Writing a contract test¶
This tutorial will take you through the steps required to write a contract test using Kotlin and Java.
The testing DSL allows one to define a piece of the ledger with transactions referring to each other, and ways of verifying their correctness.
Testing single transactions¶
We start with the empty ledger:
class CommercialPaperTest{
@Test
fun emptyLedger() {
ledger {
}
}
...
}
import static net.corda.core.testing.JavaTestHelpers.*;
import static net.corda.core.contracts.JavaTestHelpers.*;
@Test
public void emptyLedger() {
ledger(l -> {
return Unit.INSTANCE; // We need to return this explicitly
});
}
The DSL keyword ledger
takes a closure that can build up several transactions and may verify their overall
correctness. A ledger is effectively a fresh world with no pre-existing transactions or services within it.
We will start with defining helper function that returns a CommercialPaper
state:
fun getPaper(): ICommercialPaperState = CommercialPaper.State(
issuance = MEGA_CORP.ref(123),
owner = MEGA_CORP_PUBKEY,
faceValue = 1000.DOLLARS `issued by` MEGA_CORP.ref(123),
maturityDate = TEST_TX_TIME + 7.days
)
private final OpaqueBytes defaultRef = new OpaqueBytes(new byte[]{123});
private ICommercialPaperState getPaper() {
return new JavaCommercialPaper.State(
getMEGA_CORP().ref(defaultRef),
getMEGA_CORP_PUBKEY(),
issuedBy(DOLLARS(1000), getMEGA_CORP().ref(defaultRef)),
getTEST_TX_TIME().plus(7, ChronoUnit.DAYS)
);
}
It’s a CommercialPaper
issued by MEGA_CORP
with face value of $1000 and maturity date in 7 days.
Let’s add a CommercialPaper
transaction:
@Test
fun simpleCPDoesntCompile() {
val inState = getPaper()
ledger {
transaction {
input(inState)
}
}
}
@Test
public void simpleCPDoesntCompile() {
ICommercialPaperState inState = getPaper();
ledger(l -> {
l.transaction(tx -> {
tx.input(inState);
});
return Unit.INSTANCE;
});
}
We can add a transaction to the ledger using the transaction
primitive. The transaction in turn may be defined by
specifying input
-s, output
-s, command
-s and attachment
-s.
The above input
call is a bit special; transactions don’t actually contain input states, just references
to output states of other transactions. Under the hood the above input
call creates a dummy transaction in the
ledger (that won’t be verified) which outputs the specified state, and references that from this transaction.
The above code however doesn’t compile:
Error:(29, 17) Kotlin: Type mismatch: inferred type is Unit but EnforceVerifyOrFail was expected
Error:(35, 27) java: incompatible types: bad return type in lambda expression missing return value
This is deliberate: The DSL forces us to specify either this.verifies()
or this `fails with` "some text"
on the
last line of transaction
:
@Test
fun simpleCP() {
val inState = getPaper()
ledger {
transaction {
input(inState)
this.verifies()
}
}
}
@Test
public void simpleCP() {
ICommercialPaperState inState = getPaper();
ledger(l -> {
l.transaction(tx -> {
tx.input(inState);
return tx.verifies();
});
return Unit.INSTANCE;
});
}
Let’s take a look at a transaction that fails.
@Test
fun simpleCPMove() {
val inState = getPaper()
ledger {
transaction {
input(inState)
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Move() }
this.verifies()
}
}
}
@Test
public void simpleCPMove() {
ICommercialPaperState inState = getPaper();
ledger(l -> {
l.transaction(tx -> {
tx.input(inState);
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Move());
return tx.verifies();
});
return Unit.INSTANCE;
});
}
When run, that code produces the following error:
net.corda.core.contracts.TransactionVerificationException$ContractRejection: java.lang.IllegalArgumentException: Failed requirement: the state is propagated
net.corda.core.contracts.TransactionVerificationException$ContractRejection: java.lang.IllegalStateException: the state is propagated
The transaction verification failed, because we wanted to move paper but didn’t specify an output - but the state should be propagated.
However we can specify that this is an intended behaviour by changing this.verifies()
to this `fails with` "the state is propagated"
:
@Test
fun simpleCPMoveFails() {
val inState = getPaper()
ledger {
transaction {
input(inState)
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Move() }
this `fails with` "the state is propagated"
}
}
}
@Test
public void simpleCPMoveFails() {
ICommercialPaperState inState = getPaper();
ledger(l -> {
l.transaction(tx -> {
tx.input(inState);
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Move());
return tx.failsWith("the state is propagated");
});
return Unit.INSTANCE;
});
}
We can continue to build the transaction until it verifies
:
@Test
fun simpleCPMoveSuccess() {
val inState = getPaper()
ledger {
transaction {
input(inState)
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Move() }
this `fails with` "the state is propagated"
output("alice's paper") { inState `owned by` ALICE_PUBKEY }
this.verifies()
}
}
}
@Test
public void simpleCPMoveSuccess() {
ICommercialPaperState inState = getPaper();
ledger(l -> {
l.transaction(tx -> {
tx.input(inState);
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Move());
tx.failsWith("the state is propagated");
tx.output("alice's paper", inState.withOwner(getALICE_PUBKEY()));
return tx.verifies();
});
return Unit.INSTANCE;
});
}
output
specifies that we want the input state to be transferred to ALICE
and command
adds the
Move
command itself, signed by the current owner of the input state, MEGA_CORP_PUBKEY
.
We constructed a complete signed commercial paper transaction and verified it. Note how we left in the fails with
line - this is fine, the failure will be tested on the partially constructed transaction.
What should we do if we wanted to test what happens when the wrong party signs the transaction? If we simply add a
command
it will permanently ruin the transaction... Enter tweak
:
@Test
fun `simple issuance with tweak`() {
ledger {
transaction {
output("paper") { getPaper() } // Some CP is issued onto the ledger by MegaCorp.
tweak {
command(DUMMY_PUBKEY_1) { CommercialPaper.Commands.Issue() }
timestamp(TEST_TX_TIME)
this `fails with` "output states are issued by a command signer"
}
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Issue() }
timestamp(TEST_TX_TIME)
this.verifies()
}
}
}
@Test
public void simpleIssuanceWithTweak() {
ledger(l -> {
l.transaction(tx -> {
tx.output("paper", getPaper()); // Some CP is issued onto the ledger by MegaCorp.
tx.tweak(tw -> {
tw.command(getDUMMY_PUBKEY_1(), new JavaCommercialPaper.Commands.Issue());
tw.timestamp(getTEST_TX_TIME());
return tw.failsWith("output states are issued by a command signer");
});
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Issue());
tx.timestamp(getTEST_TX_TIME());
return tx.verifies();
});
return Unit.INSTANCE;
});
}
tweak
creates a local copy of the transaction. This makes possible to locally “ruin” the transaction while not
modifying the original one, allowing testing of different error conditions.
We now have a neat little test that tests a single transaction. This is already useful, and in fact testing of a single
transaction in this way is very common. There is even a shorthand top-level transaction
primitive that creates a
ledger with a single transaction:
@Test
fun `simple issuance with tweak and top level transaction`() {
transaction {
output("paper") { getPaper() } // Some CP is issued onto the ledger by MegaCorp.
tweak {
command(DUMMY_PUBKEY_1) { CommercialPaper.Commands.Issue() }
timestamp(TEST_TX_TIME)
this `fails with` "output states are issued by a command signer"
}
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Issue() }
timestamp(TEST_TX_TIME)
this.verifies()
}
}
@Test
public void simpleIssuanceWithTweakTopLevelTx() {
transaction(tx -> {
tx.output("paper", getPaper()); // Some CP is issued onto the ledger by MegaCorp.
tx.tweak(tw -> {
tw.command(getDUMMY_PUBKEY_1(), new JavaCommercialPaper.Commands.Issue());
tw.timestamp(getTEST_TX_TIME());
return tw.failsWith("output states are issued by a command signer");
});
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Issue());
tx.timestamp(getTEST_TX_TIME());
return tx.verifies();
});
}
Chaining transactions¶
Now that we know how to define a single transaction, let’s look at how to define a chain of them:
@Test
fun `chain commercial paper`() {
val issuer = MEGA_CORP.ref(123)
ledger {
unverifiedTransaction {
output("alice's $900", 900.DOLLARS.CASH `issued by` issuer `owned by` ALICE_PUBKEY)
}
// Some CP is issued onto the ledger by MegaCorp.
transaction("Issuance") {
output("paper") { getPaper() }
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Issue() }
timestamp(TEST_TX_TIME)
this.verifies()
}
transaction("Trade") {
input("paper")
input("alice's $900")
output("borrowed $900") { 900.DOLLARS.CASH `issued by` issuer `owned by` MEGA_CORP_PUBKEY }
output("alice's paper") { "paper".output<ICommercialPaperState>() `owned by` ALICE_PUBKEY }
command(ALICE_PUBKEY) { Cash.Commands.Move() }
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Move() }
this.verifies()
}
}
}
@Test
public void chainCommercialPaper() {
PartyAndReference issuer = getMEGA_CORP().ref(defaultRef);
ledger(l -> {
l.unverifiedTransaction(tx -> {
tx.output("alice's $900",
new Cash.State(issuedBy(DOLLARS(900), issuer), getALICE_PUBKEY(), null));
return Unit.INSTANCE;
});
// Some CP is issued onto the ledger by MegaCorp.
l.transaction("Issuance", tx -> {
tx.output("paper", getPaper());
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Issue());
tx.timestamp(getTEST_TX_TIME());
return tx.verifies();
});
l.transaction("Trade", tx -> {
tx.input("paper");
tx.input("alice's $900");
tx.output("borrowed $900", new Cash.State(issuedBy(DOLLARS(900), issuer), getMEGA_CORP_PUBKEY(), null));
JavaCommercialPaper.State inputPaper = l.retrieveOutput(JavaCommercialPaper.State.class, "paper");
tx.output("alice's paper", inputPaper.withOwner(getALICE_PUBKEY()));
tx.command(getALICE_PUBKEY(), new Cash.Commands.Move());
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Move());
return tx.verifies();
});
return Unit.INSTANCE;
});
}
In this example we declare that ALICE
has $900 but we don’t care where from. For this we can use
unverifiedTransaction
. Note how we don’t need to specify this.verifies()
.
Notice that we labelled output with "alice's $900"
, also in transaction named "Issuance"
we labelled a commercial paper with "paper"
. Now we can subsequently refer to them in other transactions, e.g.
by input("alice's $900")
or "paper".output<ICommercialPaperState>()
.
The last transaction named "Trade"
exemplifies simple fact of selling the CommercialPaper
to Alice for her $900,
$100 less than the face value at 10% interest after only 7 days.
We can also test whole ledger calling this.verifies()
and this.fails()
on the ledger level.
To do so let’s create a simple example that uses the same input twice:
@Test
fun `chain commercial paper double spend`() {
val issuer = MEGA_CORP.ref(123)
ledger {
unverifiedTransaction {
output("alice's $900", 900.DOLLARS.CASH `issued by` issuer `owned by` ALICE_PUBKEY)
}
// Some CP is issued onto the ledger by MegaCorp.
transaction("Issuance") {
output("paper") { getPaper() }
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Issue() }
timestamp(TEST_TX_TIME)
this.verifies()
}
transaction("Trade") {
input("paper")
input("alice's $900")
output("borrowed $900") { 900.DOLLARS.CASH `issued by` issuer `owned by` MEGA_CORP_PUBKEY }
output("alice's paper") { "paper".output<ICommercialPaperState>() `owned by` ALICE_PUBKEY }
command(ALICE_PUBKEY) { Cash.Commands.Move() }
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Move() }
this.verifies()
}
transaction {
input("paper")
// We moved a paper to another pubkey.
output("bob's paper") { "paper".output<ICommercialPaperState>() `owned by` BOB_PUBKEY }
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Move() }
this.verifies()
}
this.fails()
}
}
@Test
public void chainCommercialPaperDoubleSpend() {
PartyAndReference issuer = getMEGA_CORP().ref(defaultRef);
ledger(l -> {
l.unverifiedTransaction(tx -> {
tx.output("alice's $900",
new Cash.State(issuedBy(DOLLARS(900), issuer), getALICE_PUBKEY(), null));
return Unit.INSTANCE;
});
// Some CP is issued onto the ledger by MegaCorp.
l.transaction("Issuance", tx -> {
tx.output("paper", getPaper());
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Issue());
tx.timestamp(getTEST_TX_TIME());
return tx.verifies();
});
l.transaction("Trade", tx -> {
tx.input("paper");
tx.input("alice's $900");
tx.output("borrowed $900", new Cash.State(issuedBy(DOLLARS(900), issuer), getMEGA_CORP_PUBKEY(), null));
JavaCommercialPaper.State inputPaper = l.retrieveOutput(JavaCommercialPaper.State.class, "paper");
tx.output("alice's paper", inputPaper.withOwner(getALICE_PUBKEY()));
tx.command(getALICE_PUBKEY(), new Cash.Commands.Move());
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Move());
return tx.verifies();
});
l.transaction(tx -> {
tx.input("paper");
JavaCommercialPaper.State inputPaper = l.retrieveOutput(JavaCommercialPaper.State.class, "paper");
// We moved a paper to other pubkey.
tx.output("bob's paper", inputPaper.withOwner(getBOB_PUBKEY()));
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Move());
return tx.verifies();
});
l.fails();
return Unit.INSTANCE;
});
}
The transactions verifies()
individually, however the state was spent twice! That’s why we need the global ledger
verification (this.fails()
at the end). As in previous examples we can use tweak
to create a local copy of the whole ledger:
@Test
fun `chain commercial tweak`() {
val issuer = MEGA_CORP.ref(123)
ledger {
unverifiedTransaction {
output("alice's $900", 900.DOLLARS.CASH `issued by` issuer `owned by` ALICE_PUBKEY)
}
// Some CP is issued onto the ledger by MegaCorp.
transaction("Issuance") {
output("paper") { getPaper() }
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Issue() }
timestamp(TEST_TX_TIME)
this.verifies()
}
transaction("Trade") {
input("paper")
input("alice's $900")
output("borrowed $900") { 900.DOLLARS.CASH `issued by` issuer `owned by` MEGA_CORP_PUBKEY }
output("alice's paper") { "paper".output<ICommercialPaperState>() `owned by` ALICE_PUBKEY }
command(ALICE_PUBKEY) { Cash.Commands.Move() }
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Move() }
this.verifies()
}
tweak {
transaction {
input("paper")
// We moved a paper to another pubkey.
output("bob's paper") { "paper".output<ICommercialPaperState>() `owned by` BOB_PUBKEY }
command(MEGA_CORP_PUBKEY) { CommercialPaper.Commands.Move() }
this.verifies()
}
this.fails()
}
this.verifies()
}
}
@Test
public void chainCommercialPaperTweak() {
PartyAndReference issuer = getMEGA_CORP().ref(defaultRef);
ledger(l -> {
l.unverifiedTransaction(tx -> {
tx.output("alice's $900",
new Cash.State(issuedBy(DOLLARS(900), issuer), getALICE_PUBKEY(), null));
return Unit.INSTANCE;
});
// Some CP is issued onto the ledger by MegaCorp.
l.transaction("Issuance", tx -> {
tx.output("paper", getPaper());
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Issue());
tx.timestamp(getTEST_TX_TIME());
return tx.verifies();
});
l.transaction("Trade", tx -> {
tx.input("paper");
tx.input("alice's $900");
tx.output("borrowed $900", new Cash.State(issuedBy(DOLLARS(900), issuer), getMEGA_CORP_PUBKEY(), null));
JavaCommercialPaper.State inputPaper = l.retrieveOutput(JavaCommercialPaper.State.class, "paper");
tx.output("alice's paper", inputPaper.withOwner(getALICE_PUBKEY()));
tx.command(getALICE_PUBKEY(), new Cash.Commands.Move());
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Move());
return tx.verifies();
});
l.tweak(lw -> {
lw.transaction(tx -> {
tx.input("paper");
JavaCommercialPaper.State inputPaper = l.retrieveOutput(JavaCommercialPaper.State.class, "paper");
// We moved a paper to another pubkey.
tx.output("bob's paper", inputPaper.withOwner(getBOB_PUBKEY()));
tx.command(getMEGA_CORP_PUBKEY(), new JavaCommercialPaper.Commands.Move());
return tx.verifies();
});
lw.fails();
return Unit.INSTANCE;
});
l.verifies();
return Unit.INSTANCE;
});
}


