Part of my work is writing algorithms to analyze networks of nodes representing manufacturing systems. Each node can be one of four different types: Buffer, Constraint, Merge, and Split. A manufacturing system that we would want to simulate is typically made up of no more than 100 of these nodes. A natural way to encode these types would be to use Discriminated Unions (DU). I also use Units of Measure to annotate integers that are the ids for these manufacturing nodes.
[<Measure>] type BufferId
[<Measure>] type ConstraintId
[<Measure>] type SplitId
[<Measure>] type MergeId
[<RequireQualifiedAccess>]
type Node =
| BufferId of bufferId : int<BufferId>
| ConstraintId of constraintId : int<ConstraintId>
| MergeId of mergeId : int<MergeId>
| SplitId of splitId : int<SplitId>
The default encoding for a DU in F# is as a reference type. This means that if you have a Node array
, each array element will be a pointer to where the Node
itself is stored in memory. If you need to quickly lookup up Nodes and what type they are, you can run into a phenomenon known as Pointer Chasing. Pointer Chasing is when the CPU is trying to run your code but constantly has to look up new regions of memory because the data is spread out. As .NET developers, we tend not to think about this much, but it can become a severe problem in performance-sensitive code.
Fortunately, F# allows us to encode DUs as structs using the [<Struct>]
attribute. Here is what that looks like:
[<Struct; RequireQualifiedAccess>]
type Node =
| BufferId of bufferId : int<BufferId>
| ConstraintId of constraintId : int<ConstraintId>
| MergeId of mergeId : int<MergeId>
| SplitId of splitId : int<SplitId>
Now, if you have a Node array
, the data for the node will be stored in the array itself so you can eliminate having to perform an additional lookup. There is a serious downside to this, though. The F# compiler will allocate space for each possible case of the DU instead of only the area necessary for the instantiated individual case. This means that instead of just taking up the space of just two int
(one to encode the case and one for the value), this struct Node
will take up one int
to encode the case and four more int
for each possible case. For a deeper explanation of this, I refer you to this excellent post by Bartosz Sypytkowski. If a DU has too many cases, the benefits of the struct layout will quickly be negated by this padding.
Alternative Encoding
I was curious if there was another way to approach my problem. I like the elegance of the match ... with
syntax in F#, and I am loathed to give it up. Since my Node
type is just encoding different possible int
values, why not do some bit hacking? Now, I will be the first to say this is non-traditional F# code, but I’m curious, so why not perform the experiment?
I’ll define a new version of Node
that will use an int
to hold the data about which case it represents and the value. I will use the last 4 bits of the Value
field to encode which case the Node
represents and the top 28 bits will hold the id value. This is cutting off some of the space that int
can express, but since our networks are never more than 1,000 nodes, there is no practical loss of modeling space.
[<Struct>]
type Node =
private {
Value : int
}
static member BufferIdCode = 0
static member ConstraintIdCode = 1
static member MergeIdCode = 2
static member SplitIdCode = 3
The static members BufferIdCode
, ConstraintIdCode
, MergeIdCode
, and SplitIdCode,
will be the values I use to encode the Node cases. To still use the match...with
syntax, I will need to define an Active Pattern for unpacking the case. Active Patterns are an elegant feature of F# for decomposing data into different forms. In this case, I will take the int
held in my Node
type, check which case it is, and then return the corresponding id.
I use a mask and a bitwise AND operation to get the last 4 bits (also known as a nibble) of the Value
field, which gives me an int
. I compare that int
with the possible code values to figure out which type of node it is. I then bit shift the Value
field to the right 4 bits to convert it to the id value it stores and multiply the result by the right Unit of Measure to ensure type safety.
let (|BufferId|ConstraintId|SplitId|MergeId|) (node: Node) =
// Get the nibble which encodes the type of Id
let nodeType = node.Value &&& 0x0000000F
// Get the value of the Id
let idValue = node.Value >>> 4
if nodeType = Node.BufferIdCode then
BufferId (idValue * 1<BufferId>)
elif nodeType = Node.ConstraintIdCode then
ConstraintId (idValue * 1<ConstraintId>)
elif nodeType = Node.MergeIdCode then
MergeId (idValue * 1<MergeId>)
elif nodeType = Node.SplitIdCode then
SplitId (idValue * 1<SplitId>)
else
invalidArg (nameof node) "Node Id type does not match known Node Types"
There is a downside to this technique, though. Active Patterns will cause additional allocations and trigger additional Garbage Collection (GC). Fortunately, F# recognized this and enabled the returning of a struct from a Partial Active Pattern. The syntax is a little odd due to a limitation in the compiler, but it works for our purposes.
Here is the equivalent approach using the Partial Active Pattern that returns a ValueStruct
to reduce GC pressure. Compared to our first Active Pattern, the downside is that we have to define a separate one for each case we want to match against.
[<return: Struct>]
let (|BufferId|_|) (node: Node) =
let nodeType = node.Value &&& 0x0000000F
if nodeType = Node.BufferIdCode then
let idValue = node.Value >>> 4
ValueSome (idValue * 1<BufferId>)
else
ValueNone
[<return: Struct>]
let (|ConstraintId|_|) (node: Node) =
let nodeType = node.Value &&& 0x0000000F
if nodeType = Node.ConstraintIdCode then
let idValue = node.Value >>> 4
ValueSome (idValue * 1<ConstraintId>)
else
ValueNone
[<return: Struct>]
let (|MergeId|_|) (node: Node) =
let nodeType = node.Value &&& 0x0000000F
if nodeType = Node.MergeIdCode then
let idValue = node.Value >>> 4
ValueSome (idValue * 1<MergeId>)
else
ValueNone
[<return: Struct>]
let (|SplitId|_|) (node: Node) =
let nodeType = node.Value &&& 0x0000000F
if nodeType = Node.SplitIdCode then
let idValue = node.Value >>> 4
ValueSome (idValue * 1<SplitId>)
else
ValueNone
Benchmark Setup
We will now set up two types of tests. The first set of tests will randomly jump around a Node array
, check the Node type, and perform different work based on the case. This is most similar to the workloads I experience when writing algorithms for simulating manufacturing systems. For curiosity, I will also have a tests for a linear traversal of a Node array
and perform the same work as the random access. This should illustrate the difference in performance between a predictable access pattern and a random one. The branch predictor in the CPU will have a more challenging time with the random access, and we expect it to be slower.
To see the impact of the Active Pattern on memory allocation and GC, we will include the [<MemoryDiagnoser>]
attribute on a Benchmarks
class that holds our tests. This will tell BenchmarkDotNet to monitor how much allocation is occurring. We should see the Active Pattern approach incur more GC activity. We also include the BranchMispredictions
and CacheMisses
hardware counters to see how well the CPU can optimize our code. The ideal code has 0
Branch Mispredictions. Whenever we mispredict a branch, we can lose 20 - 30 cycles worth of work depending on the CPU. Cache Misses occur when our data is not in the cache, and the CPU has to go out to memory to retrieve the data. The CPU will do its best to predict what data it needs and fetch it ahead of time. When it guesses wrong, we can incur a severe performance penalty.
[<MemoryDiagnoser; HardwareCounters(HardwareCounter.BranchMispredictions, HardwareCounter.CacheMisses)>]
type Benchmarks () =
let rng = Random 123
let nodeCount = 100
let lookupCount = 100
let loopCount = 100_000
let nodes =
[|for i in 0 .. nodeCount - 1 ->
match rng.Next (0, 4) with
| 0 -> DuEncoding.Node.BufferId 1<BufferId>
| 1 -> DuEncoding.Node.ConstraintId 1<ConstraintId>
| 2 -> DuEncoding.Node.MergeId 1<MergeId>
| 3 -> DuEncoding.Node.SplitId 1<SplitId>
| _ -> failwith "The RNG generated a number outside the allowed bounds"
|]
At the beginning of the Benchmarks
class, we declare a random number generator rng
which we will use to produce random nodes and indices to look up. We have a nodeCount
of 100, which is the number of nodes we will generate. The lookupCount
is the number of node lookups we will perform during each test loop. The loopCount
is the number of loops we will perform in each test. nodes
is an array of randomly generated nodes for our test.
Our first test performs random lookups in the nodes
array. It matches against the case of the DU and then adds a different amount to an accumulator based on the case. This is to simulate some amount of work being done for each node that was looked up.
[<Benchmark>]
member _.DuEncodingRandomAccess () =
let mutable acc = 0
for lookupsIndex = 0 to loopCount - 1 do
let lookups = randomNodeIndices[lookupsIndex]
for lookupIndex = 0 to lookups.Length - 1 do
let randomNodeIndex = lookups[lookupIndex]
match nodes[randomNodeIndex] with
| DuEncoding.Node.BufferId bufferId -> acc <- acc + 1
| DuEncoding.Node.ConstraintId constraintId -> acc <- acc + 2
| DuEncoding.Node.MergeId mergeId -> acc <- acc + 3
| DuEncoding.Node.SplitId splitId -> acc <- acc + 4
acc
The second test does the same work, but the Node
type is the struct representation instead of the reference-based one.
[<Benchmark>]
member _.StructDuEncodingRandomAccess () =
let mutable acc = 0
for lookupsIndex = 0 to loopCount - 1 do
let lookups = randomNodeIndices[lookupsIndex]
for lookupIndex = 0 to lookups.Length - 1 do
let randomNodeIndex = lookups[lookupIndex]
match structNodes[randomNodeIndex] with
| StructDuEncoding.Node.BufferId bufferId -> acc <- acc + 1
| StructDuEncoding.Node.ConstraintId constraintId -> acc <- acc + 2
| StructDuEncoding.Node.MergeId mergeId -> acc <- acc + 3
| StructDuEncoding.Node.SplitId splitId -> acc <- acc + 4
acc
The third and fourth tests also perform the same work but with the Active Pattern and Partial Active Pattern approaches. Remember, the Active Pattern allocates objects while the Partial Active Pattern is returning a struct and therefore not causing any GC overhead.
[<Benchmark>]
member _.IntEncodingWithActivePatternRandomAccess () =
let mutable acc = 0
for lookupsIndex = 0 to loopCount - 1 do
let lookups = randomNodeIndices[lookupsIndex]
for lookupIndex = 0 to lookups.Length - 1 do
let randomNodeIndex = lookups[lookupIndex]
match intEncodedNodes[randomNodeIndex] with
| IntEncoding.ActivePattern.BufferId bufferId -> acc <- acc + 1
| IntEncoding.ActivePattern.ConstraintId constraintId -> acc <- acc + 2
| IntEncoding.ActivePattern.MergeId mergeId -> acc <- acc + 3
| IntEncoding.ActivePattern.SplitId splitId -> acc <- acc + 4
acc
[<Benchmark>]
member _.IntEncodingWithPartialActivePatternRandomAccess () =
let mutable acc = 0
for lookupsIndex = 0 to loopCount - 1 do
let lookups = randomNodeIndices[lookupsIndex]
for lookupIndex = 0 to lookups.Length - 1 do
let randomNodeIndex = lookups[lookupIndex]
match intEncodedNodes[randomNodeIndex] with
| IntEncoding.PartialActivePattern.BufferId bufferId -> acc <- acc + 1
| IntEncoding.PartialActivePattern.ConstraintId constraintId -> acc <- acc + 2
| IntEncoding.PartialActivePattern.MergeId mergeId -> acc <- acc + 3
| IntEncoding.PartialActivePattern.SplitId splitId -> acc <- acc + 4
| _ -> failwith "¯\_(ツ)_/¯"
acc
I also create four additional tests which perform the same amount of work as the above four, but iterate through the arrays in order instead of randomly jumping around. This will show us the performance difference between random versus sequential access.
Results
Note: Since I am measuring hardware counters, I have to run the terminal as admin; otherwise, I won’t have access to the data. If you want to test this yourself, you need to do the same.
When I run the tests, I get the following table:
| Method | Mean | Error | StdDev | BranchMispredictions/Op | CacheMisses/Op | Gen 0 | Allocated |
|---|---|---|---|---|---|---|---|
| DuEncodingRandomAccess | 83.09 ms | 1.122 ms | 0.995 ms | 7,414,534 | 400,225 | - | 175 B |
| StructDuEncodingRandomAccess | 83.56 ms | 0.766 ms | 0.640 ms | 7,415,626 | 409,418 | - | 175 B |
| IntEncodingWithActivePatternRandomAccess | 134.88 ms | 2.650 ms | 4.845 ms | 8,126,171 | 1,070,592 | 28500.0000 | 240,000,358 B |
| IntEncodingWithPartialActivePatternRandomAccess | 86.43 ms | 0.841 ms | 0.787 ms | 7,995,620 | 406,096 | - | 191 B |
| DuEncodingLinearAccess | 14.86 ms | 0.090 ms | 0.084 ms | 5,073 | 7,701 | - | 22 B |
| StructDuEncodingLinearAccess | 17.35 ms | 0.151 ms | 0.142 ms | 119,799 | 13,508 | - | 36 B |
| IntEncodingWithActivePatternLinearAccess | 74.67 ms | 1.018 ms | 0.903 ms | 167,078 | 677,829 | 28571.4286 | 240,000,191 B |
| IntEncodingWithPartialActivePatternLinearAccess | 22.83 ms | 0.372 ms | 0.348 ms | 8,151 | 33,225 | - | 45 B |
We see that the normal reference-based DU encoding gives us the best performance for both tests. This honestly surprised me. I would have thought that the Int Encoding would have yielded better results. There is some serious voodoo going on in the F# compiler. I wanted to dig into this more, but my work demands that I cut myself off here. I would like to get this info out for others to look at since I have not been able to find other blog posts which deal with this.
I welcome feedback and critique. You can find the code here. Let me know if I missed something obvious. Eventually, I hope to have the time to dig deeper into this. In the meantime, I plan to stick with the default DU until I can figure out if I can make something faster for my use case. Let me know if you have ideas for going faster or other ideas I should test. I can be found on Twitter @McCrews, or you can email<EMAIL_ADDRESS>.


