RESEARCH SUBJECT INFORMATION AND CONSENT FORM
TITLE: Feasibility of REDCap Cloud in a Noninternational, Bring Your Own Device (BYOD) Pilot Study
SPONSOR: Genentech
INVESTIGATOR:
SITES:
STUDY-RELATED PHONE NUMBER(S):
Cory W. Thrasher, MPH, RN
650-878-7053
650-225-3039
This consent form may contain words that you do not understand. Please ask the study staff to explain any words or information that you do not clearly understand. You may take home an unsigned copy of this consent form to think about or discuss with family or friends before making your decision.
Introductory Information and Purpose
The aim of this study is to help Roche/Genentech evaluate the feasibility of using a REDCap Cloud custom mobile app for collecting electronic patient-reported outcomes (ePRO) in clinical trials.
In order to be eligible to participate in this study, you must:
The study staff may withdraw you from the study at any time, including inability to use device as instructed for the required duration.
Roche/Genentech management does not urge, influence, or encourage anyone who works for the company to take part in this study. Your participation in this research is completely voluntary. You may withdraw from the study at any time and for any reason. Your decision to not participate in the study, or a decision on your part to withdraw, will have no effect whatsoever on your employment status at Roche/Genentech. You may refuse to participate or withdraw from the study at any time without penalty or prejudice.
Roche/Genentech intends to own all the study data. With the information from the study, Roche/Genentech may obtain patents or other legal licenses relating to potential diagnostic or treatment methods. Genentech, its scientists and physicians, and its collaborators, commercial partners, affiliates or successors may obtain financial benefit. Genentech has no plans for you to share in this financial benefit.
Description of Procedures
When you agree to participate in this study, the following actions will be taken:
Risks and Discomforts
There are no anticipated risks or discomforts associated with this study.
Expected Benefits
The benefit of participating in this research study may be the satisfaction in helping contribute towards the understanding of health and disease as well as the development of new treatments for disease, however, this cannot be guaranteed.
Payment for Participation
You will be compensated $40.00 for your time to complete all eight (8) questionnaires. Payments are processed monthly by Payroll and will appear as a line item on your paystub.
Alternatives
Your alternative is to not be in this study.
Confidentiality
The medical information that you share with Genentech study staff through the study questionnaire and the data collected from the REDCap Cloud app are treated as confidential medical information.
Your name will be replaced with a unique numeric participant ID code to ensure your identity is kept private and the data you submit is not linked to you. The coded data will be encrypted and stored on a secure REDCap Cloud server. The risk of loss of confidentiality of your data is very low. Only study staff will have access to the key that links your name to your unique participant ID number.
Although the research in this study will be done in a manner that does not identify you, it may be necessary to link the participant code assigned to you back to your data. This process is called “re-identification.” Your data will only be “re-identified” in order to contact you if an investigator would like to collect additional data from you, if the research results need to be part of the information sent to the FDA or other regulatory agency or if there is discovery of a potential medical issue that would require you to follow up with your personal physician. “Re-identification” will be done in a manner that maintains participant confidentiality.
You will have access to copies of all the data you submit.
Compensation for Injury
There is minimal risk associated with this study. There are no plans to provide you with compensation if injury occurs.
Source of Funding
Funding for this research study will be provided by Roche/Genentech.
Voluntary Participation/Withdrawal
Your participation in this study is voluntary. You may decide not to participate or you may leave the study at any time. If you change your mind, tell a member of the study staff that you no longer want to participate. You do not need to give a reason for changing your mind.
Your decision will not result in any penalty or loss of benefits to which you are entitled.
Your participation in this study may be stopped at any time by the study staff without your consent for any reason.
Questions
Please feel free to contact Cory W. Thrasher, MPH, RN<PHONE_NUMBER> or<PHONE_NUMBER>) with any general questions, if you have any questions about your participation in this study, if you have any questions about injuries or illnesses related to this study, if you have concerns or complaints about the research, or if you have any questions about your rights as a research subject.
Do not sign this consent form unless you have had a chance to ask questions and have received satisfactory answers to all of your questions.
If you agree to be in this study, you will be provided a signed copy of this consent form and the Experimental Subject's Bill of Right.
Consent
I have read the above information, and my signature below indicates my voluntary agreement to participate in the Feasibility of REDCap Cloud in a Noninterventional, Bring Your Own Device (BYOD) Pilot Study. I have had sufficient time to ask questions, and my questions have been answered satisfactorily. By signing this consent form, I have not given up any of my legal rights.
AUTHORIZATION TO USE AND DISCLOSE HEALTH INFORMATION FOR RESEARCH PURPOSES
What health information about me may be used and shared with others?
The Samples for Science study staff will get your health information. For example:
Who may use and disclose my health information?
The Samples for Science study staff.
Your information may be given to:
If the results of this study are made public, information that identifies you will not be used.
What if I decide not to give authorization to use and give out my health information?
Then you will not be able to be in this research study.
When does this authorization expire?
This authorization will be good until December 31, 2021.
May I withdraw or revoke (cancel) this authorization?
You may withdraw or take away your authorization to use and disclose your health information at any time. You do this by sending written notice to Genentech study staff. If you withdraw your authorization, you will not be able to stay in this study.
If you withdraw your authorization, no new health information identifying you will be collected after that date. Information that has already been collected may still be used and given to others.
Is my health information protected after it has been given to others?
There is a risk that your information will be given to others without your permission.
Authorization
I have been given the information about the use and disclosure of my health information for this research study. I have the right to receive a copy of this authorization.
I authorize the use and disclosure of my health information to the parties listed in this authorization section of this consent for the purposes described above.
EXPERIMENTAL SUBJECT’S BILL OF RIGHTS
California law, under Health and Safety Code Section 24172, requires that anyone who is asked to be in a research study (medical experiment), or who is asked to agree for someone else to be in a research study, has the right to know the following:


Bangladesh: Mass evacuation as Cyclone Bulbul approaches
About 55,000 volunteers mobilised to alert people as authorities plan to evacuate 1.5 million before cyclone hits.
Authorities in Bangladesh have so far evacuated about 100,000 people from its low-lying coastal villages and islands, according to officials, as the country braces for the arrival of Cyclone Bulbul.
The Meteorological Department has asked local authorities and two ports to raise their highest alert, as the cyclone is set to unleash a storm surge as high as two metres (seven feet) in coastal districts on Saturday evening.
More:
Packing a maximum wind speed of 120km/h (75m/h), Bulbul is on course to make landfall in the southwestern Khulna region, near the Sundarbans, the world's largest mangrove forest, which straddles Bangladesh and part of eastern India and is home to the endangered Bengal tigers.
The cyclone is expected to hit the Bangladesh coast at about 8pm (14:00 GMT), Shah Kamal, the country's disaster management secretary, told AFP news agency, adding there were plans to evacuate about 1.5 million people before that.
About 55,000 volunteers have been mobilised to go door-to-door and alert people about the storm.
Authorities have suspended a nationwide school test, cancelled the holidays of officials posted in coastal districts and called off a traditional fair that draws tens of thousands of people in the Sundarbans.
Operations at the country's two major ports - Mongla and Chittagong - have been suspended, Kamal said.
Increased intensity
Bangladesh's low-lying coast, home to 30 million people, is regularly battered by powerful cyclones that leave a trail of devastation in their wake.
Hundreds of thousands of people have been killed over the last few decades in cyclones, whose frequency and intensity have increased.
Bangladesh has, however, improved its preparedness in recent years, cutting the number of casualties since Cyclone Sidr killed more than 3,000 people in 2007.
In May this year, Fani became the most powerful storm to hit the country in five years, but just over a dozen people were killed.


