Package com.qoretechnologies.qorus
Class FsRemoteSend
- java.lang.Object
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- org.qore.jni.QoreObjectWrapper
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- com.qoretechnologies.qorus.FsRemoteSend
-
public class FsRemoteSend extends org.qore.jni.QoreObjectWrapper
Java wrapper for the OMQ::FsRemoteSend class in Qorus
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Constructor Summary
Constructors Constructor Description FsRemoteSend(DbRemote remote, java.lang.String path)
Constructor taking a com.qoretechnologies.qorus.DbRemote "DbRemote" object for the remote serverFsRemoteSend(java.lang.String remote, java.lang.String path, java.util.HashMap<java.lang.String,java.lang.Object> options)
Constructor taking a com.qoretechnologies.qorus.DbRemote "DbRemote" object for the remote serverFsRemoteSend(org.qore.jni.QoreObject obj)
Creates the object as a wrapper for the Qore object
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
append(byte[] data)
Append data to the ready queue Example:void
append(java.lang.String data)
Append data to the ready queue Example:void
cancel()
This method purges any queued I/O and stops the socket thread and returns when the socket I/O thread has terminated Example:void
commit()
This method flushes all buffered I/O to the remote server and returns when all data have been sent to the server and the socket I/O thread has terminated Example:StreamConfig
config()
Returns the configuration objectvoid
del()
Executes an implicit commit() and deletes the file given in "path" in the constructor() Example:void
rename(java.lang.String target, boolean man)
Executes an implicit commit() and renames/moves the file given in "path" in the constructor() Example:
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Constructor Detail
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FsRemoteSend
public FsRemoteSend(org.qore.jni.QoreObject obj)
Creates the object as a wrapper for the Qore object
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FsRemoteSend
public FsRemoteSend(java.lang.String remote, java.lang.String path, java.util.HashMap<java.lang.String,java.lang.Object> options) throws java.lang.Throwable
Constructor taking a com.qoretechnologies.qorus.DbRemote "DbRemote" object for the remote server- Parameters:
remote
- a string giving the name of the remoteconn "remote connection" for the remote serverpath
- a string with full or relative path of the target file (inluding file name)options
- optional stream-api-options as follows:- "timeout": an HTTP socket timeout value in milliseconds; used locally and in the remote for socket I/O and queue operations; default value: "5m"
- "loglevel": a default log level option for logging; see LogLevels for valid value
- "block": data block size (default 16384, minimum 4096); the number of bytes sent in each datastreamprotocol "DataStream" protocol chunk
- "queue_block_size": the number of blocks to queue for sending before the main data thread will block (default: 2)
- "queue_timeout": the number of milliseconds to wait for queue data before throwing a "QUEUE-TIMEOUT" exception
- "no_remote_timeout": if True the "timeout" option will not be sent to the remote
- Throws:
java.lang.Throwable
- Note:
- to calculate the total data queued for the socket I/O thread, multiply block by queue_block_size (default 16384 * 2 = 32768 bytes)
- the explicit or default timeout value here overrides any socket I/O timeout set for the remote connection object
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FsRemoteSend
public FsRemoteSend(DbRemote remote, java.lang.String path) throws java.lang.Throwable
Constructor taking a com.qoretechnologies.qorus.DbRemote "DbRemote" object for the remote server- Parameters:
remote
- a string giving the name of the remoteconn "remote connection" for the remote serverpath
- a string with full or relative path of the target file (inluding file name)- Throws:
java.lang.Throwable
- Note:
- the explicit or default timeout value here overrides any socket I/O timeout set for the remote connection object
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Method Detail
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commit
public void commit() throws java.lang.Throwable
This method flushes all buffered I/O to the remote server and returns when all data have been sent to the server and the socket I/O thread has terminated Example:
Log messages are created with performance information (bytes send, time of transfer, bytes / second, etc).FsRemoteSend fs = new FsRemoteSend(qrest, target_path, opts); try { // send all strings in an array of strings for (String str : array) { fs.append(str); } // finalize the data transfer fs.commit(); } catch (Throwable e) { // cancel the send stream fs.cancel(); } finally { // release the weak reference to the Qore object fs.release(); }
- Throws:
java.lang.Throwable
- See Also:
flush()
- Note:
- either this method, flush() or cancel() should be called to ensure that all data has been sent
- exceptions thrown in the socket I/O thread will be rethrown when this method is called so that errors can be handled in the main data thread
- equivalent to flush()
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cancel
public void cancel() throws java.lang.Throwable
This method purges any queued I/O and stops the socket thread and returns when the socket I/O thread has terminated Example:FsRemoteSend fs = new FsRemoteSend(qrest, target_path, opts); try { // send all strings in an array of strings for (String str : array) { fs.append(str); } // finalize the data transfer fs.commit(); } catch (Throwable e) { // cancel the send stream fs.cancel(); } finally { // release the weak reference to the Qore object fs.release(); }
- Throws:
java.lang.Throwable
- Note:
- either this method or commit() should be called to ensure that all data has been sent
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append
public void append(java.lang.String data) throws java.lang.Throwable
Append data to the ready queue Example:
Data are queued for sending when the buffered byte count reaches the "block" size.FsRemoteSend fs = new FsRemoteSend(qrest, target_path, opts); try { // send all strings in an array of strings for (String str : array) { fs.append(str); } // finalize the data transfer fs.commit(); } catch (Throwable e) { // cancel the send stream fs.cancel(); } finally { // release the weak reference to the Qore object fs.release(); }
- Parameters:
data
- data to be sent- Throws:
java.lang.Throwable
- Note:
- make sure to call either commit() (to flush all data to the server) or cancel() (to cancel the I/O operation) before continuing (see the example above)
- exceptions thrown in the socket I/O thread will be rethrown when this method is called so that errors can be handled in the main data thread
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append
public void append(byte[] data) throws java.lang.Throwable
Append data to the ready queue Example:
Data are queued for sending when the buffered byte count reaches the "block" size.FsRemoteSend fs = new FsRemoteSend(qrest, target_path, opts); try { // send all strings in an array of strings for (String str : array) { fs.append(str); } // finalize the data transfer fs.commit(); } catch (Throwable e) { // cancel the send stream fs.cancel(); } finally { // release the weak reference to the Qore object fs.release(); }
- Parameters:
data
- data to be sent- Throws:
java.lang.Throwable
- Note:
- make sure to call either commit() (to flush all data to the server) or cancel() (to cancel the I/O operation) before continuing (see the example above)
- exceptions thrown in the socket I/O thread will be rethrown when this method is called so that errors can be handled in the main data thread
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rename
public void rename(java.lang.String target, boolean man) throws java.lang.Throwable
Executes an implicit commit() and renames/moves the file given in "path" in the constructor() Example:FsRemoteSend fs = new FsRemoteSend(qrest, target_path, opts); try { // send all strings in an array of strings for (String str : array) { fs.append(str); } // finalize the data transfer fs.commit(); // rename the file after the transfer fs.rename(final_name); } catch (Throwable e) { // cancel the send stream fs.cancel(); } finally { // release the weak reference to the Qore object fs.release(); }
- Parameters:
target
- a new path with filename. Relative to user connection if the user connection constructor is used.man
- rename the file manually by copying the file's data and then deleting the source file, this is sometimes necessary on some OSes when renaming files across filesystem boundaries- Throws:
java.lang.Throwable
- Note:
- do not call this method before all data have been sent (ie call only after calling commit() first as in the example above)
- Warning:
- The original file after the rename does not exist anymore if "man" is set to True. Any additional append() call will result in an exception.
