Issue: ARGUMENT-MISMATCH-ERRORForum: Cleanup
References: ANSI CL spec (Aug 29, 1989 draft) pp.4-11,4-12,4-13,4-15,1-10
88-002R p.1-26 (appears also on p.4-20 of ANSI CL draft)
Category: CHANGE
Edit history: 1-Feb-90, Version 1 by Moon
9-May-90, Version 2 by Moon
(address another inconsistency, found by Kim Barrett)
30-Oct-90, Version 3 by Pitman
(first attempt to formalize X3J13 amendments to v2)
01-Nov-90, Version 4 by Pitman (comments by Moon, Barrett)
01-Nov-90, Version 5 by Pitman (more comments by Moon, Barrett)
24-Feb-91, Version 6 by Loosemore (propose more amendments)
14-Mar-91, Version 7 by Loosemore (change item 11)
Status: v2+amendments (reflected in v5) accepted by X3J13, June 1990
Problem description:
The draft ANSI Common Lisp specification is inconsistent about what kind
of error handling occurs when a function is called with arguments that
do not match its definition.
1. For too few arguments, p.4-11 says "there must be at least n passed
arguments," which probably (page 1-10) means "the consequences are
undefined" if there are too few arguments.
2. For too many arguments, p.4-12 says "an error of type error is
signalled."
3. For unrecognized keyword arguments, p.4-13 says "the consequences
are undefined." On the other hand, p.4-15 says "and error would be
signalled."
4. For keyword argument names that are not symbols, p.4-13 says "the
consequences are undefined."
5. For unrecognized keyword arguments supplied to generic functions,
p.4-20 and 88-002R p.1-26 say "an error is signalled."
This is Symbolics issue #5.
Proposal (ARGUMENT-MISMATCH-ERROR:MUST-SIGNAL-WHEN-SAFE-OR-SYSTEM):
Define that an error must be signalled in cases 1, 2, 3, and 5 of the
argument mismatch situations in the problem description if the caller,
the callee, and the point of functional evaluation all appear in a
context where a `safe' optimization setting is in effect (i.e.,
SAFETY=3). In all other scenarios for these situations, the
consequences are undefined.
Case 4 is treated the same, except that when :allow-other-keys t or
&allow-other-keys is involved, error detection is optional (i.e., the
consequences are undefined if the keyword argument names are not symbols).
If any of the caller, the callee, or the point of functional evaluation
was not user code, and was instead supplied by the implementation as a
pre-defined definition, or as automatically generated code (e.g., as
in method combination), then it must be treated as safe unless some
user code involved in the scenario is not safe.
Clarify that a reference to the symbolic name of the function or to the
contents of the symbol-function of a symbol does not count as a functional
evaluation. For the purposes of this definition, functional evaluation
occurs either explicitly due to a use of the FUNCTION special form, or
implicitly due to the use of a function name in the car of a normal
functional form.
The naive model which is intended is that the user can rely on error
checking in these situations if he has taken all reasonable steps to
ensure that the situations will be safe.
The exact time that the error will be signalled is implementation-dependent,
but will always be prior to the execution of the body of the function being
called.
Clarifications:
The error might be signalled at compile time or at run time. If the
error is signalled at run time, it might be signalled by either the
caller or the callee.
The reason that this terminology is used, and not the normal, "should
signal" terminology is because system code may be involved, and the
user may not know in general whether system code was compiled `safe'
or `unsafe'. An implication of this definition is that all code
compiled by the system will behave as if compiled safe unless some
user code involved in the scenario is not. So, for example, if a user
calls MAPCAR from safe code and passes a function which was compiled
safe, the system is required to ensure that MAPCAR will make a safe
call as well.
Proposal (ARGUMENT-MISMATCH-ERROR:MORE-CLARIFICATIONS):
Amend proposal MUST-SIGNAL-WHEN-SAFE-OR-SYSTEM as follows:
(1) Clarify that if the callee is a generic function, the generic function
definition (if it was defined explicitly), the method definitions for
all applicable methods, and the method-combination definition must all
be safe for the callee to be considered safe.
(2) Clarify that for a form (COERCE <lambda-expression> 'FUNCTION),
the value of the OPTIMIZE SAFETY quality in the global environment
at the time the COERCE is executed applies to the resulting function.
(3) Clarify that for a form (ENCLOSE <lambda-expression> <environment>),
the value of the OPTIMIZE SAFETY quality in the <environment>
argument applies to the resulting function.
(4) Clarify that for GENERIC-FUNCTION, GENERIC-FLET, and GENERIC-LABELS
forms, the value of the OPTIMIZE SAFETY quality in the environment
in which the form appears applies to the resulting generic functions
and method definitions.
(5) Clarify that for a form (ENSURE-GENERIC-FUNCTION . arguments), the
value of the OPTIMIZE SAFETY quality in the environment passed as
the :environment keyword applies to the resulting generic function.
(6) Clarify that for a form (COMPILE <name> <lambda-expression>), the
value of the OPTIMIZE SAFETY quality in the global environment at
the time the COMPILE is executed applies to the resulting
compiled function.
(7) Clarify that for a form (COMPILE <name>), if the original definition
of the function was at a `safe' optimization setting, then the
resulting compiled function must also be `safe'.
Rationale: although many implementations do not save the information
about the global environment for interpreted functions, this is
consistent with the idea that safety is a lexical property that is
captured at the time code promotion occurs. Implementations that do
not save the necessary information must treat interpreted functions
as being always "safe".
(8) Clarify that "automatically generated code" does not apply to the
expansions of macros defined in the standard, which inherit safety
from the environment in which the macro call appears.
Rationale: this term isn't very well defined.
(9) Replace the words "supplied by the implementation as a pre-defined
definition" with "supplied by the implementation and part of the
Common Lisp standard".
Rationale: the original wording prohibits implementations from
providing *any* functions that do no argument checking, even
internal functions that users might accidentally get their hands on
as well as officially supported extensions. There's no good
reason to prohibit this, since presumably users have to ask for
these unsafe functions explicitly.
(10) Clarify that a call to a method via CALL-NEXT-METHOD must be
considered `safe' if the generic function, applicable methods,
method-combination, and the definition and efunctuation of the
CALL-NEXT-METHOD itself are all `safe'.
(11) Specify that if CALL-NEXT-METHOD is called with arguments, the ordered
set of applicable methods for the changed set of arguments for
CALL-NEXT-METHOD must be the same as the ordered set of applicable
methods for the original arguments to the generic function, or an error
should be signaled. [This is a change -- 88-002R p2-13 says this
situation must always be detected.]
Clarify that the comparison between the set of methods applicable to the
new arguments and the set applicable to the original arguments is
insensitive to order differences among methods with the same
specializers.
(12) Clarify that if CALL-NEXT-METHOD is called with arguments that specify
a different ordered set of applicable methods and there is no next
method available, the test for different methods and the associated
error signalling (when present) takes precedence over calling
Examples:
A. Given ...
(declaim (optimize (safety 3)))
(defun foo (x) (print 'test-failed) x)
(defun bar (&key x) (print 'test-failed) x)
(defmethod baz ((a integer) &key x) (print 'test-failed) x)
(defun funcall-it (x) (funcall x))
Then every implementation must arrange for an error to be signalled
no later than the body (i.e., before (print 'test-failed) is executed)
if the following forms also occur in safe code:
A.1: (foo)
A.2: (foo 1 2)
A.3: (bar :y 1)
A.4: (bar 1 2)
A.5: (baz 1 :y 7)
A.6: (funcall #'foo)
A.7: (funcall 'foo)
A.8: (mapcar #'foo '(1 2) '(1 2))
A.9: (mapcar 'foo '(1 2) '(1 2))
A.8: (mapcar #'1+ '(1 2) '(1 2))
A.9: (mapcar '1+ '(1 2) '(1 2))
A.10: (funcall-it #'foo)
A.10: (funcall-it 'foo)
A.11: (funcall-it #'1+)
A.12: (funcall-it '1+)
A.13: (let ((x (locally (declare (optimize (safety 0))) 'foo)))
(funcall-it x))
A.14: (let ((x (locally (declare (optimize (safety 0))) '1+)))
(funcall-it x))
A.15: (let ((x (locally (declare (optimize (safety 0)))
(symbol-function 'foo))))
(funcall-it x))
A.16: (let ((x (locally (declare (optimize (safety 0)))
(symbol-function '1+))))
(funcall-it x))
B. Here are some examples of situations that might signal an error, but
are not required to signal an error. In effect, the consequences are
undefined in these cases, even if the surrounding code is declared safe:
;; Functional evaluation is not safe:
B.1: (let ((x (locally (declare (optimize (safety 2))) #'foo)))
(funcall-it x))
B.2: (let ((x (locally (declare (optimize (safety 2))) #'1+)))
(funcall-it x))
;; Point of call is not safe:
B.3: (let ((x #'foo))
(locally (declare (optimize (safety 2)))
(funcall x)))
B.4: (let ((x #'1+))
(locally (declare (optimize (safety 2)))
(funcall x)))
;; Callee is not safe:
(locally (declare (optimize (safety 2)))
(defun foo1 (x) x))
B.5: (foo1)
B.6: (mapcar #'foo1 '(1 2) '(1 2))
B.6: (funcall-it 'foo1)
Rationale:
It's important for the document to be consistent and always say the
same thing about each individual error situation. It also seems
important for the five error situations to be treated equally.
Further, it's important that programmers be able to debug their code
conveniently in a safe environment. Once they start tampering with
safety, they may run immediately into situations that expose
variations in how implementations deal with function calling, but
where safety is uniformly requested by the code, they should be able
to insulate themselves from such differences.
An exception is made in the name of efficiency for case 4 when
:ALLOW-OTHER-KEYS T or &ALLOW-OTHER-KEYS is used.
Current practice:
CLtL did not require this level of error checking, so it's entirely
likely that there are implementations which do not conform.
Symbolics Genera conforms to this proposal.
Cost to Implementors:
Some implementations already implement most or all of this, so their
cost will be minimal.
A few implementations may not implement this. The cost will vary
depending on the implementation. In some cases, it could take a fairly
substantial amount of work to make these changes.
An implementation not willing to make these changes might prefer to
identify itself as implementing only a low-safety subset of the
language, and simply refuse to compile code which was declared high
safety. This might be appropriate for certain delivery situations.
Cost to Users:
More robust code.
Cost of non-adoption:
The specification document will not even be self-consistent.
Performance impact:
None.
Benefits:
Language consistency.
Aesthetics:
This is an improvement over the inconsistent situation which preceded it.
Discussion:
The idea of making this merely "undefined" was discussed and rejected
at the June 1990 meeting. There was consensus that signalling an
error was fine, but the main sticking points were saying under what
situations the user could depend on such signalling, and at what time
the signalling might be permitted to occur. The following `amendment'
to the previous proposal was proposed and adopted, with the intent
that it be clarified later:
"should signal"
+
[ efunctuation ]
[ caller ] [ system or safe ]
[ callee ]
+
"and no later than the body of the callee"
Versions 3-5 were an attempt to clarify what was voted upon.
Version 6 is an attempt to fix some lingering problems in the
writeup from version 5, mostly related to other situations in which
function promotion can occur that were not dealt with explicitly
in version 5.