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Bob is a person. His name is bob. He was born in 1973. That's just a couple things we know about him. We know he has a name and a birthyear. Bob is a person, but this information could be found on his Driver ID. Suppose we wanted to model Bob in a ruby program. To do so we might consider creating a Ruby object -- that is, something has holds a state or set of states, and that can respond to a question (or message) -- in other words, we can ask things about an object, and given that we ask it the right question, it can give us an answer. Consider the object bob:
Bob is a new object that we've created. We've also created some behaviors for bob that will give him what he needs to be able to tell us his name and his age back to us. We ask bob what his name is with the command bob.age
and we ask him what his birthyear is with bob.birthyear
Bob can also tell us things about himself that he might not have immediate access to through a mere variable that spouts out a static answer. Bob, when endowed with the write methods, can dynamically produce the answer to a query. For example, let's ask bob what his age is. For bob to be able to respond to this message, we need to give him the ability to tell us what his age is based what what the year *currently* is. We might model this method with the following code:
def age
Time.now.year - self.age
end
Whenever we ask bob how old he is, he responds by first asking what year it currently is (Time is actually a class in ruby!) and then subtracting his own birthyear from the current one. To do so, bob actually asks himself how old he is (using self.age). Self is a cool command in which an object can recieve information about itself when it needs to. Just make sure that when you use self, that self knows how to respond to the mesage it is asking itself! The tongue, it twists.
Bob has friends, it turns out, and they all have ID's as well. Suppose we want to store that information, but we want to collect it in a way that doesn't force us to create a ton of redundant and repetetive methods for each individual ID. We know that Bob and his friends, Amy, Joel, Susan, and Chynna all have specific names and birthyears. We also know that they should be able to tell us how old they are, should we ask them. How can we create a model for these traits and behaviors without having to manually create 4 new objects? The answer is in classes.
A class is an object creator. We can create an object through a class once we give it the most basic information it needs (what we call the initialization states of an object). When we provide a class with information, it will generate a new object, endowed with the methods we assign the class we create. Enough talking, let's take a look at what a class would look like with the information we want.
Take a look at the @name
and @birthyear
notation. These are what we call instance variables. Rather than write methods that tell us exactly what those things are, we instead demand that such information be provided prior to creating the object. Because these states with onl represent the state of the current object we are creating, we consider these variable to represent specific *instances* of the class model, hence, instance variables. Let's create our four new friends, right now.
amy = UserID.new("amy love", 1955)
joel = UserID.new("joel childress", 1990)
susan = UserID.new("susan moo", 2003)
chynna = UserID.new("chynna cymmbale", 1937)
In four lines, we have four new objects, four new friends. Notice how all of these people will be able to tell us their age, immediately. All of them have been endowed with that method. It should be noted, however, that we cannot currently ask them for their name or birthyear -- this is because that, though these people contain such information, we have no way of actually reading those individual facts from them, or states. What we need to do is put what we cann an attribute reader for the UserID class -- this allows us to ask each object what it's specific attributes are.
In addition, what if we consider the possibility that one of these people might end up getting a name change? Changing your birthyear we know is not possible, but by all means, we shouldn't be so cruel as to neglect these citizens an opportunity to have a different name. To allow an object to change information about itself, we must be able to overwrite the objects state -- we do so by using a tool called an attribute writer.
In the following class, we show that, should we ask for a persons name, they can respond to that request, and should we desire to rename one of our friends, they will oblige.
Now we have the ability to read both states of each object, as well as redefine each object's name, should we choose to. This introduction to attribute readers and writers, as well as classes and objects, is a pretty minimal and basic overview, but hopefully it serves as a good introduction to not only why we would create objects, but why we would create a model for a specific type of object in the first place. Objects ultimately makeup the fundamental nature of the Ruby programming language, so it's best to embrace objects early, and become their friends, just as we became friends with our little Bobject today.
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LFE Programming Rules and Conventions
5 Processes, Servers and Messages
5.1 Implement a process in one module
Code for implementing a single process should be contained in one module. A process can call functions in any library routine but the code for the "top loop" of the process should be contained in a single module. The code for the top loop of a process should not be split into several modules - this would make the flow of control extremely difficult to understand. This does not mean that one should not make use of generic server libraries, these are for helping structuring the control flow.
Conversely, code for no more than one kind of process should be implemented in a single module. Modules containing code for several different processes can be extremely difficult to understand. The code for each individual process should be broken out into a separate module.
5.2 Use processes for structuring the system
Processes are the basic system structuring elements. But don't use processes and message passing when a function call can be used instead.
5.3 Registered processes
Registered processes should be registered with the same name as the module. This makes it easy to find the code for a process.
Only register processes that should live a long time.
5.4 Assign exactly one parallel process to each true concurrent activity in the system
When deciding whether to implement libraries and applications using sequential or parallel processes, look to the nature of the problem the software is solving; when clearly analyzed, its structure will be clear. This structure, the one inherint in the solution, should be used.
The main rule is as follows:
"Use one parallel process to model each truly concurrent activity in the
real world"
If there is a one-to-one mapping between the number of parallel processes and the number of truly parallel activities in the real world, the program will be easy to understand.
5.5 Each process should only have one "role"
Processes can have different roles in the system. Take, for example, the client and server model: one role is to serve, responding to requests; the other role is to make requests when connecting to the server(s).
As far as possible, a process implementation should cleanly mirror the separation that exists in the conceptual model of the roles of a given system. A process should only have one role, e.g., it can be a client or a server, and should not combine or conflate these roles.
Other roles which process might have are:
- Supervisor: watches other processes and restarts them if they fail.
- Worker: a normal work process (can have errors).
- Trusted Worker: not allowed to have errors.
5.6 Use generic functions for servers and protocol handlers wherever possible
In many circumstances it is a good idea to use generic server programs such as the generic server implemented in the standard libraries. Consistent use of a small set of generic servers will greatly simplify the total system structure.
The same is possible for most of the protocol handling software in the system.
5.7 Tag messages
All messages should be tagged. This makes the order in the receive statement less important and the implementation of new messages easier.
Don't program like this:
(defun loop (state)
(receive
...
; don't do the following
((tuple mod func args)
(apply mod func args)
(loop state))
...))
The new message (tuple 'get-status-info, from, option)
will introduce a
conflict if it is placed below the (tuple mod func args)
message.
If messages are synchronous, the return message should be tagged with a new
atom, describing the returned message. Example: if the incoming message is
tagged 'get-status-info
, the returned message could be tagged
'status_info
. One reason for choosing different tags is to make
debugging easier.
This is a good solution:
(defun loop (state)
(receive
...
((tuple 'execute mod func args) ; use a tagged message
(apply mod func args)
(loop state))
((tuple 'get-status-info from option)
(! from (tuple 'status-info (get-status-info option state)))
(loop state))
...))
5.8 Flush unknown messages
Every server should have an other
alternative in at least one receive
statement. This is to avoid filling up message queues.
Example:
(defun main-loop ()
(receive
((tuple 'msg-1 message-1-data)
...
(main-loop))
((tuple 'msg-2 message-2-data)
...
(main-loop))
(other ; flushes the message queue
(error-logger:error-msg
'"Error: Process ~w got unknown message ~w.~n"
(list (self) other))
(main-loop))))
5.9 Write tail-recursive servers
All servers must be tail-recursive, otherwise the server will consume memory until the system runs out of it.
Don't program like this:
(defun loop ()
(receive
((tuple msg-1 message-1-data)
...
(loop))
('stop
'true)
(other
(error-logger:error-msg
'"Error: Process ~w got unknown message ~w.~n"
(list (self) other))
(loop)))
(io:format '"Server going down" '())) ; don't do this!
; this is not tail-recursive
This is a correct solution:
(defun loop ()
(receive
((tuple msg-1 message-1-data)
...
(loop))
('stop
(io:format '"Server going down" '()))
(other
(error-logger:error-msg
'"Error: Process ~w got unknown message ~w.~n"
(list (self) other))
(loop))))
If you use some kind of server library, for example generic, you automatically avoid doing this mistake.
5.10 Interface functions
Use functions for interfaces whenever possible, avoid sending messages directly. Encapsulate message passing into interface functions. There are cases where you can't do this.
The message protocol is internal information and should be hidden to other modules.
Example of interface function:
(defmodule
(export
(start 0)
(stop 0)
(open-file 1)
...))
(defun open-file (file-name)
(! fileserver (tuple open-file-request filename))
(receive
((tuple open-file-response result)
result))
...)
5.11 Time-outs
Be careful when using (after ...)
in (receive ...)
statements. Make
sure that you handle the case when the message arrives later.
(See previous section "Flush unknown messages".)
5.12 Trapping exits
As few processes as possible should trap exit signals. Processes should either trap exits or they should not. It is usually very bad practice for a process to "toggle" trapping exits.


@@include("partials/head.html", { "pageTitle": "Goodkit" })
@@include("partials/modals.html") @@include("partials/navbar.html", { "classList": "navbar-dark bg-dark" })Create rich dashboards from your company's data without having to create events or setup tracking.
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. In urna lectus, mattis non accumsan in, tempor dictum neque.


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Inherited by Hierarchy [virtual]
.
LoggerRepository
is used to create and retrieve Loggers
.
The relation between loggers in a repository depends on the repository but typically loggers are arranged in a named hierarchy.
In addition to the creational methods, a LoggerRepository
can be queried for existing loggers, can act as a point of registry for events related to loggers.
Public Member Functions | |
| virtual | ~LoggerRepository () |
| virtual void | addHierarchyEventListener (const HierarchyEventListenerPtr &listener)=0 |
| Add a HierarchyEventListener event to the repository. | |
| virtual bool | isDisabled (int level) const =0 |
Is the repository disabled for a given level? The answer depends on the repository threshold and the level parameter. | |
| virtual void | setThreshold (const LevelPtr &level)=0 |
| Set the repository-wide threshold. | |
| virtual void | setThreshold (const LogString &val)=0 |
Another form of setThreshold accepting a string parameter instead of a Level . | |
| virtual void | emitNoAppenderWarning (const LoggerPtr &logger)=0 |
| virtual const LevelPtr & | getThreshold () const =0 |
| Get the repository-wide threshold. | |
| virtual LoggerPtr | getLogger (const LogString &name)=0 |
| virtual LoggerPtr | getLogger (const LogString &name, const spi::LoggerFactoryPtr &factory)=0 |
| virtual LoggerPtr | getRootLogger () const =0 |
| virtual LoggerPtr | exists (const LogString &name)=0 |
| virtual void | shutdown ()=0 |
| virtual LoggerList | getCurrentLoggers () const =0 |
| virtual void | fireAddAppenderEvent (const LoggerPtr &logger, const AppenderPtr &appender)=0 |
| virtual void | resetConfiguration ()=0 |
| virtual bool | isConfigured ()=0 |
| virtual void | setConfigured (bool configured)=0 |
|
|
|
|
|
Add a HierarchyEventListener event to the repository.
|
|
|
Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |
|
|
Get the repository-wide threshold. See setThreshold for an explanation. Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |
|
|
Is the repository disabled for a given level? The answer depends on the repository threshold and the See also setThreshold method. Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |
|
|
Another form of setThreshold accepting a string parameter instead of a
Implemented in Hierarchy. |
|
|
Set the repository-wide threshold.
All logging requests below the threshold are immediately dropped. By default, the threshold is set to Implemented in Hierarchy. |
|
|
Implemented in Hierarchy. |


new SpotifyAuth(clientIdopt, clientKeyopt, tokenopt)
Create a Spotify authentication handler.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
clientId |
string |
<optional> |
Client ID. |
clientKey |
string |
<optional> |
Client secret key. |
token |
string |
<optional> |
Access token (if already authenticated). |
- Source:
Members
clientId
Client ID.
- Source:
clientKey
Client secret key.
- Source:
token
Access token.
- Source:
Methods
clientsCredentialsFlow(clientId, clientKey, grantTypeopt) → {Promise|JSON}
Authenticate with the Clients Credentials Flow.
Note: this authentication method only works if the script is run from the command line. It does not work when run from a browser, because Spotify's authentication server rejects cross-site requests. In that case, authenticate with the Implicit Grant Flow instead.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
clientId |
string | Client ID. |
|
clientKey |
string | Client secret key. |
|
grantType |
string |
<optional> |
Grant type, default "client_credentials". |
- Source:
Returns:
An access token response.
- Type
- Promise | JSON
getToken() → {Promise|string}
Obtain a bearer access token.
- Source:
Returns:
A bearer access token, or the empty string if not available.
- Type
- Promise | string
implicitGrantFlow(clientIdopt, scopesopt, portopt)
Authenticate with the Implicit Grant Flow.
Runs a temporary HTTP server at port 9000 and opens a Spotify login page in a web browser. After the user has logged in, Spotify redirects to the HTTP server with an access token (included in the hash fragment of the URI). The token is picked up by the server and returned by this function in a Promise.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
clientId |
string |
<optional> |
Client ID. |
scopes |
string |
<optional> |
A space-separated list of scopes. |
port |
string |
<optional> |
Port number, default 9000. Must be white-listed by Spotify. |
- Source:
implicitGrantFlowURI(uri, clientIdopt, scopesopt) → {string}
Authentication URI for the Implicit Grant Flow.
Returns a URI that the calling web application can use to redirect the user to a Spotify login screen. After the user has logged in, Spotify redirects back to the web application with an access token (included in the hash fragment of the URI). That token can then be passed to this class.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
uri |
string | Redirect URI. |
|
clientId |
string |
<optional> |
Client ID. |
scopes |
string |
<optional> |
A space-separated list of scopes. |
- Source:
Returns:
An authentication URI.
- Type
- string
refreshToken() → {Promise|string}
Refresh the bearer access token.
- Source:
Returns:
A new bearer access token, or the empty string if not available.
- Type
- Promise | string