Robert Spence, Lisa Tweedie
Possible way of mapping products and workshop design tools on multiple dimensions.
"“The Attribute Explorer”
video demonstrates a graphical
interactive
tool for visualizing
the relationships
within
multi-attribute
data sets. Rather than using 2- or 3-
dimensional
multiple views of the data, each attribute is
mapped to a single dimensional representation (interactive
histogram)."
Unread


IN THE HIGH COURT OF JUDICATURE AT PATNA Criminal Miscellaneous No.21601 of 2012 ====================================================== 1. Atiqur Rahman S/O Fazlur Rahman M/S North Bihar Leather Industries, Industrial Area Bela, P.S. Bela, District - Muzaffarpur 2. Ajazur Rahman S/O Fazlur Rahman M/S North Bihar Leather Industries, Industrial Area Bela, P.S. Bela, District - Muzaffarpur .... .... Petitioner/s Versus 1. The State Of Bihar .... .... Opposite Party/s ====================================================== Appearance : For the Petitioner/s : Mr. Irshad Ahmad Khan, Advocate. For the Opposite Party/s : Mr. Nawin Kumar Pandey, A.P.P. ====================================================== CORAM: HONOURABLE MR. JUSTICE ASHWANI KUMAR SINGH ORAL ORDER
2 13-06-2012 Heard learned counsel for the petitioners and learned counsel for the State.
The petitioners apprehend their arrest in connection with a case in which cognizance has been taken for the offence punishable under Section 15 of the Environment (Protection) Act, 1986 as well as section 44 of the Water (Prevention and Control of Pollution) Act.
It is stated that the petitioners have committed no offence. Petitioner no.1 happens to be proprietor and petitioner no.2 is the Manager of the industry in question. Earlier the Proprietor of the North Bihar Leather Industries was one Abulain Ansari who sold the industry to petitioner 2 Patna High Court Cr.Misc. No.21601 of 2012 (2) dt.13-06-2012 2/3 no.1 on 22.5.2004 and after purchasing the said industrial unit petitioner no.1 established his own pollution and control treatment plant and applied before the Bihar State Pollution Control Board, Patna for consent under Section 21 of the Air (Prevention and Control of Pollution) Act on 14.3.2005. The Bihar State Pollution Control Board, Patna has granted permission in favour of petitioner no.1 on 21.10.2011 for a period of five years from 7.9.2011 to 6.9.2016.
Be that as it may, considering the nature of allegation, facts and circumstances of the case, let the abovenamed petitioners in the event of their arrest or surrender before the court below within a period of four weeks from the date of receipt/communication of this order, be released on bail on furnishing bail bonds of Rs. 10,000/- (Ten Thousand only) with two sureties of the like amount each to the satisfaction of the learned Sub Divisional Judicial Magistrate (Spl. J.M.), Patna in connection with Complaint Case No. 7(M) of 2005 subject to the conditions as laid down under Section 438(2) of the Code of Criminal Procedure as also on the following conditions: (i) That both the bailors will be close relative of the petitioners who will give separate affidavit giving genealogy as to how they are related with the petitioners. (ii) 3 Patna High Court Cr.Misc. No.21601 of 2012 (2) dt.13-06-2012 3/3 That the petitioners shall not directly or indirectly make any inducement, threat or promise to any person acquainted with the facts of the case so as to dissuade him to disclose such facts to the court or to any other authority. (iii) That they shall remain present before the court on the dates fixed for hearing of the case. If they wants to remain absent, then they shall take prior permission of the court and in case of compelling and unavoidable circumstances for remaining absent, they will immediately inform the court and request that they may be permitted to be present through the counsel. (iv) That liberty is given to the State to make an appropriate application for modifying/recalling the order granting bail, if for any reason, the petitioners violate any of the conditions imposed by this Court.
Vinay/- (Ashwani Kumar Singh, J)


