public class WrappedNode
extends java.lang.Object
implements org.neo4j.graphdb.Node
| Constructor and Description |
|---|
WrappedNode(org.neo4j.graphdb.Label[] labels,
java.util.Map<java.lang.String,java.lang.Object> props,
org.neo4j.graphdb.GraphDatabaseService db) |
WrappedNode(long nodeId,
org.neo4j.graphdb.GraphDatabaseService db) |
| Modifier and Type | Method and Description |
|---|---|
void |
addLabel(org.neo4j.graphdb.Label label) |
org.neo4j.graphdb.Relationship |
createRelationshipTo(org.neo4j.graphdb.Node node,
org.neo4j.graphdb.RelationshipType relationshipType) |
void |
delete() |
boolean |
equals(java.lang.Object o) |
java.util.Map<java.lang.String,java.lang.Object> |
getAllProperties() |
int |
getDegree() |
int |
getDegree(org.neo4j.graphdb.Direction direction) |
int |
getDegree(org.neo4j.graphdb.RelationshipType relationshipType) |
int |
getDegree(org.neo4j.graphdb.RelationshipType relationshipType,
org.neo4j.graphdb.Direction direction) |
org.neo4j.graphdb.GraphDatabaseService |
getGraphDatabase() |
long |
getId() |
java.lang.Iterable<org.neo4j.graphdb.Label> |
getLabels() |
java.util.Map<java.lang.String,java.lang.Object> |
getProperties(java.lang.String... strings) |
java.lang.Object |
getProperty(java.lang.String s) |
java.lang.Object |
getProperty(java.lang.String s,
java.lang.Object o) |
java.lang.Iterable<java.lang.String> |
getPropertyKeys() |
java.lang.Iterable<org.neo4j.graphdb.Relationship> |
getRelationships() |
java.lang.Iterable<org.neo4j.graphdb.Relationship> |
getRelationships(org.neo4j.graphdb.Direction direction) |
java.lang.Iterable<org.neo4j.graphdb.Relationship> |
getRelationships(org.neo4j.graphdb.Direction direction,
org.neo4j.graphdb.RelationshipType... relationshipTypes) |
java.lang.Iterable<org.neo4j.graphdb.Relationship> |
getRelationships(org.neo4j.graphdb.RelationshipType... relationshipTypes) |
java.lang.Iterable<org.neo4j.graphdb.Relationship> |
getRelationships(org.neo4j.graphdb.RelationshipType relationshipType,
org.neo4j.graphdb.Direction direction) |
java.lang.Iterable<org.neo4j.graphdb.RelationshipType> |
getRelationshipTypes() |
org.neo4j.graphdb.Relationship |
getSingleRelationship(org.neo4j.graphdb.RelationshipType relationshipType,
org.neo4j.graphdb.Direction direction) |
int |
hashCode() |
boolean |
hasLabel(org.neo4j.graphdb.Label label) |
boolean |
hasProperty(java.lang.String s) |
boolean |
hasRelationship() |
boolean |
hasRelationship(org.neo4j.graphdb.Direction direction) |
boolean |
hasRelationship(org.neo4j.graphdb.Direction direction,
org.neo4j.graphdb.RelationshipType... relationshipTypes) |
boolean |
hasRelationship(org.neo4j.graphdb.RelationshipType... relationshipTypes) |
boolean |
hasRelationship(org.neo4j.graphdb.RelationshipType relationshipType,
org.neo4j.graphdb.Direction direction) |
void |
removeLabel(org.neo4j.graphdb.Label label) |
java.lang.Object |
removeProperty(java.lang.String s) |
void |
setProperty(java.lang.String s,
java.lang.Object o) |
public WrappedNode(org.neo4j.graphdb.Label[] labels, java.util.Map<java.lang.String,java.lang.Object> props, org.neo4j.graphdb.GraphDatabaseService db)
public WrappedNode(long nodeId, org.neo4j.graphdb.GraphDatabaseService db)
public long getId()
getId
in interface org.neo4j.graphdb.Entity
getId
in interface org.neo4j.graphdb.Node
public void delete()
delete
in interface org.neo4j.graphdb.Node
public java.lang.Iterable<org.neo4j.graphdb.Relationship> getRelationships()
getRelationships
in interface org.neo4j.graphdb.Node
public boolean hasRelationship()
hasRelationship
in interface org.neo4j.graphdb.Node
public java.lang.Iterable<org.neo4j.graphdb.Relationship> getRelationships(org.neo4j.graphdb.RelationshipType... relationshipTypes)
getRelationships
in interface org.neo4j.graphdb.Node
public java.lang.Iterable<org.neo4j.graphdb.Relationship> getRelationships(org.neo4j.graphdb.Direction direction, org.neo4j.graphdb.RelationshipType... relationshipTypes)
getRelationships
in interface org.neo4j.graphdb.Node
public boolean hasRelationship(org.neo4j.graphdb.RelationshipType... relationshipTypes)
hasRelationship
in interface org.neo4j.graphdb.Node
public boolean hasRelationship(org.neo4j.graphdb.Direction direction, org.neo4j.graphdb.RelationshipType... relationshipTypes)
hasRelationship
in interface org.neo4j.graphdb.Node
public java.lang.Iterable<org.neo4j.graphdb.Relationship> getRelationships(org.neo4j.graphdb.Direction direction)
getRelationships
in interface org.neo4j.graphdb.Node
public boolean hasRelationship(org.neo4j.graphdb.Direction direction)
hasRelationship
in interface org.neo4j.graphdb.Node
public java.lang.Iterable<org.neo4j.graphdb.Relationship> getRelationships(org.neo4j.graphdb.RelationshipType relationshipType, org.neo4j.graphdb.Direction direction)
getRelationships
in interface org.neo4j.graphdb.Node
public boolean hasRelationship(org.neo4j.graphdb.RelationshipType relationshipType, org.neo4j.graphdb.Direction direction)
hasRelationship
in interface org.neo4j.graphdb.Node
public org.neo4j.graphdb.Relationship getSingleRelationship(org.neo4j.graphdb.RelationshipType relationshipType, org.neo4j.graphdb.Direction direction)
getSingleRelationship
in interface org.neo4j.graphdb.Node
public org.neo4j.graphdb.Relationship createRelationshipTo(org.neo4j.graphdb.Node node, org.neo4j.graphdb.RelationshipType relationshipType)
createRelationshipTo
in interface org.neo4j.graphdb.Node
public java.lang.Iterable<org.neo4j.graphdb.RelationshipType> getRelationshipTypes()
getRelationshipTypes
in interface org.neo4j.graphdb.Node
public int getDegree()
getDegree
in interface org.neo4j.graphdb.Node
public int getDegree(org.neo4j.graphdb.RelationshipType relationshipType)
getDegree
in interface org.neo4j.graphdb.Node
public int getDegree(org.neo4j.graphdb.Direction direction)
getDegree
in interface org.neo4j.graphdb.Node
public int getDegree(org.neo4j.graphdb.RelationshipType relationshipType, org.neo4j.graphdb.Direction direction)
getDegree
in interface org.neo4j.graphdb.Node
public void addLabel(org.neo4j.graphdb.Label label)
addLabel
in interface org.neo4j.graphdb.Node
public void removeLabel(org.neo4j.graphdb.Label label)
removeLabel
in interface org.neo4j.graphdb.Node
public boolean hasLabel(org.neo4j.graphdb.Label label)
hasLabel
in interface org.neo4j.graphdb.Node
public java.lang.Iterable<org.neo4j.graphdb.Label> getLabels()
getLabels
in interface org.neo4j.graphdb.Node
public org.neo4j.graphdb.GraphDatabaseService getGraphDatabase()
getGraphDatabase
in interface org.neo4j.graphdb.PropertyContainer
public boolean hasProperty(java.lang.String s)
hasProperty
in interface org.neo4j.graphdb.PropertyContainer
public java.lang.Object getProperty(java.lang.String s)
getProperty
in interface org.neo4j.graphdb.PropertyContainer
public java.lang.Object getProperty(java.lang.String s, java.lang.Object o)
getProperty
in interface org.neo4j.graphdb.PropertyContainer
public void setProperty(java.lang.String s, java.lang.Object o)
setProperty
in interface org.neo4j.graphdb.PropertyContainer
public java.lang.Object removeProperty(java.lang.String s)
removeProperty
in interface org.neo4j.graphdb.PropertyContainer
public java.lang.Iterable<java.lang.String> getPropertyKeys()
getPropertyKeys
in interface org.neo4j.graphdb.PropertyContainer
public java.util.Map<java.lang.String,java.lang.Object> getProperties(java.lang.String... strings)
getProperties
in interface org.neo4j.graphdb.PropertyContainer
public java.util.Map<java.lang.String,java.lang.Object> getAllProperties()
getAllProperties
in interface org.neo4j.graphdb.PropertyContainer
public boolean equals(java.lang.Object o)
equals
in class java.lang.Object
public int hashCode()
hashCode
in class java.lang.Object


C.3 Examples
Let's start off with a simple example. Suppose that
you have a saturated Internet connection, shared by many users. You
can use delay pools to limit the amount of bandwidth that Squid
consumes on the link, thus leaving the remaining bandwidth for other
applications. Use a class 1 delay pool to limit the bandwidth for all
users. For example, this limits everyone to 512 Kbit/s and keeps 1 MB
in reserve if Squid is idle:
delay_pools 1
delay_class 1 1
delay_parameters 1 65536/1048576
acl All src 0/0
delay_access 1 allow All
One of the problems with this simple approach is that some users may
receive more than their fair share of the bandwidth. If you want to
try something more balanced, use a class 2 delay pool that has
individual buckets. Recall that the individual bucket is determined
by the fourth octet of the client's IPv4 address.
Thus, if you have more than a /24 subnet, you might want to use a
class 3 pool instead, which gives you 65536 individual buckets. In
this example, I won't use the network buckets. While
the overall bandwidth is still 512 Kbit/s, each individual is limited
to 128 Kbit/s:
delay_pools 1
delay_class 1 3
delay_parameters 1 65536/1048576 -1/-1 16384/262144
acl All src 0/0
delay_access 1 allow All
You can also use delay pools to provide different classes of service.
For example, you might have important users and unimportant users. In
this case, you could use two class 1 delay pools. Give the important
users a higher bandwidth limit than everyone else:
delay_pools 2
delay_class 1 1
delay_class 2 1
delay_parameters 1 65536/1048576
delay_parameters 2 10000/50000
acl ImportantUsers src <IP_ADDRESS>/22
acl All src 0/0
delay_access 1 allow ImportantUsers
delay_access 2 allow All
|


