3. * DO 190 J = 1, 4 COLTYP( J ) = CTOT( J ) 190 CONTINUE * RETURN * * End of SLASD2 * END


The jiggle demonstration model (repast/demo/jiggle) is a simple example of a trivial network model, and the JinGirNew network model (repast/demo/jinGirNew) is a more complicated and realistic example.
The Node and Edge interfaces provide some simple methods for working with network nodes and edges. The API documentation should be clear, but the intention is that a Node keeps track of its incoming and outgoing edges as well as an optional label while an Edge keeps track of the Nodes to which and from which it is joined. In addition, an Edge also has methods for getting and setting the strength of the edge (typically to a value of 1), as well as an optional label and type. The type of the edge refers to the type of network that the edge defines (e.g. kinship, business and so forth).
Creating a link (an Edge) between two Nodes can be done is either of two ways. The first way:
// this sets up the Nodes
DefaultNode nodeA = new DefaultNode("A");
DefaultNode nodeB = new DefaultNode("B");
// constructs a DefaultEdge from nodeA to nodeB
// with a default strength of 1
// nodeA --> nodeB
DefaultEdge edge = new DefaultEdge(nodeA, nodeB);
nodeA.addOutEdge(edge);
nodeB.addInEdge(edge);
Here two Nodes are created, and Edge is created linking nodeA to nodeB. This Edge is then added as an outgoing Edge to nodeA and as an incoming Edge to nodeB.
The second and preferrable way is to use the EdgeFactory class to automate edge creation and linking for you. For example,
// this sets up the Nodes
DefaultNode nodeA = new DefaultNode("A");
DefaultNode nodeB = new DefaultNode("B");
// creates a DefaultEdge and links nodeA to nodeB
EdgeFactory.createEdge(nodeA, nodeB);
Here EdgeFactory creates a DefaultEdge and links nodeA and nodeB appropriately. EdgeFactory also as methods for setting edge strength labels and so forth during edge creation. In addition, you can also supply your own Edge implementing classes and EdgeFactory can use those. For example,
// this sets up the Nodes
DefaultNode nodeA = new DefaultNode("A");
DefaultNode nodeB = new DefaultNode("B");
// creates a DefaultEdge and links nodeA to nodeB
EdgeFactory.linkNodes(nodeA, nodeB, new MyEdge());
Here, EdgeFactory will link nodeA to nodeB using MyEdge. See the API docs for EdgeFactory for more info.
A Node can get a reference to another Node via the Edge that connects them. For example,
// this sets up the Nodes
DefaultNode nodeA = new DefaultNode("A");
DefaultNode nodeB = new DefaultNode("B");
EdgeFactory.createEdge(nodeA, nodeB);
// set up is complete. The following shows
// how to get a reference to nodeB given only
// nodeA.
ArrayList outEdges = nodeA.getOutEdges();
// we know there is only a single edge from nodeA
// because we only added one. Typically you'd
// iterate through or choose a specific edge based
// on some criteria.
DefaultEdge someEdge = (DefaultEdge)outEdges.get(0);
// the getToNode() method returns a Node so we need to
// cast to a DefaultNode. This someNode is in fact
// nodeB.
DefaultNode someNode = (DefaultNode)someEdge.getToNode();
// label = "B"
String label = someNode.getLabel();
DefaultNode also provides many convenience methods that makes coding something like the above a bit shorter. See DefaultNode's API docs for the details. For example,
// this sets up the Nodes
DefaultNode nodeA = new DefaultNode("A");
DefaultNode nodeB = new DefaultNode("B");
// constructs a DefaultEdge from nodeA to nodeB
// with a default strength of 1
// nodeA --> nodeB
DefaultEdge edge = new DefaultEdge(nodeA, nodeB);
nodeA.addOutEdge(edge);
nodeB.addInEdge(edge);
// set up is complete. The following shows
// how to get a reference to nodeB given only
// nodeA.
// we know there is only a single edge from nodeA
// because we only added one, and thus only a single
// ToNode.
DefaultNode = (DefaultNode)nodeA.getToNodes().get(0);
// label = "B"
String label = someNode.getLabel();
Here the getToNodes() methods allows us to remove some of the code from the first example. DefaultNode contains many such methods.
The DefaultNode provides much of the network type functionality you are likely to need. Consequently, you can compose your own network agents out of these DefaultNodes, either by inheritance or composition.
A typical network model will treat the network nodes as the agents, and build these nodes and any necessary Edges as part of the buildModel() template method. For example,
public class MyModel extends SimModelImpl {
ArrayList agentList = new ArrayList();
...
private void buildModel() {
// creates DefaultNodes and adds them to a list
for (int i = 0; i < numAgents; i++) {
DefaultNode node = new DefaultNode(String.valueOf(i));
agentList.add(node);
}
// iterate through the agentList creating edges between
// the current node and the previous node.
for (int i = 0; i < agentList.size(); i++) {
Node node = (Node)agentList.get(i);
Node prevNode = null;
if (i == 0) {
// current node is first node in list so
// create an edge between it and the last node in
// the list.
prevNode = (Node)agentList.get(agentList.size() - 1);
} else {
prevNode = (Node)agentList.get(i - 1);
}
EdgeFactory.createEdge(node, prevNode);
}
}
}
This is a simple example of how the structure of the initial network is created (adding edges to previous nodes). Real network models will use the same sort of code to create and add edges, but the criterion for adding edges will certainly be different. Together the Nodes and their Edges constitute the network, and all the nodes are placed in an ArrayList. Consequently this ArrayList represents the network as a whole. Many methods in the NetUtilities class operate on the entire network and expect to get such an ArrayList of nodes as an argument.