Dear all, is here someone who would like to give some instructions regarding locale.
I have been using emoncms around the year already and would like to contribute with supporting You with estonian language support to emoncms.
I actually don’t know where to start. I made myself a fork from emoncms github to https://github.com/environ/emoncms and started to change language files what i could find. so far changed admin (https://github.com/environ/emoncms/tree/9.0/Modules/admin/locale) and user https://github.com/environ/emoncms/tree/9.0/Modules/user/locale and some more locale files.
Is there some list of all the locale files I should add with et_EE versions?
I think there is need to add et_EE support to some configuration files also?
Could please someone help me to move on and to confirm that I am in right track with this github thing. I am not sure how the files are pushed to main repo.
Kristo
Re: Estonian language support to emncms. Where to start?
Take a look at https://poedit.net/
I never did any translation job so cant help much, but emoncms uses gettext functions, start looking at info for that.


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How can I try WebVR?
You need two things to experience WebVR: a VR headset and a compatible web browser.
I’ve already got a headset.
Select your headset below to find out which browsers work best for you.
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The easiest way to get started is with a basic headset like Google Cardboard. Just drop your phone in, and you’re ready to go. You can also use your phone with more advanced headsets like Samsung Gear VR and Google Daydream.
For the best performance and most features, you can use a VR headset connected to a computer, like Oculus Rift or HTC VIVE. Those will allow for higher framerates, higher resolutions, and even let you walk around in VR.
Or, on some sites, you can just use your computer or phone without a headset. You won’t be able to see in 3D or interact as fully in most VR worlds, but you can still look around in 360 degrees.


| At | u@:v _ _ _ |
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@: is equivalent to @ except that ranks are infinite.
|
x=: 1 2 3 4 y=: 7 5 3 2 x */ @: + y Applies product over sums to the entire lists 2016 x */ @ + y Applies product over sums to each item of the list 8 7 6 6 + b. 0 0 0 0 */ @: + b. 0 _ _ _ */ @ + b. 0 0 0 0


{% extends "layout.html" %} {% block main %}
{{ name | upper }}
Share this board with other players.
Spymasters: Create a keycard for this board. Pick a spymster to do this and share resulting page with other spymaster(s). Do this only once per game.
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The fourth largest city in Kerala, Thrissur or Trichur is the ‘Cultural Capital’ of God’s Own Country.
The once a capital of the Kingdom of Cochin, this metropolis is a plethora of history, rustic cultures, devout beliefs, and colourful traditions.
Known to be one of the most pious tourist destinations in Kerala, this scenic city is dotted with pious temples, and is a home to some of the most gala festivals in the entire state.
You can witness an in Thrissur array of indigenous cultures, traditions, and mystical beliefs during this celebration. Adding more to the charm and frolic of Thrissur Pooram, there is a kaleidoscopic procession of decorated elephants that is attended by a large number of devotees, national and international tourists!


lenstronomy.PointSource.Types package¶
Submodules¶
lenstronomy.PointSource.Types.base_ps module¶
-
class
lenstronomy.PointSource.Types.base_ps.
PSBase
(lens_model=None, fixed_magnification=False, additional_image=False)[source]¶ Bases:
object
base point source type class
-
image_amplitude
(kwargs_ps, *args, **kwargs)[source]¶ amplitudes as observed on the sky
Parameters: - kwargs_ps – keyword argument of point source model
- kwargs – keyword arguments of function call
Returns: numpy array of amplitudes
-
image_position
(kwargs_ps, **kwargs)[source]¶ on-sky position
Parameters: kwargs_ps – keyword argument of point source model Returns: numpy array of x, y image positions
-
source_amplitude
(kwargs_ps, **kwargs)[source]¶ intrinsic source amplitudes (without lensing magnification, but still apparent)
Parameters: - kwargs_ps – keyword argument of point source model
- kwargs – keyword arguments of function call (which are not used for this object
Returns: numpy array of amplitudes
-
lenstronomy.PointSource.Types.lensed_position module¶
-
class
lenstronomy.PointSource.Types.lensed_position.
LensedPositions
(lens_model=None, fixed_magnification=False, additional_image=False)[source]¶ Bases:
lenstronomy.PointSource.Types.base_ps.PSBase
class of a a lensed point source parameterized as the (multiple) observed image positions Name within the PointSource module: ‘LENSED_POSITION’ parameters: ra_image, dec_image, point_amp If fixed_magnification=True, than ‘source_amp’ is a parameter instead of ‘point_amp’
-
image_amplitude
(kwargs_ps, kwargs_lens=None, x_pos=None, y_pos=None, magnification_limit=None, kwargs_lens_eqn_solver=None)[source]¶ image brightness amplitudes
Parameters: - kwargs_ps – keyword arguments of the point source model
- kwargs_lens – keyword argument list of the lens model(s), only used when requiring the lens equation solver
- x_pos – pre-computed image position (no lens equation solver applied)
- y_pos – pre-computed image position (no lens equation solver applied)
- magnification_limit – float >0 or None, if float is set and additional images are computed, only those images will be computed that exceed the lensing magnification (absolute value) limit
- kwargs_lens_eqn_solver – keyword arguments specifying the numerical settings for the lens equation solver see LensEquationSolver() class for details
Returns: array of image amplitudes
-
image_position
(kwargs_ps, kwargs_lens=None, magnification_limit=None, kwargs_lens_eqn_solver=None)[source]¶ on-sky image positions
Parameters: - kwargs_ps – keyword arguments of the point source model
- kwargs_lens – keyword argument list of the lens model(s), only used when requiring the lens equation solver
- magnification_limit – float >0 or None, if float is set and additional images are computed, only those images will be computed that exceed the lensing magnification (absolute value) limit
- kwargs_lens_eqn_solver – keyword arguments specifying the numerical settings for the lens equation solver see LensEquationSolver() class for details
Returns: image positions in x, y as arrays
-
source_amplitude
(kwargs_ps, kwargs_lens=None)[source]¶ intrinsic brightness amplitude of point source When brightnesses are defined in magnified on-sky positions, the intrinsic brightness is computed as the mean in the magnification corrected image position brightnesses.
Parameters: - kwargs_ps – keyword arguments of the point source model
- kwargs_lens – keyword argument list of the lens model(s), used when brightness are defined in magnified on-sky positions
Returns: brightness amplitude (as numpy array)
-
lenstronomy.PointSource.Types.source_position module¶
-
class
lenstronomy.PointSource.Types.source_position.
SourcePositions
(lens_model=None, fixed_magnification=False, additional_image=False)[source]¶ Bases:
lenstronomy.PointSource.Types.base_ps.PSBase
class of a single point source defined in the original source coordinate position that is lensed. The lens equation is solved to compute the image positions for the specified source position.
Name within the PointSource module: ‘SOURCE_POSITION’ parameters: ra_source, dec_source, source_amp If fixed_magnification=True, than ‘source_amp’ is a parameter instead of ‘point_amp’
-
image_amplitude
(kwargs_ps, kwargs_lens=None, x_pos=None, y_pos=None, magnification_limit=None, kwargs_lens_eqn_solver=None)[source]¶ image brightness amplitudes
Parameters: - kwargs_ps – keyword arguments of the point source model
- kwargs_lens – keyword argument list of the lens model(s), only ignored when providing image positions directly
- x_pos – pre-computed image position (no lens equation solver applied)
- y_pos – pre-computed image position (no lens equation solver applied)
- magnification_limit – float >0 or None, if float is set and additional images are computed, only those images will be computed that exceed the lensing magnification (absolute value) limit
- kwargs_lens_eqn_solver – keyword arguments specifying the numerical settings for the lens equation solver see LensEquationSolver() class for details
Returns: array of image amplitudes
-
image_position
(kwargs_ps, kwargs_lens=None, magnification_limit=None, kwargs_lens_eqn_solver=None)[source]¶ on-sky image positions
Parameters: - kwargs_ps – keyword arguments of the point source model
- kwargs_lens – keyword argument list of the lens model(s), only used when requiring the lens equation solver
- magnification_limit – float >0 or None, if float is set and additional images are computed, only those images will be computed that exceed the lensing magnification (absolute value) limit
- kwargs_lens_eqn_solver – keyword arguments specifying the numerical settings for the lens equation solver see LensEquationSolver() class for details
Returns: image positions in x, y as arrays
-
source_amplitude
(kwargs_ps, kwargs_lens=None)[source]¶ intrinsic brightness amplitude of point source When brightnesses are defined in magnified on-sky positions, the intrinsic brightness is computed as the mean in the magnification corrected image position brightnesses.
Parameters: - kwargs_ps – keyword arguments of the point source model
- kwargs_lens – keyword argument list of the lens model(s), used when brightness are defined in magnified on-sky positions
Returns: brightness amplitude (as numpy array)
-
lenstronomy.PointSource.Types.unlensed module¶
-
class
lenstronomy.PointSource.Types.unlensed.
Unlensed
(lens_model=None, fixed_magnification=False, additional_image=False)[source]¶ Bases:
lenstronomy.PointSource.Types.base_ps.PSBase
class of a single point source in the image plane, aka star Name within the PointSource module: ‘UNLENSED’ This model can deal with arrays of point sources. parameters: ra_image, dec_image, point_amp
-
image_amplitude
(kwargs_ps, **kwargs)[source]¶ amplitudes as observed on the sky
Parameters: - kwargs_ps – keyword argument of point source model
- kwargs – keyword arguments of function call (which are not used for this object
Returns: numpy array of amplitudes
-
image_position
(kwargs_ps, **kwargs)[source]¶ on-sky position
Parameters: kwargs_ps – keyword argument of point source model Returns: numpy array of x, y image positions
-