is it possible that both functions, mentioned in the topic, are not working? I recently noticed it when I tried to add comments to two accounts and to the radar. It just keeps resetting. Server radar/comments are working fine though.
Greetings
Brady
BrainFooLong
Administrator
All comments and radar things work fine for me.
Do you have disabled all plugins and tried it again?
Post edited 1 x times, last by
Brady_The
Topicstarter
Brady_The
BrainFooLong wrote:
All comments and radar things work fine for me.
Do you have disabled all plugins and tried it again?
Argh, I did exactly that before I created this thread and I still had the same problem. Now everyone works fine, even with activated plugins. How do I computer...


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Required interface for an object to be versionable.
Version
| Method Summary | |
void |
create()
Mark object as created. |
void |
delete()
Mark object for deletion. |
long |
getVersionId()
Gets the version number. |
boolean |
isCreated()
Has object been newly created ? |
boolean |
isDeleted()
Has object been deleted ? |
boolean |
isDirty()
Is object dirty ? |
void |
markClean()
Resets flags. |
void |
setVersionId(long id)
Sets the version number |
void |
touch()
Touches the object. |
void |
touch(java.lang.String field)
Touches the object, detailling which field has been accessed. |
| Method Detail |
public void touch()
public void touch(java.lang.String field)
field
- which was touchedpublic void delete()
public void create()
public boolean isCreated()
public boolean isDeleted()
public boolean isDirty()
public void markClean()
public long getVersionId()
public void setVersionId(long id)
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Versions doesn't only use bandwidth when it's committing or updating changes to and from your repository. Versions also uses bandwidth for updating the bookmarks badges in the bookmarks list. If you have a very large project, these checks can result in Versions having to download a lot of data to accurately figure out the amount of changes.
You can disable "Show badges with working copy changes" in preferences to help improve performance.


This is a helpful tool, It can generate the SuperMicro's motherboard IPMI Activate License keys, And it is free and open-source.
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Github project: kasuganosoras/SuperMicro-IPMI-LicenseGenerator


I am a graduate student in the State Key Laboratory of Robotics and System, Harbin Institue of Technology (HIT). My main research interests are 3D Reconstruction and Visual SLAM.
I am a graduate student in the State Key Laboratory of Robotics and System, Harbin Institue of Technology (HIT). My main research interests are 3D Reconstruction and Visual SLAM.


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Salma Hayek Flaunts Figure In Skintight Leather & Lace Dress In Black Friday Post
November 29, 2019
Salma Hayek Flaunts Figure In Skintight Leather & Lace Dress In Black Friday Post
Actress Salma Hayek decided to get in on the Black Friday posts with something a little bit different. Instead of promoting sales with companies she has partnered with, the brunette beauty promoted her insane figure with a photo that showed off her backside in a revealing black lace dress.
The photo was interesting — and sexy — in that it was a close up of Salma’s backside. The snap captured a just a portion of the beauty’s body from her mid-back to her mid-thigh.
The ensemble featured vertical strips of leather and floral lace with a band of leather that wrapped around her waist. The strips of leather were strategically placed as not to be too revealing, but still very titillating. Her smooth skin peeked through the lace, and shadows highlighted her pert derrière, creating a sensual vibe. The outfit highlighted Salma’s hourglass shape as she stood with one hip to the side and one leg slightly forward.
Sadly for her fans, Salma’s pretty face did not make an appearance in this photo, but that did not stop it from being smoking hot. In fact, it seemed to be a smash hit among her 12.7 followers, racking up more than 108,000 likes within 30 minutes of going live.
The actress left the caption simple, mentioning what day it was.
Her fans loved the photo, and told her so.
“Well, you had my curiosity but now you have my attention,” one fan joked.
“That’s the good stuff right there!!!” a second admirer quipped.
“The most beautiful woman on the planet,” said another follower.
Loading...
One female follower told Salma that she was jealous of her body.
“Body goals!!!!! Butttttt seriously your skin is flawless & you look 1/2 your age. I’m so envious,” the fan said.
There can be no denying that Salma looks incredible. The beauty doesn’t share a lot of photos on Instagram that are racy, but when she does, she knows how to rock the look. In recent weeks, she has shared photos of herself looking incredible in a bikini. She also recently flaunted her figure in a pink dress before heading out for a night on the town.
The actress shares many interesting snapshots from her life on the photo-sharing app, including pictures of her playing with turtles to hanging out with an owl. Her fans seem to love just about everything she shares online.


hello weirdo
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Post Updated 4/27/14 9:15PM – Since writing this post, my group has finished our project. Rather than adding a separate post, I’ve updated the links below to the final recommendation.
The semester is winding down and that means final projects are wrapping up. In Applying Edtech to Practice, my group tackled the problem of “rethinking teaching.” Essentially, we came up with a suggestion for how teachers can improve their practice based on The Danielson Group’s teacher evaluation tool.
The Framework for Teaching has four components:
The instrument breaks each area down into indicators which we used to outline our proposal. Using the TPACK framework as a guide, we looked at each subgroup in the Framework and linked them to simple tools teachers can use to move toward the distinguished level of performance.
To organize everything, we’ve created a Popplet as a graphic organizer. From there, you can learn more about The Danielson Group, the TPACK framework for applying technology as well as see our white paper recommendation and video of the process we used to create the final product.
Any feedback in the comments would be appreciated. The final revision will be posted later this week.</p>
Leave a comment