Networks are displayed using the Network2DDisplay class. The Network2DDisplay class expects to be drawing a network composed of DrawableNonGridNodes and DrawableEdges. As mentioned above, this network is encapsulated by a List of Nodes, in this case, DrawableNonGridNodes whose edges are of the DrawableEdge type. Typically, this List is not passed directly to the Network2DDisplay for drawing, but is wrapped in some sort of GraphLayout first. The GraphLayout sets the x and y coordinates of the DrawableNonGridNodes according to some algorithm and then the Network2DDisplay displays the nodes at these coordinates. Repast comes with four types of GraphLayouts: CircularGraphLayout, RandomGraphLayout, FruchGraphLayout (lays out the nodes according to a modified implementation of the Fruchmen-Reingold graph layout algorithm), and KamadaGraphLayout (lays out the nodes according to a modified implementation of the Kamada Kawai graph layout algorithm). More on using GraphLayouts later below.
What all this means is that if you want your Nodes to be displayed they must implement the DrawableNonGridNode interface and your Edges must implement the DrawableEdge interface. Rather than having you implement the DrawableNonGridNode interface, Repast provides the DefaultDrawableNode class. This class extends DefaultNode, so it contains all the network functionality described above; it is a DefaultNode. And it implements DrawableNonGridNode so it can be drawn with a Network2DDisplay. The actual drawing (as a rectangle, oval or whatever) of a DefaultDrawableNode is handled by a NetworkDrawable. Repast provide two sorts of NetworkDrawables: a OvalNetworkItem, and a RectNetworkItem. These will draw your DefaultDrawableNodes as ovals or rectangles respectively.
While this mixture of DefaultNodes, DefaultDrawableNodes, and NetworkDrawables sounds quite complicated, in practice just a few lines of code are erequired to create nodes capable of being drawn by a Network2DDisplay and laid out by a GraphLayout. For example,
OvalNetworkItem item = new OvalNetworkItem(1, 10);
DefaultDrawableNode node = new DefaultDrawableNode(item);
The first line creates an OvalNetworkItem with x and y coordinates of 1 and 10. The second line creates a DefaultDrawableNode and passes the OvalNetworkItem as part of the constructor. This means that the DefaultDrawableNode will be drawn as an oval. Moreover, once you've passed the OvalNetworkItem in this way, you needn't worry about it again. In addition to the above, you can also set the NetworkDrawable for a DefaultDrawableNode with DefaultDrawableNode's setDrawable method. This allows you to easily change the shape etc. of the node while the model is running.
The above is a simple example of how NetworkDrawables (OvalNetworkItems etc.) and DefaultDrawableNodes work together. Typically, you would create your own agent class that extends DefaultDrawableNode and then set the NetworkDrawable on that. Both the demonstration network models, jiggle (repast/demo/jiggle) and jinGirNew (repast/demo/jinGirNew) work in this way.
As mentioned above, your Edges must implement the DrawableEdge interface. Repast provides a default implementation of this that should be fine for most purposes. See, DefaultDrawableEdge for more info. Edge directionality is indicated by a small square on the displayed edge. The square is closer to the "to" node and farther from the "from" node. If the nodes are too close to each other to determine directionality, you can use the mouse (click and drag) to move the nodes further away. In addition, EdgeFactory has methods for automating the creation and linking of Nodes using DefaultDrawableEdges.
Once you have constructed a Network2DDisplay it is handled like any other display. See How to Create Displays for more information.
The Network2DDisplay will layout DrawableNonGridNodes according to their x, y coordinates, and is not itself capable of doing any sort of graph layout. As mentioned above, you can use one of the graph layout classes to lay out your network for you. For example,
layout = new CircularLayout(agentList, xSize, ySize);
Network2DDisplay display = new Network2DDisplay(layout);
surface.addDisplayableProbeable(display, "Network Display");
This constructs a CircularGraphLayout by passing it the list of agents (most likely a list of DefaultDrawableNodes) and the size of the display. This layout is then passed to Network2DDisplay and that display is then handled in the usual manner. See How to Create Displays for more information. If the included graph layouts are not sufficient you can create your own using AbstractGraphLayout as a base class.
Graph layouts do not update themselves automatically every tick. You will need to schedule the updateLayout() method on your graph layout in order to update the layout. See How to Use a Schedule for more info. FruchGraphLayout and KamadaGraphLayout calculate node coordinates based on edge connections, so whenever these connections change then updateLayout() will create a new layout. Conversely, the CircularGraphLayout will create the same layout regardeless of the structure of the network, and updateLayout() will only need to be called if the model itself changes the coordinates of the nodes.
FruchGraphLayout and KamadaGraphLayout can be quite slow when laying out large networks. Consequently, you may want to update the layout infrequently. These two classes also have "hooks" in them to react to Repast toolbar (the bar with the start, stop, etc. buttons) button presses. This allows you to interrupt the layout by pressing stop, pause, or exit. Without this, your model may continue to finish network layout even though you have pressed pause. You can attach the layout to the toolbar buttons as follows:
Controller c = (Controller)getController();
c.addStopListener(graphLayout);
c.addPauseListener(graphLayout);
c.addExitListener(graphLayout);
This code would typically be placed in your model's buildDisplay() method.
You can also move the nodes around with the mouse by clicking on a node and dragging it. This is useful if the nodes are obscuring each other. Zooming in on parts of the network is also posssible. You do this by holding down the control-key and dragging the mouse to draw a box around the area you wish to zoom in on. After you've defined the zoom area, press the "z" key to zoom. Press "r" to return the network to its normal state.