Places you've visited
Name of the place
When did you visit
Location of a place
Add a place
My places
Place name:
Time of the year:
Location:


data = [[1,2,3,4],[2,2,2,2],[3,3,3,3],[4,4,4,4]]
label = [1,0,2,3]
ignore_label = 1
SoftmaxOutput(data=data, label = label,\
multi_output=true, use_ignore=true,\
ignore_label=ignore_label)
## forward softmax output
[[ 0.0320586 0.08714432 0.23688284 0.64391428]
[ 0.25 0.25 0.25 0.25 ]
[ 0.25 0.25 0.25 0.25 ]
[ 0.25 0.25 0.25 0.25 ]]
## backward gradient output
[[ 0. 0. 0. 0. ]
[-0.75 0.25 0.25 0.25]
[ 0.25 0.25 -0.75 0.25]
[ 0.25 0.25 0.25 -0.75]]
## notice that the first row is all 0 because label[0] is 1, which is equal to ignore_label.
- The parameter `grad_scale` can be used to rescale the gradient, which is often used to
give each loss function different weights.
- This operator also supports various ways to normalize the gradient by `normalization`,
The `normalization` is applied if softmax output has different shape than the labels.
The `normalization` mode can be set to the followings:
- ``'null'``: do nothing.
- ``'batch'``: divide the gradient by the batch size.
- ``'valid'``: divide the gradient by the number of instances which are not ignored.
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\softmax_output.cc:L230
Declaration
public static Symbol SoftmaxOutput(Symbol data, Symbol label, float grad_scale = 1F, float ignore_label = -1F, bool multi_output = false, bool use_ignore = false, bool preserve_shape = false, SoftmaxoutputNormalization normalization = SoftmaxoutputNormalization.Valid, bool out_grad = false, float smooth_alpha = 0F, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | Input array. |
| Symbol | label | Ground truth label. |
| System.Single | grad_scale | Scales the gradient by a float factor. |
| System.Single | ignore_label | The instances whose |
| System.Boolean | multi_output | If set to |
| System.Boolean | use_ignore | If set to |
| System.Boolean | preserve_shape | If set to |
| SoftmaxoutputNormalization | normalization | Normalizes the gradient. |
| System.Boolean | out_grad | Multiplies gradient with output gradient element-wise. |
| System.Single | smooth_alpha | Constant for computing a label smoothed version of cross-entropyfor the backwards pass. This constant gets subtracted from theone-hot encoding of the gold label and distributed uniformly toall other labels. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Softmin(Symbol, Int32, Nullable<Double>, DType, String)
Applies the softmin function.
The resulting array contains elements in the range (0,1) and the elements along the given axis sum
up to 1.
.. math::
softmin(\mathbf{z/t})_j = \frac{e^{-z_j/t}}{\sum_{k=1}^K e^{-z_k/t}}
for :math:`j = 1, ..., K`
t is the temperature parameter in softmax function. By default, t equals 1.0
Example::
x = [[ 1. 2. 3.]
[ 3. 2. 1.]]
softmin(x,axis=0) = [[ 0.88079703, 0.5, 0.11920292],
[ 0.11920292, 0.5, 0.88079703]]
softmin(x,axis=1) = [[ 0.66524094, 0.24472848, 0.09003057],
[ 0.09003057, 0.24472848, 0.66524094]]
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\nn\softmax.cc:L153
Declaration
public static Symbol Softmin(Symbol data, int axis = -1, double? temperature = default(double? ), DType dtype = null, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input array. |
| System.Int32 | axis | The axis along which to compute softmax. |
| System.Nullable<System.Double> | temperature | Temperature parameter in softmax |
| DType | dtype | DType of the output in case this can't be inferred. Defaults to the same as input's dtype if not defined (dtype=None). |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Softsign(Symbol, String)
Computes softsign of x element-wise.
.. math::
y = x / (1 + abs(x))
The storage type of ``softsign`` output is always dense
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\elemwise_unary_op_basic.cc:L145
Declaration
public static Symbol Softsign(Symbol data, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input array. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Sort(Symbol, Nullable<Int32>, Boolean, String)
Returns a sorted copy of an input array along the given axis.
Examples::
x = [[ 1, 4],
[ 3, 1]]
// sorts along the last axis
sort(x) = [[ 1., 4.],
[ 1., 3.]]
// flattens and then sorts
sort(x) = [ 1., 1., 3., 4.]
// sorts along the first axis
sort(x, axis=0) = [[ 1., 1.],
[ 3., 4.]]
// in a descend order
sort(x, is_ascend=0) = [[ 4., 1.],
[ 3., 1.]]
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\ordering_op.cc:L127
Declaration
public static Symbol Sort(Symbol data, int? axis, bool is_ascend = true, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input array |
| System.Nullable<System.Int32> | axis | Axis along which to choose sort the input tensor. If not given, the flattened array is used. Default is -1. |
| System.Boolean | is_ascend | Whether to sort in ascending or descending order. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
SpaceToDepth(Symbol, Int32, String)
Rearranges(permutes) blocks of spatial data into depth.
Similar to ONNX SpaceToDepth operator:
https://github.com/onnx/onnx/blob/master/docs/Operators.md#SpaceToDepth
The output is a new tensor where the values from height and width dimension are
moved to the depth dimension. The reverse of this operation is ``depth_to_space``.
.. math::
\begin{gather*}
x \prime = reshape(x, [N, C, H / block\_size, block\_size, W / block\_size, block\_size]) \\
x \prime \prime = transpose(x \prime, [0, 3, 5, 1, 2, 4]) \\
y = reshape(x \prime \prime, [N, C * (block\_size ^ 2), H / block\_size, W / block\_size])
\end{gather*}
where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width]
and :math:`y` is the output tensor of layout :math:`[N, C * (block\_size ^ 2), H / block\_size, W / block\_size]`
Example::
x = [[[[0, 6, 1, 7, 2, 8],
[12, 18, 13, 19, 14, 20],
[3, 9, 4, 10, 5, 11],
[15, 21, 16, 22, 17, 23]]]]
space_to_depth(x, 2) = [[[[0, 1, 2],
[3, 4, 5]],
[[6, 7, 8],
[9, 10, 11]],
[[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23]]]]
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\matrix_op.cc:L1128
Declaration
public static Symbol SpaceToDepth(Symbol data, int block_size, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | Input ndarray |
| System.Int32 | block_size | Blocks of [block_size. block_size] are moved |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
SparseAdagradUpdate(Symbol, Symbol, Symbol, Single, Single, Single, Single, Single, String)
Declaration
public static Symbol SparseAdagradUpdate(Symbol weight, Symbol grad, Symbol history, float lr, float epsilon = 1E-07F, float wd = 0F, float rescale_grad = 1F, float clip_gradient = -1F, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | weight | |
| Symbol | grad | |
| Symbol | history | |
| System.Single | lr | |
| System.Single | epsilon | |
| System.Single | wd | |
| System.Single | rescale_grad | |
| System.Single | clip_gradient | |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol |
SparseRetain(Symbol, Symbol, String)
pick rows specified by user input index array from a row sparse matrix
and save them in the output sparse matrix.
Example::
data = [[1, 2], [3, 4], [5, 6]]
indices = [0, 1, 3]
shape = (4, 2)
rsp_in = row_sparse(data, indices)
to_retain = [0, 3]
rsp_out = retain(rsp_in, to_retain)
rsp_out.values = [[1, 2], [5, 6]]
rsp_out.indices = [0, 3]
The storage type of ``retain`` output depends on storage types of inputs
- retain(row_sparse, default) = row_sparse
- otherwise, ``retain`` is not supported
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\sparse_retain.cc:L53
Declaration
public static Symbol SparseRetain(Symbol data, Symbol indices, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input array for sparse_retain operator. |
| Symbol | indices | The index array of rows ids that will be retained. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
SpatialTransformer(Symbol, Symbol, SpatialtransformerTransformType, SpatialtransformerSamplerType, Shape, Nullable<Boolean>, String)
Applies a spatial transformer to input feature map.
Declaration
public static Symbol SpatialTransformer(Symbol data, Symbol loc, SpatialtransformerTransformType transform_type, SpatialtransformerSamplerType sampler_type, Shape target_shape = null, bool? cudnn_off = default(bool? ), string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | Input data to the SpatialTransformerOp. |
| Symbol | loc | localisation net, the output dim should be 6 when transform_type is affine. You shold initialize the weight and bias with identity tranform. |
| SpatialtransformerTransformType | transform_type | transformation type |
| SpatialtransformerSamplerType | sampler_type | sampling type |
| Shape | target_shape | output shape(h, w) of spatial transformer: (y, x) |
| System.Nullable<System.Boolean> | cudnn_off | whether to turn cudnn off |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Split(Symbol, Int32, Int32, Boolean, String)
Splits an array along a particular axis into multiple sub-arrays.
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\slice_channel.cc:L107
Declaration
public static Symbol Split(Symbol data, int num_outputs, int axis = 1, bool squeeze_axis = false, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input |
| System.Int32 | num_outputs | Number of splits. Note that this should evenly divide the length of the |
| System.Int32 | axis | Axis along which to split. |
| System.Boolean | squeeze_axis | If true, Removes the axis with length 1 from the shapes of the output arrays. Note that
setting |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
SplitV2(Symbol, Shape, Int32, Boolean, Int32, String)
Splits an array along a particular axis into multiple sub-arrays.
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\matrix_op.cc:L1214
Declaration