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Write a class Repetitive, with an array rep, with 7 Integer values. The class should set all values in the array to 15, using a for-loop.
Write the class RepetitiveWithoutFor, doing the same thing as Repetitive above, though not using a for-loop.
This time write a class Repeat, with an array repeater containing 4 Real values. The values in the array shall be set to 1, 4, 7, 10, using a for-loop.
Write a function, Rapper, which calculates the sum of the values in the array.
Write an instance, where the values of that array shall be set. Then simulate the instance class and plot the result.
Why is it better not to have the same name on the variable in the iteration expression as a variable inside the for-clause in an equation?
It is not good from the programmers point of view. The variable inside the for-clause is hidden, which is all right, but they are by the programmer easily mixed together.


Projects or Assemblies Referenced via Multiple Paths¶
Identifies broken reference paths. This usually happens when projects or assemblies are moved to different locations in the repository, but not all referencing projects or solutions have their HintPaths updated.
Parameters¶
Item Name (optional):
- The name of a specific project or assembly to look up
Columns¶
Count
- The number of distinct paths used to reference an item (based on the item’s name)
Name
- The name of the project or assembly
Path
- Used in the Detail Row (described below)
Grouping¶
There is one grouping row per project or assembly that is being referenced at more than one path.
Sub-Grouping¶
Expanding the top-level grouping exposes an additional set of grouping rows. Each row represents each distinct referencing path, and the count of items that use that path.
Detail Rows¶
Each detail row under the sub-grouping represents a project that references the item using the path under which it is grouped. The Name and Path to the referencing item is provided. You can describe each detail row as “This project (at the path indicated) references that item (in the top-level grouping) using this path (in the sub-grouping).”
Sorting¶
The groupings are sorted in descending order by the count of referencing paths, then alphabetically by item name. The sub-groupings are sorted in descending order by the count of items using the referencing path, then alphabetically by path. The detail rows are sorted alphabetically by item name.
Usage¶
This report exposes broken references. Typically under each grouping you will see the largest number of referencing items using the correct path as the first row in the sub-grouping. Additional rows in the sub-grouping are projects that have a broken reference path and probably are failing to compile as a result.


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WEAVE(1) WEAVE(1) NAME weave - translate WEB to TeX SYNOPSIS weave [-x] webfile[.web] [changefile[.ch]] DESCRIPTION This manual page is not meant to be exhaustive. The complete documenta‐ tion for this version of TeX can be found in the info file or manual Web2C: A TeX implementation. The weave program is used to create a TeX file for viewing a Web program. It takes appropriate care of typographic details like page layout and the use of indentation, italics, boldface, etc., and it supplies extensive cross-index information that it gathers automatically. The command line arguments are the same as for tangle except for the option: -x says to omit the index, module name list, and table of contents pages. (A CON‐ TENTS.tex file will still be written when the TeX file is processed, how‐ ever, unless some macros in webmac.tex are redefined.) The output TeX file name is formed by adding .tex to the root of the Web file name. There are several macros that probably should be redefined by the pro‐ grammer at the beginning of the Web file. It is a good idea to set \title to the name of the program. And, to cause output of only changed modules, one can say \let\maybe=\iffalse (usually as the first change in the change file). ENVIRONMENT The environment variable WEBINPUTS is used to search for the input files, or the system default if WEBINPUTS is not set. See tex(1) for the details of the searching. FILES $TEXMFMAIN/tex/plain/base/webmac.tex TeX macros used by weave output. SEE ALSO tangle(1) for references, authors, and other information. Web2C 2012 7 April 2010 WEAVE(1)


Concept Description
Distributed call chain tracking provides timing information for calls between services, but the link call information inside the service is equally important to the developer. If you can combine the two into one, you can provide a more complete call chain, which is easier to locate. Errors and potential performance issues.
Prerequisites
- Using the custom dot function requires first configuring and enabling the Java Chassis microservice call chain.
Precautions
- The custom dot function using the
@Span
annotation only supports method calls that are requesting the same thread as the Java Chassis call. - The method to add the
@Span
annotation must be a Spring-managed bean, otherwise you need to press the [Methods mentioned] (https://stackoverflow.com/questions/41383941/load-time-weaving-for-non-spring -beans-in-a-spring-application) configuration.
Custom call chain management
This feature integrates Zipkin and provides the @Span
annotation for custom tracking of methods that need to be tracked. Java Chassis will automatically track all methods that add @Span
annotations, linking the local call information of each method to the call information between services.
Steps for usage:
Adding dependencies
Microservices based on ServiceComb Java Chassis only need to add the following dependency to pom.xml:
<dependency>
<groupId>org.apache.servicecomb</groupId>
<artifactId>tracing-zipkin</artifactId>
</dependency>
Enable custom management function {#Configure tracking processing and data collection}
Add the @EnableZipkinTracing
annotation to the application portal or Spring configuration class:
@SpringBootApplication
@EnableZipkinTracing
public class ZipkinSpanTestApplication {
public static void main(String[] args) {
SpringApplication.run(ZipkinSpanTestApplication.class);
}
}
Customized management
Add the @Span
annotation to the method that requires custom management:
@Component
public class SlowRepoImpl implements SlowRepo {
private static final Logger logger = LoggerFactory.getLogger(SlowRepoImpl.class);
private final Random random = new Random();
@Span
@Override
public String crawl() throws InterruptedException {
logger.info("in /crawl");
Thread.sleep(random.nextInt(200));
return "crawled";
}
}
In this way, by using the @Span
annotation, we started the Zipkin-based custom management function.
Customized reported data
The call chain that is escalated by custom management contains two pieces of data:
- span name defaults to the full name of the method currently being annotated.
- call.path defaults to the method signature of the current annotation.
For example, the data reported in the above example SlowRepoImp
is as follows:
| key | value |
|---|---|
| span name | crawl |
| call.path | public abstract java.lang.String org.apache.servicecomb.tests.tracing.SlowRepo.crawl() throws java.lang.InterruptedException |
If you need to customize the reported data content, you can pass in the custom parameters:
public static class CustomSpanTask {
@Span(spanName = "transaction1", callPath = "startA")
public String invoke() {
return "invoke the method";
}
}


|
IM Profile | ||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Interface common to certificates.
The features abstracted of Certificates
include subject,
issuer, type, version, serial number, signing algorithm, dates of valid use,
and serial number.
Printable Representation for Binary Values
A non-string values in a certificate are represented as strings with each byte as two hex digits (capital letters for A-F) separated by ":" (Unicode U+003A).
For example: 0C:56:FA:80
Printable Representation for X.509 Distinguished Names
For a X.509 certificate the value returned is the printable verision of the distingished name (DN) from the certificate.
An X.509 distinguished name of is set of attributes, each attribute is a sequence of an object ID and a value. For string comparison purposes, the following rules define a strict printable representation.
| Object ID | Binary | Label |
|---|---|---|
| id-at-commonName | 55:04:03 | CN |
| id-at-surname | 55:04:04 | SN |
| id-at-countryName | 55:04:06 | C |
| id-at-localityName | 55:04:07 | L |
| id-at-stateOrProvinceName | 55:04:08 | ST |
| id-at-streetAddress | 55:04:09 | STREET |
| id-at-organizationName | 55:04:0A | O |
| id-at-organizationUnitName | 55:04:0B | OU |
| emailAddress | 2A:86:48:86:F7:0D:01:09:01 | EmailAddress |
Example of a printable distinguished name:
C=US;O=Any Company, Inc.;CN=www.anycompany.com
| Method Summary | |
String |
getIssuer()
Gets the name of this certificate's issuer. |
long |
getNotAfter()
Gets the time after which this Certificate may not be used
from the validity period. |
long |
getNotBefore()
Gets the time before which this Certificate may not be used
from the validity period. |
String |
getSerialNumber()
Gets the printable form of the serial number of this Certificate .
|
String |
getSigAlgName()
Gets the name of the algorithm used to sign the Certificate .
|
String |
getSubject()
Gets the name of this certificate's subject. |
String |
getType()
Get the type of the Certificate .
|
String |
getVersion()
Gets the version number of this Certificate .
|
| Method Detail |
public String getSubject()
Certificate
;
the value MUST NOT be null
.public String getIssuer()
Certificate
;
the value MUST NOT be null
.public String getType()
Certificate
.
For X.509 Certificates the value returned is "X.509".
Certificate
;
the value MUST NOT be null
.public String getVersion()
Certificate
.
The format of the version number depends on the specific
type and specification.
For a X.509 certificate per RFC 2459 it would be "2".
Certificate
;
the value MUST NOT be null
.public String getSigAlgName()
Certificate
.
The algorithm names returned should be the labels
defined in RFC2459 Section 7.2.
null
.public long getNotBefore()
Certificate
may not be used
from the validity period.
Certificate
is not valid; it MUST be positive,
0
is returned if the certificate does not
have its validity restricted based on the time.public long getNotAfter()
Certificate
may not be used
from the validity period.
Certificate
is not valid (expiration date);
it MUST be positive; Long.MAX_VALUE
is returned if
the certificate does not have its validity restricted based on the
time.public String getSerialNumber()
Certificate
.
If the serial number within the certificate
is binary it should be formatted as a string using the binary printable
representation in class description.
For example, 0C:56:FA:80.
null
is returned
if there is no serial number.
|
IM Profile | ||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


The dashboards in TE are defined in XML profiles. Each dashboards has it's own XML profile stored within the web server. The XML file controls the SQL to run, how the columns are displayed, cross reference links, and actions. It even controls detailed information on the page itself, searching rules, how many rows to display, and much more.
You can see a sample dashboard at 'Tools --> Summary Views'
in Technology Explorer main menu.
Click here to view the XML Profile for a sample dashboard.
Summary: 1. Identify the SQL statement 2. Generate the XML profile using AdHoc SQL window 3. Create menu in Technology Explorer 4. Create dashboards using multiple views


A Go package and command line programs for working with fdx XML files used by Screen Writing software.