Importing Data
Network data is imported via the NetworkFactory
class. Using NetworkFactory.getNetwork method, you can import a
network from two file formats. The file description of the network is
transformed into a list of Nodes and Edges of some user specified
type. The supported formats are UCINet's dl format and the UCINet
compatible Excel format.
The matrix is assumed to be square for both the dl and Excel formats. For Excel, each worksheet is treated as a matrix, and any worksheets that do not contain matrices will cause an error. The worksheet name is treated as the matrix label unless the name begins with Sheet (Excel's generic worksheet name). The format for excel files is that imported and exported by UCINet. The first cell is empty, and the node labels begin on this first row in the second column. The column node labels begin in first column on the second row. The actual data begins in cell 2,2. For example,
| first_label | second_label | ...
-------------+-------------+--------------+----
first_label | 0 | 1 | ...
-------------+-------------+--------------+----
second_label | 1 | 0 | ...
-------------+-------------+--------------+----
... | ... | ... | ...
If the matrix has no node labels, Repast will expect the first row and column to be blank and for the data to begin in cell 2,2.
An example,
List list = NetworkFactory.getNetwork("./network.dl", NetworkFactory.DL,
MyNode.class, MyEdge.class,
NetworkFactory.BINARY);
NetworkFactory will parse the network.dl file creating a list of Nodes that contain the appropriate Edges to other Nodes. In this case, all the nodes will be of the MyNode class and the edges of MyEdge class. This assumes you have defined a MyNode class that implements the Node interface and have defined a MyEdge class that implements the Edge interface. DefaultNode.class and DefaultEdge.class could have been substituted here as well. (The "ClassName.class" notation returns class information about a class.) The resulting List can be used just as if you had created the network by hand (as in the previous example). The final argument specifies the size of the matrix cell items (ij values). It can be one of NetworkFactory.BINARY, NetworkFactory.SMALL, or NetworkFactory.LARGE. Use BINARY if the ij values are 0 or 1, SMALL if the values are between -127 and 127 and LARGE for anything else. The difference in memory usage between BINARY and LARGE is enormous and specifying BINARY here can make the difference between being able or unable to actually import a network into Repast.
Note: The Excel format can only be used on windows and requires that you copy the ExcelAccessor.dll from repast/lib to your system folder (i.e. windows/system or winnt/system32).
Exporting Data
Exporting data is done through the NetworkRecorder
class. NetworkRecorder records a network as a matrix or matrices in
the appropriate format. It takes a list of Nodes and writes the
corresponding adjacency matrix to a file. Three file formats are
supported: UCINet's dl, Excel, and ASCII. The actual network
matrix is written out in this format, although Repast records relevant
non-matrix information as well: a file header containing the constant
parameters for the model and a timestamp, and a block header that
contains header information relevant to the network data recorded
beneath it. The block header will contain the value of any dynamic
model parameters at the time a network was recorded, as well as any
user comments (typically the current tick count).
The actual format of the file is as follows:
file header
block_header_1
network data in the specified format
block_header_2
network data in the specified format
...
Specifying the format then specifies the format for the network data. The rest (file header and block header) will remain the same for all formats. The actual network data can easily be cut and pasted out of the file.
ASCII format will record the matrix and the matrix label in comma delimited plain text. Excel format will store it in an Excel file in a format suitable for importation into UCINet.
Note: Excel format should not be used as it is very time consuming and error prone to write to an Excel file. Use ASCII instead which is a plain text format suitable for importation into excel.
An example,
NetworkRecorder recorder = new NetworkRecorder(NetworkRecorder.DL,
"./myNetwork.dl",
this);
...
recorder.record(nodeList, "tick: " + getTickCount(), NetworkRecorder.BINARY);
...
recorder.write();
The first line creates a NetworkRecorder to record a network in dl format into the file myNetwork.dl, and passes it a reference to the model in which the recorder is constructed. The second line does the actual recording. It records the network(s) represented by the Nodes and their Edges in nodeList and appends the current tick count to the block header. The last argument refers to the size of the ij values, and works the same as for NetworkFactory.getNetwork (see above). The ij value recorded is that of the strength of the edge. By default this is 1 if an edge exists otherwise 0. recorder.record(..) should occur as part of a BasicAction to be scheduled for some sort of repeated execution. recorder.write() does the actual write of the data record by recored.record to the specified file (e.g. myNetwork.dl). Writing to disk is slow and so the call to recorder.write() should be done sporadically. See How to Collect Data from more on scheduling data collection.
Predefined Network Structures
In addtion to being used to import networks from a file, NetworkFactory
can also be used to create some common network structures. These are:
a Square Lattice Network, a Random Density Network, and the classic
Watts-Strogatz small world network (ring substrate). You create these
networks using the appropriate methods in NetworkFactory. They operate
similar to importing data. You provide the node and edge classes, as
well as some network parameters, and you get back a network of the
appropriate structure as a List of your node classes See NetworkFactory
for more info.