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To define your own custom phase, use the following pattern.
1. Extend the appropriate base class for your phase type.
class my_PHASE_phase extends uvm_task_phase; class my_PHASE_phase extends uvm_topdown_phase; class my_PHASE_phase extends uvm_bottomup_phase;
2. Optionally, implement your exec_task or exec_func method.
task exec_task(uvm_component comp, uvm_phase schedule); function void exec_func(uvm_component comp, uvm_phase schedule);
If implemented, these methods usually call the related method on the component
comp.PHASE_phase(uvm_phase phase);
3. Since the phase class is a singleton, providing an accessor method allows for easy global use, and protecting the constructor prevents misuse.
class my_PHASE_phase extends uvm_topdown_phase; or uvm_task_phase/uvm_bottomum_phase static local my_PHASE_phase m_inst; Local reference to global IMP protected function new(string name="PHASE"); Protected constructor for singleton super.new(name); endfunction : new static function my_PHASE_phase get(); Static method for accessing singleton if (m_imp == null) m_imp = new(); return m_imp; endfunction : get Optionally implement exec_func/exec_task endclass : my_PHASE_phase
4. Insert the phase in a phase schedule or domain using the uvm_phase::add method:
my_schedule.add(my_PHASE_class::get());
| User-Defined Phases | |
| To define your own custom phase, use the following pattern. |
Build up a schedule structure inserting phase by phase, specifying linkage
function void add( uvm_phase phase, uvm_phase with_phase = null, uvm_phase after_phase = null, uvm_phase before_phase = null )


| Category: containers | Component type: concept |
Iterators into a Forward Container satisfy the forward iterator requirements; consequently, Forward Containers support multipass algorithms and allow multiple iterators into the same container to be active at the same time.
| X | A type that is a model of Forward Container |
| a, b | Object of type X |
| T | The value type of X |
| Name | Expression | Type requirements | Return type |
|---|---|---|---|
| Equality | a == b | T is EqualityComparable | Convertible to bool |
| Inequality | a != b | T is EqualityComparable | Convertible to bool |
| Less | a < b | T is LessThanComparable | Convertible to bool |
| Greater | a > b | T is LessThanComparable | Convertible to bool |
| Less or equal | a <= b | T is LessThanComparable | Convertible to bool |
| Greater or equal | a >= b | T is LessThanComparable | Convertible to bool |
| Name | Expression | Precondition | Semantics | Postcondition |
|---|---|---|---|---|
| Equality | a == b | Returns true if a.size() == b.size() and if each element of a compares equal to the corresponding element of b. Otherwise returns false. | ||
| Less | a < b | Equivalent to lexicographical_compare(a,b) |
| Ordering | Two different iterations through a forward container will access its elements in the same order, providing that there have been no intervening mutative operations. |


This application combines information from the Pokemon API and PokemonGo API for users can find all information about a pokemon in one easy to use online Pokedex.
Check out the repo here
A weather dashboard that tells you the forecast, saves searchs and renders previously searched cities. Uses open weather maps api.
Check out the repo here
An application that helps you stay organized with moment.js and local storage.
Check out the repo here


A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893 and NIH R21HG005912.
| Version: | 1.3-0 |
| Depends: | R (≥ 2.13.0), methods, proxy (≥ 0.4-7) |
| Imports: | animation, clue, cluster, clusterGeneration, dbscan (≥ 1.0-0), fpc, graphics, grDevices, MASS, mlbench, Rcpp (≥ 0.11.4), stats, utils |
| LinkingTo: | Rcpp, BH |
| Suggests: | DBI, rJava, RSQLite, testthat |
| Published: | 2018-06-02 |
| Author: | Michael Hahsler [aut, cre, cph], Matthew Bolanos [aut, cph], John Forrest [aut, cph], Matthias Carnein [ctb], Dennis Assenmacher [ctb] |
| Maintainer: | Michael Hahsler <mhahsler at lyle.smu.edu> |
| BugReports: | https://github.com/mhahsler/stream |
| License: | GPL-3 |
| URL: | http://lyle.smu.edu/IDA/TRACDS/ |
| NeedsCompilation: | yes |
| Citation: | stream citation info |
| Materials: | README NEWS |
| CRAN checks: | stream results |
| Reference manual: | stream.pdf |
| Vignettes: |
Introduction to stream Extending the stream Framework |
| Package source: | stream_1.3-0.tar.gz |
| Windows binaries: | r-devel: stream_1.3-0.zip, r-release: stream_1.3-0.zip, r-oldrel: stream_1.3-0.zip |
| OS X binaries: | r-release: stream_1.3-0.tgz, r-oldrel: stream_1.3-0.tgz |
| Old sources: | stream archive |
| Reverse depends: | streamMOA, subspaceMOA |
Please use the canonical form https://CRAN.R-project.org/package=stream to link to this page.


--- layout: default title: Mark Mahoney robots: noindex ---
Hi, I'm Mark! I'm a Web Developer with a focus on Node.js. My recent obsession is Vue.js but I also have experience with the React/Redux and MongoDB stack as well as vanilla HTML/CSS/JS.
In my previous life, I had a successful run as a sales executive at several different companies. I supported the hackathon community at Major League Hacking, helped Trustev’s machine learning anti-fraud system get acquired by TransUnion, and sold social media analytics software at Brandwatch. My goal is to utilize these sales and leadership experiences to build products that people love to use every day!
A writers platform for the Dutch community. My role was to translate PSD templates and convert them into responsive webpages using HTML, LESS, and Angular.js.
Project is still under development, and will be linked here when completed.
The first site (to my knowledge) that allows users to create, share, and vote on polls using the Instant Runoff Voting method. A fullstack application, being built from the ground up using Vue.js and Firebase.
This project is still under development.
A pure JS/JQuery implementation of the classic electronic game "Simon."
An automatic widget that detects location via IP and retrieves data from the OpenWeatherMap API.
A React implementation of John Conway’s famous cellular automation.


A function object is an object to which the function call operator can be applied. Typically, it is a class that defines the function call operator (operator()()) as a member function. When a function object is used as a function, the function call operator is invoked whenever the function is called. Consider the following class definition:
class biggerThanThree { public: bool operator()(int val) const { return val > 3; } };
If we create an instance of class biggerThanThree, every time we reference this object using the function call syntax, the function call operator member function is invoked. To generalize this class, we add a constructor and a constant data member, which is set by the constructor:
class biggerThan { public: const int testValue; biggerThan(int x) : testValue(x) { } bool operator()(int val) const { return val > testValue; } };
The result is a general biggerthanX function, where the value of X is determined when we create an instance of the class. We can do so, for example, as an argument to one of the generic functions that require a predicate. In this manner the following code finds the first value in a list that is larger than 12:
std::list<int>::iterator firstBig = std::find_if(aList.begin(), aList.end(), biggerThan(12));
There are a number of situations where it is convenient to substitute function objects in place of functions: to use an existing function object provided by the C++ Standard Library instead of a new function; to improve execution by using inline function calls; and to allow a function object to access or set state information that is held by an object. Let's deal with each of these in the next three sections.
Table 5 illustrates the function objects provided by the C++ Standard Library.
Let's look at a couple of examples that show how these might be used. The first example uses std::plus<int>() to compute the by-element addition of two lists of integer values, placing the result back into the first list. This can be performed by the following code:
std::transform(listOne.begin(), listOne.end(), listTwo.begin(), listOne.begin(), std::plus<int>() );
The second example negates every element in a vector of boolean values:
std::transform(aVec.begin(), aVec.end(), aVec.begin(), std::logical_not<bool>() );
The base class templates used by the C++ Standard Library to define the functions in Table 5 are also available for creating new unary and binary function objects. The class templates unary_function and binary_function are defined in the header <functional>.
The base classes are defined as follows:
namespace std { template <class Arg, class Result> struct unary_function { typedef Arg argument_type; typedef Result result_type; }; template <class Arg1, class Arg2, class Result> struct binary_function { typedef Arg1 first_argument_type; typedef Arg2 second_argument_type; typedef Result result_type; }; }
An example of the use of these templates is found in Section 6.3. There we want to take a binary function of type Widget and an argument of type int, and compare the widget identification number against the integer value. A function to do this is written in the following manner:
struct WidgetTester : std::binary_function<Widget, int, bool>{ bool operator() (const Widget& wid, int testid) const { return wid.id == testid; } };
The importance of inheritance from one of the two base class templates becomes apparent when elementary function objects are composed to form complex expressions using negators and binders, which are explained in Section 3.5.
A second reason to consider using function objects instead of functions is faster code. The difference between a function and an object of a class that defines the member operator()() is that functions are converted to pointers when passed as arguments to other functions, thus incurring the overhead of a function call even if they are declared inline. In many cases an invocation of a function object, as in the examples on std::transform() in Section <IP_ADDRESS>, can be expanded in-line, eliminating the overhead of a function call.
The third major reason to use a function object in place of a function is when each invocation of the function must remember some state set by earlier invocations. An example of this occurs in the creation of a generator, to be used with the generic algorithm std::generate(). A generator is simply a function object that takes no arguments and returns a new (and possibly different) value each time it is invoked. The most commonly used form of generator is a random number generator, but there are other uses for the concept. A sequence generator simply returns the values of an increasing sequence of natural numbers (1, 2, 3, 4 and so on). We can call this object iotaGen after the similar operation in the programming language APL, and define it as follows:
class iotaGen { public: iotaGen (int start = 0) : current(start) { } int operator() () { return current++; } private: int current; };
An iota object maintains a current value, which can be set by the constructor, or defaults to zero. Each time the function-call operator is invoked, the current value is returned, and also incremented. Using this object, the following call on the C++ Standard Library function std::generate() initializes a vector of 20 elements with the values 1 through 20:
std::vector<int> aVec(20); std::generate(aVec.begin(), aVec.end(), iotaGen(1));
A more complex example of using a function object occurs in the radix sorting example program, which is given as an example of using the list datatype in Section 6.3. In this program references are initialized in the function object, so that during the sequence of invocations the function object can access and modify local values in the calling program.