--- notes: | L-line vertical divider. Used in "value props" within "at-a-glance" section. School of Technology (RST) colors shown. ---


--- layout: archive title: "Talks" permalink: /talks/ author_profile: true --- {% for post in site.talks reversed %} {% include archive-single-talk.html %} {% endfor %}


Classes
The following classes are available globally.
-
ACNotify
provides a simple Toast-like popup notification that is displayed at the bottom of the device’s screen for 1 second before disappearing.Example:
See more// Display popup notification ACNotify.showMessage(text: "Hello World!")
Declaration
Swift
open class ACNotify
-
Defines a set of convenience properties and functions when working on Apple devices, such as checking the device model name (
iPhone10,3
), getting the device type (iPhoneX
), getting the OS version, the current device orientation and internet connection state.Examples:
See moreif HardwareInformation.deviceType == .iPhoneX { // The app is running on an iPhone X ... } if HardwareInformation.isConnectedToNetwork { // The device has an internet connection ... }
Declaration
Swift
open class HardwareInformation


Tags: boats-and-ships, Normandy
Style: Impressionism
Artist: Monet Claude
Title: Boats in the Port of Le Havre
Year: 1883
Genre: cityscape
Categories: cliff (29.0%); volcano (6.4%); wreck (6.1%); book_jacket (5.2%); cliff_dwelling (4.4%)
<< previous image next image >>


gender : *
Profession : *
Place of living? : *
What is the most important thing that you want in a smartphone?
What are your main requirements?


type annotations
Anders Hejlsberg@Build2016: Big JavaScript codebases tend to become "read-only".
Recently published survey on the state of JavaScript
let obj: string;
obj = 'yo';
// Error: Type 'number' is not assignable to type 'string'.
obj = 10;
// types can be inferred (return type)
function sayIt(what: string) {
return `Saying: ${what}`;
}
const said: string = sayIt(obj);
class Sayer {
// mandatory
what: string;
constructor(what: string) {
this.what = what;
}
// return type if you want to
sayIt(): string {
return `Saying: ${this.what}`;
}
}
let obj: string;
obj = 'yo';
// Error: number: This type is incompatible with string
obj = 10;
function sayIt(what: string) {
return `Saying: ${what}`;
}
const said: string = sayIt(obj);
class Sayer {
what: string;
constructor(what: string) {
this.what = what;
}
sayIt(): string {
return `Saying: ${this.what}`;
}
}
Those basic features help with documentation, refactoring, and IDE support
function foo(num: number) {
if (num > 10) {
return 'cool';
}
}
console.log(foo(100).toString());
// error: call of method `toString`.
// Method cannot be called on possibly null value
console.log(foo(100).toString());
// to fix this, we need to check the result
const fooed: string|void = foo(100);
if (fooed) {
fooed.toString();
}
Types are non-nullable by default in flow
function foo(num: number) {
if (num > 10) {
return 'cool';
}
}
// same as flow
const fooed: string|void = foo(100);
if (fooed) {
fooed.toString();
}
// or tell the compiler we know better (in this case we actually do)
fooed!.toString();
Only applies to TypeScript 2.x
Only works when strictNullChecks option is checked
All types nullable by default in TypeScript 1.x
can be anything, not specified
can selectively disable type checking
function func(a: any) {
return a + 5;
}
// cool
let r1: string = func(10);
// cool
let r2: boolean = func('wat');
Types can be parameterized by others
Most common with collection types
let cats: Array<Cat> = []; // can only contain cats
let animals: Array<Animal> = []; // can only contain animals
// nope, no cat
cats.push(10);
// nope, no cat
cats.push(new Animal('Fido'));
// cool, is a cat
cats.push(new Cat('Purry'));
// cool, cat is a sub type of animal
animals.push(new Cat('Purry'));
Both Flow and TypeScript support upper, but not lower bounds
class Person {
name: string;
}
class Dog {
name: string;
}
let dog: Dog = new Dog();
// nope, nominal type compatibility violated
let person: Person = dog; // ERROR: Dog: This type is incompatible with Person
// same problem
let person: Person = { // ERROR: object literal: This type is incompatible with Person
name: "Olli"
};
class Person {
name: string;
}
class Dog {
name: string;
}
let dog: Dog = new Dog();
// yes, correct, as structurally compatible
let person: Person = dog;
// same thing, also correct
let person: Person = {
name: "Olli"
};
// this is fine as nominal typing only applies to Flow classes
let namedObject: NamedObject = dog;
// same thing, also fine
let namedObject: NamedObject = {
name: "Olli"
};
// not fine in either
let namedObject: NamedObject = {
firstName: "Olli"
};
// cool in flow, but TypeScript wants perfect match with object literal
// ERROR: Object literal may only specify known properties,
// and 'firstName' does not exist in type 'NamedObject'.
let namedObject: NamedObject = {
name: "Olli",
firstName: "Olli"
};
TypeScript has special support for classes
Makes it easier for people coming from Java/C++/C#
Flow does not feature those or any other syntactic sugar, as it is a checker only
Starting from 2016.3


fun setTitle(title: String): Note
Sets title and returns Note object
title -
title
Return Object of Note type