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Patient Home
To Play Game >
Settings
Play Game
Help


Severus Snape
Harry Potter
Severus Snape
Harry Potter
1. Enable Codepen to use your microphone.
2. Map will open if you say "I solemnly swear that I'm up to no good."
3. Map will close if you say "Mischief Managed!".
4. Remember to speak clearly.
This looks a lot better zoomed out (sorry).
If you do not want to use a microphone, press this button!


The University of Arizona has been my home as I completed both a graduate coding program and a BFA in Visual Communications. These certifications helped me develop an array of programming, animation, and analytical skills that are beneficial in a variety of work environments. My passion to become an animator has lead to an in depth understanding of both the behind the scenes coding aspect and the front end animation creation.
Over the past few years I have studied and gained an intimate understanding in programming skills including but not limited to Visual Studio Code, Adobe Creative Suite and AutoDesk. During this time my knowledge of HTML, CSS, Bootstrap, SCSS, JavaScript, HandleBars, Node.js, MySQL, and React has also grown significantly.
I welcome any challenge and look forward to showing how much value I can bring to the table.


Dear Dry,
The 42nd anniversary of “Roe v. Wade” is just a few days away. To honor “Roe” we need to *take a stand for women’s health now [ http://org.salsalabs.com/o/1400/p/dia/action3/common/public/?action_KEY=16989 ]*.
The US House of Representatives plans to vote on a 20-week extremist abortion ban this Thursday, January 22 – the anniversary of “Roe v. Wade”. This extremist bill, one that would ban abortion pre-viability, is a direct attack on “Roe”. A similar ban in Arizona was deemed unconstitutional by a federal appeals court, and the Supreme Court refused to review that decision – letting stand the decision that the 20-week ban is unconstitutional. Congress must stop wasting time and money on unconstitutional measures.
*Sign the petition to tell your representatives in Congress that the majority of Americans support safe, legal abortion. [ http://org.salsalabs.com/o/1400/p/dia/action3/common/public/?action_KEY=16989 ]*
* [ https://org.salsalabs.com/o/1400/p/dia/action3/common/public/?action_KEY=16989 ]*
That the Republican-controlled House would – on the anniversary of “Roe "– push forward an unconstitutional bill to deny women the right to an abortion is an outrage.
*Here are 2 things you can do to celebrate "Roe”:*
* *Email your representatives and senators in Congress! [ https://org.salsalabs.com/o/1400/p/dia/action3/common/public/?action_KEY=16989 ]** [ http://org.salsalabs.com/o/1400/p/dia/action3/common/public/?action_KEY=16989 ]* Tell them that you do not support an extreme, unconstitutional 20-week abortion ban. It goes too far.
* *Join us at the Supreme Court!* FMF, NOW, and DC NOW will be at the Supreme Court on Thursday, January 22, to commemorate the 42nd anniversary of" Roe v. Wade" and protest attacks on women’s reproductive rights. We will be rallying outside the Supreme Court (1 First Street NE) from 1 to 3 PM.
Together, we will fight these attacks on women’s health. We won’t go back!
For Women’s Lives,
Eleanor Smeal
President
Feminist Majority


;; title = "Error page (500)" url = "/error" layout = "default" meta_title = "Error page (500)" meta_description = "Error page (500)" robot_index = "index" robot_follow = "follow" == {##}
We're sorry, but something went wrong and the page cannot be displayed.


Ping-Pong
About Ping Pong
Created by:
Stephen Burden
GitHub Username/URL:
spburden
Level One test for:
Javascript
Let's play!
Rules:
Ping Pong will count up to your number from 1 with the following exceptions:
Numbers divisible by 3 become "ping"
Numbers divisible by 5 become "pong"
Numbers divisible by 15 become "ping-pong"
Enter your number here:
Ping Pong!


Ensembl Bacteria has been developed with the support of the Microme project, a resource for bacterial metabolism.


Playing notes
Every sequence has a variable called
note that determines what note in the scale will play.
Example: note values
0,1,2 will play notes
C,D,E if the C major scale is selected. The values than loop around the octave, such that numbers 7,8,9 again play notes C,D,E.
Variables
Variables change their value every time a note is played. They start with an
initial value and their
next value is calculated using a custom math expression.
Example: If we set the initial value for variable
note to 0 and its next expression to
note + 1, then we get a sequence
0,1,2,3,4,...
You can use multiple variables to help you construct your note sequence. Click
Load presets to see some examples.
Tips
You can use an internal variable called
note_index that is always set to the current note count.
Example: A variable with a next value set to
note_index will produce values
0,1,2,3,...
You can also use lists of numbers in your expressions. Example:
A variable set to expression
[0,1,2,3][1] will extract the first number from the list, which is 0.
Visit
this page for a full list of supported operators.
Settings
Adjust the beats per minute with the
BPM slider.