These control the basic pulse or FM width of each {_waveshaper} before modulation. The values are shown as percentages if the "saws" or "pulse" {Waveshaper_shape shape} is selected, or as a number of cycles of phase modulation if any of the FM shapes are selected. (This has no effect for the noise or impulse shapes.)
These are contained in the {Waveshaper_shape_cluster Waveshaper shape cluster} within the {Waveshaper_section}.
These are contained in the WIDTH section of the right panel, under the following parameter number:
| {sw-34.png height=48} |
These appear in the display as Wave Width.
These are accessed via the following {_NRPN NRPNs}:
| A | 1,49 | |
| B | 2,49 |
The values are displayed as percentages or cycles:
| MIDI | percent | cycles | ||
| 0 | 0% | 0cyc | (default) | |
| \b\n\b\n\b | \b\n\b\n\b | \b\n\b\n\b | ||
| 64 | 50% | 8cyc | (significant) | |
| \b\n\b\n\b | \b\n\b\n\b | \b\n\b\n\b | ||
| 127 | 100% | 16cyc |


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The Intermedia Index rises as the music of the noriji 1 becomes congruent with the poetic text, set in ōnori, and the accompaniment of the three percussion instruments in strict rhythmic setting. The intermedia interest of the noriji 1 lies in how it raises the energy and creates a transition between the adjacent shōdan.
At first, the noriji-1 is . Munechika kneels on the dais motionless as he announces that he is going to pray. The jiutai delivers the prayer, the density increasing in comparison to the single voice in the preceding notto. A moment later, Munechika begins to wave a sacred wand as the jiutai speaks for him, calling for divine help. The music builds momentum with its faster tempo and larger instrumentation, as the taiko has now joined the two hand-percussion instruments.
The noriji 1 leads to the hayafue, the entrance music that will bring on stage the second-act shite, personifying a divine being. The text and tempo artfully provide the prayer with a tone of intense reverence. Set in ōnori, the poetic text counts on average eight syllables per honji. In preparation for the swordsmith’s statement of entreaty, “hear and grant my prayer”, the tempo slows down when the taiko pattern switches . This leads to the dramatic line “”, indicating a prayer. At this point, the text setting switches to non-congruent and slows to three syllables per honji. The waki sings the first word, the jiutai the second. Against the slow-down in the vocal line, the percussion instruments create an accelerando that sets the tone for the dynamic hayafue and the entrance of the second-act shite.


"POPLAR" Hebrews libneh, “white”, (Genesis 30:37; Hos. 4:13), in all probability the storax tree (Styrax officinalis) or white poplar, distinguished by its white blossoms and pale leaves. It is common in the Anti-Libanus. Other species of the poplar are found in Palestine, such as the white poplar (P. alba) of our own country, the black poplar (P. nigra), and the aspen (P. tremula). (See WILLOW.)
"POPLAR" Hebrews libneh, “white”, (Genesis 30:37; Hos. 4:13), in all probability the storax tree (Styrax officinalis) or white poplar, distinguished by its white blossoms and pale leaves. It is common in the Anti-Libanus. Other species of the poplar are found in Palestine, such as the white poplar (P. alba) of our own country, the black poplar (P. nigra), and the aspen (P. tremula). (See WILLOW.)


teetime.stage.taskfarm.monitoring.extraction
Classes
AbstractGeneralCSVExporter
AbstractMonitoringDataExporter
AbstractStackedCSVExporter
StackedTimePullThroughput2D
StackedTimePushThroughput2D
StackedTimeSizeWithCapacity2D
TimeBoundary2D
TimeBoundaryMSPullThroughput3D
TimeBoundaryMSPushThroughput3D
TimeBoundaryStages3D


Faculty Name
Hi, I am a passionate, highly enthusiastic Android developer.
I believe I'm a fast-learner and try to learn and implement the new technologies and skills that I try to absorb. I rate myself as an out of the box thinker and a good problem solver. I can work independently using my own initiative or as a part of the team.
| CS - 202 | Computer System Organisation & Architecture |
| CS - 203 | Database Design & Management System |
| IT - 203 | OOPS wih Java |
| CS - 307 | Computer Networks |
| Contact | +91- XXX XXX XXXX |
|
<EMAIL_ADDRESS><EMAIL_ADDRESS>|


There are many ways to convert these types between each other; these are the most straightforward, least surprising ones I've found. Please see the happy path test cases for what I expect their behavior to be.
Some of these conversions, the ones that use .unwrap()
, may panic if the conversion can't be performed on the input you supply!
| Want x to be ▶ Have an x of type ▼ |
i32 | u32 | String | f64 |
|---|---|---|---|---|
| i32 | n/a | x as u32 | x.to_string() | x as f64 |
| u32 | x as i32 | n/a | x.to_string() | x as f64 |
| String* | x.parse().unwrap() | x.parse().unwrap() | n/a | x.parse().unwrap() |
| f64 | x as i32 | x as u32 | x.to_string() | n/a |
* Observant readers will notice that yes, the code for converting Strings to i32, u32, AND f64 is all the same — how can that be??? Type inference! If Rust can't infer the type that you're trying to get from the context in which you're using it, you can give it a hint by doing, for example, x.parse::<i32>().unwrap()
, but that usually isn't necessary.
If you have a variable x
, and you get the message "expected `type`, found `core::option::Option<type>`
", you can use the following code with the following consequences. There is more detail and more ways to handle Options
in the Error Handling section of the Rust Programming Language book.
| Code | Consequence when you get None |
|---|---|
x.unwrap_or(put_default_value_here) |
You will get the value you put where it says put_default_value_here . |
x.expect("My Custom Error Message") |
Panic with a custom error message |
x.unwrap() |
Panic (not recommended in production code, handle your error cases!) |
Strings are... special. It's a long story that the Strings section of the Rust Programming Language book goes into.
I've pulled the string types out into a separate table because it's not possible to get an &str without going through String, so it would add a lot of redundancy. Also, when converting between string slices (&str) and in-memory strings (String), you will need to consider the lifetime you intend these to have.
Also note that collections::string
is re-exported as std::string
, so if you get an error message saying "expected collections::string::String
", that's the same as if the error message said "expected String
".
So. Here are the ways to convert between these when the lifetimes can be inferred:
| Want x to be ▶ Have an x of type ▼ |
String | &str |
|---|---|---|
| String | n/a | &*x |
| &str | x.to_string() | n/a |
| Want x to be ▶ Have an x of type ▼ |
Vec<T> | &[T] | Box<[T]> |
|---|---|---|---|
| Vec<T> | n/a | &x[..] | x.into_boxed_slice() |
| &[T] | x.to_vec() | n/a | Box::new(*x) |
| Box<[T]> | x.into_vec() | &*x | n/a |
Bare [T], referring to some number of T in contiguous memory, are rarely useful. Usually, you want a "borrowed slice", &[T], which consists of a pointer to that memory and a count of the number of T present. If you somehow have a [T] and need &[T] instead, simply use &x
.
I hope you find this helpful! <3
I'm just a person who makes mistakes just like you, and Rust changes frequently, so if something on this page isn't right, please make a correction!
This is not an official Rust project.


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Microsoft MakeCode Arcade
Hour
of
Code™
2021
Educator Guide
About MakeCode Arcade
Microsoft MakeCode Arcade is a free, open-source, retro, 2D game development environment. Students between the ages of 9 and 18 can use MakeCode Arcade to design and create their own games online and share them with friends and family or download to hand-held game devices. Learn more about Microsoft MakeCode at http://aka.ms/makecode.
Activity Requirements
The MakeCode Arcade Hour of Code™ activities require students to have a computer with internet access. All activities run in the browser – no download or installation required.
Optionally, there are hand-held game devices students may purchase to download the games they code to play on-the-go. Learn more at https://arcade.makecode.com/hardware.
Learning Objectives
By the end of this lesson, students will have been exposed to:
-
Topic concepts:
- The impact of wildfires on the environment
- Common causes of forest fires
- How computers and technology can help combat wildfires
-
Computer Science concepts:
- Events
- Variables and constants
- Loops
- Randomization
-
Game design concepts:
- Tilemaps
- Sprites
- HUD (Head-up Display)
- Status Bar
- Art and Design
- Animations
- Effects
Teacher Preparation
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Share this Hour of Code activity as a class assignment in Microsoft Teams
Resources for Further Exploration
- Webpages and Articles
- YouTube videos
Follow us on Social Media
Tag us in your students' creations using @msmakecode or the hashtag #makecode. And don't forget to follow us to find out about the newest MakeCode Arcade updates, games, and competitions!