class: center, middle ### W4995 Applied Machine Learning # Recap & Summary 04/20/19 Andreas C. Müller ??? Only gonna talk about stuff for final, so not including first 11 lectures. Also: this lecture will not be comprehensive, there will be things in the exam not in this lecture. --- # Model inspection ??? # FIXME post-hoc vs interpretable model --- # Types of explanations ## Explain "kind of model" - How would the model change if this feature was dropped? - Doesn't explain this particular fitted model ##Explain model globally - How does the output depend on the input? - Often: some form of marginals ## Explain model locally - Why did it classify this point this way? - Explanation could look like a "global" one but be different for each point. - "What is the minimum change to classify it differently?" ??? Marginals: how does the prediction change as function of a particular Features? --- # Permutation importance Idea: measure marginal influence of one feature ```python def permutation_importance(est, X, y, n_bootstrap=100): baseline_score = estimator.score(X, y) for f_idx in range(X.shape[1]): for b_idx in range(n_bootstrap): X_new = X.copy() X_new[:, f_idx] = np.random.shuffle(X[:, f_idx]) feature_score = estimator.score(X_new, y) scores[f_idx, b_idx] = baseline_score - feature_score ``` -- - Applied on validation set given trained estimator. - Also kinda slow. - Implementation: ELI5,
https://github.com/scikit-learn/scikit-learn/pull/13146 - For RF: https://github.com/parrt/random-forest-importances (uses oob) --- --- # Partial Dependence Plots - Marginal dependence of prediction on one or two features .smaller[ ```python from sklearn.ensemble.partial_dependence import plot_partial_dependence boston = load_boston() X_train, X_test, y_train, y_test = train_test_split( boston.data, boston.target,random_state=0) gbrt = GradientBoostingRegressor().fit(X_train, y_train) fig, axs = plot_partial_dependence( gbrt, X_train, np.argsort(gbrt.feature_importances_)[-6:], feature_names=boston.feature_names, n_jobs=3, grid_resolution=50) ``` ] ??? But there's also something else that's quite interesting, which is called Partial Dependence Plots that's actually possible for all trees. Unfortunately, in scikit-learn, it’s available only for the gradient boosting. So the idea here is to not only see what parameters are important but how will they influence the target. And so after you fit your model, I'm using the Boston data set to the gradient boosting regressor, there's a thing called plot partial dependence, which gets the model and the training data set and then the features for which I want to make the partial dependence plots. So here, I'm looking at the six most important features. The feature importance is important according to the trees. So I sort this and take the six most important ones. --- # Partial Dependence Plots .center[ ![:scale 70%](images/feat_impo_part_dep.png) ] ??? And so this is what the partner dependence plots look like. So this is the most important feature, the second most important feature and so on. This is sort of the marginal contribution of each feature. Basically, in each tree, you're summing out the contribution of all the other features, and you look only at what is the contribution of this particular feature. In general, this would be hard to compute. For tree-based models you can compute is a very efficient way because you can basically just sum up over the whole space by traversing the tree. Here with increased room size, the price increases and you can see how it increases and you can see that there's like a big step function here. And you can see that for increased LSTAT, the price decreases. For the others, there aren’t any drastic effect. There seems to be some threshold for NOX. And something funky going on DIS. The question is, so I said for random forest, you can't find out the direction of which way the feature influences. But that was for the feature importance. So the feature importance tells the impurity decrease. So here I also look at the feature importance and they're just numbers so they don't give you the direction. For both gradient boosting and random forest, you can look at partial dependency plot, and they will give you a non-parametric model of how a single feature influences it. Unfortunately, it’s not available in scikit-learn. You could do the same thing for random forests. Because in the end, the way they predict is quite similar. --- # Partial Dependence - Idea: Get marginal predictions given feature. - How? "integrate out" other features using validation data - Fast methods available for trees! - Nonsensical for linear models. - sklearn master: only gradient boosting - Next sklearn: fast for gradient boosting, slow for everything else - future: fast for all tree-based models --- # Feature selection --- # Why Select Features? - Avoid overfitting (?) - Faster prediction and training - Less storage for model and dataset - More interpretable model ??? There's a couple of reasons why you might want to feature selection. One is to avoid overfitting and get a better model. In practice, I have rarely seen that happen. It's not usually what I would try to do to increase performance. If I’m only interested in performance I probably would not try to do automatic feature selection unless I think only a very small subset of my feature is actually important. There's a lot of increasing performance just by selecting only important features. What I think is more commonly, the reason to do automatic feature selection is you want to shrink your model to make faster predictions, to train your model faster, to store fewer data and possibly to collect fewer data. If you're collecting the data or to feature from some online process, maybe it means you need fewer features, you need to store less information. I think actually the top reason to do feature selection is to have a more interpretable model. If your model is smaller, if your model has 5 features instead of 500, it's probably much easier for you to grasp what the model does. If you can have a model that is as good with way fewer features, it will be much easier to explain and most people will be happy with it. --- FIXME wrapper vs univariate What happens with correlated features compare to Kuhn book --- # AutoML --- # Formulating model-selection as Hyperparameter Optimization - One big search, many conditional Hyper-Parameters - Categorical, integer, continuous, conditional - Different distributions --- # CASH problem - Find the best configuration - Global optimization on complex (high-dim?) space ??? FIXME what does cash stand for? --- # Issues with Grid-Search - Need to define Grid - Exponential in number of dims --- class: middle # Black-Box Search Procedures `$$\Lambda^* = \arg\max_\Lambda f(\Lambda)$$` Parameters $\Lambda$, model-evaluation $f$. ??? General optimization of unknown, non-differentiable f, possibly no-smooth. NP Hard in general. Function f is very slow to evaluate - think training a neural net for a week. --- # Random Search ![:scale 100%](images/bergstra_random.jpeg) --- # Bayesian Optimization, SMBO .wide-left-column[ - fit 'cheap' probabilistic function to black-box - pick next point using exploration / exploitation - Implemented as acquisition function ] .narrow-right-column[ .center[ ![:scale 110%](images/smbo.png) ]] --- # Successive Halving - Given $n$ configuration and budget $B$ - pick $\eta=2$ or $\eta=3$ (wording follows 2) - Each iteration, keep best halve of configurations - after $k=\log_\eta(n)$ left with single configuration. - initially allocate $\frac{B}{kn}$ to each configuration, double each iteration. -- ![:scale 70%](images/halving_algorithm.png) --- # Dimensionality Reduction --- # NMF & Outliers --- # Text Data --- # Topic models --- # Word Embeddings --- # Neural Networks --- # Convolutional neural Networks --- # Tricks for learning Deep Nets --- # Practical Considerations for Neural Nets --- # Time Series Data --- class: middle # Questions ?


2 writes to _cancelOperation
Microsoft.Management.Infrastructure.CimCmdlets (2)
CimSessionProxy.cs (2)
1549
this.
_cancelOperation
= null;
1567
this.
_cancelOperation
= value;
3 references to _cancelOperation
Microsoft.Management.Infrastructure.CimCmdlets (3)
CimSessionProxy.cs (3)
1545
if (this.
_cancelOperation
!= null)
1548
this.
_cancelOperation
.Dispose();
1561
return this.
_cancelOperation
;


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Project Plans
Need readers and db tables for:
- Class
- Interface
- Package
- Organization
- Method
- Fields
- Field references to classes
- Method signature references to classes
- Instruction references to classes (individual lines)
- Class annotations
- Method annotations
- Field annotations
- Annotation references to classes
- Local variable references to classes
- Attributes
Current Progress
- Traverses war/jar files nested to any depth
- Reads classes
- Updates db with class->superclass numeric id
- CDI, hibernate, logging work
- Handles hibernate persistence
- Can build for distribution with shell scripts
Pending Next
- More entities
- Read more types of info about a class
- Create a canonical set of test classes to exercise all the reader points
- Create test cases


Availability: ALL
This command copies data in range to the destination beginning at addr.
The source data and destination address do not need to be in the same address space. For example, registers can be dumped to or loaded from memory by the Move command.
Usage
M[type] range, addr
MR[type] range, addr
Arguments
Description
The data is normally copied forward from the starting addresses in the source and destination ranges, one type-sized piece at a time, with the resultant predictable destructive effect if a portion of the range implied by addr falls within range. If R is specified, the command becomes Move Reverse and the data will be copied backwards from the ending address in the source and destination ranges. In this case an overlapping upward move will be non-destructive, while an overlapping downward move will be destructive. Of course if the source and destination are in different address spaces, a move can only be destructive if the two spaces overlap in the same physical memory.
Examples
mw a0 a3, t0 // move argument registers a0..a3 to t0..t3.
mw a1000000 l 4, r1 // move four words from memory to registers
// r1..r4.
mrb 1000:1 10fe:1,1001:1 // move up one byte non-destructively.


Scopas did as Apollo said and was on his way to the fields a little before the crack of dawn. Apollo was standing in the center of the fields as he said he would.Scopas didn’t have trouble finding him at all because Apollo’s voice boomed as he said,”You’ve bragged on long enough, and now I challenge you to a contest of Floyeras.” ”Ha, this is going to go faster than I can say Floyera ,”Without a word, Apollo played his golden Floyera and made a heavenly tune that mesmerized Scopas, Scopas yelled, “Impossible! How are you so much better than me? AHHH!”, and because Apollo was so much better than Scopas, Scopas gave his Floyera a mighty throw and it shot all the way up to the Sun Chariot SNAP! The Floyera then chipped off a fragment. At that same moment Apollo exclaimed,”You Fool! Look at what you have done!” “For this you shall be thrown into my prison, and swear servitude to me for the rest of your life!” Scopas did as he was told , and Scopas was thrown in prison and was forced to work for Apollo for the rest of Scopas’ life.


Yoga for you
Begin crafting your yoga experience
Meditation
Chanting
Heated
Injuries
Live Music
Inversions


Mr. Zuhair Idris Jordan Kuwait Bank Credit Department Amman, 11191 Jordan Dear Sir/Madam, I am Zuhair Idris official of Jordan Kuwait Bank Limited .This is an urgent and very confidential business proposition. On June 6, 2000,a Foreign Oil consultant/contractor with the pakistan Asian Institute of Mining and Metallurgy, Mr.kassim Hassan made a numbered time(Fixed) Deposit for twelve calendar months, valued at US$26,500,000.00,(Twenty-six Million, five hundred thousand Dollars) in my branch. Upon maturity,I sent a routine notification to his forwarding address,but got no reply. After a month,we sent a reminder and finally we discovered from his contract employers, the National Petroleum Corporation that Mr.kassim Hassan died from an automobile accident.On further investigation,I found out that he died without making a WILL,and all attempts to trace his next of kin was fruitless. I therefore made further investigation and discovered that Mr.kassim Hassan did not declare any kin or relations in all his official documents,including his Bank Deposit paperwork in my Bank. This sum of US$26,500,000.00 has carefully been fixed in my bank for safe keeping. No one will ever come forward to claim it. According to Asian Law, at the expiration of 5 (five) years, the money will revert to the ownership of the Government if nobody applies to claim the fund. Consequently, my proposal is that I will like you as a Foreigner to stand in as the owner of the money which was fixed deposited in my bank. I am writing you because I as a public servant,i cannot operate a foreign account. I want to present you as the owner of the funds so you can be able to claim them with the help of my attorney.This is simple.I will like you to provide immediately your full names and address so that the Attorney will prepare the necessary documents which will put you in place as the beneficiary of the funds. The money will be moved out for us to share in the ratio of 80% for me and 20% for you. The paperwork for this transaction will be done by the Attorney and this will guarantees the successful execution of this transaction. If you are interested, please reply immediately via my email and Upon your response,I shall then provide you with more details and relevant documents that will help you understand the transaction. Please observe utmost confidentiality, and be rest assured that this transaction would be most profitable for both of us because I shall require your assistance to invest my share in real estate within your country. Due to the nature of confidentiality in this Transaction our communication can only be via email and fax mostly. Thanks and my regards. Mr Zuhair Idris Jordan Kuwait Bank


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Hello #displayName#,
Please click here to add this email (#email#) to your Synapse account.
If you have trouble with the link, please copy and paste the following line into your web browser:
#htmlSafelink#
This link is valid for the next 24 hours. Here are the details of your account: User Name: #username# Full Name: #displayName#
#domain# Administrator


{% extends "admin/base.html" %} {% block title %} Photos {% endblock %} {% block pagebody %}
Where's the f in photos? There's no f in photos! YOU'RE GODDAMN RIGHT!