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del
public void del() throws java.lang.Throwable
Executes an implicit commit() and deletes the file given in "path" in the constructor() Example:FsRemoteSend fs = new FsRemoteSend(qrest, target_path, opts); try { // send all strings in an array of strings for (String str : array) { fs.append(str); } // finalize the data transfer fs.commit(); // delete the file after the transfer fs.del(); } catch (Throwable e) { // cancel the send stream fs.cancel(); } finally { // release the weak reference to the Qore object fs.release(); }
- Throws:
java.lang.Throwable
- Note:
- do not call this method before all data have been sent (ie call only after calling commit() first as in the example above)
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config
public StreamConfig config() throws java.lang.Throwable
Returns the configuration object- Throws:
java.lang.Throwable
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| Class | ActionController::Routing::Segment |
| In: |
vendor/rails/actionpack/lib/action_controller/routing.rb
|
| Parent: | Object |
| is_optional | -> | optional? |
| is_optional | [RW] |
# File vendor/rails/actionpack/lib/action_controller/routing.rb, line 542 542: def initialize 543: self.is_optional = false 544: end
Return an if condition that is true if all the prior segments can be generated. If there are no optional segments before this one, then nil is returned.
# File vendor/rails/actionpack/lib/action_controller/routing.rb, line 573 573: def all_optionals_available_condition(prior_segments) 574: optional_locals = prior_segments.collect { |s| s.local_name if s.optional? && s.respond_to?(:local_name) }.compact 575: optional_locals.empty? ? nil : " if #{optional_locals * ' && '}" 576: end
Continue generating string for the prior segments.
# File vendor/rails/actionpack/lib/action_controller/routing.rb, line 551 551: def continue_string_structure(prior_segments) 552: if prior_segments.empty? 553: interpolation_statement(prior_segments) 554: else 555: new_priors = prior_segments[0..-2] 556: prior_segments.last.string_structure(new_priors) 557: end 558: end
# File vendor/rails/actionpack/lib/action_controller/routing.rb, line 546 546: def extraction_code 547: nil 548: end
Return a string interpolation statement for this segment and those before it.
# File vendor/rails/actionpack/lib/action_controller/routing.rb, line 561 561: def interpolation_statement(prior_segments) 562: chunks = prior_segments.collect { |s| s.interpolation_chunk } 563: chunks << interpolation_chunk 564: "\"#{chunks * ''}\"#{all_optionals_available_condition(prior_segments)}" 565: end
Recognition
# File vendor/rails/actionpack/lib/action_controller/routing.rb, line 580 580: def match_extraction(next_capture) 581: nil 582: end
Returns true if this segment is optional? because of a default. If so, then no warning will be emitted regarding this segment.
# File vendor/rails/actionpack/lib/action_controller/routing.rb, line 588 588: def optionality_implied? 589: false 590: end


Requirements¶
The purpose of presenting these requirements is to inform the community on the present capabilities of the WE-UQ app and features that could be added. The original set of requirements have come from a set of grand challenge reports, GC. These original requirements have been broken into a smaller set of deliverable features by the senior faculty associated with the project, SP. Additional requirements have come from users, U See section features if you have additional features you would like to see.