Funding team
Email<EMAIL_ADDRESS>Funding and audit grant return queries except early years
Guidance
Guidance for schools and ITT providers on funding available through bursaries, scholarships and subject knowledge enhancement programmes, for the academic year 2014 to 2015.
This guide refers to ITT funding for the academic year 2014 to 2015. For the academic year 2015 to 2016 please see Funding: initial teacher training (ITT), academic year 2015 to 2016 .
The National College for Teaching and Leadership (NCTL) allocates and funds teacher training places.
We provide financial incentives, such as training bursaries and scholarships, to help schools and initial teacher training providers attract high-quality graduates to the teaching profession. We do this in line with the needs of the sector and national targets set by the Department for Education.
Information in this guide relates to trainees starting their training in the academic year 2014 to 2015.
The following scholarships and bursaries are available to trainee teachers.
| Maths or physics | Computing | Chemistry | |
| Trainee with 2:1 or first | £25,000 | £25,000 | £25,000 |
A limited number of scholarships are available for trainees. These are awarded by the relevant subject association through a competitive process. Trainees must have a 2:1 or first-class honours degree to apply.
Trainees awarded a scholarship cannot receive a bursary. However, trainees who are not awarded a scholarship will continue to be eligible for a standard bursary.
| Maths or physics | Computing | Chemistry | Languages | Other priority secondary and primary | Primary maths specialist | |
| Trainee with first | £20,000 | £20,000 | £20,000 | £20,000 | £9,000 | £11,000 |
| Trainee with 2:1 | £20,000 | £20,000 | £15,000 | £15,000 | £4,000 | £11,000 |
| Trainee with 2:2 | £15,000 | £15,000 | £12,000 | £12,000 | n/a | £6,000 |
| Trainee with relevant degree and A Level maths or physics (grade B minimum) | £9,000 | n/a | n/a | n/a | n/a | n/a |
Bursaries of up to £20,000 are awarded to trainees in physics, maths, computing, chemistry and languages. Trainees must have at least a 2:2. Other relevant academic qualifications such as Masters, PhDs, overseas and medical degrees are also eligible.
Bursaries of £9,000 are available for trainees in maths and physics who have a relevant degree and an A Level (minimum B grade or equivilent) in either subject.
Bursaries of up to £9,000 are available for primary trainees and other priority secondary subjects (English, history, biology, geography, music, design and technology). Trainees must have a first or 2:1.
Bursaries of up to £12,000 are available for primary maths specialist trainees with a minimum of B at maths A Level. Those who do not meet this requirement will receive the normal primary bursary for their degree class.
School Direct trainees receive a 25% increase on these bursaries or scholarships if they spend more than 50% of their training in a school where more than 35% of pupils are eligible for free school meals.
NCTL provides the following training and salary grants to School Direct lead schools to cover training costs for salaried trainees to achieve qualified teacher status (QTS). Amounts vary on a regional basis. Find out which areas are covered by the definitions for inner London, outer London and London fringe .
| Funding per place (by region) | Amount |
| National | £19,000 |
| Inner London | £23,900 |
| Outer London | £22,600 |
| London fringe | £20,200 |
| Funding per place (by region) | Amount |
| National | £14,000 |
| Inner London | £17,600 |
| Outer London | £16,600 |
| London fringe | £14,900 |
| Funding per place (by region) | Amount |
| National | £14,000 |
| Inner London | £17,600 |
| Outer London | £16,600 |
| London fringe | £14,900 |
| Funding per place (by region) | Amount |
| National | £16,000 |
| Inner London | £19,600 |
| Outer London | £18,600 |
| London fringe | £16,900 |
This funding only applies to trainees with at least a B at maths A Level and who are on primary maths specialist courses.
Additional qualifications such as a postgraduate certificate in education (PGCE) are not funded.
School Direct trainees on the salaried route are not eligible for bursaries or scholarships.
A £2,000 incentive payment is also available for schools recruiting and employing eligible graduate service leavers. This payment is funded directly by NCTL as part of the Troops to Teachers programme .
All recipients of School Direct salaried funding must complete all highlighted sections in the Grant funding agreement: terms and conditions . You must agree to the Grant funding agreement for training bursaries ( MS Word Document , 89.7KB ) . Your accounting officer should sign Annex A of the grant offer letter and return it to<EMAIL_ADDRESS>. This document must be received and accepted by NCTL before any funding is released.
If you are a new ITT provider or lead school for School Direct in academic year 2014 to 15 and have not received training bursary or School Direct (salaried) grant funding before, you will need to complete a grant funding agreement. This is an agreement between you, as ITT provider/lead school and NCTL, and it stipulates how you should treat the grant funding.
Grant funding agreement for training bursaries ( MS Word Document , 89.7KB )
Grant funding agreement for School Direct (Salaried) ( MS Word Document , 74KB )
If you have places to fill in maths, physics, computing, chemistry, languages, design and technology, biology or geography you can request SKE funding to support your recruitment.
SKE courses can be offered to potential trainees to bring their subject knowledge up to the level required to begin training to teach their chosen subject. Courses can vary from an 8-week refresher course up to a 36-week programme, depending on the individual needs of the applicant.
NCTL provides School Direct lead schools and ITT providers with funding for SKE courses up to the following maximum rates:
| Weeks of full-time (or equivalent) study | Maximum rate |
| 8–11 | £1,910 |
| 12–15 | £2,810 |
| 16–19 | £3,705 |
| 20–23 | £4,605 |
| 24–27 | £5,500 |
| 28–31 | £6,115 |
| 32–35 | £6,725 |
| 36 | £7,340 |
For more information see our guide: Subject knowledge enhancement: an introduction .
For more information about the pre-ITT SKE programmes and SKE training bursaries read the: Subject knowledge enhancement operations (SKE) manual 2015 to 2016 .
To claim funding, please use the subject knowledge enhancement: funding request form .
To ensure payments are made promptly, the deadline for receipt of the SKE funding request form is the third Friday of the month before the programme start date. We aim to issue:
Any claim forms received after the third Friday of the month before the programme start date will be processed in the following monthly payment run.
All recipients of SKE funding must complete all highlighted sections in the Grant offer letter for SKE ( MS Word Document , 89.8KB ) . Your accounting officer should sign Annex A of the grant offer letter and return it with the SKE request form to the ITT funding team at<EMAIL_ADDRESS>. This document must be received and accepted by NCTL before any funding is released. This action only needs to be taken once for 2013 to 2014, upon submission of the first SKE request form.
If you would like to add your SKE courses to our SKE course directory , please download the SKE directory form .
Providers are allocated funds which include the postgraduate ITT training bursaries and SKE financial support referenced above. You are responsible for passing on bursary funding to trainees.
More information and guidance on the bursaries can be found in the training bursary guide for 2014 to 2015 .
You can use the ITT funding extranet to view your current training allocations, payment profile and a detailed breakdown of the funding you receive.
ITT providers who receive NCTL funding must submit independently audited accounts each year.
The financial statements must confirm that funds provided by NCTL were used only in accordance with the provision of the Education Act 1994 (as amended by the Education Act 2005 Act), the financial memorandum and all other terms and conditions that NCTL has set. All ITT providers are ultimately responsible to NCTL for the proper stewardship of the funds paid to them.
Specific requirements set out in the financial memorandum are that providers shall:
This scheme is now closed to all trainees starting their ITT in, or after, the academic year 2011 to 2012. Trainees who started their ITT course before 1 August 2011 can still receive the incentive. Local authorities and academies with eligible trainees can claim reimbursement for the Golden Hello Scheme .
Email<EMAIL_ADDRESS>Funding and audit grant return queries except early years
Email<EMAIL_ADDRESS>Published: 16 April 2014
Updated: 3 July 2014


Main
pc_main <- pc + scale_color_pwd("main")
pf_main <- pf + scale_fill_pwd("main")
pc_main + pf_main
The goal of pwdthemes
is to provide easy to use theme options for employees of the Philadelphia Water Department within R and Rmarkdown. This package provides color and fill options for ggplot2
figures. It also provides rmarkdown templates that will allow people to easy produce Rmarkdown reports with PWD branding on them.
If you are within the PWD network, you can install the released version of pwdthemes from PWD’s Internal Package Directory with:
install.packages("pwdthemes", repos = "http://pwdrstudio/bls-cran/")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("tbradley1013/pwdthemes")
NOTE: you will have to have RTools installed in order to install this package using either install.packages
or install_github
methods
ggplot2
themeThis package provides a custom ggplot2
theme with the function theme_pwd
. This function applies font and style options that are based on the PWD styleguide. There were not any definite figure style recommendations, other than the title font, so I made some decisions about style. Please feel free to suggest any changes that you think would look good.
library(pwdthemes)
library(ggplot2)
# set geoms to pwd colors
pwd_geoms()
library(dplyr)
library(ggridges)
library(patchwork)
bw <- ggplot(mtcars, aes(mpg, hp)) +
geom_point() +
labs(
title = "theme_bw",
subtitle = "mtcars dataset with Calibri font",
caption = "A really important caption"
) +
theme_bw(base_family = "Calibri")
pwd <- ggplot(mtcars, aes(mpg, hp)) +
geom_point() +
labs(
title = "theme_pwd",
subtitle = "mtcars dataset with Calibri font",
caption = "A really important caption"
) +
theme_pwd()
bw + pwd
This package provides two different fonts for use with theme_pwd
. By default, the Calibri font will be used if it is available on your computer. If it is not available, then the ggplot2
font will be used. Two other packages, Open Sans and Public Sans, are shipped with this package. These packages can be installed with the import_open_sans
and import_public_sans
functions, respectively. It is worth noting, that these likely will need to be installed on your computer, and the directory of the fonts to be installed will be displayed when you use these functions. You can then specify either of these two fonts as the base_family
option in theme_pwd
.
p <- ggplot(mtcars, aes(mpg, hp)) +
geom_point()
cal <- p +
labs(
title = "Calibri",
subtitle = "mtcars dataset with Calibri font",
caption = "A really important caption"
) +
theme_pwd()
os <- p +
labs(
title = "Open Sans",
subtitle = "mtcars dataset with Open Sans font",
caption = "A really important caption"
) +
theme_pwd(base_family = "Open Sans")
ps <- p +
labs(
title = "Public Sans",
subtitle = "mtcars dataset with Public Sans font",
caption = "A really important caption"
) +
theme_pwd(base_family = "Public Sans")
cal + os + ps + plot_layout(ncol = 1)
When pwdthemes
is loaded, the default ggplot2
color and scale values are updated to reflect PWD color palettes. If you do not want this, you can easily undo it by calling undo_pwd_geoms()
. To reapply it after undoing it, call pwd_geoms()
.
ggplot2
scalesThis package provides functions that allow you to easily add PWD palettes to ggplot2
figures. These can be added using the scale_color_pwd
and scale_fill_pwd
functions depending on whether you are using the color or fill aesthetic. The palettes provided with these functions are based on the PWD Styleguide. The available palettes are shown in the table below.
| Palette | Default n | Colors |
|---|---|---|
| main | 7 | Blue (#0078C8), Persian Green (#00B388), Cerulean (#00B5E2), Resolution Blue (#001A70), Pear (#DBE442), Ziggurat (#B8DDE1), Botticelli (#C6DAE7) |
| main_dark | 4 | Blue (#0078C8), Persian Green (#00B388), Cerulean (#00B5E2), Resolution Blue (#001A70) |
| main_light | 4 | Blue (#0078C8), Pear (#DBE442), Ziggurat (#B8DDE1), Botticelli (#C6DAE7) |
| highlights | 6 | Tango (#F37720), Burnt Sienna (#F26C51), Blue Marguerite (#7474C1), Grandis (#FDD086), Zinnwaldite (#ECC382), Gray Suit (#C6C4D2) |
| highlights_dark | 3 | Tango (#F37720), Burnt Sienna (#F26C51), Blue Marguerite (#7474C1) |
| highlights_light | 3 | Grandis (#FDD086), Zinnwaldite (#ECC382), Gray Suit (#C6C4D2) |
| map | 4 | Muddy Waters (#B9975B), Trendy Green (#76881D), Primrose (#F1EB9C), Pine Glade (#BFCC80) |
| map_dark | 2 | Muddy Waters (#B9975B), Trendy Green (#76881D) |
| map_light | 2 | Primrose (#F1EB9C), Pine Glade (#BFCC80) |
| dark | 7 | Blue (#0078C8), Persian Green (#00B388), Cerulean (#00B5E2), Resolution Blue (#001A70), Tango (#F37720), Burnt Sienna (#F26C51), Blue Marguerite (#7474C1) |
| light | 7 | Blue (#0078C8), Pear (#DBE442), Ziggurat (#B8DDE1), Botticelli (#C6DAE7), Grandis (#FDD086), Zinnwaldite (#ECC382), Gray Suit (#C6C4D2) |
| full | 13 | Blue (#0078C8), Persian Green (#00B388), Cerulean (#00B5E2), Resolution Blue (#001A70), Pear (#DBE442), Ziggurat (#B8DDE1), Botticelli (#C6DAE7), Tango (#F37720), Burnt Sienna (#F26C51), Blue Marguerite (#7474C1), Grandis (#FDD086), Zinnwaldite (#ECC382), Gray Suit (#C6C4D2) |
A few of them will be demonstrated here:
large_diamonds <- diamonds %>%
filter(carat > 2.2)
pc <- ggplot(large_diamonds, aes(price, carat, color = cut)) +
geom_point() +
theme_pwd()
pf <- ggplot(large_diamonds, aes(price, cut, fill = cut)) +
geom_density_ridges() +
theme_pwd()
pc_main <- pc + scale_color_pwd("main")
pf_main <- pf + scale_fill_pwd("main")
pc_main + pf_main
pc_main_d <- pc + scale_color_pwd("main_dark")
pf_main_d <- pf + scale_fill_pwd("main_dark")
pc_main_d + pf_main_d
pc_dark <- pc + scale_color_pwd("dark")
pf_dark <- pf + scale_fill_pwd("dark")
pc_dark + pf_dark
You can view what the palette you choose will look like for any given number of variables using the view_pwd_palette
function. You simply have to pass the palette you are interested as a character string and the number of levels of color required. If you do not pass a number to the n argument, it will show you the default number of colors for a given palette
view_pwd_palette("dark")
view_pwd_palette("dark", 4)
This package provides several Rmarkdown template. These templates will allow for users to easily create branded PWD documents to share easily. This will hopefully reduce the amount of time and headache that is spent trying to create LaTeX styles for reports that may be preventing people from embracing Rmarkdown for writing their reports. Please note that these templates are not approved by public affairs, so please consult your supervisor before using these templates to share reports outside of the department. The templates that are included with this package are:


SoftUni Halloween Party
On the 31st of October in SoftUni a Halloween party will take place. Come and join us! Wear a costume! Invite friends!
Created by Alex
On the 31st of October in SoftUni a Halloween party will take place. Come and join us! Wear a costume! Invite friends!
Created by Alex
A couple of weeks ago there was a great event: The annual ("annual" meaning that it takes place every four months :) ) SoftUni Conf in Borovetz.
Created by Petya
Do you like mountins? Do you like hiking? Well... this event maybe wasn't for you that much... Because we spent most of the time in the hut... drinking...
Created by Kiko
"There I was on a July Morning... Looking for love..." So, when you went on the SoftUni July Morning Party what were you looking for? Did you find it?
Created by Yana


Preparation time
1-2 hours
Cooking time
1 to 2 hours
Serves
Makes 12 buns
Dietary
Paul Hollywood's sticky iced fingers make wonderfully wicked tea time treats. You could make the buns in advance then ice and fill them just before serving.
Preheat the oven to 220C/425F/Gas 7.
To make the dough, place all the ingredients into a large bowl, holding back a quarter of the water. Stir the mixture with your hands, then slowly add the remaining water to form a dough and knead in the bowl for four minutes.
Turn the dough onto a lightly floured surface and knead well for 10 minutes or until the dough is smooth and elastic. Return the dough to the bowl, cover with a damp tea towel and leave to rise in a warm place for one hour.
Divide the dough into 12 pieces, each about 70g/2½oz, then roll into balls and shape into fingers about 13cm/5in long.
Place the dough fingers onto a greased baking tray, leaving space for them to double in size, then set aside in a warm place for 40 minutes. They should just touch each other when they’ve risen. Bake in the oven for 10 minutes then set them aside to cool.
For the icing, sift the icing sugar in a wide bowl and gradually stir in the cold water to form a thick paste.
Dip the top of the cooled fingers into the icing, smoothing it with your finger, then leave to set on a wire rack.
Lightly whip the cream and spoon it into a piping bag fitted with a small nozzle. Spoon the strawberry jam into another piping bag.
Sliced the iced fingers horizontally, leaving one long edge intact. Pipe in a generous line of whipped cream into the middle of each finger, then a thinner line of jam. Dust the iced fingers with icing sugar and serve.


Buttons
<mat-button>
Material buttons trigger an ink reaction on press. They may display text, imagery, or both. Flat buttons and
raised buttons are the most commonly used types.
Flat buttons:
Flat buttons are text-only buttons.
They may be used in dialogs, toolbars, or inline.
They do not lift, but fill with color on press.
Raised buttons:
Raised buttons are rectangular-shaped buttons.
They may be used inline.
They lift and display ink reactions on press.


Name:
Option:
Please Select ...
One
Two
Three
Password:
Confirm Password
Open
Close
I Agree
Start Time


4.6 Visible Hostname
Hopefully, you won't need to worry
about the
visible_hostname directive. However,
you'll need to set it if Squid
can't figure out the hostname of the machine on
which it is running. When this happens, Squid complains and refuses
to run:
% squid -Nd1
FATAL: Could not determine fully qualified hostname. Please set 'visible_hostname'
Squid wants to be sure about its hostname for a number of reasons:
The hostname appears in Squid's error messages. This
helps users identify the source of potential problems. The hostname appears in the HTTP Via header of
cache misses that Squid forwards. When the request arrives at the
origin server, the Via header contains a list of
all proxies involved in the transaction. Squid also uses the
Via header to detect forwarding loops.
I'll talk about forwarding loops in Chapter 10. Squid uses internal URLs for certain things, such as the icons for
FTP directory listings. When Squid generates an HTML page for an FTP
directory, it inserts embedded images for little icons that indicate
the type of each file in the directory. The icon URLs contain the
cache's hostname so that web browsers request them
directly from Squid. Each HTTP reply from Squid includes an X-Cache
header. This isn't an official HTTP header. Rather,
it is an extension header that indicates if the response was a cache
hit or a cache miss. Since requests and responses may flow through
more than one cache, each X-Cache header includes
the name of the cache reporting hit or miss. Here's
a sample response that passed through two caches: HTTP/1.0 200 OK
Date: Mon, 29 Sep 2003 22:57:23 GMT
Content-type: text/html
Content-length: 733
X-Cache: HIT from bo2.us.ircache.net
X-Cache: MISS from bo1.us.ircache.net Squid tries to figure out the hostname automatically at startup.
First it calls the gethostname( ) function,
which usually returns the correct hostname. Next, Squid attempts a
DNS lookup on the hostname with gethostbyname(
). This function typically returns both IP addresses and
the canonical name for the system. If
gethostbyname( ) succeeds, Squid uses the
canonical name in error messages, Via headers,
etc. Squid may be unable to determine its fully qualified hostname for a
number of reasons, including: The hostname may not be set. The hostname may be missing from the DNS zone or
/etc/hosts files. The Squid system's DNS client configuration may be
incorrect or missing. On Unix, you should check the
/etc/resolv.conf and
/etc/host.conf files.
If you see the fatal message mentioned previously, you need either to
fix the hostname and DNS information or explicitly configure the
hostname for Squid. In most cases, it is sufficient to ensure the
hostname command returns a fully qualified
hostname and add an entry to /etc/hosts. If that
doesn't work, just set the visible hostname in
squid.conf:
visible_hostname squid.packet-pushers.net
|