Class: Aws::KMS::Types::DecryptRequest
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-kms/lib/aws-sdk-kms/types.rb |
Overview
Constant Summary
Instance Attribute Summary
-
#ciphertext_blob ⇒ String
rw
Ciphertext to be decrypted.
-
#dry_run ⇒ Boolean
rw
Checks if your request will succeed.
-
#encryption_algorithm ⇒ String
rw
Specifies the encryption algorithm that will be used to decrypt the ciphertext.
-
#encryption_context ⇒ Hash<String,String>
rw
Specifies the encryption context to use when decrypting the data.
-
#grant_tokens ⇒ Array<String>
rw
A list of grant tokens.
-
#key_id ⇒ String
rw
Specifies the
::Aws::KMS
key that::Aws::KMS
uses to decrypt the ciphertext. -
#recipient ⇒ Types::RecipientInfo
rw
A signed [attestation document][1] from an Amazon Web Services Nitro enclave and the encryption algorithm to use with the enclave's public key.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#ciphertext_blob ⇒ String
(rw)
Ciphertext to be decrypted. The blob includes metadata.
# File 'gems/aws-sdk-kms/lib/aws-sdk-kms/types.rb', line 1618
class DecryptRequest < Struct.new( :ciphertext_blob, :encryption_context, :grant_tokens, :key_id, :encryption_algorithm, :recipient, :dry_run) SENSITIVE = [] include Aws::Structure end
#dry_run ⇒ Boolean
(rw)
Checks if your request will succeed. DryRun
is an optional
parameter.
To learn more about how to use this parameter, see Testing your ::Aws::KMS
API calls in the Key Management Service Developer Guide.
# File 'gems/aws-sdk-kms/lib/aws-sdk-kms/types.rb', line 1618
class DecryptRequest < Struct.new( :ciphertext_blob, :encryption_context, :grant_tokens, :key_id, :encryption_algorithm, :recipient, :dry_run) SENSITIVE = [] include Aws::Structure end
#encryption_algorithm ⇒ String
(rw)
Specifies the encryption algorithm that will be used to decrypt the
ciphertext. Specify the same algorithm that was used to encrypt the
data. If you specify a different algorithm, the Decrypt
operation
fails.
This parameter is required only when the ciphertext was encrypted
under an asymmetric ::Aws::KMS
key. The default value, SYMMETRIC_DEFAULT
,
represents the only supported algorithm that is valid for symmetric
encryption ::Aws::KMS
keys.
# File 'gems/aws-sdk-kms/lib/aws-sdk-kms/types.rb', line 1618
class DecryptRequest < Struct.new( :ciphertext_blob, :encryption_context, :grant_tokens, :key_id, :encryption_algorithm, :recipient, :dry_run) SENSITIVE = [] include Aws::Structure end
#encryption_context ⇒ Hash
<String
,String
> (rw)
Specifies the encryption context to use when decrypting the data. An
encryption context is valid only for cryptographic operations
with a symmetric encryption ::Aws::KMS
key. The standard asymmetric
encryption algorithms and HMAC algorithms that ::Aws::KMS
uses do not
support an encryption context.
An encryption context is a collection of non-secret key-value
pairs that represent additional authenticated data. When you use an
encryption context to encrypt data, you must specify the same (an
exact case-sensitive match) encryption context to decrypt the data.
An encryption context is supported only on operations with symmetric
encryption ::Aws::KMS
keys. On operations with symmetric encryption ::Aws::KMS
keys, an encryption context is optional, but it is strongly
recommended.
For more information, see Encryption context in the Key Management Service Developer Guide.
# File 'gems/aws-sdk-kms/lib/aws-sdk-kms/types.rb', line 1618
class DecryptRequest < Struct.new( :ciphertext_blob, :encryption_context, :grant_tokens, :key_id, :encryption_algorithm, :recipient, :dry_run) SENSITIVE = [] include Aws::Structure end
#grant_tokens ⇒ Array
<String
> (rw)
A list of grant tokens.
Use a grant token when your permission to call this operation comes from a new grant that has not yet achieved eventual consistency. For more information, see Grant token and Using a grant token in the Key Management Service Developer Guide.
# File 'gems/aws-sdk-kms/lib/aws-sdk-kms/types.rb', line 1618
class DecryptRequest < Struct.new( :ciphertext_blob, :encryption_context, :grant_tokens, :key_id, :encryption_algorithm, :recipient, :dry_run) SENSITIVE = [] include Aws::Structure end
#key_id ⇒ String
(rw)
Specifies the ::Aws::KMS
key that ::Aws::KMS
uses to decrypt the ciphertext.
Enter a key ID of the ::Aws::KMS
key that was used to encrypt the
ciphertext. If you identify a different ::Aws::KMS
key, the Decrypt
operation throws an IncorrectKeyException
.
This parameter is required only when the ciphertext was encrypted
under an asymmetric ::Aws::KMS
key. If you used a symmetric encryption ::Aws::KMS
key, ::Aws::KMS
can get the ::Aws::KMS
key from metadata that it adds to the
symmetric ciphertext blob. However, it is always recommended as a
best practice. This practice ensures that you use the ::Aws::KMS
key that
you intend.
To specify a ::Aws::KMS
key, use its key ID, key ::Aws::ARN
, alias name, or alias
::Aws::ARN
. When using an alias name, prefix it with "alias/"
. To specify
a ::Aws::KMS
key in a different Amazon Web Services account, you must use
the key ::Aws::ARN
or alias ::Aws::ARN
.
For example:
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN:
arn:aws:kms:us-east-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name:
alias/ExampleAlias
Alias ARN:
arn:aws:kms:us-east-2:111122223333:alias/ExampleAlias
To get the key ID and key ::Aws::ARN
for a ::Aws::KMS
key, use ListKeys or
DescribeKey. To get the alias name and alias ::Aws::ARN
, use ListAliases.
# File 'gems/aws-sdk-kms/lib/aws-sdk-kms/types.rb', line 1618
class DecryptRequest < Struct.new( :ciphertext_blob, :encryption_context, :grant_tokens, :key_id, :encryption_algorithm, :recipient, :dry_run) SENSITIVE = [] include Aws::Structure end
#recipient ⇒ Types::RecipientInfo (rw)
A signed attestation document from an Amazon Web Services Nitro
enclave and the encryption algorithm to use with the enclave's
public key. The only valid encryption algorithm is
RSAES_OAEP_SHA_256
.
This parameter only supports attestation documents for Amazon Web Services Nitro Enclaves. To include this parameter, use the Amazon Web Services Nitro Enclaves SDK or any Amazon Web Services SDK.
When you use this parameter, instead of returning the plaintext
data, ::Aws::KMS
encrypts the plaintext data with the public key in the
attestation document, and returns the resulting ciphertext in the
CiphertextForRecipient
field in the response. This ciphertext can
be decrypted only with the private key in the enclave. The
Plaintext
field in the response is null or empty.
For information about the interaction between ::Aws::KMS
and Amazon Web
Services Nitro Enclaves, see How Amazon Web Services Nitro Enclaves
uses KMS in the Key Management Service Developer Guide.
# File 'gems/aws-sdk-kms/lib/aws-sdk-kms/types.rb', line 1618
class DecryptRequest < Struct.new( :ciphertext_blob, :encryption_context, :grant_tokens, :key_id, :encryption_algorithm, :recipient, :dry_run) SENSITIVE = [] include Aws::Structure end


The Tooltip defines what will be visbile when the cursor is over an element of the chart.
Located in /googlecharttools/view/options/Tooltip.class.php (line 36)
OptionStorage | --Tooltip
Creates a new Tooltip option-set.
Sets the visibility of the element's color.
Sets which information will be shown in the tooltip of pie-charts.
Sets the text style of the chart's tooltip.
Sets which interaction causes the tooltip to be shown.
Inherited From OptionStorage
OptionStorage::encodeOptions()
OptionStorage::getOption()
OptionStorage::setOption()
OptionStorage::setOptionArray()
OptionStorage::setOptionBoolean()
OptionStorage::setOptionNumeric()
Percentage and absolute values will be displayed.
Only percentage values are displayed.
Only absolute values are displayed.
Tooltip will be displayed when the cursor is over a element.
No tooltip will be displayed.
Documentation generated on Mon, 01 Oct 2012 16:45:07 +0200 by phpDocumentor 1.4.4


1. Can you think of an analogy to describe HTML,CSS and the DOM? How would you describe to your non-technical friend?
HTML is what is on your website (headings, lists, tables)
CSS is what those things look like (borders, colours, font sizes)
DOM is how you access those things through javascript (getting nodes, adding new elements, changing their style)
2. What is meant by boxifying design?
Boxifying design is designing the websites using a concept of boxes. Before developing the websites, we need to think about each elements, and those elements are made into boxes. The technique help the developer to arrange the elements and structure the code perfectly anytime when they use.
3. What is the box model?
All HTML elements can be considered as boxes. In CSS, the term "box model" is used when talking about design and layout. The CSS box model is essentially a box that wraps around every HTML element. It consists of: margins, borders, padding, and the actual content.