quoFEM Requirements¶
QF.1.1 |
Ability to use Forward Propagtion methods listed in UQ under UF |
SP |
M |
Implemented |
_ |
QF.1.2 |
Ability to use Random Variable Distributions defeined in RV |
SP |
M |
_ |
_ |
QF.1.3 |
Ability to use Reliability Methods listed in UQ under UR |
SP |
M |
Implemented |
_ |
QF.1.4 |
Ability to use Global Sensitivity Methods listed in UQ under UG |
SP |
M |
Implemented |
_ |
QF.1.5 |
Ability to both use and create surrogates listed in UQ under US |
SP |
M |
InProgress |
_ |
QF.1.6 |
Ability to use High Dimensional UQ listed in UQ under UH |
SP |
M |
InProgress |
_ |
QF.1.7 |
Ability to use Bayesian Calibration methods listed in UQ under UB |
SP |
M |
_ |
_ |
QF.1.8 |
Ability to use Nonlinear Least Squares methods listed in UQ under UN |
SP |
M |
Implemented |
_ |
QF.2.1 |
Ability to use OpenSees |
SP |
M |
Implemented |
_ |
QF.2.2 |
Ability to use OpenSeesPy |
SP |
M |
Implemented |
_ |
QF.2.3 |
Ability to Incorporate User Own Applications |
UF |
M |
Implemented |
|
QF.3.1 |
Ability to view individual sample results |
SP |
M |
Implemented |
core |
QF.3.2 |
Ability to graphically view the results to show distribution in response |
SP |
M |
Implemented |
core |
QF.3.3 |
Ability to view statistical measures of response |
SP |
M |
Implemented |
core |
QF.4.1 |
Run application from command line, include option to run remotely |
UF |
D |
_ |
_ |
QF.7.1 |
Application to Provide Common SimCenter Research Application Requirements listed in CR |
GC |
M |
InProgresss |
_ |
UQ Requirements¶
# |
Description |
Source |
Priority |
Status |
Implementation |
|---|---|---|---|---|---|
UF.1 |
Ability to use basic Monte Carlo and LHS methods |
SP |
M |
Implemented |
_ |
UF.2 |
Ability to use Gaussian Process Regression |
SP |
M |
Implemented |
_ |
UF.3 |
Ability to use Own External UQ Engine |
SP |
M |
_ |
_ |
UF.4 |
Ability to use Multi-Scale Monte Carlo |
SP |
M |
_ |
_ |
UF.5 |
Ability to use Multi-Fidelity Models |
SP |
M |
InProgress |
_ |
UR.1 |
Ability to use First Order Reliability method |
SP |
M |
Implemented |
_ |
UR.2 |
Ability to use Second Order Reliability method |
SP |
M |
Implemented |
_ |
UR.3 |
Ability to use Surrogate Based Reliability |
SP |
M |
Implemented |
_ |
UR.4 |
Ability to use Importance Sampling |
SP |
M |
Implemented |
_ |
UG.1 |
Ability to obtain Global Sensitivity Sobol indices |
UF |
M |
Implemented |
_ |
UG.2 |
Ability to use probability model-based global sensitivity analysis (PM-GSA) |
SP |
M |
Implemented |
_ |
US.1 |
Ability to Construct Gaussian Process (GP) Regression Model from a Simulation Model |
SP |
M |
InProgress |
_ |
US.2 |
Ability to Construct GP Regression Model from Input-output Dataset |
SP |
M |
InProgress |
_ |
US.3 |
Ability to use Surrogate Model for UQ Analysis |
SP |
M |
InProgress |
_ |
US.4 |
Ability to Save the Surrogate Model |
SP |
M |
InProgress |
_ |
US.5 |
Ability to Use Adaptive Design of Experiments |
SP |
M |
InProgress |
_ |
US.6 |
Ability to Asses Reliability of Surrogate Model |
SP |
M |
Implemented |
_ |
US.7 |
Ability to Build Surrogate Under Stochastic Excitation |
SP |
M |
InProgress |
_ |
US.8 |
Ability to Use Physics-Informed Machine Learning |
SP |
M |
_ |
_ |
UN.1 |
Ability to use Gauss-Newton solvers for parameter estimation |
SP |
M |
Implemented |
_ |
UN.2 |
Ability to read calibration data from file |
UF |
M |
Implemented |
_ |
UN.3 |
Ability to handle non-scalar response quantities |
UF |
M |
Implemented |
_ |
UB.1 |