public static Symbol SplitV2(Symbol data, Shape indices, int axis = 1, bool squeeze_axis = false, int sections = 0, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input |
| Shape | indices | Indices of splits. The elements should denote the boundaries of at which split is performed along
the |
| System.Int32 | axis | Axis along which to split. |
| System.Boolean | squeeze_axis | If true, Removes the axis with length 1 from the shapes of the output arrays. Note that
setting |
| System.Int32 | sections | Number of sections if equally splitted. Default to 0 which means split by indices. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Sqrt(Symbol, String)
Returns element-wise square-root value of the input.
.. math::
\textrm{sqrt}(x) = \sqrt{x}
Example::
sqrt([4, 9, 16]) = [2, 3, 4]
The storage type of ``sqrt`` output depends upon the input storage type:
- sqrt(default) = default
- sqrt(row_sparse) = row_sparse
- sqrt(csr) = csr
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\elemwise_unary_op_basic.cc:L863
Declaration
public static Symbol Sqrt(Symbol data, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input array. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Square(Symbol, String)
Returns element-wise squared value of the input.
.. math::
square(x) = x^2
Example::
square([2, 3, 4]) = [4, 9, 16]
The storage type of ``square`` output depends upon the input storage type:
- square(default) = default
- square(row_sparse) = row_sparse
- square(csr) = csr
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\elemwise_unary_op_basic.cc:L840
Declaration
public static Symbol Square(Symbol data, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input array. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
SquareSum(Symbol, Shape, Boolean, Boolean, String)
Computes the square sum of array elements over a given axis
for row-sparse matrix. This is a temporary solution for fusing ops square and
sum together for row-sparse matrix to save memory for storing gradients.
It will become deprecated once the functionality of fusing operators is finished
in the future.
Example::
dns = mx.nd.array([[0, 0], [1, 2], [0, 0], [3, 4], [0, 0]])
rsp = dns.tostype('row_sparse')
sum = mx.nd._internal._square_sum(rsp, axis=1)
sum = [0, 5, 0, 25, 0]
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\square_sum.cc:L63
Declaration
public static Symbol SquareSum(Symbol data, Shape axis = null, bool keepdims = false, bool exclude = false, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input |
| Shape | axis | The axis or axes along which to perform the reduction. The default, |
| System.Boolean | keepdims | If this is set to |
| System.Boolean | exclude | Whether to perform reduction on axis that are NOT in axis instead. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Squeeze(SymbolList, Shape, String)
Remove single-dimensional entries from the shape of an array.
Same behavior of defining the output tensor shape as numpy.squeeze for the most of cases.
See the following note for exception.
Examples::
data = [[[0], [1], [2]]]
squeeze(data) = [0, 1, 2]
squeeze(data, axis=0) = [[0], [1], [2]]
squeeze(data, axis=2) = [[0, 1, 2]]
squeeze(data, axis=(0, 2)) = [0, 1, 2]
.. Note::
The output of this operator will keep at least one dimension not removed. For example,
squeeze([[[4]]]) = [4], while in numpy.squeeze, the output will become a scalar.
Declaration
public static Symbol Squeeze(SymbolList data, Shape axis = null, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| SymbolList | data | data to squeeze |
| Shape | axis | Selects a subset of the single-dimensional entries in the shape. If an axis is selected with shape entry greater than one, an error is raised. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Stack(SymbolList, Int32, Int32, String)
Join a sequence of arrays along a new axis.
The axis parameter specifies the index of the new axis in the dimensions of the
result. For example, if axis=0 it will be the first dimension and if axis=-1 it
will be the last dimension.
Examples::
x = [1, 2]
y = [3, 4]
stack(x, y) = [[1, 2],
[3, 4]]
stack(x, y, axis=1) = [[1, 3],
[2, 4]]
Declaration
public static Symbol Stack(SymbolList data, int num_args, int axis = 0, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| SymbolList | data | List of arrays to stack |
| System.Int32 | num_args | Number of inputs to be stacked. |
| System.Int32 | axis | The axis in the result array along which the input arrays are stacked. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
StopGradient(Symbol, String)
Declaration
public static Symbol StopGradient(Symbol data, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol |
Sum(Symbol, Shape, Boolean, Boolean, String)
Computes the sum of array elements over given axes.
.. Note::
`sum` and `sum_axis` are equivalent.
For ndarray of csr storage type summation along axis 0 and axis 1 is supported.
Setting keepdims or exclude to True will cause a fallback to dense operator.
Example::
data = [[[1, 2], [2, 3], [1, 3]],
[[1, 4], [4, 3], [5, 2]],
[[7, 1], [7, 2], [7, 3]]]
sum(data, axis=1)
[[ 4. 8.]
[ 10. 9.]
[ 21. 6.]]
sum(data, axis=[1,2])
[ 12. 19. 27.]
data = [[1, 2, 0],
[3, 0, 1],
[4, 1, 0]]
csr = cast_storage(data, 'csr')
sum(csr, axis=0)
[ 8. 3. 1.]
sum(csr, axis=1)
[ 3. 4. 5.]
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\broadcast_reduce_op_value.cc:L116
Declaration
public static Symbol Sum(Symbol data, Shape axis = null, bool keepdims = false, bool exclude = false, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input |
| Shape | axis | The axis or axes along which to perform the reduction. The default, |
| System.Boolean | keepdims | If this is set to |
| System.Boolean | exclude | Whether to perform reduction on axis that are NOT in axis instead. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Sum(Symbol, Int32, Boolean, Boolean, String)
Declaration
public static Symbol Sum(Symbol data, int axis, bool keepdims = false, bool exclude = false, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | |
| System.Int32 | axis | |
| System.Boolean | keepdims | |
| System.Boolean | exclude | |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol |
SVMOutput(Symbol, Symbol, Single, Single, Boolean, String)
Computes support vector machine based transformation of the input.
This tutorial demonstrates using SVM as output layer for classification instead of softmax:
https://github.com/dmlc/mxnet/tree/master/example/svm_mnist.
Declaration
public static Symbol SVMOutput(Symbol data, Symbol label, float margin = 1F, float regularization_coefficient = 1F, bool use_linear = false, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | Input data for SVM transformation. |
| Symbol | label | Class label for the input data. |
| System.Single | margin | The loss function penalizes outputs that lie outside this margin. Default margin is 1. |
| System.Single | regularization_coefficient | Regularization parameter for the SVM. This balances the tradeoff between coefficient size and error. |
| System.Boolean | use_linear | Whether to use L1-SVM objective. L2-SVM objective is used by default. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
SwapAxis(Symbol, Int32, Int32, String)
Interchanges two axes of an array.
Examples::
x = [[1, 2, 3]])
swapaxes(x, 0, 1) = [[ 1],
[ 2],
[ 3]]
x = [[[ 0, 1],
[ 2, 3]],
[[ 4, 5],
[ 6, 7]]] // (2,2,2) array
swapaxes(x, 0, 2) = [[[ 0, 4],
[ 2, 6]],
[[ 1, 5],
[ 3, 7]]]
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\swapaxis.cc:L70
Declaration
public static Symbol SwapAxis(Symbol data, int dim1 = 0, int dim2 = 0, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | Input array. |
| System.Int32 | dim1 | the first axis to be swapped. |
| System.Int32 | dim2 | the second axis to be swapped. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Take(Symbol, Symbol, Int32, TakeMode, String)
Takes elements from an input array along the given axis.
This function slices the input array along a particular axis with the provided indices.
Given data tensor of rank r >= 1, and indices tensor of rank q, gather entries of the axis
dimension of data (by default outer-most one as axis=0) indexed by indices, and concatenates them
in an output tensor of rank q + (r - 1).
Examples::
x = [4. 5. 6.]
// Trivial case, take the second element along the first axis.
take(x, [1]) = [ 5. ]
// The other trivial case, axis=-1, take the third element along the first axis
take(x, [3], axis=-1, mode='clip') = [ 6. ]
x = [[ 1., 2.],
[ 3., 4.],
[ 5., 6.]]
// In this case we will get rows 0 and 1, then 1 and 2. Along axis 0
take(x, [[0,1],[1,2]]) = [[[ 1., 2.],
[ 3., 4.]],
[[ 3., 4.],
[ 5., 6.]]]
// In this case we will get rows 0 and 1, then 1 and 2 (calculated by wrapping around).
// Along axis 1
take(x, [[0, 3], [-1, -2]], axis=1, mode='wrap') = [[[ 1. 2.]
[ 2. 1.]]
[[ 3. 4.]
[ 4. 3.]]
[[ 5. 6.]
[ 6. 5.]]]
The storage type of ``take`` output depends upon the input storage type:
- take(default, default) = default
- take(csr, default, axis=0) = csr
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\indexing_op.cc:L695
Declaration
public static Symbol Take(Symbol a, Symbol indices, int axis = 0, TakeMode mode = TakeMode.Clip, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | a | The input array. |
| Symbol | indices | The indices of the values to be extracted. |
| System.Int32 | axis | The axis of input array to be taken.For input tensor of rank r, it could be in the range of [-r, r-1] |
| TakeMode | mode | Specify how out-of-bound indices bahave. Default is "clip". "clip" means clip to the range. So, if all indices mentioned are too large, they are replaced by the index that addresses the last element along an axis. "wrap" means to wrap around. "raise" means to raise an error, not supported yet. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Tan(Symbol, String)