Interested in Contributing?
- Check out available resources.
- Create an account and start submitting your own systems.
Scored Systems
| System | Submitter | System Notes | Constraint | Run Notes | BLEU | BLEU-cased | TER | BEER 2.0 | CharactTER |
|---|---|---|---|---|---|---|---|---|---|
| uedin-nmt-ensemble (Details) | rsennrich University of Edinburgh |
BPE neural MT system with monolingual training data (back-translated). ensemble of 4, reranked with right-to-left model. | yes |
34.8 |
34.2 |
0.543 |
0.626 |
0.468 |
|
| UniMelb-NMT-Transformer-BT (Details) | vhoang The University of Melbourne, Australia |
NMT with Transformer architecture (medium size, 4 heads + 4 encoder/decoder layers) enhanced with WMT'16 back-translation data. (decoding with single best system) | yes |
34.7 |
34.0 |
0.533 |
0.625 |
0.475 |
|
| KIT-cG-mA-single 2016 (Details) | thanhleha KIT |
yes |
33.4 |
32.8 |
0.551 |
0.620 |
0.480 |
||
| metamind-ensemble (Details) | jekbradbury Salesforce MetaMind |
Neural MT system based on Luong 2015 and Sennrich 2015, using Morfessor for subword splitting, with back-translated monolingual augmentation. Ensemble of 3 checkpoints from one run plus 1 Y-LSTM (see entry). | yes |
32.8 |
32.3 |
0.546 |
0.617 |
0.490 |
|
| uedin-nmt-single (Details) | rsennrich University of Edinburgh |
BPE neural MT system with monolingual training data (back-translated). single model. (contrastive) | yes |
32.2 |
31.6 |
0.570 |
0.611 |
0.492 |
|
| metamind-single (Details) | jekbradbury Salesforce MetaMind |
Neural MT system based on Luong 2015 and Sennrich 2015, using Morfessor for subword splitting, with back-translated monolingual augmentation. Single model. | yes |
32.1 |
31.6 |
0.553 |
0.616 |
0.495 |
|
| KIT correct BT single 2016 (Details) | thanhleha KIT |
yes |
32.1 |
31.5 |
0.556 |
0.613 |
0.514 |
||
| NYU-UMontreal-NMT-BPE-Char (Details) | jychung08 University of Montreal |
BPE to Character neural machine translation system http://arxiv.org/abs/1603.06147 | yes |
31.3 |
30.8 |
0.583 |
0.600 |
0.511 |
|
| uedin-syntax (Details) | rsennrich University of Edinburgh |
string-to-tree syntax-based SMT system, mostly corresponding to system described in http://www.aclweb.org/anthology/D/D15/D15-1248.pdf , with added News 2015 LM. | yes |
31.4 |
30.6 |
0.585 |
0.610 |
0.505 |
|
| nmt-editdistance-hifst (Details) | fs439 University of Cambridge |
Loose coupling of NMT and Hiero via edit distance transducer | yes | update LM to news2015, fix surnames |
31.3 |
30.6 |
0.580 |
0.599 |
0.531 |
| NYU-UMontreal-NMT-BPE-Char (Details) | jychung08 University of Montreal |
BPE to Character neural machine translation system http://arxiv.org/abs/1603.06147 | yes |
31.1 |
30.6 |
0.585 |
0.599 |
0.511 |
|
| nmt-editdistance-hifst (Details) | fs439 University of Cambridge |
Loose coupling of NMT and Hiero via edit distance transducer | yes |
31.1 |
30.5 |
0.578 |
0.597 |
0.536 |
|
| nmt-cgru-ensemble (Details) | ozancaglayan LIUM - Le Mans University |
Ensemble of 3 small models, CGRU-based NMT, subword. | yes |
30.7 |
30.1 |
0.605 |
0.599 |
0.516 |
|
| metamind-ylstm (Details) | jekbradbury Salesforce MetaMind |
Novel neural MT system with source and target LSTM-LMs coupled to each other at the middle layer. Uses Morfessor for subword splitting, with back-translated monolingual augmentation. | yes |
29.8 |
29.3 |
0.575 |
0.598 |
0.511 |
|
| KIT/LIMSI (Details) | niehues KIT |
Phrase-based MT system with NMT and SOUL in rescoring | yes |
29.7 |
29.1 |
0.594 |
0.600 |
0.533 |
|
| KIT (Details) | niehues KIT |
Phrase-based MT with NMT in rescoring | yes |
29.7 |
29.0 |
0.598 |
0.602 |
0.533 |
|
| uedin-pbt-wmt16-en-de (Details) | Matthias Huck University of Edinburgh |
Phrase-based Moses | yes |
29.1 |
28.4 |
0.600 |
0.594 |
0.556 |
|
| Moses Phrase-Based (Details) | jhu-smt Johns Hopkins University |
Phrase-based model, word clusters for all model components (LM, OSM, LR, sparse features), neural network joint model, large cc LM | yes | [26-7] |
29.0 |
28.3 |
0.629 |
0.595 |
0.537 |
| uedin-pbt-wmt16-en-de-contrastive (Details) | Matthias Huck University of Edinburgh |
Phrase-based Moses (contrastive, 2015 system) | yes |
29.0 |
28.3 |
0.618 |
0.594 |
0.540 |
|
| jhu-syntax (Details) | sding Johns Hopkins University |
yes |
27.3 |
26.6 |
0.637 |
0.593 |
0.533 |
||
| ParFDA (Details) | bicici |
en-de ParFDA Moses phrase-based SMT system | yes | en-de (after the deadline) |
24.5 |
23.9 |
0.680 |
0.569 |
0.590 |
| PROMT Rule-based (Details) | Alex Molchanov PROMT LLC |
no |
23.8 |
23.4 |
0.651 |
0.570 |
0.538 |


We should work toward a universal linked information system, in which generality and portability are more important than fancy graphics techniques and complex extra facilities. The aim would be to allow a place to be found for any information or reference which one felt was important, and a way of finding it afterwards.
The USS Berners-Lee is a proud Excelsior class retrofit dedicated to advancing Starfleet's mission of discovery, innovation, and ease of communication. Commissioned on Stardate 90193.45, the mission of the USS Berners-Lee has been one of constant discovery and enlightenment.


Great website about hobbies
Hobby Page
I have couple of hobbies:
1.Sports
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).
2.Education
Michelangelo said that when he began with a solid block of stone, he could sense the sculpture within, asking him to reveal it. This was likely a result of his early years of working in a marble quarry, where he was taught to read the lines of stress and potential fracture within the stone itself. This hobby is an ancient one that many men have elevated to an art form over time. The art of being a mason has even been entangled with mysticism — those amazing individuals who created stone lace and structures weighing millions of tons that seemed made of light — in the great Gothic cathedrals of Europe. This skill requires that you hone planning and understand the application of force and directionality. What could be more awesome?
3.Amazing stuff
This element of outdoor design will sharpen your planning skills and your understanding of nested interrelated systems. Not only do shapes work together, but also water and earth features, species of plants, and symbiotic animal species working within the microenvironment that you’re landscaping.
4. Chess
Mastering the techniques of a champion may take you the rest of your life, but learning the basics of strategy — observation, planning, judging the quality of an opponent’s skill — can be picked up relatively swiftly. Moreover, these are skills that you can apply to every life situation, and which will stand you in good stead in the realm of interpersonal interaction. Chess sets can be procured for relatively small sums, and technique guides are available in both print and online for free.
Kucha vsyakoy information
meta
data
More amazing hobbies


?6
7
8
9
10
T
XT
v
The
New
York
copyright
reserved
botanical
Garden
BOTANICAL
GARDE**
3-55
W|
Flora
of
the
Bahamian
Archipelago
Family
Apiaceae
Genus
Centella
Specific
epithet
asiatica
(L.)
Urb.
Island
Abaco
Settlement
or
location
Abaco
National
Park
X
Coordinate
2873425
Y
Coordinate
18R
0279576
Habitat
Wetland
in
Pine
Woodland
Collector
1
Ethan
Freid
Other
Collectors
Brandi
Mueller
Collection
Date
Monday,
June
21,
2004
Identified
bv
Ethan
Freid
Collection
#
04-090
01500895
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Welcome to the Polk County High School Youth Connection! Want to get involved in the community or at school? Many service-oriented school clubs, including FBLA, FCA, FFA, HOSA, NHS, SkillsUSA, and TSA, are described here along with activities the clubs participate in. There are also recent photos relating to each club and a promotional video showing what each is all about. A list of upcoming events including all of the various clubs' events is on the home page, and each club's specific activities are listed on its individual page. Feel free to contact us, check out the local government's website to learn more ways to participate, or utilize our Mobile Site by using the links at the bottom. Also, find out more about this summer's FBLA national conference through the link on the right and connect with us and other youth through our social network pages by using the Facebook, Twitter and Google+ icons!


SD/London
Home
News
Jobs
About
More
Add New Startup
My Settings
Sign in
Google Inc.
A leading search engine.
13
25500
2045
Address
Belgrave House
76 Buckingham Palace Rd
London
Greater London
SW1W 9TQ
Phone
020 7031 3000
Website
google.co.uk
View Larger Map


Profile
{{model.message}}
Follow
following
UserName
Password
First Name
Last Name
Email
Phones
Update
Favorites
No favorites!
Song (click for song details)
Comment
{{song.title}}
{{song.comment}}
People you follow:
No one!
{{user.following_username}}
People following you:
No one!
{{user.follower_username}}


<?php namespace CodeIgniter\Config; /** * CodeIgniter * * An open source application development framework for PHP * * This content is released under the MIT License (MIT) * * Copyright (c) 2014-2019 British Columbia Institute of Technology * * 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. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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. * * @package CodeIgniter * @author CodeIgniter Dev Team * @copyright 2014-2019 British Columbia Institute of Technology (https://bcit.ca/) * @license https://opensource.org/licenses/MIT MIT License * @link https://codeigniter.com * @since Version 3.0.0 * @filesource */ /** * Class Config * * @package CodeIgniter\Config */ class Config { /** * Cache for instance of any configurations that * have been requested as "shared" instance. * * @var array */ static private $instances = []; //-------------------------------------------------------------------- /** * Create new configuration instances or return * a shared instance * * @param string $name Configuration name * @param boolean $getShared Use shared instance * * @return mixed|null */ public static function get(string $name, bool $getShared = true) { $class = $name; if (($pos = strrpos($name, '\\')) !== false) { $class = substr($name, $pos + 1); } if (! $getShared) { return self::createClass($name); } if (! isset( self::$instances[$class] )) { self::$instances[$class] = self::createClass($name); } return self::$instances[$class]; } //-------------------------------------------------------------------- /** * Helper method for injecting mock instances while testing. * * @param string $class * @param $instance */ public static function injectMock(string $class, $instance) { self::$instances[$class] = $instance; } //-------------------------------------------------------------------- /** * Resets the instances array */ public static function reset() { static::$instances = []; } //-------------------------------------------------------------------- /** * Find configuration class and create instance * * @param string $name Classname * * @return mixed|null */ private static function createClass(string $name) { if (class_exists($name)) { return new $name(); } $locator = Services::locator(); $file = $locator->locateFile($name, 'Config'); if (empty($file)) { return null; } $name = $locator->getClassname($file); if (empty($name)) { return null; } return new $name(); } //-------------------------------------------------------------------- }