Ability to use DREAM algorithm for Bayesian inference |
SP |
M |
Implemented |
_ |
UB.2 |
Ability to use TMCMC algorithm for Bayesian inference |
SP |
M |
Implemented |
_ |
UB.3 |
Ability to read calibration data from file |
UF |
M |
Implemented |
_ |
UB.4 |
Ability to handle non-scalar response quantities |
UF |
M |
Implemented |
_ |
UB.5 |
Ability to calibrate multipliers on error covariance |
UF |
M |
Implemented |
_ |
UB.6 |
Ability to use a default log-likelihood function |
UF |
M |
Implemented |
_ |
UB.7 |
Ability to use Kalman Filtering |
UF |
M |
_ |
_ |
UB.8 |
Ability to use Particle Filtering |
UF |
M |
_ |
_ |
UH.1 |
Ability to sample from manifold |
SP |
M |
Implemented |
_ |
UH.2 |
Ability to build Reduced Order Model |
SP |
M |
_ |
_ |
UO.1 |
Ability to use User-Specified External UQ Engine |
SP |
M |
Implemented |
_ |
UO.2 |
Ability to use Own External FEM Application |
UF |
M |
Implemented |
_ |
UM.1 |
Ability to use various Reliability Methods |
_ |
_ |
_ |
_ |
UM.1.1 |
Ability to use First Order Reliability method |
UF |
M |
Implemented |
_ |
UM.1.2 |
Ability to use Surrogate Based Reliability |
UF |
M |
_ |
_ |
UM.1.3 |
Ability to use Own External Application to generate Results |
UF |
M |
Implemented |
_ |
UM.2 |
Ability to user various Sensitivity Methods |
_ |
_ |
_ |
_ |
UM.2.1 |
Ability to obtain Global Sensitivity Sobol’s indices |
UF |
M |
Implemented |
_ |
RV Requirements¶
# |
Description |
Source |
Priority |
Status |
Implementation |
|---|---|---|---|---|---|
RV.1 |
Various Random Variable Probability Distributions |
_ |
_ |
_ |
_ |
RV.1.1 |
Normal |
SP |
M |
Implemented |
_ |
RV.1.2 |
Lognormal |
SP |
M |
Implemented |
_ |
RV.1.3 |
Uniform |
SP |
M |
Implemented |
_ |
RV.1.4 |
Beta |
SP |
M |
Implemented |
_ |
RV.1.5 |
Weibull |
SP |
M |
Implemented |
_ |
RV.1.6 |
Gumbel |
SP |
M |
Implemented |
_ |
RV.2 |
User defined Distribution |
SP |
M |
_ |
_ |
RV.3 |
Define Correlation Matrix |
SP |
M |
Implemented |
_ |
RV.4 |
Random Fields |
SP |
M |
_ |
_ |
RV.5 |
Ability to View Graphically the density function when defining the RV |
UF |
D |
Implemented |
Common Research Application Requirements¶
# |
Description |
Source |
Priority |
Status |
Implementation |
|---|---|---|---|---|---|
CR.1 |
Open-source software where developers can test new data and develop algorithms |
_ |
_ |
_ |
_ |
CR.1.1 |
Provide open-source applications utilizing code hosting platforms, e.g. GitHub |
SP |
M |
Implemented |
|
CR.1.2 |
Assign an open-source licensce that allows free use. |
SP |
M |
Implemented |
|
CR.2 |
Ability of Practicing Engineers to use multiple coupled resources (applications, databases, viz tools) in engineering practice |
_ |
_ |
_ |
_ |
CR.2.1 |
Allow users to launch scientific workflows |
SP |
M |
Implemented |
_ |
CR.3 |
Ability to utilize resources beyond the desktop including HPC |
_ |
_ |
_ |
_ |
CR.3.1 |
Allow users to utilize HPC resources at TACC through DesignSafe |
SP |
M |
Implemented |
_ |
CR.4 |
Efficient use of multiple coupled and linked models requiring sharing and inter-operability of databases, computing environments, networks, visualization tools, and analysis systems |
_ |
_ |
_ |
_ |
CR.4.1 |
Identify and include external analysis systems |
SP |
M |
InProgress |
_ |
CR.4.2 |
Identify and include external databases |
SP |
M |
InProgress |
_ |
CR.4.3 |
Identify and include external viz tools |
SP |
M |
InProgress |
_ |
CR.4.4 |
Identify and include external computing env |