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Ethan Frome
by Edith Wharton
Ethan Frome is a tragic novel about the unrequited love between Ethan Frome and his wife's cousin, Mattie Silver.
This novel has elements in common with gothic fiction, realism, modernism, romance, and tragedy.
Source: Wharton, E. (1912). Ethan Frome. New York: Scribner's.
- Introduction
- The narrator tells of his first encounter with Ethan Frome in the town of Starkfield, Massachusetts. The narrator seeks information on the reclusive Frome.
- Chapter 1
- A younger Ethan Frome walks the streets of Starkfield stopping at a dance in the community church. Ethan quietly watches Mattie.
- Chapter 2
- Ethan approaches Mattie after the dance concludes.
- Chapter 3
- Ethan thinks about the previous evenings events. Zeena leaves for Bettsbridge.
- Chapter 4
- The circumstances of Ethan's marriage to Zeena are explained. With Zeena gone for the evening, Mattie prepares dinner for herself and Ethan.
- Chapter 5
- After dinner, Mattie and Ethan talk. Although she is absent, Zeena's presence is still felt in the house.
- Chapter 6
- Zeena's return fills Ethan with a sense of foreboding.
- Chapter 7
- Upon returning, Zeena gives Ethan some unsettling news concerning Mattie.
- Chapter 8
- Ethan is plagued with indecision and doubt.
- Chapter 9
- Ethan insists on taking Mattie to the station.


Constant structure associated with SPI port. More...
#include <qspi_dma.h>
Public Attributes | |
| Qspid_t | Qspid |
| sXdmad * | pXdmad |
| Pointer to DMA driver. More... | |
| uint8_t | Polling |
| Polling. More... | |
| uint8_t | TxChNum |
| Tx ch num. More... | |
| uint8_t | RxChNum |
| Rx ch num. More... | |
| volatile uint8_t | progress |
| QSPI Xfr state. More... | |
| XdmadTransferCallback | fCallbackTx |
| Tx callback function. More... | |
| void * | pArgTx |
| Tx callback argument. More... | |
| XdmadTransferCallback | fCallbackRx |
| Rx callback function. More... | |
| void * | pArgRx |
| Rx callback argument. More... | |
Constant structure associated with SPI port.
This structure prevents client applications to have access in the same time.
| XdmadTransferCallback _Qspid::fCallbackRx |
Rx callback function.
| XdmadTransferCallback _Qspid::fCallbackTx |
Tx callback function.
| void* _Qspid::pArgRx |
Rx callback argument.
| void* _Qspid::pArgTx |
Tx callback argument.
| uint8_t _Qspid::Polling |
Polling.
| volatile uint8_t _Qspid::progress |
QSPI Xfr state.
| sXdmad* _Qspid::pXdmad |
Pointer to DMA driver.
| Qspid_t _Qspid::Qspid |
| uint8_t _Qspid::RxChNum |
Rx ch num.
| uint8_t _Qspid::TxChNum |
Tx ch num.


|
Wayland Server API
1.16.0
|
#include <wayland-server-protocol.h>
Data Fields | |
| void(* | sync )(struct wl_client *client, struct wl_resource *resource, uint32_t callback) |
| asynchronous roundtrip More... | |
| void(* | get_registry )(struct wl_client *client, struct wl_resource *resource, uint32_t registry) |
| get global registry object More... | |
| void(* wl_display_interface::get_registry) (struct wl_client *client, struct wl_resource *resource, uint32_t registry) |
get global registry object
This request creates a registry object that allows the client to list and bind the global objects available from the compositor.
It should be noted that the server side resources consumed in response to a get_registry request can only be released when the client disconnects, not when the client side proxy is destroyed. Therefore, clients should invoke get_registry as infrequently as possible to avoid wasting memory.
| registry | global registry object |
| void(* wl_display_interface::sync) (struct wl_client *client, struct wl_resource *resource, uint32_t callback) |
asynchronous roundtrip
The sync request asks the server to emit the 'done' event on the returned wl_callback object. Since requests are handled in-order and events are delivered in-order, this can be used as a barrier to ensure all previous requests and the resulting events have been handled.
The object returned by this request will be destroyed by the compositor after the callback is fired and as such the client must not attempt to use it after that point.
The callback_data passed in the callback is the event serial.
| callback | callback object for the sync request |


Chapter 1 Doc - Updated 1/10/2020 - Finished
Chapter 2 Doc - Updated 1/26/2020 - Finished
Chapter 3 Doc - Updated 2/18/2020 - Questions 3.50 forward ignored


--- layout: basic category: EmpowerHack ---
Bringing people together to build solutions empowering women and girls in humanitarian contexts and creating sustainable tech that crosses borders.
EmpowerHack is a collective that creates sustainable design for women and girls in humanitarian contexts. Bridging the gap between technology, design, and NGOs to address key challenges, EmpowerHack galvanises volunteers to create code that crosses borders.
Despite up to 80% of world’s refugees being women and children, the problems faced by them are never at the forefront. EmpowerHack was born to change just that by taking collaborative design to scale. Female refugees are among the most vulnerable, facing sexual violence, coercion, forced and early marriage, social isolation, and a lack of access to gender-specific health care and education.


subject = "Sent after creating a support ticket" == Hello, Your ticket has been created. Use the link below to access it. You will be informed about any updates with separate e-mails. Please its number {{ ticket_number }} when contacting support by e-mail or phone. {{ ticket_link }} ==
Hello,
Your ticket has been created. Use the link below to access it. You will be informed about any updates with separate e-mails.
Please its number {{ ticket_number }} when contacting support by e-mail or phone.


Annotated moves
Javascript part
var pgn = '1. e4$1 e5$2 2. Nf3$3 Nc6$4 3. Bb5$5 a6$6 4. Ba4$7 Nf6$10 5. O-O$13 Be7$14 6. Re1$15 b5$16 7. Bb3$17 O-O$18 8. c3$19 d5$3';
pgnView('b1', {pgn: pgn});
HTML part

See the back link to the original examples page.
Here the normal layout of the moves, with some special characters, the so called NAGs. PNGViewerJS knows the NAGs, and translates them in the correct notation. The following game is not really correct commented, but it shows the different options.
Not all characters are available in all fonts, so we should check that ....


In order to test your skills and compete against other players in Head To Head Matches and Tournament Play, you will need to become a Royal Flush Club member by signing up for our monthly subscription membership.
Things To Know:
- Free Play Available (Limited Access- Individual Play Only).
- No obligation to upgrade account.
- Complete levels and increase your chip count.
- Reward Chips awarded on every level (Individual Play).
- Chance your chips to compete against other players in Head To Head Matches & Tournament Play.


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--- title: How to Use Foundation Sass ---
Making changes is simple, in ```_settings.scss``` find the element you want to style (command/ctrl+f). Find the variable, uncomment the style, and change its value. Be sure to to run grunt (libsass) or compass watch (compass) in order see any changes.
Here we've made some example changes to the buttons.
Now just need to save your changes and your Sass will compile. Just refresh the page to see yours changes!
A mixin lets you make groups of CSS declarations that you want to reuse throughout your site. You can even pass in values to make your mixin more flexible. You can create custom mixins to speed up development, another example regarding buttons:
{{#markdown}} ```scss {{> examples_custom_button_include}} ``` {{/markdown}}Here is an example of some variables you can customize:
{{#markdown}} ```scss {{> examples_button_variable_include}} ``` {{/markdown}}Then you can use your new custom button by simply include the following html:
{{#markdown}} ```html {{> examples_button_html_include}} ``` {{/markdown}}By default the _settings.scss is imported here - ```@import "settings"``` as well as all of Foundation SCSS - ```@import "foundation"```. If you don't want all of Foundation you can comment it out and then activate the individual imports you want below including the ```@import``` on line 5.
Note: Some Foundation components depend on other styles.
After the ```@import``` tag is a great place to keep your custom Sass because it will get compiled into CSS right from here.
Here is an example of how you can write Sass inside the ```app.scss``` file.
The Sass version of Foundation still allows you to write Vanilla CSS as well. If you want to do so, it is best practice to write these CSS styles in the ```app.scss``` file.
Note: Any CSS files in the stylesheets folder will be overwritten when your Sass is compiled, be sure keep any changes away from the files in this folder.


What to watch on Netflix, Showmax, and Video Play this weekend
by Jamie McKaneHere are the latest shows you can watch on Netflix, Showmax, and Video Play this weekend.
Mars – Season 2
IMDb – 7.5
Mean Girls 2
IMDb – 4.1
Dead Like Me: Season 2
IMDb – 8.1
Victoria & Abdul
IMDb – 6.8
EuroTrip
IMDb – 6.6
Let’s be Cops
IMDb – 6.4
Little Things – Season 3
IMDb – 8.3
Napoleon Dynamite
IMDb – 6.9
Sex and the City 2
IMDb – 4.4
The Dark Tower
IMDb – 5.6
Narco Wars: In Their Own Words
IMDb – 7.0
The Original Kings of Comedy
IMDb – 6.9
Greatest Events of World War II in HD Colour – Season 1
IMDb – N/A
Mission: Impossible – Fallout
IMDb – 7.8
What Happens in Vegas
IMDb – 6.1


Get all the licenses that are included in GitHub.
All licenses are properties of an initiated License class, which takes the name, and optionally, the year. For example:
const Licenses = require("github-licenses");
const licenses = new Licenses("Deez Nuts")
licenses.MIT;
/* MIT License
Copyright (c) 2021 Deez Nuts
Permission is hereby granted...*/
const Licenses = require("github-licenses");
const licenses = new Licenses("Deez Nuts", 2012)
licenses.MIT;
/* MIT License
Copyright (c) 2012 Deez Nuts
Permission is hereby granted...*/
The list of licenses that are included are:
| License | Function | Takes name + year |
|---|---|---|
| Academic Free License v3.0 | AFL | No |
| Apache License 2.0 | APACHE | No |
| Artistic License 2.0 | ART | No |
| Boost Software License 1.0 | BSL | No |
| BSD 2-clause "Simplified" License | BSD2C | Yes |
| BSD 3-clause "New" or "Revised" License | BSD3C | Yes |
| BSD 3-clause Clear License | BSD3CC | Yes |
| Creative Commons Zero v1.0 Universal | CC0 | No |
| Creative Commons Attribution 4.0 | CCBY | No |
| Creative Commons Attribution Share Alike 4.0 | CCBYSA | No |
| Do What The F You Want To Public License | WTFPL | No |
| Educational Community License v2.0 | ECL | No |
| Eclipse Public License 1.0 | EPL1 | No |
| Eclipse Public License 2.0 | EPL2 | No |
| European Union Public License 1.1 | EUPL | No |
| GNU Affero General Public License v3.0 | AGPL | No |
| GNU General Public License v2.0 | GPL2 | No |
| GNU General Public License v3.0 | GPL3 | No |
| GNU Lesser General Public License v2.1 | LGPL21 | No |
| GNU Lesser General Public License v3.0 | LGPL3 | No |
| ISC License | ISC | Yes |
| LaTeX Project Public License v1.3c | LPPL | No |
| Microsoft Public License | MSPL | No |
| MIT License | MIT | Yes |
| Mozilla Public License 2.0 | MPL | No |
| Open Software License 3.0 | OSL | No |
| PostgreSQL License | POSTSQL | Yes |
| SIL Open Font License 1.1 | OFL | Yes |
| University of Illinois/NCSA Open Source License | NCSA | Yes |
| The Unlicense | UNL | No |
| zLib License | ZLIB | Yes |
The CLI allows you to choose a license and write to a file. (Default: (cwd)/LICENSE)
Format: licenses (license) (option(s))
Options:
| Name | Description |
|---|---|
| --output/-n? | The output of the license |
| --name/-n | The name of the licensor |
| --year/-y? | The year the license was created |
This project is licensed under the MIT License.
Generated using TypeDoc


This is a test to see if people can tell the difference between music composed by humans and music composed by an algorithm.