|
SphinxBase
5prealpha
|
Generic linked-lists maintenance. More...
Go to the source code of this file.
Data Structures | |
| struct | gnode_s |
| A node in a generic list. More... | |
Macros | |
| #define | gnode_ptr(g) ((g)->data.ptr) |
| Head of a list of gnodes. More... | |
| #define | gnode_int32(g) ((g)->data.i) |
| #define | gnode_uint32(g) ((g)->data.ui) |
| #define | gnode_float32(g) ((float32)(g)->data.fl) |
| #define | gnode_float64(g) ((g)->data.fl) |
| #define | gnode_next(g) ((g)->next) |
Typedefs | |
| typedef struct gnode_s | gnode_t |
| A node in a generic list. | |
| typedef gnode_t * | glist_t |
Functions | |
| SPHINXBASE_EXPORT glist_t | glist_add_ptr (glist_t g, void *ptr) |
| Create and prepend a new list node, with the given user-defined data, at the HEAD of the given generic list. More... | |
| SPHINXBASE_EXPORT glist_t | glist_add_int32 (glist_t g, int32 val) |
| Create and prepend a new list node containing an integer. More... | |
| SPHINXBASE_EXPORT glist_t | glist_add_uint32 (glist_t g, uint32 val) |
| Create and prepend a new list node containing an unsigned integer. More... | |
| SPHINXBASE_EXPORT glist_t | glist_add_float32 (glist_t g, float32 val) |
| Create and prepend a new list node containing a single-precision float. More... | |
| SPHINXBASE_EXPORT glist_t | glist_add_float64 (glist_t g, float64 val) |
| Create and prepend a new list node containing a double-precision float. More... | |
| SPHINXBASE_EXPORT gnode_t * | glist_insert_ptr (gnode_t *gn, void *ptr) |
| Create and insert a new list node, with the given user-defined data, after the given generic node gn. More... | |
| SPHINXBASE_EXPORT gnode_t * | glist_insert_int32 (gnode_t *gn, int32 val) |
| Create and insert a new list node containing an integer. More... | |
| SPHINXBASE_EXPORT gnode_t * | glist_insert_uint32 (gnode_t *gn, uint32 val) |
| Create and insert a new list node containing an unsigned integer. More... | |
| SPHINXBASE_EXPORT gnode_t * | glist_insert_float32 (gnode_t *gn, float32 val) |
| Create and insert a new list node containing a single-precision float. More... | |
| SPHINXBASE_EXPORT gnode_t * | glist_insert_float64 (gnode_t *gn, float64 val) |
| Create and insert a new list node containing a double-precision float. More... | |
| SPHINXBASE_EXPORT glist_t | glist_reverse (glist_t g) |
| Reverse the order of the given glist. More... | |
| SPHINXBASE_EXPORT int32 | glist_count (glist_t g) |
| Count the number of element in a given link list. More... | |
| SPHINXBASE_EXPORT void | glist_free (glist_t g) |
| Free the given generic list; user-defined data contained within is not automatically freed. More... | |
| SPHINXBASE_EXPORT gnode_t * | gnode_free (gnode_t *gn, gnode_t *pred) |
| Free the given node, gn, of a glist, pred being its predecessor in the list. More... | |
| SPHINXBASE_EXPORT gnode_t * | glist_tail (glist_t g) |
| Return the last node in the given list. | |
Generic linked-lists maintenance.
Only insert at the head of the list. A convenient little linked-list package, but a double-edged sword: the user must keep track of the data type within the linked list elements. When it was first written, there was no selective deletions except to destroy the entire list. This is modified in later version.
(C++ would be good for this, but that's a double-edged sword as well.)
Definition in file glist.h.
| #define gnode_ptr | ( | g | ) | ((g)->data.ptr) |
Head of a list of gnodes.
Access macros, for convenience
Definition at line 109 of file glist.h.
Referenced by cmd_ln_free_r(), jsgf_grammar_free(), listelem_alloc_free(), listelem_get_item(), listelem_stats(), ngram_model_read_classdef(), ngram_model_set_read(), and parse_subvecs().
Create and prepend a new list node containing a single-precision float.
| g | a link list |
| val | a float32 vlaue |
Definition at line 110 of file glist.c.
References ckd_calloc, and gnode_s::next.
Referenced by ngram_model_add_class().
Create and prepend a new list node containing a double-precision float.
| g | a link list |
| val | a float64 vlaue |
Definition at line 122 of file glist.c.
References ckd_calloc, and gnode_s::next.
Create and prepend a new list node containing an integer.
| g | a link list |
| val | an integer value |
Definition at line 86 of file glist.c.
References ckd_calloc, and gnode_s::next.
Referenced by parse_subvecs().
Create and prepend a new list node, with the given user-defined data, at the HEAD of the given generic list.
Return the new list thus formed. g may be NULL to indicate an initially empty list.
| g | a link list |
| ptr | a pointer |
Definition at line 74 of file glist.c.
References ckd_calloc, and gnode_s::next.
Referenced by hash_table_tolist(), ngram_model_set_read(), and parse_subvecs().
Create and prepend a new list node containing an unsigned integer.
| g | a link list |
| val | an unsigned integer value |
Definition at line 98 of file glist.c.
References ckd_calloc, and gnode_s::next.
| SPHINXBASE_EXPORT int32 glist_count | ( | glist_t | g | ) |
Count the number of element in a given link list.
| g | input link list |
Definition at line 145 of file glist.c.
References gnode_s::next.
Referenced by ngram_model_set_read(), and parse_subvecs().
| SPHINXBASE_EXPORT void glist_free | ( | glist_t | g | ) |
Free the given generic list; user-defined data contained within is not automatically freed.
The caller must have done that already.
Definition at line 133 of file glist.c.
References ckd_free(), and gnode_s::next.
Referenced by cmd_ln_free_r(), jsgf_grammar_free(), listelem_alloc_free(), ngram_model_add_class(), ngram_model_read_classdef(), ngram_model_set_read(), and parse_subvecs().
Create and insert a new list node containing a single-precision float.
| gn | a generic node which a value will be inserted after it |
| val | float32 inserted |
Definition at line 230 of file glist.c.
References ckd_calloc, and gnode_s::next.
Create and insert a new list node containing a double-precision float.
| gn | a generic node which a value will be inserted after it |
| val | float64 inserted |
Definition at line 244 of file glist.c.
References ckd_calloc, and gnode_s::next.
Create and insert a new list node containing an integer.
| gn | a generic node which a value will be inserted after it |
| val | int32 inserted |
Definition at line 201 of file glist.c.
References ckd_calloc, and gnode_s::next.
Create and insert a new list node, with the given user-defined data, after the given generic node gn.
gn cannot be NULL. Return ptr to the newly created gnode_t.
| gn | a generic node which ptr will be inserted after it |
| ptr | pointer inserted |
Definition at line 187 of file glist.c.
References ckd_calloc, and gnode_s::next.
Create and insert a new list node containing an unsigned integer.
| gn | a generic node which a value will be inserted after it |
| val | uint32 inserted |
Definition at line 215 of file glist.c.
References ckd_calloc, and gnode_s::next.
Reverse the order of the given glist.
(glist_add() adds to the head; one might ultimately want the reverse of that.) NOTE: The list is reversed "in place"; i.e., no new memory is allocated.
| g | input link list |
Definition at line 169 of file glist.c.
References gnode_s::next.
Referenced by ngram_model_add_class(), and ngram_model_set_read().
Free the given node, gn, of a glist, pred being its predecessor in the list.
Return ptr to the next node in the list after the freed node.
Definition at line 257 of file glist.c.
References ckd_free(), and gnode_s::next.


Next: No Arguments or Values in the ``Syntax'' Section, Previous: Requiring Non-Null Rest Parameters in the ``Syntax'' Section, Up: Interpreting Dictionary Entries
An evaluation arrow "⇒ " precedes a list of values to be returned. For example:
F
a b c ⇒ x
indicates that F is an operator that has three required parameters (i.e., a, b, and c) and that returns one value (i.e., x). If more than one value is returned by an operator, the names of the values are separated by commas, as in:
F
a b c ⇒ x, y, z


Chindōgu (珍道具) is the Japanese art of inventing ingenious everyday gadgets.
1. It has to be possible to make (i.e., it has to actually exist), in spite of its absurdity;
2. It has to remain in the public domain (i.e., it cannot be given a patent);
3. It must not be exclusively a vehicle for humor, or the warped satirical worldview of the inventor.


{{ partial "head.html" . }}
{{ partial "topbar.html" . }}IPLD is the data model of the content-addressable web. It allows us to treat all hash-linked data structures as subsets of a unified information space, unifying all data models that link data with hashes as instances of IPLD.
A data model for interoperable protocols.
Content addressing through hashes has become a widely-used means of connecting data in distributed systems, from the blockchains that run your favorite cryptocurrencies, to the commits that back your code, to the web’s content at large. Yet, whilst all of these tools rely on some common primitives, their specific underlying data structures are not interoperable.
Enter IPLD: IPLD is a single namespace for all hash-inspired protocols. Through IPLD, links can be traversed across protocols, allowing you to explore data regardless of the underlying protocol.
The sky’s the limit as IPLD allows you to work across protocol boundaries. The point is that IPLD provides libraries that make the underlying data interoperable across tools and across protocols by default.
{{ partial "use" (dict "title" "Bitcoin to Git" "desc" "Reference your latest commits in a git branch to a bitcoin transaction to timestamp your work. By linking your git commit, you can view the commit from your blockchain explorer.") }} {{ partial "use" (dict "title" "Ethereum to IPFS" "desc" "Have your next contract refer to media on IPFS, perhaps modifying it and tracking its changes on each function execution. Seamlessly jump from function to object through IPLD addressing." "swap" true) }}A self-contained descriptive model that uniquely identifies any hash-based data structure and ensures the same logical object always maps to the exact same sequence of bits.
IPLD brings isolated systems together, making integration with existing protocols simple.
With Multiformats support, IPLD is easily upgradeable and will grow with your favorite protocols.
Express your IPLD objects in various serializable formats like JSON, CBOR, YAML, XML and many more, making IPLD a cinch to use with any framework.
Non-intrusive resolvers make IPLD easy to integrate within your existing work.
IPLD allows you to explore data across protocols seamlessly, binding hash-based data structures together through a common namespace.
IPLD is an open source project from the IPFS community. There are many ways you can contribute by getting involved. Learn more about IPLD from the community.