SP |
M |
Inprogress |
_ |
CR.5 |
Tool available for download from web |
_ |
_ |
_ |
_ |
CR.5.1 |
Tool downloadable from DesignSafe website |
GC |
M |
Implemented |
|
CR.6 |
Ability to benefit from programs that move research results into practice and obtain training |
_ |
_ |
_ |
_ |
CR.6.1 |
Ability to use educational provisions to gain interdisclipinary education so as to gain expertise in earth sciences and physics, engineering mechanics, geotechnical engineering, and structural engineering in order to be qualified to perform these simulations |
GC |
D |
_ |
_ |
CR.6.2 |
Documentation exists demonstrainting application usage |
SP |
M |
Implemented |
_ |
CR.6.3 |
Video Exists demonstrating application usage |
SP |
M |
Implemented |
_ |
CR.6.4 |
Tool Training through online and in person training events |
SP |
M |
Implemented |
_ |
CR.7 |
Verification Examples Exist |
SP |
M |
Implemented |
_ |
CR.8 |
validation of proposed analytical models against existing empirical datasets |
_ |
_ |
_ |
_ |
CR.8.1 |
Validation Examples Exist, validated against tests or other software |
GC |
M |
_ |
|
CR.9 |
Tool to allow user to load and save user inputs |
SP |
M |
Implemented |
core |
CR.10 |
Installer which installs application and all needed software |
UF |
D |
_ |


Binary Exploitation Challenges from iCTFF 2019
Bin100
We are given a PE32 executable file which when run asks user for a serial key.
Simply opening the file in ghidra
. We see strings given in the output including a base64 text.
Trying to decode this base64 data, we seem to get a version of the encrypted flag.
Going through the source code in ghidra, we see that the flag seems to be bit shifted.
Learning more about bit shifting from https://wiki.python.org/moin/BitwiseOperators.
We can write a small python script that would shift the contents of the encrypted flag to 1.
import base64
cipher = '6tLo2vauZpjGYJpmvuievo7c0qTKZpxijtyK+g=='
a = base64.b64decode(cipher)
c = []
for char in a:
c.append(chr(ord(char) >> 1))
d = ''.join(c)
print("Flag: "+d)
Running this gives us the flag.
Solution 2:
If we want to want to avoid python scripting and stop worrying about the bit shift, we could decode the initial base64 just by using Cyberchef (https://gchq.github.io/CyberChef).
Just add From Base64
and Bit Shift Right
options.
Bin200
We are given 2 files named decryptor and flag. Viewing the contents of these two files, file ‘flag’ seems to be encrypted data, which we assume when encrypted gives us the flag we are looking for.
Running file command against ‘decrytor’.
We can see that it is python 3.6 compiled. We can get the source code decompiled by using uncompyle6
. In order to do that we have to rename decryptor
to decryptor.pyc
, essentially add a .pyc
extension.
Here we have the decompiled code:
Looking at this python code we see that the program asks the user for a key and simply XORes the flag in plaintext with the key…
Now we know that flag is encrypted using a XOR algorithm… We also know that the flag format would be uitm{….}.
Since we know the cipher text (flag) and plaintext (uitm{..}). We could guess the key by XORing each other.
To get the key, we try to give the key as uitm{
, which would be the first 5 characters of our flag.
We get a byte array of values yFk*}T@M\x18Cm\\F\x18E~Vo\x136Sfe\x18Cm\\f:
.
We try to do the same thing again, but this time giving the first 5 bytes of our last output which is ` yFk*}`.
Flag: uitm{XoR_EasY_CrypT0_Iz_Easy}.