--- title: Red Butte Photo of the Day August 28, 2009 date: 2009-08-28 00:00:00 -06:00 categories: - whats-blooming layout: post blog-banner: whats-blooming-now-summer.jpg post-date: August 28, 2009 post-time: 8:11 AM blog-image: wbn-default.jpg ---
This large, native cousin to Bitterbrush (Purshia tridentata) grows 3-12 feet high and sometimes becomes tree-like in the wild.
It's evergreen foliage and pale yellow flowers are pleasantly fragrant. Feathery seed heads cover the plant after flowering is through.
Cliffrose is becoming more and more common in the plant trade every year. It makes a wonderfully unique addition to your waterwise garden.
Watch for this plant at next years Red Butte Garden's annual benefit plant sale in the native and waterwise plant section


Kelly J. Hill SCC Portal IT1262
Welcome
Willkommen, ようこそ, Ceud mìle fàilte, Yin Dee Dtôn Ráp, Bienvenidos, Aloha, Benvenute, Wemukelekile, أهلاً و سهلاً, Ngiyanemukela: To one and all.
I am currently working to become a Web Designer in Seattle, WA. I have a background in Event Management and hold a degree in Art History. My design style ambition is to marry traditional beauty with future technology. My inspiration comes from travels, water, finding direction, movement and fasinating people.
Fun Facts about me:
- I have a cat with 22 toes.
- I Lucy Ball my way out of awkward situations.
- I lived on the edge of the Grand Canyon for a year.
Something to ponder: I am an only child who has the a bother. Hint: He is not a stepbother.


License
Consent-O-Matic is made available on
Github under the MIT license
MIT License
Copyright (c) 2019,2020,2021,2022 Janus Bager Kristensen and Rolf Bagge,
CAVI - Center for Advanced Visualization and Interaction, Aarhus University
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


Affordances
-
Asynchronous interactions for students and instructors offer flexibility in
scheduling so that engaging the course can happen during convenient blocks
of time for all parties involved.
-
There is greater opportunity for instructors and students to reflect on
their participation and prepare more thoughtful responses in asynchronous
discussion forums.
-
Asynchronous discussion environments may be less threatening for some
students and thus support richer collaborative experiences and co-creation
of knowledge (Zhong & Norton, 2018).
-
Communication can be more widely and evenly distributed in the learning
environment with more opportunities for participants to respond to each
other.
-
Assignment feedback is an essential feature in asynchronous interactions
with students and offers both students and instructors evidence of student
progress in an asynchronous environment.
Limitations
-
Feedback lacks immediacy and therefore it takes longer for the feedback
cycle to complete: submission > instructor feedback > student review >
revision.
-
Students and faculty may feel less engaged without a regular, physical
meeting time.
-
Written and nonverbal communications lack nonverbal cues and can be
misinterpreted.
-
Information must be organized more thoughtfully so students can access
learning materials without direct intervention.
-
Discussion boards can be long and difficult to engage specific ideas and
students. The amount of text information to process is much higher in an
asynchronous environment creating a higher cognitive load for students which
takes more time to encode and process.
Instructional Strategies
-
Provide a consistent interval to communicate with students so that they can
form an expectation of when they will hear from faculty.
-
Have a new announcement at a regular interval for your class. That
includes reminders of topics addressed, due dates, etc., as well as what
students can expect from faculty (availability, feedback on assessments,
etc.). Choose a timeframe for when students should expect feedback from
assignments and stick to it. Make sure that this feedback is given so
students have enough time to incorporate feedback into the next assignment
that is due.
-
Offer students a clear expectation for when you will respond to emails –
usually within 24 hours is recommended.
-
One student comments: “Couldn’t agree with this more. Also, from my
experience I’ve received emails, yet no Canvas notifications and vice
versa. So I’d strongly encourage teachers to use both and all means
consistently.”
-
Active discussion participation is essential to maintaining visible
instructor presence in the class (Garrison & Akyol, 2013).
- Model the kind of interaction you are expecting of your students.
-
Highlight strengths of discussion posts and spur the discussion with
questions to expand participation (Garrison & Akyol, 2013).
-
Create a detailed syllabus and maintain those stated expectations throughout
the course: explicitly communicate any changes when they are made.
-
Include in announcements and other communications what students need to
expect for a given learning unit and any information to help them navigate
the course and find what they need for each assignment.
-
Include any general impressions you might have from an assignment you have
just assessed to guide students in their performance to improve as the
course progresses. Trends you see in feedback to individual students are
often helpful if included in announcements to the entire class.
-
Consider using audio or video feedback for assignments to build more nuanced
social presence (Garrison & Akyol, 2013).
-
Offer synchronous office hours, but consider students in different time
zones and have various meeting time options. Consider office hours by
appointment. Microsoft bookings, Doodle polls, and Canvas schedule tools
might help.
-
Consider using video introductions so that students get to know you on a
more personal basis.
-
Consider breaking class discussion forums into randomized groups in Canvas.
Randomized discussion groups each week ensure all students have an
opportunity to interact with each other.


The correct sequence was blood - gold - silver. Ha! Better luck next time!


Provides a set of R bindings for the 'Selenium 2.0 WebDriver' (see <https://seleniumhq.github.io/docs/wd.html> for more information) using the 'JsonWireProtocol' (see <https://github.com/SeleniumHQ/selenium/wiki/JsonWireProtocol> for more information). 'Selenium 2.0 WebDriver' allows driving a web browser natively as a user would either locally or on a remote machine using the Selenium server it marks a leap forward in terms of web browser automation. Selenium automates web browsers (commonly referred to as browsers). Using RSelenium you can automate browsers locally or remotely.
| Version: | 1.7.4 |
| Depends: | R (≥ 3.0.0) |
| Imports: | XML, methods, caTools, tools, utils, openssl, httr, wdman (≥ 0.2.2), binman |
| Suggests: | testthat, knitr, Rcompression, covr, rmarkdown |
| Published: | 2018-09-18 |
| Author: | John Harrison [aut] (original author), Ju Yeong Kim [cre] (rOpenSci maintainer) |
| Maintainer: | Ju Yeong Kim <jkim2345 at fredhutch.org> |
| BugReports: | http://github.com/ropensci/RSelenium/issues |
| License: | AGPL-3 |
| URL: | http://ropensci.github.io/RSelenium |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | RSelenium results |
| Reference manual: | RSelenium.pdf |
| Vignettes: |
Vignette Title Vignette Title Vignette Title Vignette Title Vignette Title Vignette Title |
| Package source: | RSelenium_1.7.4.tar.gz |
| Windows binaries: | r-devel: RSelenium_1.7.4.zip, r-release: RSelenium_1.7.4.zip, r-oldrel: RSelenium_1.7.4.zip |
| OS X binaries: | r-release: RSelenium_1.7.4.tgz, r-oldrel: RSelenium_1.7.4.tgz |
| Old sources: | RSelenium archive |
| Reverse imports: | carbonate |
Please use the canonical form https://CRAN.R-project.org/package=RSelenium to link to this page.


Based on HN stories
Using mostly all the stories published in the site news.ycombinator.com I grouped and filtered all stories related to programming languages. The core idea is to know which programming languages are the most discussed and have more relevance in the HN ecosystem.
Disclaimer: This is a very rough estimate. In special for 'Go', 'C' and 'R' which are difficult to separate from context. Besides, I'm mostly a backend developer so if you think something is wrong with this web or can be modified, please feel free to comment about it!
In my opinion, HN is a fantastic information source. Not only for entrepreneurs but also for many hackers trying to do something interesting with code. I believe a great amount of HN stories are relevant, not only to devs, but to a whole ecosystem trying not to drown in an ocean of irrelevant information.
For simplicity I use some Python scripts for connect to the HN API in Firebase. The process of gathering all the stories was painfully slow. Then I tried to filter stories related to some programming language using Github's linguist languages.yml file. For some cases (strings with no more than 3 characters), I had to filter with exact match for each word in the story title. Therefore, this is a very rough estimate; as I said before, hope you could hack this scripts if you want to :)
Finally I did some processing using Pandas, and this is the result:
When looking at this chart, it's important to know that for instance 'Go' has a lot of false positives, due to the difficult to filter in context. In the other hand Javascript has many stories not counted because of typos in the title of the story (ex: [Something]+JS and the relevant Node framework)
It's interesting to see that 2015 was the year of Javascript without any doubt. In 2014, we witnessed a decline of Python and Java langs. Go-lang gained a lot of popularity last year, maybe at the same level of Python (ignoring false positives).
Aside from the quantity of HN readers, this is only one source of thousands of other sources of information related to programming (Twitter, GitHub, groups, other ezines, etc). So this information can't be used to select one programming language in particular. Use the correct tool for the job.
If you want to contribute with more analytics, feel free to fork HN dataset. It would be nice seeing some trends about Web Frameworks, counting votes of each story, or doing some sentiment analysis of each article. Another interesting thing would be using HN Jobs information grouping by technology.


| Entry | Title | Description |
|---|---|---|
| Virtual Reality Training & Development | Virtual Reality enables anyone in an educational setting to deliver large amounts of complex information in a visually attractive way. | |
| Augmented Reality Training & Development | Augmented Reality allows anyone in an educational setting to be subjected to dangerous environments in a completely safe manner. | |
| Gamification in Training & Development | Gamification allows anyone in an educational setting to bring forth elements of game playing to reach a level of engagement with participants. |
Head over to the polls and cast your vote now!