PhD Preliminary Exam Studying
How do you study for a preliminary exam? I don’t know, I was hoping you could tell me.
I might write something insightful up here at some point. For now, just trying to make a daily log of what I’ve been doing.
This section will eventually go-away, as I read these papers and put summaries in the daily logs.
Hockenberry 2018 for some hypotheses about why translation initiation varies.
Giess 2017 for another method that may help identifying translation start sites.
Hersch 2014 can affect stalling.
Verma 2019 early elongation events matter for efficiency.
Is should eventually read
A leaderless GFP mRNA, has 17 out-of-frame ACA sites that don’t interfere with expression after mazF induction. Adding an ACA site in the ORF prevented expression.
Really not impressed by the quality control in this paper. E.g., it sites Fig S1 about the effect of nalidixic acid on their reporter, but S1 has nothing to do with nalidixic acid–that’s S2. & Their “OD600” axis is in the hundreds–is it OD * 100?
Basically we’re seeing that induction of mazF reduces the GFP level in constructs with in-frame ACA sites. There’s no difference in a mazEF knockout, and it increases GFP level in WT and when you add an AC before the start codon (they call this leaderless due to cleavage, but don’t show that).
They make an argument that only in-frame ACA sites are cleaved, substantiating their claim by comparing PCR band intensities.
Then they find that their GFP-up/down phenotype +/- mazF induction can be reserved by making a mutation to the EDF-like sequence in bS1.
They conclude that there may be a bias away from ACAs being in frame, to protect from mazF cleavage.
I’m not convinced by this paper–the figures are not very clean, there are not multiple veins of evidence for each conclusion. It highlights that I really need to make sure I can think critically about the techniques raised in the controversy over the formation of stress-induced translation machinery, should also read Wade 2019 and the response Nigam 2019 and Vesper 2011, and Culviner 2018.
Take a $\Delta$mazF strain, put mazF on an ara-inducible promoter, low-copy plasmid. Induce mazF for just 5 minutes, take paired-end sequencing reads, and look for regions where density decreases. Confirm patterns with single-gene RT-qPCR.
82% of genes were highly-cleaved (2-fold or more downregulated, compared to empty vector) after mazF induction.
They also compare this to 5’-OH sequencing, but that’s not well-correlated–likely because of different 5’-end stabilities.
Not all ACAs are cleaved at the same rate, so they took some sequence logos and think that there’s a ~7-nt region.
They confirmed that these flanking sequences are important by selecting some of them across different RNA-seq scores, taking them out of context into a reporter, and then qPCR quantifying them, and that correlated as-expected.
They find a few genes that, by this method, may be leaderless after mazF induction (just 41). They measure ribosome footprints for all genes, and find that none of those leaderless genes have any substantial increase in footprints.
To put the nail in the coffin, they added an ACA site between the RBS and start codon of YFP, and did not see any increase in flourescence after mazF induction.
They also saw traffic jams of ribosomes upstream of identified cleavage sites.
I want to focus on a few things over the course of this study period:
Summary: Used a fluorescent reporter +/- ACA sequences and +/- a leader under expression of mazF.
Relevance to project: They use a “leaderless” construct, but they don’t specify the promoter (native GFP promoter?) or show a 5’ start, but that may have been shown in reference paper (Oron-Gottesman 2016, will read next). In both plasmids, they are not able to see expression of leaderless GFP over background, except in the high-copy-number-plasmid after 6h mazF induction. So, their lmRNA GFP is not highly expressed.
Relevant data points:
Figure summary:
Figure 1 shows GFP (under leaderless or canonical mRNA, both without ACA sequences) after induction of mazF. In a high copy plasmid, there’s clear increase in GFP concentration compared to no-plasmid control.
Figure 2 shows that the amount of increase of flourescence after mazF induction is lower for mCherry (with ACA) than GFP (without ACA)