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Thoughts on nonprofit technology, movement analytics, data and testing.
Digital Innovation Director @ Stand.earth, formerly SumOfUs, Mozilla and OpenMedia
#Rstats #BI #nptech #analytics #datascience
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Max sits at a lunch table, multitasking by eating and reviewing for an upcoming chemistry exam. He’s not taking it because of an overwhelming enthusiasm for the subject, it’s just a required class for graduation.They’re going over Lewis dot diagrams in class today. Max is able to understand the concepts fairly well, but it takes him more time to complete the problems than his classmates. He’s unable to finish the assignment before class is over, so he resigns himself to adding the rest to his ever growing mound of homework.
Visuals are used in subjects like science, technology, engineering, and math (STEM). For example, chemistry lessons on bonding typically includes the visuals shown in Figure 1. While we usually assume that these visuals help students learn because they make abstract concepts clearer, they can also harm students’ learning if students do not know how the visuals show information. To learn from visuals, students need representational competencies — knowledge about how visual representations show information. For example, a chemistry student needs to learn that the dots in the Lewis structure (Figure 1a) show electrons and that the spheres in the space-filling model (Figure 1b) show areas where electrons may live.
Lessons that help students learn representational competencies mostly focuse on conceptual representational competencies. These include the ability to connect visual features to concepts, support conceptual reasoning with visuals, and choose the right visuals to illustrate a given concept. Less research has focused on a second type of representational competency — perceptual fluency. This is the ability to quickly and effortlessly see meaningful information in visuals. For example, chemists can effortlessly see that both visuals in Figure 1 show water. Perceptual fluency plays an important role in students’ learning because it frees mental energy for more complex reasoning. This allows students to learn from visuals.
Students get perceptual fluency through learning processes that are implicit and inductive. To understand this, think about the the way you learned your first language. You didn't need to effortfully think about the grammatical rules. Instead, you got a "feel" for the rules (implicit learning) that came from many learning opportunities (inducing process). Because of this, it is thought that perceptual fluency should be taught by giving students many simple tasks in which they must quickly judge what a visual shows. For example, a perceptual fluency task may ask students to quickly and intuitively judge whether two visuals like the ones in Figure 1 show the same molecule. They ask students to rely on implicit intuitions. The problem sequence is typically chosen so that (1) students are exposed to a variety of visuals and (2) consecutive visuals vary irrelevant features while drawing attention to relevant features.
However, these general guidelines leave many possible sequences open. So far, we do not have a principle-based way of identifying the best problem sequences. In our study, we used a created a computer model of how undergraduates learn to solve perceptual fluency problems in chemistry. Then, we had a computer algorithm teach the first model. Finally, we took the problem sequence that worked best for the model and gave it to real humans on the Internet. We found that the sequence of chemistry visuals (practice problems) created by the machine was better for learning than a random problem sequence and a sequence generated by a human expert who knew about chemistry and perceptual learning.
Representations used when teaching are defined as external representations because they are external or outside of the viewer. By contrast, internal representations are mental objects that students can imagine and mentally manipulate. External representations can be symbolic like the text in a book or visual like Lewis structures in chemistry.
Perceptual fluency research is based on findings that experts like doctors and pilots can automatically see meaningful connections among representations, that it takes them little cognitive effort to translate among representations, and that they can quickly and effortlessly mix information distributed across representations. For example, chemists can see at a glance that the Lewis structure in Figure 1A shows the same molecule as the space-filling model in Figure 1B. This kind of perceptual expertise frees up cognitive resources for more complex reasoning.
According to two learning theories, perceptual fluency involves building accurate internal representations of visuals and connecting them to each other.
Cognitive science suggests that students get perceptual fluency by perceptual induction processes. Here, inductive means that students can figure out how visual properties relate to concepts through practice. Students become better at seeing meaning in visuals by treating each visual feature property as one perceptual chunk that relates to multiple concepts (perceptual chunking). Perceptual induction processes are thought to be nonverbal happen unconsciously.
Lessons that target perceptual fluency are fairly new. Some researchers have created math and science lessons in which students translate between different visuals quickly. In our chemistry study, students judged whether two visuals like the ones shown in Figure 1 show the same molecule. Students would get dozens of problems like these in a row. These interventions can raise test scores even if the problems are a bit different from the problems used during the lesson.
Perceptual learning depends on the practice sequence. To design good sequences, tasks should give students a variety of problems so that irrelevant features vary but relevant features are constant across several tasks. But we know that visuals differ from each other in many ways. So there are many possible sequences that could vary visual features. To address this problem, we used a new computer science approach called machine teaching.
Machine teaching is a computer science technique in which a computer algorithm helps improve human learning. We took the following steps.
Which problem sequence is best for learning? We wondered whether an algorithm run by a machine could improve learning beyond a random problem sequence and a sequence created by a human expert.
First, we needed to train a learning algorithm that behaved like real humans on perceptual learning lessons. To do that, we ran a small experiment. We compared the learning algorithm’s predictions to humans' test scores. In our pilot experiment, we recruited 47 undergraduate chemistry students. They were randomly assigned to two conditions using random problem sequences -- training with feedback or training without feedback.
Perceptual fluency problems ask students to make simple perceptual judgments. In our case, students were given two images. One image was of a molecule represented by a Lewis structure and the other image was a molecule represented by a space-filling model. Students judged whether or not the two images show the same molecule.
Now, we describe how we created the cognitive model of how humans learn with chemistry visuals. To do this, we describe the:
In our experiment, we used visual representations of chemical molecules common in undergraduate classes. To find these molecules, we reviewed textbooks and web-based content. We listed the frequency of different molecules using their chemical names (e.g., H2O) and chose the 142 most common molecules. To precisely describe the visual representations, we counted visual features like the number of lines or dots in the Lewis structure and the colors of spheres in the space-filling models. This allowed us to create lists of visual properties called feature vectors. Each molecule had a feature vector that described its visual features Feature vectors of Lewis structures had 27 features. Feature vectors of space-filling models had 24 features. These feature vectors were used by the learning algorithm.
Our learning algorithm plugged in the two molecules (Lewis structure and space-filling model) as inputs in the form of feature vectors. It was important to represent the molecules as feature vectors because this translates the visual information to a "language" that software can understand.
Given these feature vectors as inputs, the learning algorithm was able to place them in the same "world". This world is the golden vector space. It can be thought of as a coordinate plane similar to the one in which you would place the points (0,4) and (9,3). Just as the distance between two points in a coordinate plane can be calculated using the distance formula, the distance between two molecules can also be calculated. The distance between two molecules represents their similarity. Two similar molecules will be very close together in the golden vector space and two dissimilar molecules will be far apart. Next, we calculate the probability that the two molecules were the same. When given the correct answer, the algorithm updated itself. Over many opportunities, this allows the algorithm to learn.
Our first problem was to consider the length of the problem sequences. Should we create sequences of 50 problems? Or 100? Searching over all possible sequences would be too complex for most computers. We settled on 60 as the length of the problem sequence because this was closest to other studies.
Finding the best problem sequence was a computationally tough problem. For each problem in the 60-problem sequence, there could be 5,041 possible options. This means that there were 504160 or 1.4 * 10222 possible sequences. We can not hope to find the "best" or optimal sequence. So we settle for a sup-optimal or "good enough" sequence.
The algorithm that searchs for a suboptimal solution is called a modified hill climbining algorithm. We consider a landscape with different training sets scattered about. We begin by looking at a random random set and checking its neighbors. If the neighboring problem sequences result in better learning for the cognitive model, we move to the better training set.
We continued this process until no better neighbors are found. Because of computer limits, we only checked 500 neighbors of a particular training set. The problem sequence that remained was our solution -- the machine-generated problem sequence.
To find out whether the machine problem sequence is best for learning, we ran a randomized experiment with humans on the Internet.
We recruited 368 participants using Amazon’s Mechanical Turk (MTurk). Of these, 216 were male and 131 were female. The rest did not report their sex. Most participants (86%) were below the age of 45.
We compared three training conditions. In the machine training sequence condition, we used the problem sequence found by the search algorithm.
Participants judged whether the two molecules were the same and received feedback. In the condition using the human expert sequence, the training set was constructed by an expert in perceptual learning and chemistry.
In the random training sequence condition, each problem was randomly created.
We hosted the experiment online. Participants first received a brief description of the study and then completed a sequence of 126 judgment problems (yes or no). Tasks were divided into three phases. Phase one was the pretest and it included 20 test problems without feedback. Phase two was for training and it included 60 training problems with correctness feedback. We assumed that participants learned during this phase. Phase three was the posttest with 40 test problems displayed without feedback.
In addition, one guard task was inserted after every 19 tasks throughout all three phases. A guard question either showed two perfectly identical molecules shown by the same representation or two highly dissimilar molecules shown by Lewis structures. We used these easy guard questions to filter out participants who randomly clicked through the tasks. We ignored the guard tasks during modeling. When the images of the two molecules were shown to participants, the position (left or right) was randomized so that no representation was on one side more often.
Of the 368 participants, we filtered out 43 participants who failed any of the guard questions. The final sample size was 325. The final number of participants in the conditions random, human, and machine training sequence were 108, 117 and 100 respectively.
Our goal was to determine whether machine learning can help find a sequence of visual representations that raises students’ learning from perceptual-fluency tasks. To do this, we used machine teaching to reverse-engineer an optimal training sequence for a machine-learning algorithm. Next, we conducted an experiment with humans that compared the machine teaching sequence to a random sequence and to a sequence generated by a human expert on perceptual learning. The machine teaching sequence resulted in lower training performance, but higher posttest scores. The fact that the machine learning sequence yielded lower performance during training, but higher posttest scores suggests that this sequence induced desirable difficulties. Desirable difficulties refers to interventions yielding lower performance during training, but higher long-term learning. Given that visual representations are so common, we think that our findings will be broadly useful.


EdLab Seminar: K12Meets?
Aug 9, 2012 5:32 PM
K12meets is a product of ApprenNet LLC, an education technology company that brings online learning to life by providing educators the tools to build "Meets."
Meets are three step virtual exercises that give educators the ability to scale significant predictors of achievement:
- observation of self and others
- social capital between peers
- mentorship
Meets can be used to teach any practical skills. Thus, Meets can be used to train and support teachers and they can be used in the classroom to teach students skills.
Upon graduating from the University of Pennsylvania, Emily Foote spent five years teaching in high-poverty schools as a TFA corps member and KIPP Public Charter School teacher. During those years, Emily attended night school and received her teaching certification in Early Childhood Education from Georgia State University and a masters of science in Secondary Education from the University of Pennsylvania. Following her teaching career, Emily earned a law degree (with honors) from Drexel University and then practiced special education law. As an attorney, Emily's primary focus was representing special needs students from high-poverty school districts. Last year, with funding from a National Science Foundation grant, Emily co-founded ApprenNet LLC, with Karl Okamoto and Paul Tzen. For the last year, the team focused specifically on legal education. This year they plan to expand their focus to the K12 space.
Open to all members of the university community.
If you are coming from outside the Edlab, please RSVP by November 12th by sending a message to Edlab at http://ednode.com/profiles/edlabtc/about (You must have/create an Ednode account and click on
).
EdLab Seminars take place on the 5th floor of Russell Hall at Teachers College Columbia University (525 W. 120th Street, NY, NY). For detailed directions within the College visit TC Route Finder: http://maps.tc-library.org


Logan EngstromEmail<EMAIL_ADDRESS>Scholar: here CV/Resume: here GitHub: @lengstrom |
I'm a third year PhD student at MIT EECS advised by Aleksander Mądry and funded by a Google PhD Fellowship. I'm most interested in machine learning robustness and generalization.
I went to MIT for undergrad and grew up in beautiful Lincoln, MA. Outside of research I contribute to open source and play pickup soccer.
Andrew Ilyas, Sam Park, Logan Engstrom, Guillaume LeClerc, Aleksander Madry. Datamodels: Predicting Predictions from Training Data. ICML 2022.
Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor. Unadversarial Examples: Designing Objects for Robust Vision. NeurIPS 2021.
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry. Noise or signal: The role of image backgrounds in object recognition. ICLR 2021.
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry. Do Adversarially Robust ImageNet Models Transfer Better?. NeurIPS 2020 Oral Presentation.
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, and Aleksander Mądry. Identifying Statistical Bias in Dataset Replication. ICML 2020.
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, and Aleksander Madry. From imagenet to image classification: Contextualizing progress on benchmarks. ICML 2021.
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Mądry. Implementation Matters in Deep RL: A Case Study on PPO and TRPO. ICLR 2020 Oral Presentation.
Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Mądry. A Closer Look at Deep Policy Gradients. ICLR 2020 Oral Presentation.
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. Adversarial examples are not bugs, they are features. NeurIPS 2019 Spotlight Presentation.
Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, and Aleksander Madry. Computer vision with a single (robust) classifier.. NeurIPS 2019.
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Mądry. Robustness May Be at Odds with Accuracy. ICLR 2019.
Andrew Ilyas, Logan Engstrom, Ludwig Schmidt, and Aleksander Mądry. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors. ICLR 2019.
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Mądry. A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations. ICML 2019.
Logan Engstrom, Andrew Ilyas, and Anish Athalye. Evaluating and Understanding the Robustness of Adversarial Logit Pairing. NIPS 2018 Machine Learning and Computer Security Workshop.
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin. Black-box Adversarial Attacks with Limited Queries and Information. ICML 2018.
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok. Synthesizing Robust Adversarial Examples. ICML 2018, Demo at NeurIPS 2017 Machine Learning and Computer Security Workshop.
Daniel Kang, Richard Sherwood, Amira Barkal, Tatsunori Hashimoto, Logan Engstrom, and David Gifford. DNase-capture Reveals Differential Transcription Factor Binding modalities. PloS one, 2017.
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, and Aleksander Madry. Learning perceptually-aligned representations via adversarial robustness. 2019.