--- title: Home permalink: / --- {% include head.html %}
Spatialys is a company founded by Even Rouault that is dedicated to providing to its clients the best of its expertise around Open Source geospatial software and standards.
Being strongly involved in the core development teams of the GDAL/OGR and MapServer projects, Spatialys can take care of designing and developing new features that will be included into the mainline of those projects, with the appropriate level of quality insurance and documentation.
Spatialys can assist you in deploying those technologies in your infrastructure, and optimizing their use to your needs.


Telemetry
Commands
Pad 3
Pad 4
SPEEDOMETER
289,3
km/h
COOLING
BATTERY
POD STATE
SPEEDOMETER
ROLL
PITCH
YAW
PRESSURE
TEMPERATURE
MESSAGES
Click to say "Hello, world"


The Web is full of useful resources for Python developers. The most important and obvious ones were already mentioned earlier but here they are repeated to keep this list consistent:
The other resources such as books and tutorials are useful but often get outdated very fast. What does not get outdated are the resources that are actively curated by the community or released periodically. The two that are mostly worth recommending are:
These two resources will provide the reader with tons of additional reading for several months.


|
eccodes
|
Data Structures | |
| struct | codes_handle |
| struct | codes_multi_handle |
Functions | |
| int | codes_count_in_file (codes_context *c, FILE *f, int *n) |
| Counts the messages contained in a file resource. More... | |
| grib_handle * | codes_handle_new_from_file (grib_context *c, FILE *f, ProductKind product, int *error) |
| Create a handle from a file resource. More... | |
| grib_handle * | codes_grib_handle_new_from_file (grib_context *c, FILE *f, int *error) |
| Create a GRIB handle from a file resource. More... | |
| grib_handle * | codes_bufr_handle_new_from_file (grib_context *c, FILE *f, int *error) |
| Create a BUFR handle from a file resource. More... | |
| int | codes_write_message (codes_handle *h, const char *file, const char *mode) |
| Write a coded message to a file. More... | |
| codes_handle * | codes_handle_new_from_message (codes_context *c, const void *data, size_t data_len) |
| Create a handle from a user message in memory. More... | |
| codes_handle * | codes_grib_handle_new_from_multi_message (codes_context *c, void **data, size_t *data_len, int *error) |
| Create a handle from a user message in memory. More... | |
| codes_handle * | codes_handle_new_from_message_copy (codes_context *c, const void *data, size_t data_len) |
| Create a handle from a user message. More... | |
| codes_handle * | codes_grib_handle_new_from_samples (codes_context *c, const char *sample_name) |
| Create a handle from a GRIB message contained in a samples directory. More... | |
| codes_handle * | codes_bufr_handle_new_from_samples (codes_context *c, const char *sample_name) |
| Create a handle from a BUFR message contained in a samples directory. More... | |
| codes_handle * | codes_handle_clone (codes_handle *h) |
| Clone an existing handle using the context of the original handle, The message is copied and reparsed. More... | |
| int | codes_handle_delete (codes_handle *h) |
| Frees a handle, also frees the message if it is not a user message. More... | |
| codes_multi_handle * | codes_grib_multi_handle_new (codes_context *c) |
| Create an empty multi-field GRIB handle. More... | |
| int | codes_grib_multi_handle_append (codes_handle *h, int start_section, codes_multi_handle *mh) |
| Append the sections starting with start_section of the message pointed by h at the end of the multi-field GRIB handle mh. More... | |
| int | codes_grib_multi_handle_delete (codes_multi_handle *mh) |
| Delete multi-field GRIB handle. More... | |
| int | codes_grib_multi_handle_write (codes_multi_handle *mh, FILE *f) |
| Write a multi-field GRIB handle in a file. More... | |
The codes_handle is the structure giving access to parsed message values by keys.
| grib_handle* codes_bufr_handle_new_from_file | ( | grib_context * | c, |
| FILE * | f, | ||
| int * | error | ||
| ) |
Create a BUFR handle from a file resource.
The file is read until a BUFR message is found. The message is then copied. Remember always to delete the handle when it is not needed anymore to avoid memory leaks.
| c | : the context from which the handle will be created (NULL for default context) |
| f | : the file resource |
| error | : error code set if the returned handle is NULL and the end of file is not reached |
| codes_handle* codes_bufr_handle_new_from_samples | ( | codes_context * | c, |
| const char * | sample_name | ||
| ) |
Create a handle from a BUFR message contained in a samples directory.
The message is copied at the creation of the handle
| c | : the context from which the handle will be created (NULL for default context) |
| sample_name | : the name of the sample file (without the .tmpl extension) |
| int codes_count_in_file | ( | codes_context * | c, |
| FILE * | f, | ||
| int * | n | ||
| ) |
Counts the messages contained in a file resource.
| c | : the context from which the handle will be created (NULL for default context) |
| f | : the file resource |
| n | : the number of messages in the file |
| grib_handle* codes_grib_handle_new_from_file | ( | grib_context * | c, |
| FILE * | f, | ||
| int * | error | ||
| ) |
Create a GRIB handle from a file resource.
The file is read until a GRIB message is found. The message is then copied. Remember always to delete the handle when it is not needed anymore to avoid memory leaks.
| c | : the context from which the handle will be created (NULL for default context) |
| f | : the file resource |
| error | : error code set if the returned handle is NULL and the end of file is not reached |
| codes_handle* codes_grib_handle_new_from_multi_message | ( | codes_context * | c, |
| void ** | data, | ||
| size_t * | data_len, | ||
| int * | error | ||
| ) |
Create a handle from a user message in memory.
The message will not be freed at the end. The message will be copied as soon as a modification is needed. This function works also with multi field messages.
| c | : the context from which the handle will be created (NULL for default context) |
| data | : the actual message |
| data_len | : the length of the message in number of bytes |
| error | : error code |
| codes_handle* codes_grib_handle_new_from_samples | ( | codes_context * | c, |
| const char * | sample_name | ||
| ) |
Create a handle from a GRIB message contained in a samples directory.
The message is copied at the creation of the handle
| c | : the context from which the handle will be created (NULL for default context) |
| sample_name | : the name of the sample file (without the .tmpl extension) |
| int codes_grib_multi_handle_append | ( | codes_handle * | h, |
| int | start_section, | ||
| codes_multi_handle * | mh | ||
| ) |
Append the sections starting with start_section of the message pointed by h at the end of the multi-field GRIB handle mh.
Remember always to delete the multi-handle when it is not needed any more to avoid memory leaks.
| h | : The handle from which the sections are copied. |
| start_section | : section number. Starting from this section all the sections to the end of the message will be copied. |
| mh | : The multi-field handle on which the sections are appended. |
| int codes_grib_multi_handle_delete | ( | codes_multi_handle * | mh | ) |
Delete multi-field GRIB handle.
| mh | : The multi-field handle to be deleted. |
| codes_multi_handle* codes_grib_multi_handle_new | ( | codes_context * | c | ) |
Create an empty multi-field GRIB handle.
Remember always to delete the multi-handle when it is not needed any more to avoid memory leaks.
| c | : the context from which the handle will be created (NULL for default context) |
| int codes_grib_multi_handle_write | ( | codes_multi_handle * | mh, |
| FILE * | f | ||
| ) |
Write a multi-field GRIB handle in a file.
Remember always to delete the multi-handle when it is not needed any more to avoid memory leaks.
| mh | : The multi-field GRIB handle to be written. |
| f | : File on which the file handle is written. |
| codes_handle* codes_handle_clone | ( | codes_handle * | h | ) |
Clone an existing handle using the context of the original handle, The message is copied and reparsed.
| h | : The handle to be cloned |
| int codes_handle_delete | ( | codes_handle * | h | ) |
Frees a handle, also frees the message if it is not a user message.
| h | : The handle to be deleted |
| grib_handle* codes_handle_new_from_file | ( | grib_context * | c, |
| FILE * | f, | ||
| ProductKind | product, | ||
| int * | error | ||
| ) |
Create a handle from a file resource.
The file is read until a message is found. The message is then copied. Remember always to delete the handle when it is not needed anymore to avoid memory leaks.
| c | : the context from which the handle will be created (NULL for default context) |
| f | : the file resource |
| product | : the kind of product e.g. PRODUCT_GRIB, PRODUCT_BUFR |
| error | : error code set if the returned handle is NULL and the end of file is not reached |
| codes_handle* codes_handle_new_from_message | ( | codes_context * | c, |
| const void * | data, | ||
| size_t | data_len | ||
| ) |
Create a handle from a user message in memory.
The message will not be freed at the end. The message will be copied as soon as a modification is needed.
| c | : the context from which the handle will be created (NULL for default context) |
| data | : the actual message |
| data_len | : the length of the message in number of bytes |
| codes_handle* codes_handle_new_from_message_copy | ( | codes_context * | c, |
| const void * | data, | ||
| size_t | data_len | ||
| ) |
Create a handle from a user message.
The message is copied and will be freed with the handle
| c | : the context from which the handle will be created (NULL for default context) |
| data | : the actual message |
| data_len | : the length of the message in number of bytes |
| int codes_write_message | ( | codes_handle * | h, |
| const char * | file, | ||
| const char * | mode | ||
| ) |
Write a coded message to a file.
| h | : codes_handle to be written |
| file | : name of the output file |
| mode | : mode |


{% extends "base/base.html" %} {% load staticfiles %} {% load thumbnail %} {% block page_title %} View your shoppint cart {% endblock %} {% block content %}
Please, fill this form with your personal data to checkout your purchase:
{% include "base/forms/form.html" %}


Your web browser must have JavaScript enabled in order for this application to display correctly.
Torus Focal Decomposition
View the source


UC Berkeley's Electrical Engineering and Computer Sciences honor society
Today's Tutoring Hours
April 22, 2019
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Eta Kappa Nu (HKN) is the national Electrical and Computer Engineering honor society. The Berkeley chapter is among the most active engineering societies at Berkeley, providing services to fellow undergraduates. Welcome to our website!
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We host a variety of acdemic, industry, and service events to help grow our EECS community. Here's a taste of some of what we do.
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Find past exams and solutions from various EE and CS classes.
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Congratulations {{ abstract.participant.author }}!
Your abstract titled {{ abstract.title }} in the category {{ abstract.category.name }} has been selected for the next round of Paper Presentation 2019.
In this round, you will have to submit your paper by 17th February 2019. You would have to upload the paper on the portal. Click here to see the format for the paper. The participants shortlisted after this round will present their paper during APOGEE 2019 - The Reality Roulette
Feedback for abstract: As an attempt to promote undergraduate research, we have introduced the system of feedback to help you improve and refine your paper and give proper direction to first time participants. PFA the feedback for your abstract as well.
To add value to the competition and help the students get international recognition for their study, we have collaborated with SpringerPublications. The winners from each category will be given access to E-books worth 100 euros.
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 Both were great. [WISH YOU WERE WERE HERE is reviewed in another article. -ecl]
WISH YOU WERE HERE is based on the first part of the biography of Cynthia Payne (the character that PERSONAL SERVICES is based on. It is directed and written by David Leland, the writer of MONA LISA (with Neil Jordan) and PERSONAL SERVICES. The star, Emily Lloyd, is the brightest thing to hit movie screens this year. The story is about a young girl who tries to rebel against her father and society by swearing like a truck driver and using sexual promiscuity to shock people. Her favorite saying (she says it often) is "up your bum." But as she realizes what her actions have done, she begins to realize the consequences that she must begin to face. The movie ends with her making some very crucial decisions about how she will live her life.
This is one of my favorite movies of the year. Go see it. I give it a 9 out of 10.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