[dscrdeql] <CowboyCrypto- Founder> @cryptic1 where at?
[dscrdeql] <cryptic1> @CowboyCrypto- Founder at a Green Solution in IL actually
GatewayBOT08-Sep-18 03:40 AM
[dscrdeql] <CowboyCrypto- Founder> Nice didn’t know il was rockin like that
[dscrdeql] <CowboyCrypto- Founder> I’m a state over in Missouri
GatewayBOT08-Sep-18 03:52 AM
[dscrdeql] <cryptic1> We have a location in Sauget just across the border, it requires you to be an Illinois patient but pay attention man because it'll be recreational really soon and then you should have no problem making a visit
[dscrdeql] <Jellysmash> maybe like those in amsterdam?
[dscrdeql] <Jellysmash> never been to the 'green solution'
[dscrdeql] <Jellysmash> never really tried cannabis
[dscrdeql] <Jellysmash> I tried a space cake in amsterdam last year
[dscrdeql] <Jellysmash> damn made me sick
[dscrdeql] <Jellysmash> wouldn't bother with that again
[dscrdeql] <CowboyCrypto- Founder> Lol I’m fine here in Missouri
[dscrdeql] <CowboyCrypto- Founder> Be cool to be able to go to il and get it though
[dscrdeql] <CowboyCrypto- Founder> I’m hoping we can get med here
[dscrdeql] <CowboyCrypto- Founder> Lots of opportunities in that sector
[dscrdeql] <CowboyCrypto- Founder> Off topic, but fun fact. Did you know that with our homes geothermal cameras will be ineffective? Could be 160 degrees inside and outside will read around outside temp
[dscrdeql] <Jellysmash> interesting
[dscrdeql] <cryptic1> That is interesting
[dscrdeql] <cryptic1> Yeah @Jellysmash it's becoming big business over here
[dscrdeql] <cryptic1> We could def use some blockchain solutions lol
GatewayBOT08-Sep-18 04:24 AM
[dscrdeql] <CowboyCrypto- Founder> Are you in sauget
[dscrdeql] <CowboyCrypto- Founder> I cross the river occasionally lol
[dscrdeql] <CowboyCrypto- Founder> I go to the city from time to time
GatewayBOT08-Sep-18 04:43 AM
[tlgrm] <Raghuharris94> Hy
[tlgrm] <AndronicusBass> (re @Raghuharris94: Hy)
[tlgrm] <Raghuharris94> It's real and it's paying
20% paid in 24 hours!
40% paid in 48 hours!
100% paid in 48 hours!
My BTC wallet keeps growing with this NEW MINING COMPANY.
JOIN ME
TESTED AND TRUSTED BITCOIN MINING INVESTMENT
GatewayBOT08-Sep-18 05:14 AM
[dscrdeql] <Jellysmash> @cryptic1 blockchain for what purpose? Just curious
[dscrdeql] <Jellysmash> Do you mean for sauget?(edited)
[dscrdeql] <CowboyCrypto- Founder> I think he was referring to our building being resistant to thermal scanning
[dscrdeql] <CowboyCrypto- Founder> Govt uses it to locate grow houses
GatewayBOT08-Sep-18 05:44 AM
[dscrdeql] <ghøst> good morning!
GatewayBOT08-Sep-18 05:58 AM
[dscrdeql] <Jellysmash> good morning @ghøst
[dscrdeql] <Jellysmash> hope your weekend is good
[dscrdeql] <AndronicusBass-Founder> Praise the sunshine, nice day
GatewayBOT08-Sep-18 06:09 AM
[dscrdeql] <Jellysmash> Praise the sunshine, praise the day
[dscrdeql] <Jellysmash> it's there for the taking
GatewayBOT08-Sep-18 06:47 AM
[dscrdeql] <CowboyCrypto- Founder> Lol I’m supposed to be st the car lot in 6 hrs
[dscrdeql] <CowboyCrypto- Founder> I’m prob not gonna make it
GatewayBOT08-Sep-18 06:58 AM
[dscrdeql] <x2maano> good morning guys
[dscrdeql] <x2maano>
GatewayBOT08-Sep-18 07:25 AM
[dscrdeql] <Jellysmash> watching sat arvo footy..
[dscrdeql] <Jellysmash> swans vs gws
[dscrdeql] <Jellysmash> gws ahead at half time
[dscrdeql] <Jellysmash> be hard for sydney swans to come back from behind
GatewayBOT08-Sep-18 08:06 AM
[dscrdeql] <Adrian > morning guys !!!!!
[dscrdeql] <Adrian >
GatewayBOT08-Sep-18 08:24 AM
[dscrdeql] <AndronicusBass-Founder> good evening
GatewayBOT08-Sep-18 08:34 AM
[dscrdeql] <Max834> good morning!(edited)
[dscrdeql] <ghøst> Morning!
[dscrdeql] <Max834>
[dscrdeql] <Jellysmash> hi there
[dscrdeql] <Jellysmash> morning @ghøst
[dscrdeql] <Jellysmash> hi @Adrian
[dscrdeql] <Adrian > hey Paul
[dscrdeql] <Jellysmash> thanks for the coffee
[dscrdeql] <Adrian >
[dscrdeql] <Adrian > well u need some
[dscrdeql] <Jellysmash> my team is losing the footy
[dscrdeql] <Adrian >
[dscrdeql] <Adrian > or
[dscrdeql] <Jellysmash> terrible to watch
[dscrdeql] <Jellysmash> yea, I'll have some red wine
[dscrdeql] <Jellysmash> make a bbq
[dscrdeql] <Jellysmash> I find it hard to watch my team lose so badly in teh finals
[dscrdeql] <Jellysmash> I get so angry
[dscrdeql] <Adrian > that's nice
[dscrdeql] <Adrian > i'm sorry to hear that (edited)
GatewayBOT08-Sep-18 09:10 AM
[dscrdeql] <Jellysmash> anyway drinking red wine now
[dscrdeql] <Jellysmash> feel a little better
[dscrdeql] <Jellysmash> full up on bbq pork chops and sausages
GatewayBOT08-Sep-18 09:20 AM
[dscrdeql] <AndronicusBass-Founder> Nice. Pizza and movie night up in here.

GatewayBOT08-Sep-18 10:10 AM
[dscrdeql] <Combat Wom-mod> congrats jelly
[dscrdeql] <Combat Wom-mod> hello everyone
[dscrdeql] <Jellysmash> thanks
[dscrdeql] <Jellysmash> hi there @Combat Wom-mod
[dscrdeql] <Jellysmash> so I guess your weekend starts tomorrow right
[dscrdeql] <Combat Wom-mod> how you doing bud
[dscrdeql] <Jellysmash> you get sunday and monday as your weekend
[dscrdeql] <Combat Wom-mod> normally yes but i took a day off today
[dscrdeql] <Jellysmash> have you been watching much afl finals
[dscrdeql] <Jellysmash> my team sydney swans lost to GWS
[dscrdeql] <Combat Wom-mod> got a bad back last couple of days can barely walk today lol feel like an old man
[dscrdeql] <Combat Wom-mod> yeah i seen that
[dscrdeql] <Jellysmash> boo hoo Swans are out now
[dscrdeql] <Combat Wom-mod> sydney were terrible
[dscrdeql] <Jellysmash> that's bad news about your back
[dscrdeql] <Jellysmash> an injured back is really bad
[dscrdeql] <Jellysmash> I've injured mine before ....lifting weights


BBLog Background Changer is not working - Better Battlelog Forums #98672
Sitemap
15. May 2013 13:04
Post edited 2 x times, last by
BrainFooLong
Administrator
27. May 2013 09:07
Topicstarter
KILROYalex
As i said Background changer is not working right on curent BBLog version on Firefox.
No i'm trying to fix it
15. May 2013 13:15
Post edited 1 x times, last by
ORFK
Global Moderator
15. May 2013 13:16
ORFK
Global Moderator
BBLog does currently work
but probably not stable because of a big battlelog update
!
We working on BBLog 4 but this will take a couple of weeks/month. Pls wait for the update/fix.
15.  May 2013 13:18
Topicstarter
KILROYalex
Okay.Thanks for answer and Good luck)