To access the internet, you use a web browser to enter the URL (uniform resource locator) of a webpage. Your computer then reaches out to a DNS which sends HTTP responses back to your computer. HTTP stands for hypertext transfer protocol and is the language used to communicate between web browsers and servers.  Get requests are simple lookup requests (i.e. get document-name or get /login).
Obviously, the more image, video, other HTTP requests, the slower a webpage will load. When sending information to a server, your computer sends “Post” requests to servers. One simple and common example is a post requests to websites with login information. Web browsers then take this back and forth of Gets and Ports and stores any relevant information in cookies. Cookies are temporary files that allow websites to save data and remember things like who you are.
HTTP communications are handled via plain text requests and are susceptible to hackers and other security vulnerabilities. This led to creation of SSL (secure socket layer) and TSL (transport layer security) incorporated into HTTPS. This is done via digital certificates that provided by verified websites. If the certificates being sent by websites have not been verified, you web browser will warn you.
Encryption (scrambling or changing a message to hide it) allows us to safely transfer private data over the open and unsecured internet. Decryption is the process of unscrambling am encrypted message to make it readable. Caesar's Cypher was one of the first encryption methods.
Based on Caesar's idea of "encrypting" messages so that intercepted messages were unreadable to his enemies, letters in the messages were shifted a certain number of letters up or down in the alphabet, for example, all letters were shifted up or down 3 letters. Keys are the number of letters that a sender and receiver have agreed upon so that the messages can be unlocked. HELLO with a key of 5 would be MJQQT.
This cypher is extremely simple and easy to crack. So much so, that using brute force would only take at most 26 different shift combinations. A 10-digit encryption key on the other hand would create 10 billion possible solutions. While this would take a human being several centuries to decrypt, it would still only take a computer seconds to do the same. Today's encryption keys are made up of 256 bits.
Symmetric encryption is when the same key is used to encrypt and decrypt messages. Asymmetric encryption is what is used by communication on the internet. There is a public key for encrypting and a private key for decrypting. The public key is shared so that anyone can encrypt messages (normally sent by websites to clients). This way messages can only be decrypted once received back to website servers via their private keys.
One real world analogy for this would be that public keys can open delivery slots on mailboxes, private keys can open the mailbox itself. This pubic / private key encryption method is the basis for SSL and TLS encryption protocols. Web browsers indicate this with the lock icon and https.


Animated Value
animated-value
is an imperative animation API for declarative UI renderers, like React, Preact, and Torus. It allows us to build rich, fully interactive animations with the full benefits of a JavaScript-driven imperative animation system -- custom tweening and physics including spring-based interactive physics, full interruptibility and redirectability, reliable chaining and callbacks, and more -- within the robust declarative UI frameworks we use to build apps today. Read more on GitHub.
Annotated source files


Welcome to SOABA!
This software was developed by João Pinho, during his free time and it's not affiliated with any corporative or university entity.
I am also a MSc Student at Instituto Superior Técnico, Taguspark.
For professional references, please check my linked in profile
here!
This software is provided "AS IS", without warranties of any kind.
If you liked this software, pay me a coffee, I'll be glad ;).
To get access to this software, please contact me at
jpe.pinho@gmail.com.


I’ll be starting a position in STEM Education at the University of Tennessee, Knoxville, beginning this August. As part of this position, I’ll teach science teaching methods (and science and STEM education research-related) courses. Hearing about the first course I’ll teach sparked me to gather these.
So, this is a loosely-organized collection of some resources–especially some new(er) books and National Academies Press (NAP) reports–related to science teaching and learning. I leave “oldies but goodies”, like Taking Science to School (2007), the Framework for K-12 Science Education (2012), and America’s Lab Report (2006) for example.
As a few notes, while I didn’t add annotation on why they’re included, I think these are all worth (at least my) attention. Also, these are just what I’ve found and am trying to read right now. I am sure there are a number of other great, new resources that I missed–and look forward to finding out about!
Books
NAP reports
Science Teachers’ Learning by Wilson, Schweingruber, and Nielsen (eds., 2015)
Seeing Students Learn Science by Beatty and Schweingruber (eds., 2017)


Mauricio Villamayor
Mauricio designed a project which exposes Paraguayan web idiosyncrasy through a curated selection of news and people's comments on social media. His project was published through different platforms, with distinctive features on each of them.
The print-on-demand book is available on the Blurb platform and it contains an English translation to the whole book. The digital publication through the Internet Archive contains an ePub with an additional section of news videos. The project was also published on ISSUU which also allows print-on-demand in addition to its standard PDF viewer.


Previous Seminars
Graduate Students in International Economics - Seminar
2014
June 30th, 2014 - MSE Room S19 (12:00-13:00)
Jamal Haidar (Paris School of Economics):
“Sanctions and Exports Deflection”.
Find the paper here.
May 19th, 2014 - MSE Room S19 (12:00-13:00)
Clément Nedoncelle (EQUIPPE - Université de Lille):
“Real Exchange-Rate Volatility, Third Market Effect and Trade: Evidence from French Firm-level Data”, with Jérôme Héricourt (EQUIPPE - Université de Lille, CEPII, Paris 1).
Find the abstract here.
April 7th, 2014 - MSE Room S19 (12:00-13:00)
Anna Ray (Paris School of Economics - Paris 1):
“Expanding Multinationals - Industry Relatedness and Conglomerate M&A”.
March 24th, 2014 - MSE Room S19 (12:00-13:00)
Sophie Hatte (Université de Rouen - PSE):
“It’s not Who You Are, it’s Who You Know: Political Connections and the Media Capture”.
Find the abstract here.
February 24th, 2014 - MSE Room S19 (12:00-13:00)
Anthony Edo (Paris School of Economics - Paris 1):
“Minimum Wage and the Labor Market Effects of Migration: Evidence from France”, with Farid Toubal (ENS, PSE, CESifo, CEPII).
Find the abstract here.
February 10th, 2014 - MSE Room S19 (12:00-13:00)
Geoffrey Barrows (UC Berkeley, ARE):
“Exporting and the environment: evidence from the end of the multi-fibre agreement”, with Helene Ollivier (PSE - Paris 1).
Find the abstract here.
January 20th, 2014 - MSE Room S2 (12:00-13:00)
Brahim Boualam (Université de Génève):
“Getting a first job: quality of the labor matching in French cities”.
Find the abstract here.
2013
December 9th, 2013 - MSE Room S2 (12:00-13:00)
Julian Hinz (Paris School of Economics - Paris 1):
“The ties that bind: Geopolitical motivation for PTAs”.
November 25th, 2013 - MSE Room S2 (12:00-13:00)
Duy-Manh Nguyen (Paris School of Economics - Paris 1):
“Heterogeneous Firms and Trade in a Dynamic Perspective”.
October 28th, 2013 - MSE Room S2 (12:00-13:00)
Sophie Hatte (Université de Rouen, PSE):
“News Coverage Distortion by the U.S. Government”.
October 14th, 2013 - MSE Room S2 (12:00-13:00)
Silja Baller (University of Oxford):
“Does Product Quality Matter or Gains from Trade? Theory and Evidence”
September 30th, 2013 - MSE Room S2 (12:00-13:00)
Mauro Lanati (University of Pisa):
“Estimating the elasticity of trade: the trade share approach”. Find the abstract here.
September 16th, 2013 - MSE Room S3 (12:00-13:00)
Emmanuel Milet (Paris School of Economics - Paris 1):
“Networks in International Trade in Services: Geography and Communication”.
September 2nd, 2013 - MSE Room S2 (12:00-13:00)
Margarita Lopez Forero (Paris School of Economics - Paris 1):
“Exports and FDI at the firm-level in France: complements or substitutes?”, with Jean-Charles Bricogne (Banque de France). Find the abstract here.
June 10th, 2013 - MSE Room S17 (12:00-13:00)
Marcelo Olarreaga (University of Geneva - CEPR):
“Cooperation in WTO’s Tariff Waters”, with Alessandro Nicita (UNCTAD) and Peri Silva (Kansas State University, Centro Studi Luca d’Agliano). Find the paper here.
May 27th, 2013 - MSE Room S3 (12:00-13:00)
Florian Le Bris (Paris School of Economics):
“Linking Firms’ Intermediate Inputs and Export Performances”, with Anne-Célia Disdier (Paris School of Economics), and Mélise Jaud (World Bank). Find the abstract here.
April 22nd, 2013 - MSE Room S3 (12:00-13:00)
Thannaletchimy Thanagopal (Université Paris 1):
“Price versus Quality War: a Case Study of France and Germany”. Find the paper here.
March 11th, 2013 - MSE Room S3 (12:00-13:00)
Ramezan Ali Marvi (Bocconi):
“Global Value Chains as Networks of Queues and the Great Trade Collapse”, with Carlo Altomonte. Find the abstract here.
February 25th, 2013 - MSE Room S3 (12:00-13:00)
Mélise Jaud (World Bank):
“Regime Change, Democracy and Growth”, with Carolina Freund. Find the paper here.
2012
December 10th, 2012 - MSE Room 3.1 (12:00-13:00)
Christian Gormsen (PSE):
“Disaporas and Foreign Direct Investments”.
December 3rd, 2012 – MSE Room 3.1 (12:00-13:00)
Jamal Ibrahim Haidar (PSE):
“Do Sanctions Matter? Exporter-Level Evidence from Iran”.
November 5th, 2012 - MSE Room 3.1 - (12:00-13:00)
Elizaveta Archanskai (SciencesPo):
“Proximity as a Source of Comparative Advantage”.
October 1st, 2012 - MSE Room 3.1 - (12:00-13:00)
Joachim Jarreau (Paris 1):
“The impact of resident status regulations on immigrants’ labor supply: evidence for France”.
September 24th, 2012 - MSE Room B.3.1 - (12:00-13:00)
Svetlana Ledyaeva (Aalto School of Economics, Helsinki):
“If foreign investment is not foreign: round-trip versus genuine foreign investment in Russia”. Find the paper here.
September 17th, 2012 - MSE Room B.3.1 - (12:00-13:00)
Guillaume Daudin (Sciences Po):
“Heterogeneity and the Distance Puzzle”. Find the paper here.
June 20th, 2012 - MSE Room 115 - (17:00-18:00)
Sean Dougherty (OECD):
“Contract enforcement, market size and firm scale in Mexico”.
June 18th, 2012 - MSE Room 115 - (12:00-13:00)
Sarra Ben Yahmed (Université d’Aix-Marseille, GREQAM):
“Gender Wage Gaps across Skills with Statistical Discrimination and Trade Openness”.
June 11th, 2012 - MSE Room 115 - (12:00-13:00)
Charles Fe Doukoure (Paris 1):
“A New Assessment of the Economic Partnership Agreements Impacts”.
May 21st, 2012 - Jourdan- (12:00-13:00)
Chiara Tomasi (University of Trento):
“Export activities under financial constraints: margins, quantities and prices”, with Angelo Secchi and Federico Tamagni. Find the paper here.
May 14th, 2012 - Jourdan - (12:00-13.00)
Sanne Hiller (Leuphana Unversität Lüneburg):
“Does Export Promotion work in Denmark? Evidence from a Matching Approach”.
April 30th, 2012 - Jourdan - (1200-13:00)
Thannaletchimy Thanagopal (Paris 1):
“Re-estimating International Elasticity of Substitution: A Preliminary Study of Price and Quality Competition”. Find the paper here.
April 2nd, 2012 - Jourdan - (12:00-13:00)
Sophie Hatte (Université de Rouen):
“Media exposure and NGO activity: crowding in or crowding out?”, with Matthieu Couttenier.
March 26th, 2012 - Jourdan - (12:00-13:00)
Pauline Bourgeon (Banque de France - PSE):
“Do financial frictions and trading time matter for trade?”.
March 19h, 2012 - Jourdan - (12:00-13:00)
Chiara Tomasi (University of Trento):
“Intermediaries in International Trade: Direct versus indirect modes of export”, with Andrew Bernard and Marco Grazzi. Find the paper here.
March 12th, 2012 - Jourdan - (12:00-13:00)
Felipe Starosta de Waldemar (Paris 1):
“Product relatedness and firm exports in China”, with Sandra Poncet.
March 5th, 2012 - Jourdan - (12:00-13:00)
Mathieu Couttenier (HEC Lausanne - DEEP):
“External shocks, internal shots: on the geography of civil conflict”, with Nicolas Berman.
February 20th, 2012 - Jourdan (12:00-13:00)
Jamal Haidar (PSE):
“Product diversification and Exporter Sales: Evidence from Jordan”.