Computes the element-wise tangent of the input array.
The input should be in radians (:math:`2\pi` rad equals 360 degrees).
.. math::
tan([0, \pi/4, \pi/2]) = [0, 1, -inf]
The storage type of ``tan`` output depends upon the input storage type:
- tan(default) = default
- tan(row_sparse) = row_sparse
- tan(csr) = csr
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\elemwise_unary_op_trig.cc:L83
Declaration
public static Symbol Tan(Symbol data, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input array. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Tanh(Symbol, String)
Returns the hyperbolic tangent of the input array, computed element-wise.
.. math::
tanh(x) = sinh(x) / cosh(x)
The storage type of ``tanh`` output depends upon the input storage type:
- tanh(default) = default
- tanh(row_sparse) = row_sparse
- tanh(csr) = csr
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\elemwise_unary_op_trig.cc:L234
Declaration
public static Symbol Tanh(Symbol data, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input array. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Tile(Symbol, Shape, String)
Declaration
public static Symbol Tile(Symbol data, Shape reps, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | |
| Shape | reps | |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol |
Topk(Symbol, Nullable<Int32>, Int32, TopkRetTyp, Boolean, DType, String)
Declaration
public static Symbol Topk(Symbol data, int? axis, int k = 1, TopkRetTyp ret_typ = TopkRetTyp.Indices, bool is_ascend = false, DType dtype = null, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | |
| System.Nullable<System.Int32> | axis | |
| System.Int32 | k | |
| TopkRetTyp | ret_typ | |
| System.Boolean | is_ascend | |
| DType | dtype | |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol |
Transpose(Symbol, Shape, String)
Permutes the dimensions of an array.
Examples::
x = [[ 1, 2],
[ 3, 4]]
transpose(x) = [[ 1., 3.],
[ 2., 4.]]
x = [[[ 1., 2.],
[ 3., 4.]],
[[ 5., 6.],
[ 7., 8.]]]
transpose(x) = [[[ 1., 5.],
[ 3., 7.]],
[[ 2., 6.],
[ 4., 8.]]]
transpose(x, axes=(1,0,2)) = [[[ 1., 2.],
[ 5., 6.]],
[[ 3., 4.],
[ 7., 8.]]]
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\matrix_op.cc:L399
Declaration
public static Symbol Transpose(Symbol data, Shape axes = null, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | Source input |
| Shape | axes | Target axis order. By default the axes will be inverted. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Trunc(Symbol, String)
Return the element-wise truncated value of the input.
The truncated value of the scalar x is the nearest integer i which is closer to
zero than x is. In short, the fractional part of the signed number x is discarded.
Example::
trunc([-2.1, -1.9, 1.5, 1.9, 2.1]) = [-2., -1., 1., 1., 2.]
The storage type of ``trunc`` output depends upon the input storage type:
- trunc(default) = default
- trunc(row_sparse) = row_sparse
- trunc(csr) = csr
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\elemwise_unary_op_basic.cc:L802
Declaration
public static Symbol Trunc(Symbol data, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input array. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
UnravelIndex(Symbol, Shape, String)
Converts an array of flat indices into a batch of index arrays. The operator follows numpy conventions so a single multi index is given by a column of the output matrix. The leading dimension may be left unspecified by using -1 as placeholder.
Examples::
A = [22,41,37]
unravel(A, shape=(7,6)) = [[3,6,6],[4,5,1]]
unravel(A, shape=(-1,6)) = [[3,6,6],[4,5,1]]
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\ravel.cc:L67
Declaration
public static Symbol UnravelIndex(Symbol data, Shape shape = null, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | Array of flat indices |
| Shape | shape | Shape of the array into which the multi-indices apply. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
UpSampling(SymbolList, Int32, UpsamplingSampleType, Int32, Int32, UpsamplingMultiInputMode, UInt64, String)
Performs nearest neighbor/bilinear up sampling to inputs. Bilinear upsampling makes use of deconvolution. Therefore, provide 2 inputs - data and weight.
Declaration
public static Symbol UpSampling(SymbolList data, int scale, UpsamplingSampleType sample_type, int num_args, int num_filter = 0, UpsamplingMultiInputMode multi_input_mode = UpsamplingMultiInputMode.Concat, ulong workspace = 512UL, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| SymbolList | data | Array of tensors to upsample. For bilinear upsampling, there should be 2 inputs - 1 data and 1 weight. |
| System.Int32 | scale | Up sampling scale |
| UpsamplingSampleType | sample_type | upsampling method |
| System.Int32 | num_args | Number of inputs to be upsampled. For nearest neighbor upsampling, this can be 1-N; the size of output will be(scaleh_0,scalew_0) and all other inputs will be upsampled to thesame size. For bilinear upsampling this must be 2; 1 input and 1 weight. |
| System.Int32 | num_filter | Input filter. Only used by bilinear sample_type.Since bilinear upsampling uses deconvolution, num_filters is set to the number of channels. |
| UpsamplingMultiInputMode | multi_input_mode | How to handle multiple input. concat means concatenate upsampled images along the channel dimension. sum means add all images together, only available for nearest neighbor upsampling. |
| System.UInt64 | workspace | Tmp workspace for deconvolution (MB) |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Where(Symbol, Symbol, Symbol, String)
Return the elements, either from x or y, depending on the condition.
Given three ndarrays, condition, x, and y, return an ndarray with the elements from x or y,
depending on the elements from condition are true or false. x and y must have the same shape.
If condition has the same shape as x, each element in the output array is from x if the
corresponding element in the condition is true, and from y if false.
If condition does not have the same shape as x, it must be a 1D array whose size is
the same as x's first dimension size. Each row of the output array is from x's row
if the corresponding element from condition is true, and from y's row if false.
Note that all non-zero values are interpreted as ``True`` in condition.
Examples::
x = [[1, 2], [3, 4]]
y = [[5, 6], [7, 8]]
cond = [[0, 1], [-1, 0]]
where(cond, x, y) = [[5, 2], [3, 8]]
csr_cond = cast_storage(cond, 'csr')
where(csr_cond, x, y) = [[5, 2], [3, 8]]
Defined in C:\Jenkins\workspace\mxnet\mxnet\src\operator\tensor\control_flow_op.cc:L57
Declaration
public static Symbol Where(Symbol condition, Symbol x, Symbol y, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | condition | condition array |
| Symbol | x | |
| Symbol | y | |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
WhileLoop(Symbol, Symbol, SymbolList, Int32, Int32, Int32, Int32, Tuple<Double>, Tuple<Double>, Tuple<Double>, String)
Run a while loop over with user-defined condition and computation
From:C:\Jenkins\workspace\mxnet\mxnet\src\operator\control_flow.cc:1317
Declaration
public static Symbol WhileLoop(Symbol cond, Symbol func, SymbolList data, int num_args, int num_outputs, int num_out_data, int max_iterations, Tuple<double> cond_input_locs, Tuple<double> func_input_locs, Tuple<double> func_var_locs, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | cond | Input graph for the loop condition. |
| Symbol | func | Input graph for the loop body. |
| SymbolList | data | The input arrays that include data arrays and states. |
| System.Int32 | num_args | Number of input arguments, including cond and func as two symbol inputs. |
| System.Int32 | num_outputs | The number of outputs of the subgraph. |
| System.Int32 | num_out_data | The number of outputs from the function body. |
| System.Int32 | max_iterations | Maximum number of iterations. |
| Tuple<System.Double> | cond_input_locs | The locations of cond's inputs in the given inputs. |
| Tuple<System.Double> | func_input_locs | The locations of func's inputs in the given inputs. |
| Tuple<System.Double> | func_var_locs | The locations of loop_vars among func's inputs. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
Zeros(Shape, Context, DType, String)
fill target with zeros
Declaration
public static Symbol Zeros(Shape shape = null, Context ctx = null, DType dtype = null, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Shape | shape | The shape of the output |
| Context | ctx | Context of output, in format cpu|gpu|cpu_pinned.Only used for imperative calls. |
| DType | dtype | Target data type. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
ZerosLike(Symbol, String)
Return an array of zeros with the same shape, type and storage type
as the input array.
The storage type of ``zeros_like`` output depends on the storage type of the input
- zeros_like(row_sparse) = row_sparse
- zeros_like(csr) = csr
- zeros_like(default) = default
Examples::
x = [[ 1., 1., 1.],
[ 1., 1., 1.]]
zeros_like(x) = [[ 0., 0., 0.],
[ 0., 0., 0.]]
Declaration
public static Symbol ZerosLike(Symbol data, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Symbol | data | The input |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |
ZerosWithoutDtype(Shape, Context, DType, String)
fill target with zeros without default dtype
Declaration
public static Symbol ZerosWithoutDtype(Shape shape = null, Context ctx = null, DType dtype = null, string symbol_name = "")
Parameters
| Type | Name | Description |
|---|---|---|
| Shape | shape | The shape of the output |
| Context | ctx | Context of output, in format cpu|gpu|cpu_pinned.Only used for imperative calls. |
| DType | dtype | Target data type. |
| System.String | symbol_name |
Returns
| Type | Description |
|---|---|
| Symbol | returns new symbol |