DAV classes are roughly broken down into the following packages:
The acegisecurity
subpackage contain classes responsible for handling access decision
management for certain dav requests
The acl
subpackages contains classes relating to the
minimal WebDAV ACL support the server provides.
The caldav
subpackages
contain classes that define and implement CalDAV.
The servlet
subpackage
contains the request handler implementation that handles all dav requests.
The ticket
subpackages
contains classes that define and implement WebDAV tickets
The impl
subpackage
contains implementations of dav interfaces


This page contains webassembly and javascript content, please enable javascript in your browser.
←
→
Shot


title = "home.htm" url = "/" layout = "default" is_hidden = 0 ==
Bekend met Symfony4 React (Native) Docker WebPack


{% extends '_layouts/layout_2-3.html' %} {% block page_title %}Connect to NHS Wifi{{serviceName}}{% endblock %} {% block pageTitle %}Connect to NHS Wifi{% endblock %} {% block content %}
To connect to NHS Wifi you need to give us some details, this will also create an NHS account which will make it easier for you to use future services
For example, 31 / 12 / 1980


These are the colors that compose the palette. Replace them directly on your CSS or as the variable value on the SASS files.
Primary
#6C63FFPrimary Dark
#463affAccent
#06d19cSecondary
#4641FFText
#666Headings
#111Alternate background
#f7f7f7Dark background
#333C44Lines
#eeeEvie should be easily customizable and really fast, so only one font (Lato) is imported and at a single weight (normal - 400).
To achieve some kind of vertical rhythm and more eye-pleasing results the typography loosely follows the Minor Third scale. Here are the examples:
Finally, there is the plain paragraph you can use as always.
To cover the most common cases, there are some buttons but you can easily create custom ones by changing only the color/border-color both on normal and hover state. Here are the buttons:
Link
The form elements share the border radius and are designed to stand out in a white background. You can see those here:


Home
Salmon Cookies Sales
Create a New Salmon Cookie Store
Location Name
Min Customer Per Hour
Max Customer Per Hour
Avg Cookie Sale Per Hour
Create Store


To get started, authorize your account to work with this extension and all your calendars and associated events from your Google Calendar will be automatically synchronized.
Note: This extension uses the account currently logged into your Google Chrome Browser.


Dear {{member_me.user.username}},
{{username}} has invited you to join the organization {{member_me.organization.name}} on SolarPermit.org.
As a member of {{member_me.organization.name}}, you'll be able to connect with your colleagues on the site and {{member_me.organization.name}} will share credit for data that you contribute to the database.
Please sign in to SolarPermit.org to accept or decline this invitation.
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| 26 Jul 1977 |
Noelle Lucila Bush born, to Jeb and Columba Bush. She is the niece of George W Bush. |
| 1995 |
A female with Noelle Bush's name and identical birthdate arrested for shoplifting at an Arizona mall. If so, Noelle does not have a clean criminal record required for her to avoid harsh sentencing under Florida's drug laws. Caveat: While this might not actually be her, how many Noelle Bush's were likely born on July 26th, 1977? |
| 1999 |
Admissions of drug offenders to Florida's prison system up 19% from 1998. |
| 2000 |
Noelle Bush graduates Tallahassee Community College. She resides with her parents in the Florida Governor's mansion, which in some sense qualifies as "state subsidized housing." |
| 29 Jan 2002 |
Noelle Bush arrested at Walgreen's on a charge of prescription fraud, attempting to obtain Xanax. |
| 29 Jan 2002 |
"Columba and I are deeply saddened over an incident that occurred last night involving our daughter Noelle. This is a very serious problem. Unfortunately, substance abuse is an issue confronting many families across our nation... We ask the public and the media to respect our family's privacy during this difficult time so that we can help our daughter. We will have no further comment on this issue. Thank you for your understanding." |
| 9 Sep 2002 |
"One of the women here was caught buying crack cocaine tonight. And a lot of the women are upset because she's been caught about five times. And we want something done because our children are here, and they just keep letting it slip under the counter and carpet... She does this all the time and she gets out of it because she's the governor's daughter. But we're sick of it here 'cause we have to do what's right, but she gets treated like some kind of princess. And everybody's tired of it, you know. We're just trying to get our lives together, and this girl's bringing drugs on property." Transcript, Orlando Police Department, receiving an anonymous call from within the Center for Drug-Free Living, where Noelle Bush is currently in rehab. |
| 9 Sep 2002 |
Employee Julia Elias discovers 0.2g crack cocaine hidden in Noelle Bush's shoe, while Bush is a patient at the Center for Drug-Free Living.  Lacking the document or cooperation, police were unable to make an arrest. |
| 16 Sep 2002 |
"While Noelle has been given every break in the book -- and then some -- her father has made it harder for others in her position to get the help they need by cutting the budgets of drug treatment and drug court programs in his state. He has also actively opposed a proposed ballot initiative that would send an estimated 10,000 non-violent drug offenders into treatment instead of jail." Arianna Huffington. |
| 30 Sep 2002 |
A judge blocks police from questioning employees that found the crack cocaine in Noelle's shoe earlier in the month, citing privacy laws. Father Jeb Bush expresses pleasure at the court's decision. |
| 8 Oct 2002 |
Noelle Bush asks that drug court records be closed on her case, citing a ludicrous theory involving "expectation of privacy." The real reason, of course, is the extra scrutiny her celebrity status is costing her. |
| 15 Oct 2002 |
A judge denies Bush's request to seal drug court records. |
| 17 Oct 2002 |
Noelle Bush sentenced to ten days in Orange County jail for contempt, because of the crack cocaine. |
| 8 Aug 2003 |
Noelle Bush released from rehab to the custody of her parents, Governor Jeb Bush and wife Columba. |


A minisite for book and article highlights: highlights.melanie-richards.com, built with Eleventy. Highlights and covers are copyright to their respective authors. Let’s be book friends
The master
branch was renamed to main
in 2020. Folks who have forked the repo can update their local clones using these instructions.
The primary purpose of this repo is to deploy highlights.melanie-richards.com, though others may feel free to remix the code for their own highlights sites. As of late 2020, according to my current development practices, this site has been converted from Jekyll to Eleventy. If you'd still prefer to use Jekyll instead, you may access prior releases or branch off of jekyll
.
npm install
npx @11ty/eleventy --serve
localhost:8080
I've chosen to deploy my highlights to the root path of a subdomain, but others might prefer a subdirectory (e.g. example.com/highlights/
). To do so, change the pathPrefix
in .eleventy.js
to specify your subdirectory (e.g. /highlights/
). You should not need to prepend any permalink
frontmatter or URLs referenced in templates with this subpath.
| Command | Purpose |
|---|---|
| npm run start | Serve project + watch Sass |
| netlify build | Kick off a Netlify build locally. Useful for local validation w/ Netlify plugins. |
title: ""
book: dash-separated
author:
kindle: true
spoilers: false
date: YYYY-MM-DD
bookshop_id:
_data
file, JPG, and SVG.bookshop_id
is used for bookshop.org buy links. The base template uses my affiliate link structure; feel free to replace with your own or remove these buy links entirely (though it’s nice to support authors!).- text:
page:
attribution:


The majority of services offered by The Met.Office in the UK are also available for overseas sites. Thus we provide forecasts to Formula 1 Grand Prix teams as they travel around the world and to coffee growers in Brazil.
Developing countries may not have the infrastructure of observers and Weather Centres that we have in the UK but we welcome the opportunity to collaborate and co-operate in setting up and establishing the necessary computer and communication systems for running an effective meteorological service.
For further information contact:
David Smith
Tel.No. +44 (0)1344 856283
E-mail<EMAIL_ADDRESS>Postal Address: Met.Office, Sutton House, London Road, Bracknell, Berks. RG12 2SY. United Kingdom
or simply fill in our Response Form.


Web application development consultancy from Rob Howell.
Latest GitHub projects
- Weather forecast demo app [live demo]: 5-day weather forecast using the openweatherapp.com API
- Clean React Boilerplate: A simple boilerplate for React-based JavaScript projects


2048
New Game
HOW TO PLAY:
Use your arrow keys to move the tiles. Tiles with the same number merge into one when they touch. Add them up to reach 2048!
Join the tiles and get to
2048!
score
0
best
0
Game over!
New Game