Comments
#
# Single line comment
/* Multi-line
comment */
String
""
"foo\"bar"
"foo
bar"
''''
''foo'''bar''
''
foo
bar
''
String interpolation
"foo${42}bar"
"foo\${42}bar" # This is not interpolated
''foo${42}bar''
''foo''${42}bar'' # This is not interpolated
URLs and paths
ftp://ftp.nluug.nl/pub/gnu/hello/hello-2.1.1.tar.gz
http://example.org/foo.tar.bz2
/bin/sh
./builder.sh
~/foo.bar
Integers, booleans and null
0
42
true
false
null
Builtin functions
name = baseNameOf (toString url);
imap =
if builtins ? genList then
f: list: genList (n: f (n + 1) (elemAt list n)) (length list)


| Module | ScrollPaginate::Helpers::ViewHelpers |
| In: |
lib/scroll_paginate/helpers/view_helpers.rb
|
DECRIPTION: will add require css and javascript file to layout. EXAMPLE: <%= include_scroll_paginate %>
DESCRIPTION: will generete the javascript block for enebaling scroll pagination. EXAMPLE: <%= scroll_paginate(:url => ‘example.com’,
:total_results => 100, :container => "mycontainer", :current_page => 0, :per_page => 20, :pager_var => 'page', :loader_img_path => 'images/loader.gif', :loader_text => 'Loading...', :debug => 1) %>
:url ( required field :EXAMPLE => ‘example.com’, :NOTE => Dont put http;// ) :total_results ( required field :EXAMPLE => User.count ) :container ( required field :EXAMPLE => Container Div for scroll pagination ) :current_page ( optional :DESCRIPTION => Gets appended to url as a GET value to help your backend script keep track of which page of results the user is on. Defaults to 0 (first page) ), :per_page ( optional :DESCRIPTION => Used for calculation and display purposes, tell the plugin how many results are being displayed per page. Defaults to 50.), :pager_var ( optional :DESCRIPTION => Related to currentPage, gets appended to url as a GET var to help your backend script keep track of which page of results the user is on (e.g. index.php?page=2). Defaults to "page" ) :loader_img_path ( optional :DESCRIPTION => Tell the plugin where to find the loader img relative to the page calling the plugin. Defaults to "images/scroll_paginate/default/loader.gif" ), :loader_text ( optional :DESCRIPTION => Add a text msg to Ajax loader ) :debug ( optional :DESCRIPTION => When set to 1, the plugin will print debugging information to the console (firebug). Defaults to 0 ) %>


To install and use m0n0wall-CMI, you will need following things:
Latest version of this software could be found on sourceforge here
To gather latest source, you can use a CVS client. From UNIX, type following commands:
First, extract the latest archive to a web-directory of your choice.
If you use CVS version, just copy the directory gathered from CVS server.
tar xzvf m0n0wall-CMI-1.0-alpha1.tar.gz
Then, be sure to have the information of your MySQL server (credentials, rights, database) under hands,
and point your browser to http://yoursite.com/
As you can see, there is a requirement check. To correct the two first errors, issues following commands inside your m0n0wall-CMI directory:
chmod 777 tmp/
chmod 777 inc/config.inc.php
Refresh the page and you should be able to see the installation page like the following:
Fill in the information according to your MySQL credentials. Note that the database should exist and that you need right to create table inside. You should then see the detailled installation of m0n0wall-CMI, and, if you're lucky, the process should run without any error:
Your m0n0wall-CMI installation is now complete ! You can still do a little bit of cleaning in your installation by issuing commands:
chmod 444 inc/config.inc.php
rm -rf install/
If you encounter any issue during the installation, or find any bug, please report it through the sourceforge project page at https://sourceforge.net/projects/m0n0wall-cmi


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Preface
Math 253 and the Macalester statistics curriculum
Math 253, Statistical Computing and Machine Learning, is a course at Macalester College. The course comes early in our curriculum for the statistics major. Currently, that curriculum looks like:
- Comp 110, Data Computing, a no-prerequisite course in data wrangling and visualization. The main tools in the course are R, dplyr, and ggplot.
- Math 125, Epidemiology. This course also has no pre-requisites. It’s designed to teach quantitative and statistical literacy in the context of public health and decision making. Unlike the other courses in our curriculum, Math 125 is not computer intensive. This is an elective for the statistics major.