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[gecode-users] Atmost constraint
Christian Schulte
cschulte at kth.se
Wed Sep 29 17:31:29 CEST 2010
Sorry, but I do not understand your question. How about an example?
Christian
--
Christian Schulte, www.ict.kth.se/~cschulte/
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of frank lemoine
Sent: Wednesday, September 29, 2010 2:37 PM
To: gecode gecode
Subject: [gecode-users] Atmost constraint
hello,
i'm searching for a CSP constraint that detect and prevents the repetition
of elements in solutions. I thought that the atmost constraint could
resolve it, but this constraint just prevent the repetition in each
solution. i'm wondering if there are in Gecode a globale constraint that
propagates the atmost effect on all solutions.
hope this was clear
Best Cheers.
Frank
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Your final score is .
Enter initials: Submit


Common QuestionsCommon Questions — Find answers to common questions in the GTK+ manual |
This is an "index" of the reference manual organized by common "How do I..." questions. If you aren't sure which documentation to read for the question you have, this list is a good place to start.
1. General | |||||||||||
|
1.1. |
How do I get started with GTK+? |
||||||||||
The GTK+ website offers some tutorials and other documentation (most of it about GTK+ 2.x, but mostly still applicable). More documentation ranging from whitepapers to online books can be found at the GNOME developer's site. After studying these materials you should be well prepared to come back to this reference manual for details. |
|||||||||||
|
1.2. |
Where can I get help with GTK+, submit a bug report, or make a feature request? |
||||||||||
See the documentation on this topic. |
|||||||||||
|
1.3. |
How do I port from one GTK+ version to another? |
||||||||||
|
See Migrating from GTK+ 2.x to GTK+ 3. You may also find useful information in the documentation for specific widgets and functions. If you have a question not covered in the manual, feel free to ask on the mailing lists and please file a bug report against the documentation. |
|||||||||||
|
1.4. |
How does memory management work in GTK+? Should I free data returned from functions? |
||||||||||
|
See the documentation for GObject and GInitiallyUnowned. For GObject note
specifically
For strings returned from functions, they will be declared "const"
if they should not be freed. Non-const strings should be
freed with |
|||||||||||
|
1.5. |
Why does my program leak memory, if I destroy a widget immediately after creating it ? |
||||||||||
|
If GtkFoo isn't a toplevel window, then
is a memory leak, because no one assumed the initial floating reference. If you are using a widget and you aren't immediately packing it into a container, then you probably want standard reference counting, not floating reference counting. To get this, you must acquire a reference to the widget and drop the floating reference (“ref and sink” in GTK+ parlance) after creating it:
When you want to get rid of the widget, you must call
When you immediately add a widget to a container, it takes care of
assuming the initial floating reference and you don't have to worry
about reference counting at all ... just call |
|||||||||||
|
1.6. |
How do I use GTK+ with threads? |
||||||||||
This is covered in the |
|||||||||||
|
1.7. |
How do I internationalize a GTK+ program? |
||||||||||
|
Most people use GNU
gettext, already required in order to install GLib. On a UNIX
or Linux system with gettext installed, type
The short checklist on how to use gettext is: call
You use Code using these macros ends up looking like this:
Libraries using gettext should use
With the convention that the macro
|
|||||||||||
|
1.8. |
How do I use non-ASCII characters in GTK+ programs ? |
||||||||||
|
GTK+ uses Unicode (more exactly UTF-8) for all text. UTF-8 encodes each Unicode codepoint as a sequence of one to six bytes and has a number of nice properties which make it a good choice for working with Unicode text in C programs:
More information about Unicode and UTF-8 can be found in the
UTF-8 and Unicode
FAQ for Unix/Linux.
GLib provides functions for converting strings between UTF-8 and other
encodings, see
Text coming from external sources (e.g. files or user input), has to be
converted to UTF-8 before being handed over to GTK+. The following example
writes the content of a IS0-8859-1 encoded text file to
For string literals in the source code, there are several alternatives for handling non-ASCII content:
Here is an example showing the three approaches using the copyright sign
© which has Unicode and ISO-8859-1 codepoint 169 and is represented
in UTF-8 by the two bytes 194, 169, or
If you are using |
|||||||||||
|
1.9. |
How do I use GTK+ with C++? |
||||||||||
|
There are two ways to approach this. The GTK+ header files use the subset of C that's also valid C++, so you can simply use the normal GTK+ API in a C++ program. Alternatively, you can use a "C++ binding" such as gtkmm which provides a native C++ API. When using GTK+ directly, keep in mind that only functions can be connected to signals, not methods. So you will need to use global functions or "static" class functions for signal connections. Another common issue when using GTK+ directly is that C++ will not implicitly convert an integer to an enumeration. This comes up when using bitfields; in C you can write the following code:
while in C++ you must write:
There are very few functions that require this cast, however. |
|||||||||||
|
1.10. |
How do I use GTK+ with other non-C languages? |
||||||||||
See the list of language bindings on https://www.gtk.org. |
|||||||||||
|
1.11. |
How do I load an image or animation from a file? |
||||||||||
|
To load an image file straight into a display widget, use
To load an image or animation file asynchronously (without blocking), use GdkPixbufLoader. |
|||||||||||
|
1.12. |
How do I draw text ? |
||||||||||
|
To draw a piece of text, use a Pango layout and
See also the Cairo Rendering section of Pango manual. |
|||||||||||
|
1.13. |
How do I measure the size of a piece of text ? |
||||||||||
|
To obtain the size of a piece of text, use a Pango layout and
See also the Layout Objects section of Pango manual. |
|||||||||||
|
1.14. |
Why are types not registered if I use their |
||||||||||
|
The A common workaround for this problem is to store the result in a volatile variable, which keeps the compiler from optimizing the call away.
|
|||||||||||
|
1.15. |
How do I create a transparent toplevel window ? |
||||||||||
|
To make a window transparent, it needs to use a visual which supports that.
This is done by getting the RGBA visual of the screen with
To fill the alpha channel on the window simply use cairos RGBA drawing capabilities.
Note that the presence of an RGBA visual is no guarantee that the
window will actually appear transparent on screen. On X11, this
requires a compositing manager to be running. See
|
|||||||||||
2. Which widget should I use... | |||||||||||
|
2.1. |
...for lists and trees? |
||||||||||
See tree widget overview — you should use the GtkTreeView widget. (A list is just a tree with no branches, so the tree widget is used for lists as well). |
|||||||||||
|
2.2. |
...for multi-line text display or editing? |
||||||||||
|
See text widget overview — you should use the GtkTextView widget.
If you only have a small amount of text, GtkLabel may also be appropriate
of course. It can be made selectable with |
|||||||||||
|
2.3. |
...to display an image or animation? |
||||||||||
GtkImage can display images in just about any format GTK+ understands. You can also use GtkDrawingArea if you need to do something more complex, such as draw text or graphics over the top of the image. |
|||||||||||
|
2.4. |
...for presenting a set of mutually-exclusive choices, where Windows would use a combo box? |
||||||||||
With GTK+, a GtkComboBox is the recommended widget to use for this use case. This widget looks like either a combo box or the current option menu, depending on the current theme. If you need an editable text entry, use the “has-entry” property. |
|||||||||||
3. GtkWidget | |||||||||||
|
3.1. |
How do I change the color of a widget? |
||||||||||
|
See To change the background color for widgets such as GtkLabel that have no background, place them in a GtkEventBox and set the background of the event box. |
|||||||||||
|
3.2. |
How do I change the font of a widget? |
||||||||||
|
This has several possible answers, depending on what exactly you want to
achieve. One option is
If you want to make the text of a label larger, you can use
This is preferred for many apps because it's a relative size to the
user's chosen font size. See You can also change the font of a widget by putting .my-widget-class { font: Sans 30; }
in a CSS file, loading it with |
|||||||||||
|
3.3. |
How do I disable/ghost/desensitize a widget? |
||||||||||
In GTK+ a disabled widget is termed "insensitive."
See |
|||||||||||
4. GtkTextView | |||||||||||
|
4.1. |
How do I get the contents of the entire text widget as a string? |
||||||||||
|
See
|
|||||||||||
|
4.2. |
How do I make a text widget display its complete contents in a specific font? |
||||||||||
|
If you use
To ensure that all text has the desired appearance, use
|
|||||||||||
|
4.3. |
How do I make a text view scroll to the end of the buffer automatically ? |
||||||||||
|
A good way to keep a text buffer scrolled to the end is to place a mark at the end of the buffer, and give it right gravity. The gravity has the effect that text inserted at the mark gets inserted before, keeping the mark at the end.
To ensure that the end of the buffer remains visible, use
The gtk-demo application contains an example of this technique. |
|||||||||||
5. GtkTreeView | |||||||||||
|
5.1. |
How do I associate some data with a row in the tree? |
||||||||||
Remember that the GtkTreeModel columns don't necessarily have to be
displayed. So you can put non-user-visible data in your model just
like any other data, and retrieve it with |
|||||||||||
|
5.2. |
How do I put an image and some text in the same column? |
||||||||||
You can pack more than one GtkCellRenderer into a single GtkTreeViewColumn
using |
|||||||||||
|
5.3. |
I can set data easily on my GtkTreeStore/GtkListStore models using
|
||||||||||
Both the GtkTreeStore and the GtkListStore implement the GtkTreeModel
interface. Consequentially, you can use any function this interface
implements. The easiest way to read a set of data back is to use
|
|||||||||||
|
5.4. |
How do I change the way that numbers are formatted by GtkTreeView? |
||||||||||
|
Use The following example demonstrates this:
|
|||||||||||
|
5.5. |
How do I hide the expander arrows in my tree view ? |
||||||||||
Set the expander-column property of the tree view to a hidden column.
See |
|||||||||||
6. Using cairo with GTK+ | |||||||||||
|
6.1. |
How do I use cairo to draw in GTK+ applications ? |
||||||||||
|
The “draw” signal gets a ready-to-use cairo context as parameter that you should use.
All drawing in GTK+ is normally done in a draw handler, and GTK+
creates a temporary pixmap for double-buffering the drawing.
It is possible to turn off double-buffering, with
|
|||||||||||
|
6.2. |
Can I improve the performance of my application by using the Glitz or GL backend of cairo ? |
||||||||||
No. The GDK X11 backend uses the cairo X backend (and the other GDK backends use their respective native cairo backends). The GTK+ developers believe that the best way to improving the GDK drawing performance is to optimize the cairo X backend and the relevant code paths in the X server that is uses (mostly the Render extension). |
|||||||||||
|
6.3. |
Can I use cairo to draw on a GdkPixbuf ? |
||||||||||
No, at least not yet. The cairo image surface does not support the pixel format used by GdkPixbuf. |
[1] If the file load fails,
gtk_image_new_from_file()
will display no image graphic — to detect
a failed load yourself, use gdk_pixbuf_new_from_file()
directly, then
gtk_image_new_from_pixbuf()
.


$D12F/53551: Get buffer and channel numbers Jump from $CD3C, $CDC9, $D137, $D3DE, $E01D, $E127, $E138, $E156: D12F: 20 93 DF JSR $DF93 ; get buffer number D132: 0A ASL D133: AA TAX D134: A4 82 LDY $82 D136: 60 RTS Previous Page / Next Page


Barca Suffer Shock Loss, Despite Messi's Heroics
by SL InternationalLaLiga
Result: Levante 3-1 FC Barcelona
Date: 02 November 2019
Venue: Ciutat de Valencia
FC Barcelona failed to strengthen their lead at the top of LaLiga following a 3-1 defeat away to Levante on Saturday.
Lionel Messi, Luis Suarez and Antoine Griezmann started for the Catalans, but Sergi Roberto and Arthur Melo came in for Jordi Alba and Sergio Busquets respectively.
Barca were off to a flying start at the midway point after Griezmann fired into the side-netting from outside the area following a through ball by Nelson Semedo.
Barca went in front six minutes later when Messi drilled home his fifth strike of the season from the penalty spot after Semedo went down in the area.
Ernesto Valverde was forced to make a substitution four minutes to halftime after Suarez requested to leave the pitch to be replaced by Carles Perez.
Barca made a tentative start to the second half as Messi, Griezmann and joined by Perez struggled to open up their hosts who defended strongly.
Lapse of concentration drew the home side level when Jose Campana smashed home from close range 61 minutes in after the Catalans failed to eradicate a loose ball in the six yard box.
Levante hit the front two minutes later when Camapana turned provider to Borja Mayoral, who eliminated the last line to finish beyond Marc Andre Ter Stegen.
Barca however failed to prosper as Nemanja Radoja put the hosts further in front four minutes later when his shot wrong-footed Ter Stegen after coming off Busquets.
Messi reduced the deficit on 75 minutes when he combined with Griezmann to find the bottom corner, but the Video Assistant Referee disallowed for offside.
No frantic finish from either side cropped up, as Barca failed to open up a four-point gap over second-place Real Madrid, who now have the chance move top tonight.