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Standard: <IP_ADDRESS>
IP Address of the WiFi-Module receiving Control-Data.
Standard: 9750
Port, through which communication with the WiFi-Module will take place.
Standard: 2000
Time (in milliseconds) until a connection with the WiFi-Module has to be established.
Standard: false
Invert the motor axis
Standard: false
Invert the wheel axis.
Standard: false
Causes the robot to freeze.
Standard (Motor): Pitch
Standard (Wheel): Roll
The axis which will be used for the steering (wheel) or the velocity adjustment (motor) respectively.
Standard (Motor): 10
Standard (Wheel): 2
Absolute minimal deviation from the relative zero point in degree, from which on a velocity is transmitted to the WiFi-Module.
Standard (Motor): 45
Standard (Wheel): 10
Absolute maximal deviation from the relative zero point, from which on the highest possible Velocity is transmitted to the WiFi-Module.


Toggle navigation
Reports
Dashboard
Population
Dashboard Report
CDM Summary
Data Density
Mapping Status
Year of Birth
Gender Distribution by Age
Population
Population at Start of Observation
Population by Year
Birth Date
Persons with continuous observation by year
Cumulative Observation
Observation Length
Number of observation periods
Persons with continuous observation by month
Report generated on 2019-06-10 4:16 pm


Basic Tables
Basic example
For basic styling—light padding and only horizontal dividers—add the base class .table
to any <table>
.
| Name | Phone Number | Date of Birth | Active? |
|---|---|---|---|
| Risa D. Pearson |<PHONE_NUMBER> | July 24, 1950 |
|
| Ann C. Thompson |<PHONE_NUMBER> | January 25, 1959 |
|
| Paul J. Friend |<PHONE_NUMBER> | September 1, 1939 |
|
| Linda G. Smith |<PHONE_NUMBER> | May 3, 1962 |
|
Inverse table
You can also invert the colors—with light text on dark backgrounds—with .table-dark
.
| Name | Phone Number | Date of Birth | Active? |
|---|---|---|---|
| Risa D. Pearson |<PHONE_NUMBER> | July 24, 1950 |
|
| Ann C. Thompson |<PHONE_NUMBER> | January 25, 1959 |
|
| Paul J. Friend |<PHONE_NUMBER> | September 1, 1939 |
|
| Sean C. Nguyen |<PHONE_NUMBER> | February 5, 1994 |
|
Striped rows
Use .table-striped
to add zebra-striping to any table row
within the <tbody>
.
Bordered table
Add .table-bordered
for borders on all sides of the table and cells.
Hoverable rows
Add .table-hover
to enable a hover state on table rows within a <tbody>
.
| Product | Price | Quantity | Amount |
|---|---|---|---|
| ASOS Ridley High Waist | $79.49 | 82 Pcs | $6,518.18 |
| Marco Lightweight Shirt | $128.50 | 37 Pcs | $4,754.50 |
| Half Sleeve Shirt | $39.99 | 64 Pcs | $2,559.36 |
| Lightweight Jacket | $20.00 | 184 Pcs | $3,680.00 |
Small table
Add .table-sm
to make tables more compact by cutting cell padding in half.
| Product | Price | Quantity | Amount |
|---|---|---|---|
| ASOS Ridley High Waist | $79.49 | 82 Pcs | $6,518.18 |
| Marco Lightweight Shirt | $128.50 | 37 Pcs | $4,754.50 |
| Half Sleeve Shirt | $39.99 | 64 Pcs | $2,559.36 |
| Lightweight Jacket | $20.00 | 184 Pcs | $3,680.00 |
| Marco Shoes | $28.49 | 69 Pcs | $1,965.81 |
| ASOS Ridley High Waist | $79.49 | 82 Pcs | $6,518.18 |
Table head options
Use one of two modifier classes to make <thead>
s appear light or dark gray.
| Product | Courier | Process | Status |
|---|---|---|---|
| ASOS Ridley High Waist | FedEx |
|
Delivered |
| Marco Lightweight Shirt | DHL |
|
Shipped |
| Half Sleeve Shirt | Bright |
|
Order Received |
| Lightweight Jacket | FedEx |
|
Delivered |
Contextual with background color
Use contextual classes to color table rows or individual cells.
| Product | Courier | Process | Status |
|---|---|---|---|
| ASOS Ridley High Waist | FedEx |
|
Delivered |
| Marco Lightweight Shirt | DHL |
|
Shipped |
| Half Sleeve Shirt | Bright |
|
Order Received |
| Lightweight Jacket | FedEx |
|
Delivered |
Always responsive
Across every breakpoint, use
.table-responsive
for horizontally scrolling tables. Use
.table-responsive{-sm|-md|-lg|-xl}
as needed to create responsive tables up to a particular breakpoint. From that breakpoint and up, the table will behave
normally and not scroll horizontally.
| # | Heading | Heading | Heading | Heading | Heading | Heading | Heading | Heading | Heading |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Cell | Cell | Cell | Cell | Cell | Cell | Cell | Cell | Cell |
| 2 | Cell | Cell | Cell | Cell | Cell | Cell | Cell | Cell | Cell |
| 3 | Cell | Cell | Cell | Cell | Cell | Cell | Cell | Cell | Cell |


Asciidoctor DZSlides light container
Link version
SCRIPT IS RUNNING?
-
Click to generate file (Regenerate every 2 seconds)
View Slides
On stage mode
Embedder mode
Standalone version
SCRIPT IS RUNNING?
-
Click to generate file.
View Slides


Owners of GitHub Apps can now uninstall an app using the Apps API.
Custom media types: gambit-preview
gambit-preview


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Enabling Collaborative Cybercartography with MapBlender
Proceedings of the 25th AGILE Conference on Geographic Information Science (AGILE'22)
We are excited to share that MapBlender has won the first price in the CyberCartography Competition at AGILE 2022!
Authors
Marius Hogräfer Jens Emil Grønbæk Jana Puschmann Sebastian Krog Knudsen Hans-Jörg SchulzAbstract
One of the challenges that GIS users in diverse, distributed teams face these days is being able to efficiently collaborate, both across workspaces and tools. To that end, we present MapBlender, a cybercartographic application that fosters geocollaboration by adapting a collaboration-first approach, placing users and their GIS tools on equal footing. MapBlender allows all connected users to share video feeds of their GIS tools and webcams, which can then be freely rearranged and re-sized, adopting simple and familiar interaction techniques from modern window managers. In addition, the rendering and translucency of these feeds can be adjusted, allowing users to align and analyze information across tools. We demonstrate the utility of MapBlender in a hybrid teaching scenario, wherein a representative of a software company uses our application to instruct co-located and remote learners from another company on the features of their GIS tool.
MapBlender is publicly available under open source licenses and runs in the browser, with no local installation necessary. Thus, with MapBlender in place, one of our goals is to promote an HCI perspective on future work in geocollaboration.
Citation in BibTeX
To cite this article, we encourage you to use the following bibtex entry in your citation manager:
@inproceedings{Hograefer2022_mapblender, title = {Enabling Collaborative Cybercartography with {MapBlender}}, author = {Hogr\"afer, Marius and Gr\o{}nb\ae{}k, Jens Emil and Puschmann, Jana and Knudsen, Sebastian Krog and Schulz, Hans-J\"org}, booktitle = {Proceedings of the 25th AGILE Conference on Geographic Information Science (AGILE'22)}, year = {2022}, pages = {37:1--37:6} editor = {Parseliunas, Eimuntas and Mansourian, Ali and Partsinevelos, Panagiotis and Suziedelyte-Visockiene, Jurate}, publisher = {Copernicus Publications}, doi = {10.5194/agile-giss-3-37-2022} }


{% extends '_layouts/layout_2-3_no_title_block.html' %} {% block page_title %} Stomach ache symptoms and conditions - NHS.UK {% endblock %} {% block pageTitle %} See a GP {% endblock %} {% block pageIntro %} {% endblock %} {% block content %}
To book an appointment with your GP online you must already be registered with a GP surgery.
You will need to tell us:
Not know your GP? Find your GP.
{% endblock %} {% block emergencyContent %} {% endblock%} {% block mutedCallout%} {% endblock%} {% block body_after %} {% endblock %}


---
layout: page
weight: 0
title: Variations (A/B Testing)
navigation:
show: true
---
This module allows you to add new Variations to your drafted
Marketing Emails. These Variations can be used in A/B testing (also sometimes
called split testing).
Create a new Variation.
| Parameter |
Required |
Requirements |
Description |
| name |
Yes |
Must be an existing Marketing Email in draft mode. |
The name of the Marketing Email you are adding Variations to. |
| variation |
Yes |
None |
The name of the Variation you will be adding. |
| subject |
Yes |
None |
The subject that will be used for the Variation being created. |
| text |
Yes |
None |
The text portion of the Marketing Email Variation being added. |
| html |
No |
If the type parameter is set to "html" this parameter is required. |
The html portion of the Marketing Email Variation being added. |
| type |
Yes |
Value must be either "text" or "html". |
The Variation type, either plain text, or multipart-alternative text and html. |
| identity_name |
No |
Must be an existing Marketing Email Identity |
Identity name that is needed if the Marketing Email name does not already exist. Will be used to create a parent Marketing Email. |
{% apiexample add POST https://api.sendgrid.com/api/newsletter/variations/add api_user=your_sendgrid_username&api_key=your_sendgrid_password&name=existing_draft&type=html&variation=variation_name&subject=variation_subject&html=variation_body %}
{% response json %}
//success
{
"message": "success"
}
//error
{
"message": "error",
"errors": [
"...error messages..."
]
}
{% endresponse %}
{% response xml %}
| Parameter |
Required |
Requirements |
Description |
| name |
Yes |
Must be an existing Marketing Email |
Select the Marketing Email you'de like to schedule Variation testing on. |
| percent |
Yes |
Must be a number 0-100 |
Percentage of the total recipients that will be emailed in the test. |
| delay |
No |
Must be numeric. |
The number of units specified by delay_type by which to delay the send. |
| delay_type |
No |
"minutes", "hours", or "days" |
The units used to specify the delay |
| date |
No |
Date / Time must be provided in ISO 8601 format (YYYY-MM-DDTHH:MM:SS+-HH:MM) |
Date / Time to schedule marketing email Delivery. |
| timezone |
No |
Must be an existing Variation |
An optional timezone of specific date (timezones are in the Olson Database name format). |
{% apiexample schedule POST https://api.sendgrid.com/api/newsletter/variations/schedule api_user=your_sendgrid_username&api_key=your_sendgrid_password&name=existing_draft&percent=50 %}
{% response json %}
//success
{
"message": "success"
}
//error
{
"message": "error",
"errors": [
"...error messages..."
]
}
{% endresponse %}
{% response xml %}