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| Top |
Functions
Types and Values
| struct | NcmIntegrand2dim |
| struct | NcmIntegrand3dim |
| struct | NcmIntegralFixed |
| #define | NCM_INTEGRAL_PARTITION |
| #define | NCM_INTEGRAL_ALG |
| #define | NCM_INTEGRAL_ERROR |
| #define | NCM_INTEGRAL_ABS_ERROR |
Functions
ncm_integral_get_workspace ()
gsl_integration_workspace **
ncm_integral_get_workspace (void
);
This function provides a workspace to be used by numerical integration functions of GSL. It keeps a internal pool of workspaces and allocate a new one if the function is called and the pool is empty. It is designed to be used in a multi-thread environment. The workspace must be unlocked in order to return to the pool. This must be done using the ncm_memory_pool_return.
[skip]
_NcmIntegrand2dimFunc ()
gdouble (*_NcmIntegrand2dimFunc) (gdouble x
,gdouble y
,gpointer userdata
);
NcmIntegralPeakfinder ()
void (*NcmIntegralPeakfinder) (const gint *ndim
,const gdouble b[]
,gint *n
,gdouble x[]
,void *userdata
);
_NcmIntegrand3dimFunc ()
gdouble (*_NcmIntegrand3dimFunc) (gdouble x
,gdouble y
,gdouble z
,gpointer userdata
);
ncm_integral_locked_a_b ()
gint ncm_integral_locked_a_b (gsl_function *F
,gdouble a
,gdouble b
,gdouble abstol
,gdouble reltol
,gdouble *result
,gdouble *error
);
This function uses a workspace from the pool and gsl_integration_qag function to perform the numerical integration in the [a, b] interval.
[skip]
Parameters
F |
a gsl_function wich is the integrand. |
|
a |
lower integration limit. |
|
b |
upper integration limit. |
|
abstol |
absolute tolerance. |
|
reltol |
relative tolerance. |
|
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integral_locked_a_inf ()
gint ncm_integral_locked_a_inf (gsl_function *F
,gdouble a
,gdouble abstol
,gdouble reltol
,gdouble *result
,gdouble *error
);
This function uses a workspace from the pool and gsl_integration_qagiu function to perform the numerical integration in the $[a, \infty]$ interval.
[skip]
ncm_integral_cached_0_x ()
gint ncm_integral_cached_0_x (NcmFunctionCache *cache
,gsl_function *F
,gdouble x
,gdouble *result
,gdouble *error
);
This function searches for the nearest x_near value previously chosen as the upper integration limit and perform the integration at [x_near, x] interval. This result is summed to that obtained at [0, x_near] and then it is saved in the cache.
[skip]
Parameters
cache |
a pointer to NcmFunctionCache. |
|
F |
a gsl_function wich is the integrand. |
|
x |
upper integration limit. |
|
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integral_cached_x_inf ()
gint ncm_integral_cached_x_inf (NcmFunctionCache *cache
,gsl_function *F
,gdouble x
,gdouble *result
,gdouble *error
);
This function searchs for the nearest x_near value previously chosed as the lower integration limit and perform the integration at $[x, x_{near}]$ interval. This result is summed to that obtained at $[x_{near}, \infty]$ and then it is saved in the cache.
[skip]
Parameters
cache |
a pointer to NcmFunctionCache. |
|
F |
a gsl_function wich is the integrand. |
|
x |
lower integration limit. |
|
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integrate_2dim ()
gboolean ncm_integrate_2dim (NcmIntegrand2dim *integ
,gdouble xi
,gdouble yi
,gdouble xf
,gdouble yf
,gdouble epsrel
,gdouble epsabs
,gdouble *result
,gdouble *error
);
This function FIXME
Parameters
integ |
a pointer to NcmIntegrand2dim. |
|
xi |
gbouble which is the lower integration limit of variable x. |
|
yi |
gbouble which is the lower integration limit of variable y. |
|
xf |
gbouble which is the upper integration limit of variable x. |
|
yf |
gbouble which is the upper integration limit of variable y. |
|
epsrel |
relative error |
|
epsabs |
absolute error |
|
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integrate_2dim_divonne ()
gboolean ncm_integrate_2dim_divonne (NcmIntegrand2dim *integ
,gdouble xi
,gdouble yi
,gdouble xf
,gdouble yf
,gdouble epsrel
,gdouble epsabs
,const gint ngiven
,const gint ldxgiven
,gdouble xgiven[]
,gdouble *result
,gdouble *error
);
This function FIXME
Parameters
integ |
a pointer to NcmIntegrand2dim |
|
xi |
gbouble which is the lower integration limit of variable x. |
|
yi |
gbouble which is the lower integration limit of variable y. |
|
xf |
gbouble which is the upper integration limit of variable x. |
|
yf |
gbouble which is the upper integration limit of variable y. |
|
epsrel |
relative error |
|
epsabs |
absolute error |
|
ngiven |
number of peaks |
|
ldxgiven |
the leading dimension of xgiven, i.e. the offset between one point and the next in memory (ref. libcuba documentation) |
|
xgiven |
list of points where the integrand might have peaks (ref. libcuba documentation) |
|
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integrate_2dim_divonne_peakfinder ()
gboolean ncm_integrate_2dim_divonne_peakfinder (NcmIntegrand2dim *integ
,gdouble xi
,gdouble yi
,gdouble xf
,gdouble yf
,gdouble epsrel
,gdouble epsabs
,const gint ngiven
,const gint ldxgiven
,gdouble xgiven[]
,const gint nextra
,NcmIntegralPeakfinder peakfinder
,gdouble *result
,gdouble *error
);
This function FIXME
Parameters
integ |
a pointer to NcmIntegrand2dim |
|
xi |
gbouble which is the lower integration limit of variable x. |
|
yi |
gbouble which is the lower integration limit of variable y. |
|
xf |
gbouble which is the upper integration limit of variable x. |
|
yf |
gbouble which is the upper integration limit of variable y. |
|
epsrel |
relative error |
|
epsabs |
absolute error |
|
ngiven |
number of peaks |
|
ldxgiven |
the leading dimension of xgiven, i.e. the offset between one point and the next in memory (ref. libcuba documentation) |
|
xgiven |
list of points where the integrand might have peaks (ref. libcuba documentation) |
|
nextra |
the maximum number of extra points the peakfinder will return. |
|
peakfinder |
[scope call] | |
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integrate_2dim_vegas ()
gboolean ncm_integrate_2dim_vegas (NcmIntegrand2dim *integ
,gdouble xi
,gdouble yi
,gdouble xf
,gdouble yf
,gdouble epsrel
,gdouble epsabs
,const gint nstart
,gdouble *result
,gdouble *error
);
This function FIXME
Parameters
integ |
a pointer to NcmIntegrand2dim |
|
xi |
gbouble which is the lower integration limit of variable x. |
|
yi |
gbouble which is the lower integration limit of variable y. |
|
xf |
gbouble which is the upper integration limit of variable x. |
|
yf |
gbouble which is the upper integration limit of variable y. |
|
epsrel |
relative error |
|
epsabs |
absolute error |
|
nstart |
number of samples to start the first round of integration with. |
|
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integrate_3dim ()
gboolean ncm_integrate_3dim (NcmIntegrand3dim *integ
,gdouble xi
,gdouble yi
,gdouble zi
,gdouble xf
,gdouble yf
,gdouble zf
,gdouble epsrel
,gdouble epsabs
,gdouble *result
,gdouble *error
);
This function FIXME
Parameters
integ |
a pointer to NcmIntegrand3dim. |
|
xi |
gbouble which is the lower integration limit of variable x. |
|
yi |
gbouble which is the lower integration limit of variable y. |
|
zi |
gbouble which is the lower integration limit of variable z. |
|
xf |
gbouble which is the upper integration limit of variable x. |
|
yf |
gbouble which is the upper integration limit of variable y. |
|
zf |
gbouble which is the upper integration limit of variable z. |
|
epsrel |
relative error |
|
epsabs |
absolute error |
|
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integrate_3dim_divonne ()
gboolean ncm_integrate_3dim_divonne (NcmIntegrand3dim *integ
,gdouble xi
,gdouble yi
,gdouble zi
,gdouble xf
,gdouble yf
,gdouble zf
,gdouble epsrel
,gdouble epsabs
,const gint ngiven
,const gint ldxgiven
,gdouble xgiven[]
,gdouble *result
,gdouble *error
);
This function FIXME
Parameters
integ |
a pointer to NcmIntegrand3dim |
|
xi |
gbouble which is the lower integration limit of variable x. |
|
yi |
gbouble which is the lower integration limit of variable y. |
|
zi |
gbouble which is the lower integration limit of variable z. |
|
xf |
gbouble which is the upper integration limit of variable x. |
|
yf |
gbouble which is the upper integration limit of variable y. |
|
zf |
gbouble which is the upper integration limit of variable z. |
|
epsrel |
relative error |
|
epsabs |
absolute error |
|
ngiven |
number of peaks |
|
ldxgiven |
the leading dimension of xgiven, i.e. the offset between one point and the next in memory (ref. libcuba documentation) |
|
xgiven |
list of points where the integrand might have peaks (ref. libcuba documentation) |
|
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integrate_3dim_vegas ()
gboolean ncm_integrate_3dim_vegas (NcmIntegrand3dim *integ
,gdouble xi
,gdouble yi
,gdouble zi
,gdouble xf
,gdouble yf
,gdouble zf
,gdouble epsrel
,gdouble epsabs
,const gint nstart
,gdouble *result
,gdouble *error
);
This function FIXME
Parameters
integ |
a pointer to NcmIntegrand3dim |
|
xi |
gbouble which is the lower integration limit of variable x. |
|
yi |
gbouble which is the lower integration limit of variable y. |
|
zi |
gbouble which is the lower integration limit of variable z. |
|
xf |
gbouble which is the upper integration limit of variable x. |
|
yf |
gbouble which is the upper integration limit of variable y. |
|
zf |
gbouble which is the upper integration limit of variable z. |
|
epsrel |
relative error |
|
epsabs |
absolute error |
|
nstart |
number of samples to start the first round of integration with. |
|
result |
a pointer to a gdouble in which the function stores the result. |
|
error |
a pointer to a gdouble in which the function stores the estimated error. |
ncm_integral_fixed_new ()
NcmIntegralFixed * ncm_integral_fixed_new (gulong n_nodes
,gulong rule_n
,gdouble xl
,gdouble xu
);
This function prepares the NcmIntegralFixed with a grid with n_nodes - 1 intervals beteween xl and xu. In each interval it uses a fixed order (rule_n) Gauss-Legendre integration rule to determine the interval inner points. This results in a grid with (n_nodes - 1) * rule_n points.
[skip]
ncm_integral_fixed_free ()
void
ncm_integral_fixed_free (NcmIntegralFixed *intf
);
This function frees the memory associated to NcmIntegralFixed.
ncm_integral_fixed_calc_nodes ()
void ncm_integral_fixed_calc_nodes (NcmIntegralFixed *intf
,gsl_function *F
);
This function FIXME
[skip]
ncm_integral_fixed_nodes_eval ()
gdouble
ncm_integral_fixed_nodes_eval (NcmIntegralFixed *intf
);
This function
ncm_integral_fixed_integ_mult ()
gdouble ncm_integral_fixed_integ_mult (NcmIntegralFixed *intf
,gsl_function *F
);
This function
[skip]
ncm_integral_fixed_integ_posdef_mult ()
gdouble ncm_integral_fixed_integ_posdef_mult (NcmIntegralFixed *intf
,gsl_function *F
,gdouble max
,gdouble reltol
);
This function
[skip]