Eyewitness This: Shaquille O'Neal wants to hire you to work at his chicken restaurant in Glendale
GLENDALE, Calif. (KABC) -- If you're looking for a job, NBA legend Shaquille O'Neal wants to hire you to work at his restaurant Big Chicken in Glendale.
The restaurant is looking to fill positions for cashiers, cooks and supervisors to run the joint.
Those interested can attend a job fair through Friday, Nov. 22 at the Community Room inside the Grand Lobby at the Americana. Interested candidates should take their resume, work cards and be prepared to complete an application and a possible interview on-site.
The new restaurant will be located on South Brand Avenue and is set to open in mid-December.


iccv iccv2013 iccv2013-123 iccv2013-123-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Fatemeh Mirrashed, Mohammad Rastegari
Abstract: We propose an unsupervised domain adaptation method that exploits intrinsic compact structures of categories across different domains using binary attributes. Our method directly optimizes for classification in the target domain. The key insight is finding attributes that are discriminative across categories and predictable across domains. We achieve a performance that significantly exceeds the state-of-the-art results on standard benchmarks. In fact, in many cases, our method reaches the same-domain performance, the upper bound, in unsupervised domain adaptation scenarios.
[1] R. G. ad R. Li and R. Chellappa. Domain adaptation for object recognition: An unsupervised approach. In ICCV, 2011. 1, 2, 4, 5, 6
[2] H. Bay, T. Tuytelaars, and L. V. Gool. Surf: Speeded up robust features. In In ECCV, 2006. 5
[3] A. Bergamo and L. Torresani. Exploiting weakly-labeled web images to improve object classification: A domain adaptation approach. In NIPS, 2010. 1
[4] J. Blitzer, M. Dredze, and F. Pereira. Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification. In ACL, 2007. 1, 2, 4, 5
[5] J. Blitzer, R. McDonald, and F. Pereira. Domain adaptation with structural correspondence learning. In Conference on Empirical Methods in Natural Language Processing, 2006. 1, 2, 5, 6
[6] H. Daum e´, III and D. Marcu. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 2006. 2
[7] H. Daume III. Frustratingly easy domain adaptation. 2007. 1
[8] H. Daume III, A. Kumar, and A. Saha. Co-regularization based semisupervised domain adaptation. In NIPS, 2010. 1
[9] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88, 2010. 6
[10] R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin. LIBLINEAR: A library for large linear classification. JMLR, 2008. 6
[11] A. H. G. Griffin and P. Perona. Caltech-256 object category dataset. In Technical report, 2007. 5, 6
[12] B. Gong, Y. Shi, K. Grauman, and F. Sha. Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation. In ICML, 2013. 1, 2, 4, 5, 6
[13] B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In CVPR, 2012. 1, 2, 4, 5, 6
[14] Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In CVPR, 2011. 2
[15] H. J., A. Gretton, K. Borgwardt, and B. Scholkopf. Correcting sample selection bias by unlabeled data. In NIPS, 2007. 5, 6 The dashed part of the arrow indicates that the same hyperplane which
[16] A. Khosla, T. Zhou, T. Malisiewicz, A. Efros, and A. Torralba. Undoing the damage of dataset bias. In ECCV, 2012. 2, 4, 6, 7
[17] B. Kulis, K. Saenko, and T. Darrell. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In CVPR, 2011. 4, 5
[18] D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. IJCV, 2, 2004. 6
[19] S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2), 2011. 1, 2, 4, 5, 6
[20] M. Rastegari, A. Farhadi, and D. Forsyth. Attribute discovery via predictable discriminative binary codes. In ECCV, 2012. 1, 3, 4
[21] B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. Labelme: A database and web-based tool for image annotation. IJCV,
[22]
[23]
[24]
[25] 2007. 6 K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV, 2010. 1, 2, 4, 5, 6 A. Torralba and A. Efros. Unbiased look at dataset bias. In CVPR, 2011. 2, 4, 6 A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http : / /www .vlfe at . org/ , 2008. 6 J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010. 6 2615


Tools & Frameworks
<@title>Realtime Terrain Generation@title>
<@year>2021@year>
<@main-link>https://github.com/JustoSenka/HtmlGenerator@main-link>
<@subtitle>
Editor extension for Unity game engine for terrain generation.
@subtitle>
<@links>@links>
<@description>
Terrain can be generated in both editmode and in game time fully asynchronously using C# threads or untiy job system.
Terrain height map is based on perlin, water and mountain areas as well.
Supports generating texture and normal maps which are selected based on height and slope.
There is a dynamic resolution feature, where zooming in would regenerate parts of terrain to increase resolution and detail,
zooming out will decrease terrain resolution automatically.
No github link since the project might be put on the market.
@description>
<@lang>Unity C#@lang>
<@tags>3D, Procedural Terrain, Perlin Noise@tags>
<@collapse-id>collapseRTG@collapse-id>
<@images-text>Show Screenshots@images-text>
<@collapse-id>collapseRTG@collapse-id>
<@cols>
@cols>
<@title>Html Generator@title>
<@year>2020@year>
<@main-link>https://github.com/JustoSenka/HtmlGenerator@main-link>
<@subtitle>Reusable web components, html includes, templates for static webpages.@subtitle>
<@links>Github@links>
<@description>
Html Generator is a standalone command line tool which adds additional features to html.
It can merge, include and otherwise postprocess html files.
It is a pure html script-less solution alternative to many web component frameworks.
This website is created with it.
@description>
<@lang>C#@lang>
<@tags>Web Components, HTML Include, Reusable HTML Components@tags>
<@collapse-id>@collapse-id>
<@images-text>@images-text>
<@title>Mouse Robot@title>
<@year>2017 - 2019@year>
<@main-link>https://www.rotesting.com@main-link>
<@subtitle>GUI Automated Testing Framework.@subtitle>
<@links>Github | Official Webpage.@links>
<@description>
Mouse Robot is a desktop application for building automated UI tests.
It can also be used to create automated bots for doing various menial tasks, playing browser games,
launching builds, clicking on buttons or links. Its functionality is based on simulating mouse and keyboard input.
@description>
<@lang>C#@lang>
<@tags>Automation, Gui Testing, UI Automated Tests@tags>
<@collapse-id>collapseMR@collapse-id>
<@images-text>Show Screenshots@images-text>
<@collapse-id>collapseMR@collapse-id>
<@cols>
@cols>
<@title>8086 16-Bit Disassembler@title>
<@year>2014@year>
<@main-link>https://github.com/JustoSenka/8086-16-Bit-Disassembler@main-link>
<@subtitle>8086 16-Bit Disassembler written in Assembly.@subtitle>
<@links>Github@links>
<@description>
Disassembler for 16-Bit 8086 Intel CPU byte code while it itself was written in 16-Bit MASM/TASM assembler language.
Does not recognize all opcodes, supports MOV PUSH POP ADD INC SUB DEC CMP MUL DIV CALL RET JMP LOOP INT and most of Jumps.
@description>
<@lang>Assembly@lang>
<@tags>Intel 8086, 16-Bit, TASM, MASM@tags>
<@collapse-id>collapseASM@collapse-id>
<@images-text>Show Screenshots@images-text>
<@collapse-id>collapseASM@collapse-id>
<@cols>
@cols>
Games
<@title>RTS Game@title>
<@year>2016@year>
<@main-link>https://github.com/JustoSenka/RTS-Game@main-link>
<@subtitle>Real Time Strategy Warcraft-like Game draft in Unity Engine.@subtitle>
<@links>Github@links>
<@description>Features top-down RTS controls, unit movement with animations, pathfinding, hero skills and particle systems, enemy AI. @description>
<@lang>Unity C#@lang>
<@tags>3D, RPG Elements, Real Time Strategy, Top-Down@tags>
<@collapse-id>collapseRTS@collapse-id>
<@images-text>Show Screenshots@images-text>
<@collapse-id>collapseRTS@collapse-id>
<@cols>
@cols>
<@title>Battle City@title>
<@year>2015@year>
<@main-link>https://github.com/JustoSenka/BattleCity@main-link>
<@subtitle>Battle City (NES) game recreated in Unity Engine 5.@subtitle>
<@links>Github@links>
<@description>
A complete copy of battle city game with identical mechanics, multiplayer, powerups and tileset.
Contains 5 starting maps, which are stored in easily editable .txt files.
@description>
<@lang>Unity C#@lang>
<@tags>2D, Shooter, Multiplayer@tags>
<@collapse-id>collapseBATTLE@collapse-id>
<@images-text>Show Screenshots@images-text>
<@collapse-id>collapseBATTLE@collapse-id>
<@cols>
@cols>
<@title>Quadcopter Simulator@title>
<@year>2015@year>
<@main-link>#@main-link>
<@subtitle>Drone flying simulator on desktop which is controlled via mobile phone.@subtitle>
<@links>Quadcopter Github | Controller App Github@links>
<@description>
Main application is written with JOGL (Java OpenGL library), map is rendered from .obj 3D model file.
The drone is controlled using app on Android phone, the app itself uses accelerometer data which goes directly into TCP socket.
Drone will try to match the phone's orientation, and if it's tilted, will fly to same direction. Github needs description and screenshots
@description>
<@lang>Java@lang>
<@tags>3D, OpenGL, Android@tags>
<@collapse-id>collapseQuad@collapse-id>
<@images-text>@images-text>
<@title>Andoid Apps@title>
<@year>2014@year>
<@main-link>#@main-link>
<@subtitle>Various small math-based games mainly for children.@subtitle>
<@links>Official Webpage@links>
<@description>
Some of my Android apps managed to get onto google playstore.
Sadly, it's been awhile and they got removed since I did not update them.
Some screenshots can be still visible on the original website.
@description>
<@lang>Java@lang>
<@tags>Android, Google Playstore, AdMob@tags>
<@collapse-id>@collapse-id>
<@images-text>@images-text>
<@title>GMan RPG Game@title>
<@year>2012@year>
<@main-link>https://github.com/JustoSenka/GMan-RPG-Game@main-link>
<@subtitle>A Diablo-like 2.5D RPG game created with Borland C++ Builder 6.@subtitle>
<@links>Github@links>
<@description>
Game with standard RPG mechanics, leveling, skill and magic systems, mobs with randomized loot, inventory and shops, bosses.
Also includes Map Creator where world map as well as mob and item databases can be created using simple and intuitive drag&drop mechanics.
@description>
<@lang>C++@lang>
<EMAIL_ADDRESS>RPG Elements, Hack & Slash@tags>
<@collapse-id>collapseGMAN@collapse-id>
<@images-text>Show Screenshots@images-text>
<@collapse-id>collapseGMAN@collapse-id>
<@cols>
@cols>


Orders create lots of events you can use to track their status. These events are passed into the OnOrderEvent event handler with the relevant order.
OnOrderEvent


https://tha-imax.de/git/root/fimap
https://github.com/OsandaMalith/LFiFreak/
- last update 2015
Kadabra
- Automatic LFI exploiter and scanner.
Kadimus
- LFI scan and exploit tool.
liffy
- LFI exploitation tool.