Disney sets release dates for next five Marvel movies
While the rest of us are still in mourning after Avengers: Endgame, Disney have been keeping themselves busy, setting the release dates for the next five Marvel movies.
Buckle in guys.
Despite us having a pretty huge Marvel gap at the moment, with 10 months between the release of Spider-Man: Far From Home in July 2019 and Black Widow in February 2020, we’re getting spoiled down the line.
According to The Hollywood Reporter, the franchise has set currently unknown releases for October 7, 2022, February 17, 2023, May 5, 2023, July 28, 2023, and November 3, 2023.
So we’ll be getting two Marvel movies in 2020, four in 2021, four in 2022, and another four in 2023. This is not a drill guys.
Meanwhile, in the midst of the Great Marvel Drought Of 2019 as we’ve officially named it, fans have been getting their fix from Disney+ after its launch.
Advertisement Advertisement
In fact, they’ve gone full force and added much of the Marvel back catalogue to the service.
They also announced a new special called Expanding The Universe, teasing the new TV series that will be attached to the larger MCU.
This includes Falcon and The Winter Soldier, which has just started production, and stars characters Sam Wilson (Anthony Mackie) Bucky Barnes (Sebastian Stan) as the heroes themselves.
WandaVision, starring Scarlet Witch (Elizabeth Olsen) and Vision (Paul Bettany), has also been announced, as has a spin-off series focusing on Loki (Tom Hiddleston).
Joining Avengers: Endgame on the streaming service is a large collection of Marvel favourites. Captain Marvel, the Iron Man trilogy, Guardians of the Galaxy 1+2 and Avengers Assemble and Age of Ultron.
Although there are still some notable absences from the collection, including Thor: Ragnarok, Ant-Man and The Wasp, and the Spider-Man collection.
Meanwhile, although we’ve got to wait a while for a new Marvel movie, Disney are cracking on with Star Wars: The Rise Of Skywalker, which is set for release in December.
Too much Disney to handle!
Got a showbiz story?
If you've got a story, video or pictures get in touch with the Metro.co.uk Entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


Cities in New York
The 111 largest cities in New York (the most populous cities in NY are New York, Buffalo, and Rochester) have an average Walk Score of 48. The best cities in New York ranked by Walk Score are New York, Mineola, and Port Chester, while the least walkable cities are Dix Hills, Coram, and Melville.
Follow the links for city maps, photos, and all neighborhoods.
The list of New York cities below is sorted by Walk Score, but can also be sorted by population or alphabetically (click table's heading to sort).
City |
Zip Code |
Walk Score |
Transit Score |
Bike Score |
Population |
|---|---|---|---|---|---|
| New York (the largest city in New York) | 11368 | 88 | 84 | 70 | 8,175,133 |
| Buffalo | 14213 | 68 | 49 | 65 | 261,310 |
| Rochester | 14621 | 65 | 43 | 61 | 210,565 |
| Yonkers | 10701 | 70 | 54 | 39 | 195,976 |
| Syracuse | 13210 | 60 | 44 | 50 | 145,170 |
| Albany | 12208 | 65 | 54 | 56 | 97,856 |
| New Rochelle | 10801 | 65 | 44 | 48 | 77,062 |
| Cheektowaga | 14225 | 34 | 28 | 44 | 75,178 |
| Mount Vernon | 10550 | 74 | 56 | 44 | 67,292 |
| Schenectady | 12308 | 65 | 37 | 51 | 66,135 |
| Utica | 13501 | 58 | -- | 49 | 62,235 |
| Brentwood | 11717 | 38 | -- | 42 | 60,664 |
| Tonawanda | 14223 | 47 | 31 | 50 | 58,144 |
| White Plains | 10605 | 62 | 51 | 40 | 56,853 |
| Hempstead | 11550 | 74 | 53 | 55 | 53,891 |
| Levittown | 11756 | 50 | 21 | 46 | 51,881 |
| Irondequoit | 14617 | 32 | 33 | 40 | 51,692 |
| Niagara Falls | 14301 | 49 | 30 | 55 | 50,193 |
| Troy | 56 | 39 | 44 | 50,129 | |
| Binghamton | 56 | -- | 51 | 47,376 | |
| West Seneca | 14224 | 25 | -- | 37 | 44,711 |
| West Babylon | 11704 | 46 | -- | 44 | 43,213 |
| Freeport | 11520 | 63 | 37 | 50 | 42,860 |
| Hicksville | 11801 | 60 | 30 | 50 | 41,547 |
| Coram | 11727 | 18 | -- | 36 | 39,113 |
| East Meadow | 11554 | 52 | 25 | 46 | 38,132 |
| Valley Stream | 66 | 51 | 51 | 37,511 | |
| Brighton | 28 | 32 | 47 | 36,609 | |
| Commack | 11725 | 30 | -- | 38 | 36,124 |
| Central Islip | 11722 | 37 | -- | 42 | 34,450 |
| Rome | 35 | -- | 45 | 33,725 | |
| New City | 10956 | 21 | -- | 29 | 33,559 |
| Long Beach | 70 | 29 | 53 | 33,275 | |
| Elmont | 11003 | 62 | 40 | 49 | 33,198 |
| Huntington Station | 51 | -- | 50 | 33,029 | |
| Poughkeepsie | 63 | 30 | 54 | 32,736 | |
| Oceanside | 11572 | 55 | 32 | 48 | 32,109 |
| Centereach | 11720 | 22 | -- | 36 | 31,578 |
| North Tonawanda | 38 | -- | 51 | 31,568 | |
| Spring Valley | 53 | 20 | 41 | 31,347 | |
| Jamestown | 53 | -- | 43 | 31,146 | |
| Ithaca | 14853 | 71 | -- | 57 | 30,014 |
| Franklin Square | 11010 | 67 | 38 | 51 | 29,320 |
| Elmira | 50 | -- | 55 | 29,200 | |
| Port Chester | 76 | 43 | 43 | 28,967 | |
| Newburgh | 69 | -- | 43 | 28,866 | |
| West Islip | 11795 | 33 | -- | 39 | 28,335 |
| Middletown | 57 | -- | 38 | 28,086 | |
| Shirley | 11967 | 23 | -- | 37 | 27,854 |
| Deer Park | 11729 | 45 | -- | 43 | 27,745 |
| Auburn | 52 | 28 | 44 | 27,687 | |
| Harrison | 10528 | 37 | 32 | 31 | 27,472 |
| Lindenhurst | 53 | -- | 46 | 27,253 | |
| Holbrook | 11741 | 32 | -- | 40 | 27,195 |
| Watertown | 50 | -- | 46 | 27,023 | |
| Glen Cove | 11542 | 52 | 22 | 37 | 26,964 |
| Dix Hills | 12 | -- | 25 | 26,892 | |
| Saratoga Springs | 42 | 24 | 42 | 26,586 | |
| Smithtown | 23 | -- | 31 | 26,470 | |
| Bay Shore | 50 | -- | 48 | 26,337 | |
| Plainview | 11803 | 48 | 15 | 46 | 26,217 |
| Ossining | 61 | 36 | 35 | 25,060 | |
| Uniondale | 11553 | 63 | 38 | 52 | 24,759 |
| Medford | 20 | -- | 36 | 24,142 | |
| Baldwin | 63 | 36 | 51 | 24,033 | |
| Rockville Centre | 11570 | 62 | 40 | 55 | 24,023 |
| Kingston | 57 | -- | 53 | 23,893 | |
| Peekskill | 10566 | 61 | 33 | 30 | 23,583 |
| Copiague | 11726 | 56 | -- | 48 | 22,993 |
| East Patchogue | 24 | -- | 38 | 22,469 | |
| Garden City | 54 | 46 | 48 | 22,371 | |
| Merrick | 55 | 25 | 47 | 22,097 | |
| Massapequa | 48 | 23 | 47 | 21,685 | |
| Lockport | 44 | -- | 52 | 21,165 | |
| Hauppauge | 11788 | 26 | -- | 36 | 20,882 |
| Rotterdam | 39 | -- | 45 | 20,652 | |
| East Northport | 39 | -- | 39 | 20,217 | |
| Kiryas Joel | 41 | -- | 29 | 20,175 | |
| Lake Ronkonkoma | 41 | -- | 42 | 20,155 | |
| Plattsburgh | 12903 | 57 | -- | 57 | 19,989 |
| North Bellmore | 53 | 18 | 46 | 19,941 | |
| Selden | 35 | -- | 40 | 19,851 | |
| Holtsville | 11742 | 22 | -- | 36 | 19,714 |
| Eastchester | 10709 | 61 | 41 | 44 | 19,554 |
| Lynbrook | 11563 | 72 | 51 | 57 | 19,427 |
| Cortland | 54 | -- | 48 | 19,204 | |
| Ronkonkoma | 27 | -- | 35 | 19,082 | |
| East Massapequa | 47 | 24 | 47 | 19,069 | |
| Melville | 11747 | 20 | -- | 31 | 18,985 |
| North Bay Shore | 39 | -- | 48 | 18,944 | |
| Mamaroneck | 64 | 41 | 50 | 18,929 | |
| Wantagh | 49 | 28 | 47 | 18,871 | |
| West Hempstead | 65 | 42 | 53 | 18,862 | |
| Syosset | 44 | -- | 42 | 18,829 | |
| Mineola | 11501 | 77 | 52 | 51 | 18,799 |
| Islip | 11751 | 41 | -- | 44 | 18,689 |
| Amsterdam | 34 | -- | 29 | 18,620 | |
| Monsey | 40 | -- | 35 | 18,412 | |
| Oswego | 58 | -- | 44 | 18,142 | |
| Lackawanna | 14218 | 42 | 27 | 49 | 18,141 |
| Huntington | 36 | -- | 30 | 18,046 | |
| North Massapequa | 44 | 13 | 47 | 17,886 | |
| Nanuet | 36 | -- | 33 | 17,882 | |
| North Amityville | 42 | -- | 44 | 17,862 | |
| North Babylon | 11703 | 43 | -- | 43 | 17,509 |
| Kings Park | 11754 | 25 | -- | 33 | 17,282 |
| Scarsdale | 40 | 37 | 38 | 17,166 | |
| Woodmere | 46 | -- | 45 | 17,121 | |
| Massapequa Park | 43 | 28 | 50 | 17,008 | |
| Sayville | 35 | -- | 39 | 16,853 | |
| North Valley Stream | 62 | 38 | 49 | 16,628 |
Find apartments and off campus housing near universities in New York
Find apartments and rentals near large employers in New York


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Welcome to Pippin's Tasty Pizzas!
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Choose a Single Topping!
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Pinneapple.
Ham.
Pick a Sauce!