RobBERT is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging task. As such, it has been successfully used by many researchers and practitioners for achieving state-of-the-art performance for a wide range of Dutch natural language processing tasks, including:
- Emotion detection
- Sentiment analysis (book reviews, news articles*)
- Coreference resolution
- Named entity recognition (CoNLL, job titles*, SoNaR)
- Part-of-speech tagging (Small UD Lassy, CGN)
- Zero-shot word prediction
- Humor detection
- Cyberbulling detection
- Correcting dt-spelling mistakes*
and also achieved outstanding, near-sota results for:
* Note that several evaluations use RobBERT-v1, and that the second and improved RobBERT-v2 outperforms this first model on everything we tested
(Also note that this list is not exhaustive. If you used RobBERT for your application, we are happy to know about it! Send us a mail, or add it yourself to this list by sending a pull request with the edit!)
To use the RobBERT model using
HuggingFace transformers, use the name
pdelobelle/robbert-v2-dutch-base
.
Get started with our models
We release our pretrained models for both Hugging Face's transformers and Facebook's Fairseq. For some downstream tasks, we also have models in either or both formats.
Awesome that you're using 🤗 Transformers. You can import our models directly using:
from transformers import RobertaTokenizer, RobertaForSequenceClassification tokenizer = RobertaTokenizer.from_pretrained("pdelobelle/robbert-v2-dutch-base") model = RobertaForSequenceClassification.from_pretrained("pdelobelle/robbert-v2-dutch-base")
With the pretrained model being either the base model, that was trained on language modeling (LM) or a finetuned model that we provide.
| Model | Parameters | Year | Corpus | Huggingface id | |
|---|---|---|---|---|---|
| RobBERT-2022 RobBERT-2022 is the newest release of our RobBERT model. Since the original release in January 2020, some things happened and our language evolved. To account for this, we release a new Dutch BERT model trained on data from 2022. |
119 M | 2022 | 77 GB | DTAI-KULeuven/ robbert-2022-dutch-base | |
| RobBERTje Non-shuffled Smaller, distilled RobBERT model trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. |
74 M | 2019 | 1 GB | DTAI-KULeuven/ robbertje-1-gb-non-shuffled | |
| RobBERTje Shuffled Smaller, distilled RobBERT model trained on the publicly available and shuffled OSCAR corpus. |
74 M | 2019 | 1 GB | DTAI-KULeuven/ robbertje-1-gb-shuffled | |
| RobBERTje Merged (p=0.5) Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. |
74 M | 2019 | 1 GB | DTAI-KULeuven/ robbertje-1-gb-merged | |
| RobBERTje BORT A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). |
46 M | 2019 | 1 GB | DTAI-KULeuven/ robbertje-1-gb-bort | |
| RobBERT v2 RobBERT is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging task. |
116 M | 2019 | 39 GB | pdelobelle/ robbert-v2-dutch-base | |
| RobBERT | 2019 | 39 GB | pdelobelle/ robBERT-base |
Great that you're using Fairseq! We pre-trained our model with this library. You can import this pre-trained version of RobBERT by downloading it directly.
| Model | Task | Accuracy (%) |
|---|---|---|
| Latest release RobBERT-v2-dutch-base Download (1.2 GB) | LM | — |
| RobBERT-base Download (1.4 GB) | LM | — |
| RobBERT-diedat Download (1.4 GB) | Die vs dat | 98.4% |
We're releasing all our models under MIT.
The advent of neural networks in natural language processing (NLP) has significantly improved state-of-the-art results within the field. While recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) initially dominated the field, recent models started incorporating attention mechanisms and then later dropped the recurrent part and just kept the attention mechanisms in so-called transformer models. This latter type of model caused a new revolution in NLP and led to popular language models like GPT-2 and ELMo. BERT improved over previous transformer models and recurrent networks by allowing the system to learn from input text in a bidirectional way, rather than only from left-to-right or the other way around. This model was later re-implemented, critically evaluated and improved in the RoBERTa model.
These large-scale transformer models provide the advantage of being able to solve NLP tasks by having a common, expensive pre-training phase, followed by a smaller fine-tuning phase. The pre-training happens in an unsupervised way by providing large corpora of text in the desired language. The second phase only needs a relatively small annotated data set for fine-tuning to outperform previous popular approaches in one of a large number of possible language tasks.
While language models are usually trained on English data, some multilingual models also exist. These are usually trained on a large quantity of text in different languages. For example, Multilingual-BERT is trained on a collection of corpora in 104 different languages and generalizes language components well across languages. However, models trained on data from one specific language usually improve the performance over multilingual models for this particular language. Training a RoBERTa model on a Dutch dataset thus has a lot of potential for increasing performance for many downstream Dutch NLP tasks.
Intro in RobBERT, BERT and RoBERTa
In NLP, encoder-decoder models have been used for some time. These models, often called sequence-to-sequence or seq2seq, are good at various sequence-based tasks: translations, token labeling, named entity recognition (NER), etc. Historically, these seq2seq models were usually LSTMs or other recurrent networks. A major improvement in these networks was an attention mechanism, that allowed to communicate more than one feature vector. (For those coming from computer vision, this looks a bit like the connections in UNet).
The by now famous transformer model was based solely on this attention mechanism. It features 2 stacks: (i) an encoder stack that uses multiple layers of self-attention and (ii) a decoder stack with attention layers that connect back to the encoder outputs.
Encoders
So this attention-based encoder generates a transformed representation of the input sequence at once.
We could also interpret this probabilistically, we have a language model
$$P(\text{``giraf"} \mid \text{``ik zie een <mask> in mijn tuin."})<0.0001$$
Or a more probable:
$$P(\text{``boom"} \mid \text{ik zie een <mask> in mijn tuin."})=0.1498$$
In fact, we can even query the most likely results. For this sentence, RobBERT gives us:
[('Ik zie een lamp in mijn tuin.', 0.39584335684776306, ' lamp'),
('Ik zie een boom in mijn tuin.', 0.1497979462146759, ' boom'),
('Ik zie een camera in mijn tuin.', 0.089895099401474, ' camera'),
('Ik zie een ster in mijn tuin.', 0.046020057052373886, ' ster'),
('Ik zie een stip in mijn tuin.', 0.009481011889874935, ' stip'),
('Ik zie een man in mijn tuin.', 0.009198895655572414, ' man'),
('Ik zie een slang in mijn tuin.', 0.009129301644861698, ' slang'),
('Ik zie een stem in mijn tuin.', 0.007939961738884449, ' stem'),
('Ik zie een bos in mijn tuin.', 0.007785357069224119, ' bos'),
('Ik zie een storm in mijn tuin.', 0.0077188946306705475, ' storm')]
Ok, I'll be honest. I thought a tree (een boom) would be the most likely, I didn't even think of a lamp (een lamp). But I guess it makes sense anyway. And giraffes are not even in the top 10k suggestions, so that's disappointing.
Decoders
So that’s the encoder side. For some language tasks, it is enough (NER, POS tagging, etc.). But for others, like translation, we need the decoder as well. Translation could thus be formulated as a task $P(A) P(B\mid A)$ with a decoder that depends on the outputs of the encoder or language model. Practically, this looks a bit like this:
$$P(\text{``Ik zie een giraf in mijn tuin"})\newline \cdot P(\text{``I see a giraffe in my garden"}\mid \text{``Ik zie een giraf in mijn tuin"})$$
To actually get to this outcome, the attention mechanism implicitly uses marginal probabilities over all tokens in the encoder, which are then also used by the decoder. It’s also possible to use only the decoder, which is what GPT-2 does: they can generate an output sequence based on only the input without an encoder. But of course, GPT-2 doesn’t translate sentences, as it misses that part.
$$P(\text{``I see a giraffe"}|\text{``I see a"})=0.01$$
$$P(\text{``I see a giraffe in"}|\text{``I see a giraffe"})=0.6$$
But since RobBERT is only an encoder stack, we won’t dive deeper into this. From now on, we will only describe an encoder we happen to have laying around (hint: it’s RobBERT).
Great, but how do you input a sentence?
Word embeddings like word2vec were quite popular to use in seq2seq models. Especially since they happened to encode some semantic and grammatical meaning, so they were a quick way to boost the utility. But these models had two drawbacks: (i) if a word is not in the vocabulary, it has no vector (word2vec trained on Google News had 3 million words so usually it was fine) and (ii) word embeddings give the same vector regardless of context. The go-to example is “stick”, that is both a verb and a noun.
This was addressed by ELMo, which could generate contextualized embeddings. But BERT and RobBERT have a similar trick up their sleeves. If we look back at the probabilistic interpretation, we see that the input of the language model is not just a bag-of-words or TF-IDF input, but the actual text with one word masked. The input is namely the whole sentence, or even multiple sentences.
To deal with all these words, we need something to convert words into an embedding. All transformer-based models—including RobBERT—use a tokenizer. This tokenizer splits the input into words and, if a word is not in its vocabulary, it will split it into subwords. BERT uses WordPiece and RobBERT uses a byte-level BPE tokenizer. These these tokenizers have the benefit that all input sentences can be represented, even if some words are missing. Yay, no more out-of-vocabulary (OOV) issues!
For all these tokens, we take a vector (similar to word embeddings) from our embeddings matrix. Hold up, wasn’t that problematic for word2vec? Yes, but transformer-based models do something else before the attention heads: they multiply the embeddings with a positional encoding, which is a sine and cosine concatenated. These altered embeddings then get fed into the first layer of attention heads.
Self-attention mechanism
So we have multiple layers (12 in the case of RobBERT) that each have multiple attention heads (also 12). Each head calculates scaled dot-product attention based on Query (Q), Key (K) and Value (V) vectors, where these vectors are calculated from three respective weight matrices that are learned during training.
$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$
After calculating the self-attention for each head, the outputs are fed into a linear layer and then concatinated.
If you want to learn more about the attention mechanism specifically, there are some good resources, like this post from Towards Data Science and Jay Alammar's The Illustrated Transformer!
Pre-training and finetuning
As it happens, language models are quite expensive to train. We used
a high-performance computing cluster with ~80 Nvidia P100’s for
several days. This is because we train it on a large dataset (39 GB
of text) with the word-masking objective, which means we randomly
replace some words by a <mask>
token (or another
word or the same word, but those are less likely). After a few
epochs, we have something that resembles the probabilistic language
model we described earlier.
But this language model can do more than just filling in some
<mask>
tokens! This is one of the so-called
heads, the one we use to pre-train our language
model on. After training, we can easily take it off—perhaps calling
them heads was a bit insensitive?—and replace it another one. This
step is then called finetuning. All the weights of
the model stay the same and we add a newly initialized head that we
train on the data that we want. And since most weights are from the
trained base model, we only need a fraction of the data. So it will
go a lot faster as well!
Custom heads: sentence and token classification
So to train our model, we used a language modeling head. But as it turns out, we could also add other heads that do other things. These heads can be trained on a lot less data and are faster to train, so it's really easy to train your own on a custom task.
Of course, not all tasks are the same. Roughly, there are kinds of tasks: (i) sentence-level prediction and (ii) token-level prediction. As the names imply, the difference is that one predicts something on a sentence or document level versus making a prediction for each token.
If you’re interested in the custom heads we have trained and their performance, see Downstream tasks.
Wrapping it up and why monolingual models matter
So we discussed language models—and their probabilistic interpretation—and how they are related to transformer models. With a lot of self-supervised training on large corpora, these models are relatively good for these tasks. Multilingual models also perform very well, but they do mix a lot of languages (over 100 for Google's mBERT).
As a language model, a multilingual model will then have to deal with very different linguistic properties. For the tokenizer, this means either a lot more tokens, or less tokens that represent actual words in one language. Despite these drawbacks, multilingual models might leverage some features from related languages, which could increase performance.
This is also what we observed, multilingual models do perform well on a variety of tasks, especially if sufficient training data is available. If this is not the case, monolingual models like RobBERT have a slight edge.
Downstream tasks
Anaphora resolution with die and dat
We evaluated RobBERT's performance on a task that is specific to Dutch, namely disambiguating "die" and "dat" (= "that" in English). In Dutch, depending on the sentence, both terms can be either demonstrative or relative pronouns; in addition they can also be used in a subordinating conjunction, i.e. to introduce a clause. The use of either of these words depends on the gender of the word it refers to. Distinguishing these words is a task introduced by Allein et al. (2020), who presented multiple models trained on the Europarl and SoNaR corpora. Their results ranged from an accuracy of 75.03% on Europarl to 84.56% on SoNaR.
For this task, we use the Dutch version of the Europarl corpus,
which we split in 1.3M utterances for training, 319k for validation,
and 399k for testing. We then process every sentence by checking if
it contains "die" and "dat", and if so, add a training
example for every occurrence of this word in the sentence, where a
single occurrence is masked. For the test set for example, this
resulted in about 289k masked sentences. We then test two different
approaches for solving this task on this dataset. The first approach
is making the BERT models use their MLM task and guess which word
should be filled in this spot, and check if it has more confidence
in either "die" and "dat" (by checking the first 2,048
guesses at most, as this seemed sufficiently large). This allows us
to compare the zero-shot BERT models, i.e. without any fine-tuning
after pre-training.. The second approach uses the same data, but
creates two sentences by filling in the mask with both
"die" and "dat", appending both with the
<sep>
token and making the model predict which of
the two sentences is correct.
Demo on disambiguating 'die' and 'dat'
With our language model, you can easily disambiguate between 'die' and 'dat' in Dutch. Just enter a sentence in Dutch, and our model will automatically identify which word should be used.
We use a distilled model for this demo, since it is cheaper to deploy and still has pretty good performance. The model is not finetuned, but a zero-shot model, meaning we never had to train it explicitly on this task. The model also takes casing into account.
High-level sentiment analysis
we compare its performance with other BERT-models and state-of-the-art systems in sentiment analysis, to show its performance for classification tasks. We replicated the high-level sentiment analysis task used to evaluate BERTje to be able to compare our methods. This task uses a dataset called Dutch Book Reviews Dataset (DBRD), in which book reviews scraped from hebban.nl are labeled as positive or negative. Although the dataset contains 118,516 reviews, only 22,252 of these reviews are actually labeled as positive or negative.
We trained our model for 2000 iterations with a batch size of 128 and a warm-up of 500 iterations, reaching a learning rate of 10⁻⁵. We found that our model performed better when trained on the last part of the book reviews than on the first part. This is likely due to this part containing concluding remarks summarizing the overall sentiment.
Finally, a note on the name and the logo
We named our model RobBERT, or to be more precise: our model named itself RobBERT. When we used word masking in a sentence to introduce itself, it picked RobBERT as the most likely name. In an serendipitous way, this also highlighted the link to RoBERTa, so that name was perfect!
The word rob also means seal in Dutch, hence our logo is a seal being dressed up as Bert. Special thanks to Thomas Winters for the logo!