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U.S. Educational Attainment
Percentage of adults age 25 and older with a bachelor's degree or higher (2010-2014)
Source


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This tracker is used to determine a triangle type depending on the length values that a user inputs.
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WITNESS LIST Redistricting Committee (SUBCOMMITTEE ON Congressional Redistricting Two) June 26, 2003-9:00A Congressional Redistricting For: Bradford, Mike (Self and Midland County Commissioners) Breier, John (Midland Chamber of Commerce) Caddel, Jerry (Self) Durand-Hollis, Margarita (Self) Frazier, Terri (Self) Garcia, Jessica (Self) Isett, Dan (Lubbock County Republican Party) Jones, Fred (Odessa Chamber of Commerce) Mechler, Tom (Self) Menchaca, Rick (City of Midland) Meurer, Larry (Self) Morton, Richard (City of Odessa) Schiermeyer, Ruth (Self) Sims, Jonell (Self) Winn, Chris (Self) Workman, Skeet (Self) Against: Adcock, Daniel (Self) Barnes, Melanie (Self) Batson, Todd (Self) Belisle, Mavis (Self) Benson, Kenneth (Self) Blackburn, David (City of Killeen) Blocker, Corky (Martin County) Callaway, Cass (Self) Cantu, Ernestine (Self) Christian, A. Duane (West Texas Organizing Strategy) Collins, Gene (Self and NAACP, Texas; Highland & Bunche Church of Christ; Farmers Insurance) Crooker, Bill (Howard County) Dalby, Giaes (Garza County Judge) Davis, Celia (Abilene City Council; Abilene Chamber of Commerce) Davis, Evelyn (Self) Dunn, Charles (Self) Earl, Lewis (Self and Common Cause Texas) Eiland, Paige (Self) Estrada, Eduardo (Self) Everheart, Harvey (Mesa Underground Water Conservation Ditrict) Fitzgerald, Lynn (Self) Flores, Gilbert (Self and Lubbock County Commissioner, Precinct 3) Guerrero, Irma (Self and Lubbock County Democratic Party) Gutierrez, Ysidro (Self) Haldenby, Roger (Self and Plains Cotton Growers Inc.) Harris, Jay (Self) Hatfield, Mary (Self) Hicks, Roberta (Self) Hoch, Michael (Self and Glasscock County Commissioner, Precinct 4) Hurt, Steve (Self) Jackson, Carlton (Self) Jackson, Dan (Self) James, Clyde (Self) Luna, Ysabel (Self) Marble, Don (Self) Martinez, Christy (Self and Her family) McBride, Eddie (Lubbock Chamber of Commerce) McEwen, Russ (City of Big Spring) McMahon, Kevin (Self and Lubbock Chamber of Commerce) Mullan, Carolie (League of Women Voters of Texas) Pearson, Neale (Self) Pope, Dan (Self and Lubbock Chamber of Commerce) Reese, Zane (Self) Richardson, Eddie (Self) Ricketts, Robert (Self) Rogers, Robert (Self) Spychaj, Sherrie (West Texas Organizing Strategy) Stafford, Gwen (Self) Stahl, Carol (Self) Stockhausen, Lupe (West Texas Organizing Strategy) Stravato, Claudia (Self) Weninger, Betty (Self) Williams, J. Leon (West Texas Organizing Strategy) Wolfe, Rosalyn (Self) On: Heflin, Joe (Self and Crosby County) Wilbanks, Wendell (Self)


Prism - Evoking empathy for autism
Project Info
| Duration | 15 weeks (Jan 15 - May 5, 2018) |
| Team Size | 5 |
| Team members | Ridima Ramesh, Xueyang Wang, Dan Wolpow, Yutian Zheng |
| Links |
Introduction
Prism is a technological solution to an important problem society faces today—helping neurotypical people to empathize with those on the autism spectrum. Working initially with 3rd and 4th graders at the Beech Bottom Primary School in in Brooke County, West Virginia, the team behind Prism is creating the tools to help a generation of children grow up with increased awareness and understanding for their autistic peers. If successful, the technology that powers Prism can be expanded to reach additional age groups, parents of autistic children, educators, and beyond.
Notable Contributions
Programming:
- Implemented an interactive branching dialogue system
- Implemented a framework to drive easy creation of open-world narrative scenarios
- Implemented a contextualized hint system to help players (which are grade 3-4 primary school students!) progress through the game
- Implemented a framework to allow easy localization of the game
Design:
- Designed, modeled, and populated the game’s map to support different interaction scenarios
- Iterated on the map through playtesting to make it easier for players to navigate
Awards & Recognitions:
- Games for Change Festival 2019 - Best Student Game (Hey it appeared on Gamasutra too!)
- Gold Medal Winner - 2018 International Serious Play Awards
- Finalist - Serious Games Showcase & Challenge at Interservice/Industry Training, Simulation, and Education Conference 2018
- Published under Joint International Conference on Serious Games as Prism, a Game to Promote Autism Acceptance Among Elementary School Students
- Our producer, Dan, gave a talk at 4th Integrative Conference on Technology, Social Media and Behavioral Health


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Types in Code Blocks // Use single quotes when referring to identifiers // Variable 'p' is of type 'string' const p = "";Try Title Casing


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E2FREEFRAG
Section: Maintenance Commands (8)Updated: May 2010
Index Return to Main Contents
NAME
e2freefrag - report free space fragmentation informationSYNOPSIS
e2freefrag [ -c chunk_kb ] [ -h ] filesysDESCRIPTION
e2freefrag is used to report free space fragmentation on ext2/3/4 file systems. filesys is the filesystem device name (e.g. /dev/hdc1, /dev/md0). The e2freefrag program will scan the block bitmap information to check how many free blocks are present as contiguous and aligned free space. The percentage of contiguous free blocks of size and of alignment chunk_kb is reported. It also displays the minimum/maximum/average free chunk size in the filesystem, along with a histogram of all free chunks. This information can be used to gauge the level of free space fragmentation in the filesystem.OPTIONS
- -c chunk_kb
- If a chunk size is specified, then e2freefrag will print how many free chunks of size chunk_kb are available in units of kilobytes (Kb). The chunk size must be a power of two and be larger than filesystem block size.
- -h
- Print the usage of the program.
EXAMPLE
# e2freefrag /dev/vgroot/lvhomeDevice: /dev/vgroot/lvhome
Blocksize: 4096 bytes
Total blocks: 1504085
Free blocks: 292995 (19.5%)
Min. free extent: 4 KB
Max. free extent: 24008 KB
Avg. free extent: 252 KB
HISTOGRAM OF FREE EXTENT SIZES:
Extent Size Range : Free extents Free Blocks Percent
4K... 8K- : 704 704 0.2%
8K... 16K- : 810 1979 0.7%
16K... 32K- : 843 4467 1.5%
32K... 64K- : 579 6263 2.1%
64K... 128K- : 493 11067 3.8%
128K... 256K- : 394 18097 6.2%
256K... 512K- : 281 25477 8.7%
512K... 1024K- : 253 44914 15.3%
1M... 2M- : 143 51897 17.7%
2M... 4M- : 73 50683 17.3%
4M... 8M- : 37 52417 17.9%
8M... 16M- : 7 19028 6.5%
16M... 32M- : 1 6002 2.0%
AUTHOR
This version of e2freefrag was written by Rupesh Thakare, and modified by Andreas Dilger<EMAIL_ADDRESS>and Kalpak Shah.SEE ALSO
debugfs(8), dumpe2fs(8), e2fsck(8)
Index
This document was created by man2html, using the manual pages.
Time: 05:34:25 GMT, December 24, 2015


KEY:
No space is available for state name, i.e. Only a single state can be inserted per name, no duplicate states allowed!
Σ =