|
Assimp
v3.1.1 (June 2014)
|
A node in the imported hierarchy. More...
Public Member Functions | |
| const aiNode * | FindNode (const aiString &name) const |
| Searches for a node with a specific name, beginning at this nodes. More... | |
| aiNode * | FindNode (const aiString &name) |
| const aiNode * | FindNode (const char *name) const |
| aiNode * | FindNode (const char *name) |
| mChildren (NULL) | |
| mChildren (NULL) | |
| mMeshes (NULL) | |
| mMeshes (NULL) | |
| mMetaData (NULL) | |
| mMetaData (NULL) | |
| mNumChildren (0) | |
| mNumChildren (0) | |
| mNumMeshes (0) | |
| mNumMeshes (0) | |
| mParent (NULL) | |
| mParent (NULL) | |
| ~aiNode () | |
| Destructor. More... | |
Public Attributes | |
| __pad0__: mName("") | |
| Constructor. More... | |
| __pad1__: mName(name) | |
| Construction from a specific name. More... | |
| aiNode ** | mChildren |
| The child nodes of this node. More... | |
| unsigned int * | mMeshes |
| The meshes of this node. More... | |
| aiMetadata * | mMetaData |
| Metadata associated with this node or NULL if there is no metadata. More... | |
| aiString | mName |
| The name of the node. More... | |
| unsigned int | mNumChildren |
| The number of child nodes of this node. More... | |
| unsigned int | mNumMeshes |
| The number of meshes of this node. More... | |
| aiNode * | mParent |
| Parent node. More... | |
| aiMatrix4x4 | mTransformation |
| The transformation relative to the node's parent. More... | |
A node in the imported hierarchy.
Each node has name, a parent node (except for the root node), a transformation relative to its parent and possibly several child nodes. Simple file formats don't support hierarchical structures - for these formats the imported scene does consist of only a single root node without children.
|
inline |
Destructor.
Searches for a node with a specific name, beginning at this nodes.
Normally you will call this method on the root node of the scene.
| name | Name to search for |
|
inline |
|
inline |
| aiNode::mChildren | ( | NULL | ) |
| aiNode::mChildren | ( | NULL | ) |
| aiNode::mMeshes | ( | NULL | ) |
| aiNode::mMeshes | ( | NULL | ) |
|
inline |
|
inline |
| aiNode::mNumChildren | ( | 0 | ) |
| aiNode::mNumChildren | ( | 0 | ) |
| aiNode::mNumMeshes | ( | 0 | ) |
| aiNode::mNumMeshes | ( | 0 | ) |
| aiNode::mParent | ( | NULL | ) |
| aiNode::mParent | ( | NULL | ) |
| aiNode::__pad0__ |
Constructor.
| aiNode::__pad1__ |
Construction from a specific name.
| aiNode** aiNode::mChildren |
The child nodes of this node.
NULL if mNumChildren is 0.
| unsigned int* aiNode::mMeshes |
The meshes of this node.
Each entry is an index into the mesh
| aiMetadata* aiNode::mMetaData |
Metadata associated with this node or NULL if there is no metadata.
Whether any metadata is generated depends on the source file format. See the importer_notes page for more information on every source file format. Importers that don't document any metadata don't write any.
| aiString aiNode::mName |
The name of the node.
The name might be empty (length of zero) but all nodes which need to be referenced by either bones or animations are named. Multiple nodes may have the same name, except for nodes which are referenced by bones (see aiBone and aiMesh::mBones). Their names must be unique.
Cameras and lights reference a specific node by name - if there are multiple nodes with this name, they are assigned to each of them.
There are no limitations with regard to the characters contained in the name string as it is usually taken directly from the source file.
Implementations should be able to handle tokens such as whitespace, tabs, line feeds, quotation marks, ampersands etc.
Sometimes assimp introduces new nodes not present in the source file into the hierarchy (usually out of necessity because sometimes the source hierarchy format is simply not compatible). Their names are surrounded by
<>
e.g.
<DummyRootNode>
.
| unsigned int aiNode::mNumChildren |
The number of child nodes of this node.
| unsigned int aiNode::mNumMeshes |
The number of meshes of this node.
| aiNode* aiNode::mParent |
Parent node.
NULL if this node is the root node.
| aiMatrix4x4 aiNode::mTransformation |
The transformation relative to the node's parent.


Staff
Yps , Mohali is the school of wonders .Where there are wonderful students who will be the future of the world .