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| src/ | 97.96% | 1392/1421 | 93.82% | 835/890 | 96.52% | 222/230 | 96.74% | 831/859 | |
| src/__tests__/ | 99.2% | 3331/3358 | 98.98% | 195/197 | 95.13% | 586/616 | 99.55% | 2902/2915 | |
| src/events/ | 100% | 100/100 | 98.39% | 61/62 | 100% | 21/21 | 100% | 66/66 | |
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The ovm_void class is the base class for all OVM classes. It is an abstract class with no data members or functions. It allows for generic containers of objects to be created, similar to a void pointer in the C programming language. User classes derived directly from ovm_void inherit none of the OVM functionality, but such classes may be placed in ovm_void-typed containers along with other OVM objects.


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dials.export¶
Introduction¶
This program is used to export the results of dials processing in various formats.
The output formats currently supported are:
MTZ format exports the files as an unmerged mtz file, ready for input to downstream programs such as Pointless and Aimless. For exporting integrated, but unscaled data, the required input is a models.expt file and an integrated.refl file. For exporting scaled data, the required input is a models.expt file and a scaled.refl file, also passing the option intensity=scale.
NXS format exports the files as an NXmx file. The required input is a models.expt file and an integrated.refl file.
MMCIF format exports the files as an mmcif file. The required input is a models.expt file and an integrated.refl file.
XDS_ASCII format exports intensity data and the experiment metadata in the same format as used by the output of XDS in the CORRECT step - output can be scaled with XSCALE.
SADABS format exports intensity data (and geometry by direction cosines) as an ersatz-SADABS format reverse engineered from the file format used by EvalCCD for input to SADABS.
MOSFLM format exports the files as an index.mat mosflm-format matrix file and a mosflm.in file containing basic instructions for input to mosflm. The required input is an models.expt file.
XDS format exports a models.expt file as XDS.INP and XPARM.XDS files. If a reflection file is given it will be exported as a SPOT.XDS file.
PETS format exports intensity data and diffraction data in the CIF format used by PETS. This is primarily intended to produce files suitable for dynamic diffraction refinement using Jana2020, which requires this format.
Examples:
# Export to mtz
dials.export models.expt integrated.refl
dials.export models.expt integrated.refl mtz.hklout=integrated.mtz
dials.export models.expt scaled.refl intensity=scale mtz.hklout=scaled.mtz
# Export to nexus
dials.export models.expt integrated.refl format=nxs
dials.export models.expt integrated.refl format=nxs nxs.hklout=integrated.nxs
# Export to mmcif
dials.export models.expt integrated.refl format=mmcif
dials.export models.expt integrated.refl format=mmcif mmcif.hklout=integrated.mmcif
# Export to mosflm
dials.export models.expt integrated.refl format=mosflm
# Export to xds
dials.export strong.refl format=xds
dials.export indexed.refl format=xds
dials.export models.expt format=xds
dials.export models.expt indexed.refl format=xds
Basic parameters¶
format = *mtz sadabs nxs mmcif mosflm xds xds_ascii json shelx pets
intensity = *auto profile sum scale
debug = False
mtz {
combine_partials = True
partiality_threshold = 0.4
min_isigi = -5
force_static_model = False
filter_ice_rings = False
d_min = None
hklout = auto
crystal_name = XTAL
project_name = DIALS
best_unit_cell = None
wavelength_tolerance = 1e-4
}
sadabs {
hklout = integrated.sad
run = 1
predict = False
}
xds_ascii {
hklout = DIALS.HKL
}
nxs {
hklout = integrated.nxs
instrument_name = Unknown
instrument_short_name = Unknown
source_name = Unknown
source_short_name = Unknown
}
mmcif {
hklout = auto
compress = gz bz2 xz
pdb_version = *v5 v5_next
}
mosflm {
directory = mosflm
}
xds {
directory = xds
}
json {
filename = rlp.json
compact = True
n_digits = 6
}
shelx {
hklout = dials.hkl
ins = dials.ins
scale = True
scale_range = -9999.0, 9999.0
}
pets {
filename_prefix = dials_dyn
id = None
partiality_cutoff = 0.99
flag_filtering = False
virtual_frame {
excitation_error_cutoff = 0.04
n_merged = 1
step = 1
}
}
output {
log = dials.export.log
}
Full parameter definitions¶
format = *mtz sadabs nxs mmcif mosflm xds xds_ascii json shelx pets
.help = "The output file format"
.type = choice
intensity = *auto profile sum scale
.help = "Choice of which intensities to export. Allowed combinations:
"
" scale, profile, sum, profile+sum, sum+profile+scale. Auto"
"will
default to scale or profile+sum depending on if"
"the data are scaled."
.type = choice(multi=True)
debug = False
.help = "Output additional debugging information"
.type = bool
mtz {
combine_partials = True
.help = "Combine partials that have the same partial id into one
"
"reflection, with an updated partiality given by the sum of the
"
" individual partialities."
.type = bool
partiality_threshold = 0.4
.help = "All reflections with partiality values above the partiality
"
" threshold will be retained. This is done after any combination"
"of
partials if applicable."
.type = float(allow_none=True)
min_isigi = -5
.help = "Exclude reflections with unfeasible values of I/Sig(I)"
.type = float(allow_none=True)
force_static_model = False
.help = "Force program to use static model even if scan varying is present"
.type = bool
filter_ice_rings = False
.help = "Filter reflections at ice ring resolutions"
.type = bool
d_min = None
.help = "Filter out reflections with d-spacing below d_min"
.type = float(allow_none=True)
hklout = auto
.help = "The output MTZ filename, defaults to integrated.mtz or"
"scaled_unmerged.mtz depending on if the input data are scaled."
.type = path
crystal_name = XTAL
.help = "The name of the crystal, for the mtz file metadata"
.type = str
project_name = DIALS
.help = "The project name for the mtz file metadata"
.type = str
best_unit_cell = None
.help = "Best unit cell value, to use when performing resolution cutting,"
"and as the overall unit cell in the exported mtz. If None, the"
"median cell will be used."
.type = unit_cell
wavelength_tolerance = 1e-4
.help = "An absolute tolerance on the wavelength (in A)"
.type = float(allow_none=True)
}
sadabs {
hklout = integrated.sad
.help = "The output raw sadabs file"
.type = path
run = 1
.help = "Batch number / run number for output file"
.type = int(allow_none=True)
predict = False
.help = "Compute centroids with static model, not observations"
.type = bool
}
xds_ascii {
hklout = DIALS.HKL
.help = "The output raw hkl file"
.type = path
}
nxs {
hklout = integrated.nxs
.help = "The output Nexus file"
.type = path
instrument_name = Unknown
.help = "Name of the instrument/beamline"
.type = str
instrument_short_name = Unknown
.help = "Short name for instrument/beamline, perhaps the acronym"
.type = str
source_name = Unknown
.help = "Name of the source/facility"
.type = str
source_short_name = Unknown
.help = "Short name for source, perhaps the acronym"
.type = str
}
mmcif {
hklout = auto
.help = "The output CIF file, defaults to integrated.cif or"
"scaled_unmerged.cif
depending on if the data are scaled."
.type = path
compress = gz bz2 xz
.help = "Choose compression format (also appended to the file name)"
.type = choice
pdb_version = *v5 v5_next
.help = "This controls which pdb mmcif dictionary version the output mmcif"
"file should comply with. v5_next adds support for recording"
"unmerged data as well as additional scan metadata and statistics,"
"however writing can be slow for large datasets."
.type = choice
}
mosflm {
directory = mosflm
.help = "The output directory for mosflm output"
.type = path
}
xds {
directory = xds
.help = "The output directory for xds output"
.type = path
}
json {
filename = rlp.json
.type = path
compact = True
.type = bool
n_digits = 6
.help = "Number of decimal places to be used for representing the"
"reciprocal lattice points."
.type = int(value_min=1, allow_none=True)
}
shelx {
hklout = dials.hkl
.help = "The output hkl file"
.type = path
ins = dials.ins
.help = "The output ins file"
.type = path
scale = True
.help = "Scale reflections to maximise output precision in SHELX 8.2f"
"format"
.type = bool
scale_range = -9999.0, 9999.0
.help = "minimum or maximum intensity value after scaling."
.type = floats(size=2, value_min=-999999, value_max=9999999)
}
pets {
filename_prefix = dials_dyn
.help = "The prefix for output files, where the default will produce"
"dials_dyn.cif_pets"
.type = str
id = None
.help = "The experiment ID to export from a multi-experiment list"
.type = int(allow_none=True)
partiality_cutoff = 0.99
.help = "Cutoff for determining which reflections are deemed to be fully"
"recorded"
.type = float(allow_none=True)
flag_filtering = False
.help = "If true, keep only the reflections where the relevant"
"`integrated` flag is set (either `integrated_sum` or"
"`integrated_prf`). This seems to be quite restrictive compared to"
"PETS, so is not set by default."
.type = bool
virtual_frame {
excitation_error_cutoff = 0.04
.help = "Excitation error cutoff determining which reflections are"
"included in virtual frames"
.type = float(allow_none=True)
n_merged = 1
.help = "Number of frames to merge in a virtual frame"
.type = int(allow_none=True)
step = 1
.help = "Step between frames"
.type = int(allow_none=True)
}
}
output {
log = dials.export.log
.help = "The log filename"
.type = path
}


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Ant McPartlin pinpoints Katie Hopkins as his 'least favourite' I'm A Celebrity contestant
"She didn't really shine," TV host reflects
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But he's due back on the box alongside Dec to welcome the latest group of celebrities into the Australian jungle tonight - and he couldn't be happier.
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This year sees the likes of Caitlyn Jenner, Nadine Coyle, and Ian Wright take part, with Dec, 44, has dubbed them as "one of the best casts in years".
Asked what they're most looking forward to about this series, Dec said: "The cast. We've been privy to a few of the names taking part this year. We're very, very excited as it's one of the best casts in years.
"It's a time of year we really look forward to and we love being in Australia. This will be our 19th series and it's a show we absolutely love doing."
However, while reflecting on the various famous faces to have gone to Oz for the long-running series over the years, Ant seemed to make a slight dig at former contestant Katie Hopkins.
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Asked about their least favourite celebrity campmate, Dec pointed out Gillian McKeith's histrionic approach to Bushtucker trials could be frustrating.
He also noted that the least entertaining stars are those who are not very memorable - leading to Ant's dismissal of 2007 contestant Katie.
"You wouldn't really remember her doing it, as she didn't really shine," he said of the ex Apprentice contestant who has gone on to become a polarising figure on social media.
Last year's series of the jungle reality show saw football manager Harry Redknapp crowned King of the Jungle.
- I'm A Celebrity 2019 begins on ITV tonight, Sunday November 17, at 9pm
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