‘We’re talking to him’ – Eric Abidal confirms Barcelona have opened talks with talented Blaugrana ace over new contract
by Sam WilkinsBarcelona chief Eric Abidal has confirmed that the Blaugrana have opened talks with Spanish youngster Ansu Fati regarding a new deal for the 17-year-old talent.
Fati, who only made his first team debut for the club earlier this year, has massively impressed fans and critics alike with his consistent displays so far this term.
And now, it seems like the youngster is going to be rewarded for this fine form in the shape of a new contract.
According to AS, Barca are in talks with Messi and Fati regarding new contracts for the duo, with Abidal confirming these talks recently.
The Frenchman, who was brought back to Barcelona as the club’s sporting director last summer, confirmed the club’s talks with Fati over a new deal, stating “We are also talking to him. He is a young player that we have to protect. We have to talk to his family and representatives.”
So far this year, Fati has bagged two goals and one assist in 10 games for Barca in all competitions, a decent return for a player who only turned 17 last month.
A lot of hype has been made in regards to Fati recently, with fans everywhere becoming increasingly excited with the player’s potential given his fast start to life in Barca’s first team.
And now, following Abidal’s words, it seems like Barcelona themselves are willing to give the player an increased chance to shine at the Nou Camp in the future…


NASA is asking Earthlings to participate in the first-ever interplanetary photo bomb.
At 3:27 p.m. on Friday, the Cassini spacecraft will be in a unique position to photograph Saturn, and for 15 minutes, it will be able to capture Earth from deep within the solar system for the third time ever.
—
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"Earth will appear as a tiny blue speck just outside of E ring on Saturn," said Paulmichael Maxfield, gallery programs coordinator at the Natural History Museum of Utah.
The museum and the University of Utah’s Department of Physics and Astronomy will host activities from 2 to 4 p.m., including setting up special telescopes where viewers can see the sun and possibly Saturn during the daytime. U. scientists Benjamin Bromley, who specializes in the rings and moons of Saturn, and Tabitha Buehler, who specializes in new stars, will be on hand to answer questions.
"It’s a great time to visit the museum to talk with local experts and participate in hands-on activities," Maxfield said.
For the Saturn photo, staff will provide fun costumes and signs.
Downtown, folks who signed up previously will pose for the Saturn photo on the rooftop garden of the Salt Lake City Main Library for a BBC documentary called "Human Universe," which will look at humans’ continual search for their place in the cosmos.
But Patrick Wiggins, NASA/JPL solar system ambassador to Utah, reminds everyone they don’t need a formal gathering to say hi to the sixth planet.
Simply grab your colleagues or kids, walk outside, look low on the southeast horizon and remember to smile.
"We don’t want Saturnians thinking we’re a bunch of unhappy people," he joked.
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North America will be illuminated at the time of the photo, though of course no one will be identifiable from hundreds of millions of miles away. It will take the light from Earth 1 hour and 20 minutes to travel to Cassini’s cameras, Wiggins said.
"But maybe a couple of photons from your area made it into the photo," he said.
The conditions to actually aim a camera back at Earth from such a distance are rare as the sun’s bright rays fry delicate camera equipment. On Friday, Cassini’s orbit will take it to the far-side of Saturn from the sun. The planet will block those harsh rays, allowing the spacecraft’s cameras to safely capture images of a backlit Saturn’s atmosphere and rings and our tiny, blue dot of a planet.
While Wiggins is always excited about people getting excited about space exploration, he’s even more excited to find out what the photographs of Saturn turn up this time. In 2006, Cassini photographed Saturn and its rings, giving researchers new details about Saturn’s dusty, icy E ring and capturing Earth from the distance of 926 million miles away. (The first time Earth was photographed from deep inside our solar system was 23 years ago by Voyager I from 4 billion miles away.)
"You never know what you’re going to find when they backlight the rings," Wiggins said. "The structures and systems of the rings are fascinating, with shepherding moons and the gravitational interactions. You just never know what they’ll discover."
Twitter: @sheena5427
Copyright 2013 The Salt Lake Tribune. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


ireland-weather-station-county: Overview
back to
ToC
This ontology has the following classes and properties.
Classes
Antrim
April
Armagh
August
Carlow
Cavan
Clare
Cork
County
County Name
December
Derry
Donegal
Down
Dublin
February
Fermanagh
Galway
Grid Square Location
January
July
June
Kerry
Kildare
Kilkenny
Laois
Latitude Longitude Location
Leitrim
Limerick
Location
Longford
Louth
March
May
Mayo
Meath
Monaghan
Month Name
November
October
Offaly
Rainfall Record
Roscommon
September
Sligo
Temperature Record
Tipperary
Tyrone
Waterford
Weather Record
Weather Station
Westmeath
Wexford
Wicklow
Object Properties
adjacent to
county is named
happened at
Higher Than
Located at
Located In
Lower Than
month happened
Data Properties
area
close year
easting
height
latitude
longitude
northing
open year
rainfall
station name
station number
Temperature Maximum
Temperature Mean
Temperature Minimum


@(employeeForm: Form[models.users.Worker], aForm: Form[models.Address], dForm: Form[models.Department], user: models.users.Employees, env: play.api.Environment) @import helper._ @main("Add Worker", user) {
Add a new Worker
@form(action=routes.HomeController.addWorkerSubmit(), 'class -> "form-horizontal", 'role -> "form", 'method -> "POST", 'enctype -> "multipart/form-data") { @* CSRF attack Prevention *@ @CSRF.formField @inputText(employeeForm("email"), '_label -> "Email", 'class -> "form-control") @inputText(employeeForm("fName"), '_label -> "fName", 'class -> "form-control") @inputText(employeeForm("lName"), '_label -> "lName", 'class -> "form-control") @inputText(employeeForm("password"), '_label -> "Password", 'class -> "form-control") @inputText(employeeForm("id"), '_label -> "", 'hidden -> "hidden") @inputText(aForm("street1"), '_label -> "Street 1", 'class -> "form-control") @inputText(aForm("street2"), '_label -> "Street 2", 'class -> "form-control") @inputText(aForm("town"), '_label -> "Town", 'class -> "form-control") @inputText(aForm("county"), '_label -> "County", 'class -> "form-control") @inputText(aForm("eircode"), '_label -> "Eircode",'class -> "form-control") @inputText(employeeForm("role").copy(value=Some("worker")), '_label -> "", 'hidden -> "hidden") @inputText(employeeForm("salary").copy(value=Some("32500")), '_label -> "", 'hidden -> "hidden") } }


Surname
First Name
Middle name
You are male female
Age
Country of Residence Russia Germany China Italy France
City Ryazan Moscow
Photography
Title Web Master Database Administrator Computer-aided design Economic Planning Management accouting Budgeting and planning
Salary
Site
Email
Phone
Password


Maui jail penalizes 18 inmates involved in riot and fire
WAILUKU, Hawaii (AP) - A Hawaii jail has penalized 18 prisoners for their participation in a riot that caused millions of dollars in damage.
The Maui News reported Wednesday that the Maui Community Correctional Center assessed fines of $2,716 each against inmates found guilty of rioting and setting fires in March.
The state Department of Public Safety says inmates who were only found guilty of setting fires were each assessed $1,358 fines.
Authorities say damages beyond the fires included tearing nonworking phones off walls and ransacking cells.
[Read more: Former Maui inmate shares near-death experience during March riot ]
[Read more: Lawmakers set aside millions for overcrowding at Maui jail to prevent further riots ]
Jail staff and inmates say they feared for their lives as fights broke out and smoke and water filled the modules.
Officials say the inmates were sanctioned under corrections administration policies regarding misconduct violations.
Fine payments will be deposited in the state’s general fund.
Copyright 2019 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


I helped Obrafour with ‘Back to Taifa’ concert but he betrayed me during ‘Pae Mu Ka’ concert – Yaw Labito spills more secrets
by Nana YawObrafour’s old friend, Yaw Labito is really speaking from the pain he has harboured all these years.
The highlife artiste who sang the chorus on Obrafour’s ‘Odo’ song has revealed that after putting his silky voice on the song, he was forced to live under the shadow.
To him, his shine was taken from him as Obrafour intentionally refused to feature him in the music video.
According to Yaw Labito in an interview with Sammy Flex, Obrafour intentionally neglected his presence during the video shoot of the ‘Odo’ song.
“We recorded ‘Odo’ and the song became an instant hit. We went behind Achimota forest to shoot the video for the song. When it got to my part of the song, the director asked Obrafour to let me sing my part of the, but to my surprise, he told the director in my presence that the video will be used for a documentary.”
“I felt bad but told people who asked why I wasn’t featured in the video that I had travelled during the shoot,” he narrated.
After all these years, he thought the dust will finally settle for him to get his shine but he was once again neglected during Obrafour’s recent concert, ‘Pae Mu Ka at 20’.
According to Yaw Labito, he was called by Obraofur to help him with the ‘Back to Taifa’ which was held in October, some few weeks before the grand ‘Pae Mu Ka’ concert.
He agreed and performed with Obrafour on the same ‘Odo’ song but during the ‘Pae Mu Ka’ concert, he was substituted for Bisa Kdei who sang his part of the song.
Narrating what happened, he revealed that he reached out to Obrafour prior to the concert but was told he was granting a radio interview and will be attended to after the interview but that didn’t happen. He tried several times but all proved futile.
He later saw the video after several people started calling him on why he didn’t make an appearance during the performance of the song.
Watch the interview below:


Before Change
with warnings.catch_warnings(record=True) as w:
bad_gt = gradient_table(expected_bvals, expected_bvecs,
b0_threshold=200)
assert len(w) == 1
def test_GradientTable_btensor_calculation():
After Change
b0_threshold=200)
// Select only UserWarning
selected_w = [w for w in l_warns
if issubclass(w.category, UserWarning)]
assert len(selected_w) >= 1
msg = [str(m.message) for m in selected_w]
npt.assert_equal("b0_threshold has a value > 199" in msg, True)
def test_GradientTable_btensor_calculation():