Exports the Trackable object obj
to SavedModel format.
tf.compat.v1.saved_model.experimental.save
tf.compat.v1.saved_model.save
tf.compat.v2.saved_model.save
tf.saved_model.save( obj, export_dir, signatures=None, options=None )
class Adder(tf.Module): @tf.function(input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)]) def add(self, x): return x + x + 1. to_export = Adder() tf.saved_model.save(to_export, '/tmp/adder')
The resulting SavedModel is then servable with an input named "x", its value having any shape and dtype float32.
The optional signatures
argument controls which methods in obj
will be
available to programs which consume SavedModel
s, for example serving
APIs. Python functions may be decorated with
@tf.function(input_signature=...)
and passed as signatures directly, or
lazily with a call to get_concrete_function
on the method decorated with
@tf.function
.
If the signatures
argument is omitted, obj
will be searched for
@tf.function
-decorated methods. If exactly one @tf.function
is found, that
method will be used as the default signature for the SavedModel. This behavior
is expected to change in the future, when a corresponding
tf.saved_model.load
symbol is added. At that point signatures will be
completely optional, and any @tf.function
attached to obj
or its
dependencies will be exported for use with load
.
When invoking a signature in an exported SavedModel, Tensor
arguments are
identified by name. These names will come from the Python function's argument
names by default. They may be overridden by specifying a name=...
argument
in the corresponding tf.TensorSpec
object. Explicit naming is required if
multiple Tensor
s are passed through a single argument to the Python
function.
The outputs of functions used as signatures
must either be flat lists, in
which case outputs will be numbered, or a dictionary mapping string keys to
Tensor
, in which case the keys will be used to name outputs.
Signatures are available in objects returned by tf.saved_model.load
as a
.signatures
attribute. This is a reserved attribute: tf.saved_model.save
on an object with a custom .signatures
attribute will raise an exception.
Since tf.keras.Model
objects are also Trackable, this function can be
used to export Keras models. For example, exporting with a signature
specified:
class Model(tf.keras.Model): @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string)]) def serve(self, serialized): ... m = Model() tf.saved_model.save(m, '/tmp/saved_model/')
Exporting from a function without a fixed signature:
class Model(tf.keras.Model): @tf.function def call(self, x): ... m = Model() tf.saved_model.save( m, '/tmp/saved_model/', signatures=m.call.get_concrete_function( tf.TensorSpec(shape=[None, 3], dtype=tf.float32, name="inp")))
tf.keras.Model
instances constructed from inputs and outputs already have a
signature and so do not require a @tf.function
decorator or a signatures
argument. If neither are specified, the model's forward pass is exported.
x = input_layer.Input((4,), name="x") y = core.Dense(5, name="out")(x) model = training.Model(x, y) tf.saved_model.save(model, '/tmp/saved_model/') # The exported SavedModel takes "x" with shape [None, 4] and returns "out" # with shape [None, 5]
Variables must be tracked by assigning them to an attribute of a tracked
object or to an attribute of obj
directly. TensorFlow objects (e.g. layers
from tf.keras.layers
, optimizers from tf.train
) track their variables
automatically. This is the same tracking scheme that tf.train.Checkpoint
uses, and an exported Checkpoint
object may be restored as a training
checkpoint by pointing tf.train.Checkpoint.restore
to the SavedModel's
"variables/" subdirectory. Currently variables are the only stateful objects
supported by tf.saved_model.save
, but others (e.g. tables) will be supported
in the future.
tf.function
does not hard-code device annotations from outside the function
body, instead using the calling context's device. This means for example that
exporting a model which runs on a GPU and serving it on a CPU will generally
work, with some exceptions. tf.device
annotations inside the body of the
function will be hard-coded in the exported model; this type of annotation is
discouraged. Device-specific operations, e.g. with "cuDNN" in the name or with
device-specific layouts, may cause issues. Currently a DistributionStrategy
is another exception: active distribution strategies will cause device
placements to be hard-coded in a function. Exporting a single-device
computation and importing under a DistributionStrategy
is not currently
supported, but may be in the future.
SavedModels exported with tf.saved_model.save
strip default-valued
attributes
automatically, which removes one source of incompatibilities when the consumer
of a SavedModel is running an older TensorFlow version than the
producer. There are however other sources of incompatibilities which are not
handled automatically, such as when the exported model contains operations
which the consumer does not have definitions for.
obj
: A trackable object to export.export_dir
: A directory in which to write the SavedModel.signatures
: Optional, either a tf.function
with an input signature
specified or the result of f.get_concrete_function
on a
@tf.function
-decorated function f
, in which case f
will be used to
generate a signature for the SavedModel under the default serving
signature key. signatures
may also be a dictionary, in which case it
maps from signature keys to either tf.function
instances with input
signatures or concrete functions. The keys of such a dictionary may be
arbitrary strings, but will typically be from the
tf.saved_model.signature_constants
module.options
: Optional, tf.saved_model.SaveOptions
object that specifies
options for saving.ValueError
: If obj
is not trackable.Not well supported when graph building. From TensorFlow 1.x,
tf.compat.v1.enable_eager_execution()
should run first. Calling
tf.saved_model.save in a loop when graph building from TensorFlow 1.x will
add new save operations to the default graph each iteration.
May not be called from within a function body.


--- layout: post esbma_id: 1748 title: My Favorite Books date: 2008-05-22 13:00:00 ---
People often ask me for a list of my favorite books. So here it is.
I reserve the right to update this list from time to time.
I tend to read a lot of stuff. The fact that I recommend a book here does not mean that I agree with everything in it.
I think it's out of print, but I really liked Writing Solid Code. It's very oriented toward C/C++, so if you're mostly in C#/Java/Ruby/Python/Perl/VB, it may not be worth your time. Still, it's an outstanding book.
And of course Code Complete is a classic.
Dynamics of Software Development is one of my favorites.
I'm a big fan of Built to Last and its sequel, Good to Great. The sequel is easier to read and a bit more relevant to smaller companies.
The Silicon Valley Way is a great book, and it's a very visual book with nice short chapters. Easy to just pick up and browse..
If you get the opportunity, go hear Guy Kawasaki speak. He's much better in person than he is on paper. But if that doesn't work out, Rules for Revolutionaries is a good read.
If you read only one book on marketing, it should be Crossing the Chasm.
But actually you should read at least a dozen books on marketing.
Here's your second one: Differentiate or Die
Now go find ten more.
I think Selling the Wheel is an outstanding book. At first you'll be tempted to stop because it's kind of cheesy. Don't. Finish it all the way to the end.
I really like the "Prey" series of novels by John Sandford. Start at the beginning with Rules of Prey
I have all of the following WPF books:
These are all good, each in a different way. If you're going to do anything serious with WPF, it seems to me that you should own them all.
The Seven Habits of Highly Effective People is still worth reading. None of Covey's other books are nearly as good.
Any serious pool player has a copy of Byrne's New Standard Book of Pool and Billiards.
My favorite literary novel is The Count of Monte Cristo. The unabridged version is worth the extra trouble.
For dog lovers, Marley & Me is terrific.