---
layout: slides
title: "On individual intelligence"
category: hidden
type: course
semester: Fall 2020
college: Stevens Institute of Technology
permalink: /cognition/intelligence/
---
# On individual intelligence.
???
---
class: center, middle, statement, dark
< recap >
---
class: center, middle, statement, dark
The mind faces many maddeningly hard engineering problems, and we have an arsenal of mental faculties for solving each of them approximately.
---
class: center, middle, statement, dark
We have representations of space, quantity, value, the future, and the past.
---
class: center, middle, statement, dark
We also have representations of objects, time, cause and effect, and other people and their thoughts about other people, &c.
---
class: center, middle, statement, dark
Language comprehension must go beyond learning statistics of transitions between words, given that "Colorless green ideas sleep furiously" is absurd but syntactically unobjectionable.
---
class: center, middle, statement, dark
The organizational framework that linguists and cognitive psychologists use to think about language begins at phonology, and proceeds through morphology, syntax, semantics, and pragmatics.
---
class: center, middle, statement, dark
At each level of the organizational framework are phenomena characteristic of it, including the McGurk effect, cross-culutral differences phonemic repertoire, generativity (infinite use of finite means), polysemy, indeterminacy of reference, semantic ambiguity, and the four Gricean maxims of conversational cooperation.
---
class: center, middle, statement, dark
Language is also central to several of cognition's great debates, including nativism vs. empiricism, linguistic relativity (the "Sapir-Whorf hypothesis"), and prescriptivism vs. descriptivism.
---
class: center, middle, statement, dark
We need categories for all the reasons Rosch identifies — in particular, features are correlated across objects.
---
class: center, middle, statement, dark
One theory of what concepts are, the definitional theory, with its intensions and extensions, is lacking because not all categories can be precisely defined, and because some category members are better than others.
---
class: center, middle, statement, dark
Another theory of what concepts are, the prototype theory, suggests that concepts are defined by a prototype and a family resemblance structure among the members.
---
class: center, middle, statement, dark
Categories are organized hierarchically, with basic-level categories, subordinate-level categories, and superordinate-level categories.
---
class: center, middle, statement, dark
Basic-level categories are special, and Rosch delineates their unique properties.
---
class: center, middle, statement, dark
The primacy of basic-level categories may resolve Quine's indeterminacy of reference ("gavagai").
---
class: center, middle, statement, dark
And the shape bias, where the learner assumes that category labels refer to the shape of an object over its surface properties, provides another constraint that helps us acquire new concepts.
---
class: center, middle, statement, dark
Analogies are not just for standardized exams.
---
class: center, middle, statement, dark
Consider the myriad examples of everyday analogical reasoning described in Hofstadter's chapters, including "sour grapes", "exactly the same thing", and idioms from other languages.
---
class: center, middle, statement, dark
One way to think of analogies is to first "embed" concepts in a metric space, using a technique like multi-dimensional scaling (MDS) or word2vec.
---
class: center, middle, statement, dark
Then the vector (direction and distance) from one concept to another describes how those concepts relate, so if one pair of concepts has the same vector as another, they are analogous.
---
class: center, middle, statement, dark
recap >
---
# Administrivia
- Attendance sheet
- Paper checklist coming soon
---
# What is intelligence?
An exercise.
---
# Some possibilities, I.
- the ability to acquire and use knowledge;
- the ability to reason;
- the ability to solve problems;
- the ability to plan;
- the ability to achieve goals;
- the ability to remember important information;
- the ability to adapt to new situations;
- the ability to understand complex ideas;
---
# Some possibilities, II.
- the ability to think abstractly;
- the ability to learn and apply skills;
- the ability to profit from experience;
- the ability to perceive and recognize;
- the ability to create products of value;
- the ability to attain what one seeks;
- the ability to think rationally;
- the ability to improve.
---
# A troubled history of studying intelligence.
Listen to **Bricks + Mortals**!
---
# Charles Darwin
???
Darwin's book introduced the scientific theory that populations evolve over the course of generations through a process of natural selection.
Selection.
---
class: center, middle
---
---
---
---
---
---
---
---
---
---
---
---
# Francis Galton
The most famous scientist you've never heard of.
- Darwin's cousin via Erasmus Darwin
- A "gentleman scientist"
- Application of Darwin's ideas to people
- Psychometrics
- Author of "Inquiries into Human Faculty and Its Development"
---
# Eugenics
Claims:
- There is measurable variation across people (scientific)
- Some of the variants are desirable (moral/political)
- Selective breeding CAN in principle be used to increase abundance of those variants (scientific)
- Selected breeding SHOULD be used to that end (moral/political)
???
---
# American eugenics
???
In the first half of the 20th century, approximately 60,000 people were sterilized under U.S. eugenics programs. Eugenic laws in 32 states empowered government officials in public health, social work and state institutions to render people they deemed “unfit” infertile.
California led the nation in this effort at social engineering. Under California’s eugenic law, first passed in 1909, anyone committed to a state institution could be sterilized. Between the early 1920s and the 1950s, approximately 20,000 people – one-third of the national total – were sterilized in California state institutions for the mentally ill and disabled.
---
# Nazi eugenics
"You will be interested to know that your work has played a powerful part in shaping the opinions of the group of intellectuals who are behind Hitler in this epoch-making program. Everywhere I sensed that their opinions have been tremendously stimulated by American thought... I want you, my dear friend, to carry this thought with you for the rest of your life, that you have really jolted into action a great government of 60 million people."
— C. M. Goethe, founder of the Eugenics Society of Northern California, in 1934
???
The Nazis were well aware of the American eugenics movement and drew inspiration from it.
Upon returning from Germany in 1934, where more than 5,000 people per month were being forcibly sterilized, the California eugenics leader C. M. Goethe bragged to a colleague:
people with congenital cognitive and physical disabilities (including people who were "feebleminded", epileptic, schizophrenic, manic-depressive, cerebral palsy, muscular dystrophy, deaf, blind)
---
# General, crystalized, and fluid intelligence.
- General intelligence (G, g, or "g factor")
- Factor analysis of performance, leads to...
- Fluid intelligence (G_f) vs. crystallized (Gc) intelligence.
---
# Multiple intelligences
Howard Gardner
- musical-rhythmic
- visual-spatial
- verbal-linguistic
- logical-mathematical
- bodily-kinesthetic
- interpersonal
- intrapersonal
???
The theory of multiple intelligences differentiates human intelligence into specific 'modalities', rather than seeing intelligence as dominated by a single general ability.
---
# Intelligence tests, IQ tests
- Old vs. new definition of IQ
- Wechsler Adult Intelligence Scale (WAIS)
- Stanford–Binet Intelligence Scales
- Raven’s progressive matrices
???
 The resulting fraction is multiplied by 100 to obtain the IQ score
Now it's based on a bell curve.
---
# Raven's progressive matrices example
---
# The Bell Curve
???
The Bell Curve: Intelligence and Class Structure in American Life is a 1994 book by psychologist Richard J. Herrnstein and political scientist Charles Murray, in which the authors argue that human intelligence is substantially influenced by both inherited and environmental factors and that it is a better predictor of many personal dynamics, including financial income, job performance, birth out of wedlock, and involvement in crime than are an individual's parental socioeconomic status. They also argue that those with high intelligence, the "cognitive elite", are becoming separated from those of average and below-average intelligence. The book was controversial, especially where the authors wrote about racial differences in intelligence and discussed the implications of those differences.
---
# Larry Summers
---
class: center, middle
# Fin.


Hobbies
I'm kind of happy person, laughing every time even for nothing. I like to play basket-ball, soccer and tennis.
Family
we are 4 children, and I'm the only one boy.
I'm Didier, form west africa, precisely Ivory Coast.
I was born in Ivory Coast and after my highSchool Diploma, I went in Moscow (Russia) for studies. I was a little bit scared about to go somewhere else because I was very young and I never lived alone. So it was difficult to me to adapt to it but after a few months, I found my way to live better. I received my bachelor degree after 4 years. I began a master degree but I finally join the United States. Now I'm a student of CodeCrew Codde School.
As a CodeCrew student, my first goal is to receive my certification at the end of the session. I every second convict myself that I'll do it.
I'm kind of happy person, laughing every time even for nothing. I like to play basket-ball, soccer and tennis.
we are 4 children, and I'm the only one boy.


White House calls anonymous Trump critic a 'coward'
by EndPlayWASHINGTON (AP) - The White House is objecting to a forthcoming book by an anonymous author who describes President Donald Trump as volatile, incompetent and unfit to be commander in chief.
The Washington Post acquired a copy of the book, called "A Warning," and first reported on its contents Thursday.

She says reporters should "cover the book as what it is - a work of fiction."
The author is identified only as "a senior official in the Trump administration."
According to the Post, the book describes racist and misogynist behind-the-scenes statements by Trump and says he "stumbles, slurs, gets confused, is easily irritated, and has trouble synthesizing information."


How to bend objects correctly in Blender 2.8x
by Widhi MuttaqienWidhi Muttaqien writes:
Assalamu’alaikum. Hi guys.
My name is Widhi Muttaqien from Expose Academy.
In this tutorial video, I want to show you how to use the new “bend” modifier feature that exists in Blender 2.8x which is part of the “simple deform” modifier. One of the most important things to know when using the bend feature is the correlation between the rotational axis and the working axis. Without this basic knowledge, you’ll get into trouble sooner or later when trying to bend objects in Blender.
In this tutorial, I also explain why you shouldn't use the "limits" feature and how to work around it, which basically using an empty object and the vertex group feature.
I hope this short tutorial can be helpful. Happy Blending!
Regards,
Widhi Muttaqien