Top Terms¶
Given a set of Etsy shops, determine each shop’s top 5 terms.
To set up environment: pip install < requirements.txt
To build documentation: cd docs; make html
./terms.py -h provides usage information with options.
The full pipeline consists of the following steps:
- Download data via Etsy API for shops and their listings.
- Extract a list of candidate phrases from each shop’s listings’ tags.
- Exploit structural information – make use of title and announcement.
- Perform topic modeling on product descriptions with NMF or LDA.
- Do a mapping of candidate phrases to topic keywords
Optionally, the –quick option can be passed to omit steps 4 and 5 and simply return the top stemmed and lemmatized stags.
Does a mapping of candidate phrases (tags) to topic keywords.
- Scans for matches of tags in order of their ranking.
- Scans topics for matches of each given tag.
- Does a serpentine scanning of topics, in order to maintain balance across topics.
- Identifies tags that match a topic word.
- Stops when N_TOP_TERMS tags have been identified.
Parameters: - shop_name (str) – Shop name.
- position (int) – Position in list of names, for positioning of progress bar.
- tags (list) – Ranked list of tags for the shop.
- topics (list) – List of the shop’s topics (which are each lists of words)
- lock (_thread.lock) – Thead lock for non-thread-safe tqdm progress bar updates.
Returns: Tags that match the shop’s topic, padded with tags if less than N_TOP_TERMS.
Return type: list
-
terms.
download_data
(shop_names)[source]¶ Downloads data via Etsy API for shops and their listings.
Uses multiprocessing to retrieve multiple shop’s data at a time.
Parameters: shop_names (list) – Shop names. Returns: Shop data indexed by shop name Return type: dict Todo
Cache downloaded data for reprocessing without redownloading.
-
terms.
exploit_structural_info
(shop_name_and_tags)[source]¶ - Re-ranks a shop’s top 100 tags based on:
- Tag contains a word present in shop announcement AND shop title.
- Tag contains a word present in shop title.
- Tag contains a word present in shop announcement.
Parameters: shop_name_and_tags (tuple) – - shop name (str) for selecting shop data from data_by_shop.
- shop tags (tuple) two lists
- stemmed and lemmatized top tags,
- tags sorted by frequency (used for secondary ranking).
Returns: List of top tags, reordered by ranking criteria. Return type: list
-
terms.
parse_CLI_args
()[source]¶ Parses CLI arguments for various options. :returns: Variables from CLI argument parsing. :rtype: argparse.Namespace
-
terms.
perform_topic_modeling
(data_by_shop, method='NMF')[source]¶ - Performs topic modeling on product descriptions using one of two methods:
- Non-negative Matrix Factorization or Latent Dirichlet Allocation.
- With default configuration:
- Extracts N_TOPICS = N_TOP_TERMS = 5
- Selects top N_TOP_WORDS = 20 keywords for each topic
Parameters: - data_by_shop (dict) – Shop data indexed by shop name
- method (str) – Topic modeling method, ‘NMF’ or ‘LDA’
Returns: Lists of shop topics (which are each lists of words), indexed by shop name
Return type: dict
Todo
Make use of mutual information across shops.
Parallelize.
Extracts a list of candidate phrases from the specified shop’s listings’ tags.
- Rank by frequency of occurance across a shop’s products
- Select top 100 tags
- Perform stemming and lemmatizing
Parameters: shop_data (list) – Shop and listings data. Returns: - The list of tuples containing:
- stemmed and lemmatized top tags,
- tags sorted by frequency (useful for later secondary ranking).
Return type: list


PUL GitHub Issues
2023-06-12
#4 Paragraph tag was used as a heading.
- Creator:
- Liz Garcia
- Created:
- 2023-06-12 20:00:27
- Status:
- Open
- Events:
-
-
assigned
by Liz Garcia at 2023-06-12 20:01:15.
-
closed
by Liz Garcia at 2023-06-12 20:01:25.
-
reopened
by Liz Garcia at 2023-06-12 20:05:14.
#5 Missing labels for links of icons
- Creator:
- Liz Garcia
- Created:
- 2023-06-12 20:03:43
- Status:
- Open
- Events:
-
-
assigned
by Liz Garcia at 2023-06-12 20:04:16.
-
closed
by Liz Garcia at 2023-06-12 20:04:21.
-
reopened
by Liz Garcia at 2023-06-12 20:05:23.
#7
- Creator:
- Liz Garcia
- Created:
- 2023-06-12 20:09:33
- Status:
- Open
- Events:
-
-
assigned
by Liz Garcia at 2023-06-12 20:09:36.
#8 Wrong heading order
- Creator:
- Liz Garcia
- Created:
- 2023-06-12 20:12:22
- Status:
- Open
- Events:
-
-
assigned
by Liz Garcia at 2023-06-12 20:13:01.
#6 Missing language of page
- Creator:
- Liz Garcia
- Created:
- 2023-06-12 20:07:29
- Status:
- Open
- Events:
-
-
assigned
by Liz Garcia at 2023-06-12 20:07:48.
#9 Missing image description to be announced with screen reader
- Creator:
- Liz Garcia
- Created:
- 2023-06-12 20:14:53
- Status:
- Open
- Events:
-
-
assigned
by Liz Garcia at 2023-06-12 20:15:49.
-
renamed
by Liz Garcia at 2023-06-12 20:18:15.
#1694 Events page with only PUL data
- Creator:
- esmé cowles
- Created:
- 2021-10-11 12:56:57
- Status:
- Closed
- Comments:
- Events:
-
-
closed
by esmé cowles at 2023-06-12 18:09:02.
#1465 Remove 2 spotlight resources/iiif harvesters in DPUL site
- Creator:
- Kim Leaman
- Created:
- 2023-06-12 16:28:14
- Status:
- Open
- Events:
-
-
labeled
by Kim Leaman at 2023-06-12 16:28:31.
-
labeled
by Kim Leaman at 2023-06-12 16:28:31.
#3976 [sftp] create local user
- Creator:
- Francis Kayiwa
- Created:
- 2023-06-11 11:49:15
- Status:
- Closed
(pull request)
- Events:
-
-
referenced
by Kevin Reiss at 2023-06-12 13:13:38.
-
merged
by Kevin Reiss at 2023-06-12 13:13:38.
-
closed
by Kevin Reiss at 2023-06-12 13:13:38.
-
head_ref_deleted
by Kevin Reiss at 2023-06-12 13:13:39.
#3977 Delete the `confluence` role
- Creator:
- Alicia Cozine
- Created:
- 2023-06-12 20:57:09
- Status:
- Open
#184 Add a bunch more stuff to new mac setup
- Creator:
- hackartisan
- Created:
- 2023-06-12 16:03:42
- Status:
- Closed
(pull request)
- Events:
-
-
head_ref_force_pushed
by None at 2023-06-12 16:10:23.
-
referenced
by Hector Correa at 2023-06-12 17:14:56.
-
merged
by Hector Correa at 2023-06-12 17:14:56.
-
closed
by Hector Correa at 2023-06-12 17:14:56.
-
head_ref_deleted
by Hector Correa at 2023-06-12 17:14:57.
#9 [CONTENT] - About
- Creator:
- beth-german
- Created:
- 2023-06-06 13:43:55
- Status:
- Open


T
Assisted by teachers
P
Not assisted by parents
Goal
Steps
Learn to spell 10 new words.
1. Find examples of 10 words that I’ve spelt incorrectly.
Due: 15-06-2016
2. Search the internet one picture that reminds me of each word.
Due: N /A
3. Each week, print out a copy of my spelling words page and write the words next to each image. Show teacher and spell them.
Due: 27-06-2016
Extra bonus after completion:
Please note that for assisted goals, any changes will send to teachers and/or parents.
What Will You Benefit From ‘PLP’?


Photobooth App
Please Capture Video To Get Started
Capture Your Moments
Capture video
Screenshot
Download
Format
PNG
JPEG
JPG
TIFF
Clear All
Click 'Screenshot' to take a picture.


fun String.stringify(): String
Returns a string representation with some characters escaped to be represented as the content of a quoted string, i.e. "...".


AppRun is a JavaScript library for building reliable, high-performance web applications using the Elm inspired Architecture, events, and components.
AppRun is an MIT-licensed open source project. Please consider supporting the project on Patreon. 👍❤🙏
Applications built with AppRun have ** fewer lines of code, **smaller js files, and better performance. See a comparison from A Real-World Comparison of Front-End Frameworks with Benchmarks (2019 update). You can also see the performance results compared to other frameworks and libraries in the js-framework-benchmark project.
Application logic is broken down into three separate parts in the AppRun architecture.
AppRun ties the three parts together and drives the applications using events.
AppRun is distributed on npm.
npm install apprun
You can also load AppRun directly from the unpkg.com CDN:

Or use it as ES module from unpkg.com:

Below is a counter application using AppRun (Online Demo).

<meta charset="utf-8">
<title>Counter</title>

Below is a counter web component using AppRun as ES module (Online Demo).

<meta charset="utf-8">
<title>Counter Web Component</title>

<wc-lit-html></wc-lit-html>

We recommend using TypeScript and JSX. TypeScript provides strong typing. JSX provides more advanced features. For examples:
import app from 'apprun';
import * as d3 from 'd3';
import * as topojson from 'topojson';
const map = ({ datum }) => { /* D3 logic */};
const state = d3.json('world-110m.json').then(world => ({
datum: topojson.feature(world, world.objects.land)
}));
const view = state => <svg id="svg" ref={() => map(state)}></svg>;
const view = state => {
const canvas = document.createElement('canvas');
const ctx = canvas.getContext('2d');
state.chart = new Chart(ctx, state.data);
return (
<Card header={
Chart JS
}>

{canvas}

</Card>
);
};
app.on('$', ({key, props}) => {
if (key === '$animation') {
const value = props[key];
if (typeof value === 'string') {
props.class = `animated ${value}`;
}
}
});
const view = () => <>

Try the AppRun Playground to see more examples.
Use the AppRun CLI to initialize a TypeScript and webpack configured project:
npx apprun --init
npm start
You can also initialize a SPA project.
npx apprun --init --spa
To initialize a project that targets ES5, use the AppRun CLI with the --es5 flag:
npx apprun --init --spa --es5
Optionally, you can directly scaffold AppRun projects from the AppRun starter templates.
npx degit apprunjs/apprun-rollup my-app
npx degit apprunjs/apprun-rollup-lit-html my-app
npx degit apprunjs/apprun-webpack my-app
npx degit apprunjs/apprun-parcel my-app
npx degit apprunjs/apprun-web-components my-app
npx degit apprunjs/apprun-bootstrap my-app
npx degit apprunjs/apprun-coreui my-app
npx degit apprunjs/apprun-pwa my-app
npx degit apprunjs/apprun-pwa-workbox my-app
npx degit yysun/apprun-d3 my-app
npx degit yysun/apprun-electron my-app
npx degit yysun/apprun-electron-forge my-app
npx degit yysun/apprun-websockets my-app
npx degit yysun/apprun-websockets-sqlite my-app
In the past, the AppRun default version on npm is 1.x. The CLI creates tsconfig for es5. You can use --es6 option to create tsconfig for 2.x.
On Feb 21, 2020, the default version on npm has been changed from 1.x to 2.x. And the CLI creates tsconfig for es2015. You can use --es5 option for 1.x.
When upgrading projects to the latest version (2.x), please modify the tsconfig from targeting es5 to es2015.
Currently, the npm tags are as following:
AppRun CLI also runs in the console.
To use the AppRun dev-tools CLI, include the dev-tools script.


{% extends "base.html" %} {% block content %} You shouldn't be able to see this page without first logging in. {{ session }}