The first thing I noticed when I clicked on the Nebraska website was all of the different colors. I think it was really appealing to the viewer's eye. There is a nice image of the city as the background and the colors of the page compliment that picture.The website is compatible for cellphones and the text size gets bigger as the screen shrinks making it easier to read on a cell phone.
The colors of the entire website are awesome. The tabs each have their own colors that still compliment the entire website. It is a very interactive website that makes you want to click more and find out more about Nebraska.
I think the design of this website is really good. When you click on the tabs it takes you to that tab page and then gives you options on different topics that people look for under those tabs. The options on each page are also interactive and very big making it easier to use for cell phones.
The navigation of this website is very easy and clear to find waht you are looking for.
The coding of the website is very organized and easy to navigate through if you needed to add something to a section or fix something.
Overall, I really like this website and think it is very appealing to the viewer's eye right away making it more likely to draw a viewer deeper into the website and learn more about the state of Nebraska.


Jet Li, Jack Ma and former UN Secretary General arrive in Ghana for Africa Netpreneurship Summit
by Nana YawPopular Chinese film actor and producer Jet Li and a number of high profile Chinese personalities including business mogul and philanthropist Jack Ma and former UN Secretary-General, Ban Ki-Moon are in Ghana.
According to what we were told, the above personalities and a Chinese entourage are in the country for the 2019 Africa Netpreneurship Summit which will be held at the Accra International Conference, on Saturday, 16th November 2019.
The personalities who arrived in Ghana on Friday, November 15, 2019, were first captured in photos at the Kempinski Hotel before they made their way to the Jubilee House to call on the President of the Republic of Ghana, H.E. Nana Addo Dankwa Akufo – Addo.
Jack Ma is the co-founder and former executive chairman of Alibaba Group, a multinational technology conglomerate.
See photos below:


Hunters head out for start of deer firearms season in northeast Missouri
by Matt GunnKIRKSVILLE, Mo. — Deer firearm season officially kicked off on November 16, 2019, for the state of Missouri.
However, if you are hunting in one of the 29 counties in the CWD Management Zone you will have to have your harvested deer tested for chronic wasting disease (CWD) at one of the sampling stations.
Of the 29 counties, seven of those counties reside in northeast Missouri.
Those counties are Adair, Knox, Linn, Macon, Mercer, Putnam, and Sullivan counties.
The goal of these sampling stations is to ensure that the deer the hunters harvested are safe to consume and help the Missouri Department of Conservation monitor the landscape of CWD.
CWD sampling and their test results are free.
Sampling stations will be opened from 7:30 a.m. to 8 p.m. this weekend.
You can find the closest sampling station near you by heading to this website.


pd(1) pd(1) NAME pd-gem - Graphical Environment for Multimedia - Pure Data external DESCRIPTION Gem (Graphics Environment for Multimedia) is a library to be used with Miller Puckette's Max-like realtime-computermusic-software "Pure Data" (Pd), which allows one to draw and control openGL- and pixel-graphics (like video) in realtime from within pd. This script starts Pd with Gem enabled. All arguments passed to it, will be forwarded to Pd. To get a list of allowable arguments to Pd (pd-gem), type pd-gem -help and for more documentation either start pd-gem and get help, or consult http://gem.iem.at SEE ALSO pd(1) LICENSE Gem is covered by the GNU General Public License (GPL). HOMEPAGE Gem homepage: http://gem.iem.at AUTHOR IOhannes m zmoelnig<EMAIL_ADDRESS>et al. The manpage was written by IOhannes m zmoelnig<EMAIL_ADDRESS>for the Debian GNU/Linux system (but may be used by others). GNU 1996 Mar 20 pd(1)


--- layout: page title: How Medieval Europe was Ruled ---
The vast majority of studies on rulership in medieval Europe focus on one kingdom; one type of rule; or one type of ruler. This volume attempts to break that mold and demonstrate the breadth of medieval Europe and the various kinds of rulership within it.
How Medieval Europe was Ruled aims to demonstrate the multiplicity of types of rulers and polities that existed in medieval Europe. The contributors discuss not just kings or queens, but countesses, dukes, and town leadership. We see that rulers worked collaboratively with one another both across political boundaries and within their own borders in ways that are not evident in most current studies of kingship, inhibited by too narrow a focus. The volume also covers the breadth of medieval Europe from Scandinavia in the north to the Italian peninsula in the south, Iberia and the Anglo-Normans in the west to Rus, Byzantium and the Khazars in the east.
This book is geared towards a wide audience and thus provides a broad base of understanding via a clear explanation of concepts of rule in each of the areas that is covered. The book can be utilized in the classroom, to enhance the presentation of a medieval Europe survey or to discuss rulership more specifically for a region or all of Europe. Beyond the classroom, the book is accessible to all scholars who are interested in continuing to learn and expand their horizons.


public class GradeBook
extends java.lang.Object
Uses an input text file (inFile
) to instantiate student information,
then calculates their average grade and letter grade,
finally writes the formatted data to a new text file.
| Modifier and Type | Field and Description |
|---|---|
private static double |
average
Average score of the class as a whole.
|
private double |
averageScore
Student's average grade score (total percentage / total number of scores).
|
private java.lang.String |
firstName
A personal name given to the student at birth or baptism and used before a family name.
|
private static int |
flag
Flag to prevent file being re-opened each iteration.
|
private char |
grade
Student's letter grade (A, B, C, D, or F) of average score.
|
private static java.util.Scanner |
inFile
Input file to be scraped.
|
private java.lang.String |
lastName
Student's surname.
|
private double[] |
scores
Collection of student's scores (in percentage).
|
private double |
sum
Total amount of percentage of scores.
|
| Constructor and Description |
|---|
GradeBook()
Constructs a
GradeBook object by reading the text from an input text file,
then formats it and writes it to a new text file. |
GradeBook(GradeBook obj)
Constructs a shallow copy of the
GradeBook object. |
GradeBook(java.lang.String firstName,
java.lang.String lastName,
double[] scores)
|
| Modifier and Type | Method and Description |
|---|---|
double |
classAverage()
Calculates the class's
averageScore . |
int[] |
copyArray(int[] array)
Copies the student's
scores into a collection. |
boolean |
findTheSame(int[] one,
int[] two)
Determines if two collections of test
scores are
the same. |
GradeBook |
getCopy()
Retrieves a deep copy of the
GradeBook object. |
java.lang.String |
getFirstName()
Retrieves the student's
firstName . |
java.lang.String |
getLastName()
Retrieves the student's
lastName . |
double[] |
getScores()
Retrieves the student's test
scores . |
boolean |
isEqual(GradeBook obj)
Determines if two
GradeBook objects are the same. |
void |
makeCopy(GradeBook obj)
Shallow copies the
GradeBook object. |
void |
setFirstName(java.lang.String fName)
Updates the student's
firstName . |
void |
setLastName(java.lang.String lName)
Updates the student's
lastName . |
void |
setScores(double[] scores)
Updates the student's
scores . |
java.lang.String |
toString()
Converts the
GradeBook object's data to a String. |
private java.lang.String firstName
private java.lang.String lastName
private double[] scores
private static java.util.Scanner inFile
private static int flag
private double averageScore
private char grade
private double sum
private static double average
public GradeBook() throws java.io.FileNotFoundException
GradeBook
object by reading the text from an input text file,
then formats it and writes it to a new text file.java.io.FileNotFoundException
- Invalid file pathpublic GradeBook(java.lang.String firstName, java.lang.String lastName, double[] scores)
public void makeCopy(GradeBook obj)
GradeBook
object.obj
- GradeBook
object to be shallow copiedpublic GradeBook getCopy() throws java.io.FileNotFoundException
GradeBook
object.GradeBook
objectjava.io.FileNotFoundException
- Invalid file pathpublic double classAverage()
averageScore
.averageScore
public void setFirstName(java.lang.String fName)
firstName
.fName
- firstName
of the studentpublic void setLastName(java.lang.String lName)
lastName
.lName
- lastName
of the studentpublic void setScores(double[] scores)
scores
.scores
- Collection of scores
of the studentpublic java.lang.String getFirstName()
firstName
.firstName
public java.lang.String getLastName()
lastName
.lastName
public double[] getScores()
scores
.scores
public boolean isEqual(GradeBook obj)
GradeBook
objects are the same.obj
- GradeBook
object to be comparedpublic int[] copyArray(int[] array)
scores
into a collection.public boolean findTheSame(int[] one, int[] two)
scores
are
the same.public java.lang.String toString()
GradeBook
object's data to a String.toString
in class java.lang.Object