- Math 155, Introduction to Statistical Modeling. This is our main entry-level statistics course. It also has no pre-requisites, but we encourage students to take a course in our calculus sequence: Applied Multivariate Calculus I, II, and III. The course covers important concepts in modeling: model architectures and fitting using mainly linear and logistic regression, covariation and adjustment, interpretation of model coefficients, inference techniques such as analysis of variance and co-variance and the ways these techniques can inform the decisions needed to build useful models, causality including techniques for making reasonable conclusions about causation from observational data. The course makes very extensive and intensive use of R and the
mosaic
package for R. This course, Math 253, Statistical Computing and Machine Learning. Math 253 introduces a broader set of model architectures (e.g. those associated with “machine learning” such as support vector machines) and the trade-offs that make machine learning a human skill rather than a push button mechanism. The main text is Introduction to Statistical Learning in R. Computing in R is used intensively in the course. Math 155 is a pre-requisite. The computing used in that course is the only pre-requisite. A small part of Math 253 is given to basic programming in R, but the large majority is about the mathematical and statistical concepts involved in learning from data and the exercise of a variety of architectures for classification and regression models.
Math 253 is placed intentionally in the middle of our statistics curriculum to make it accessible to many students who are interested and may have use for the techniques, but whom are not primarily interested in statistics per se.- Upper level courses including:
- Math 454: Bayesian statistics
- Math 453: Biostatistics
- Math 454: Mathematical statistics
- Supporting courses including:
- Math 354: Probability
- Math 135: Applied Multivariate Calculus I
- Math 236: Linear algebra
- Comp 123: Core conceptsin computer science
- Electives such as
- Math 432: Mathematical modeling
- Math/Comp 365: Numerical linear algebra
- Comp 302: Introduction to database management systems
These notes are written in Bookdown
The document uses an elaboration on R/Markdown, called “Bookdown.” I don’t yet know if this will be a good way to maintain and distribute class notes.
Some advantages:
- All the notes are in one place.
- Students and other instructors can clone the notes for their own uses.
- People spotting mistakes can contribute corrections via the “pull request” mechanism supported by Bookdown working with GitHub.
- Multiple instructors can contribute to the notes via GitHub. This is the stated purpose behind Bookdown — allowing book-length publications to be authored by many authors.
Disadvantages:
- The notes are always a work in progress. Don’t be mislead by the polish that RMarkdown and Bookdown give to the notes.


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Simply email us to initiate a return. The return address is listed on the shipment you received.
We recommend shipping to us with a trackable shipping method so you can stay on top of the timeline of your return. If you’re in the USA, we recommend USPS First Class shipping, which is inexpensive and provides a tracking number. Pack items securely in a sturdy box and cushion with packing bubbles or newspaper.


Nelson Mandela
Education is the Most Powerful Weapon which you can use to Change The World
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Consists of Power Source Board, Digital Multimeter, 5 PCB Boards, Resistors, Capacitors, Diodes, Bulbs, LEDs, Transistors, Solar Cell, Motor, Fan, Potentiometers, LDR and other Electronic Components. *A very good foundation kit to master the basics of Electrical and Electronics.
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Feel free to contact us any time. We will get back to you as soon as we can. We are eagerly waiting for resolve your problems with a good solution.
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User Guide:Zoom and rotate using mouse/trackpad.Move XY Plane using keys Z/E.Move YZ Plane using keys A/D.Move XZ Plane using keys W/S.Click to select a liquid molecule.


Arguments¶
Arguments work similarly to options but are positional. They also only support a subset of the features of options due to their syntactical nature. Click will also not attempt to document arguments for you and wants you to document them manually in order to avoid ugly help pages.
Basic Arguments¶
The most basic option is a simple string argument of one value. If no
type is provided, the type of the default value is used, and if no default
value is provided, the type is assumed to be STRING
.
Example:
@click.command()
@click.argument('filename')
def touch(filename):
click.echo(filename)
And what it looks like:
$ touch foo.txt
foo.txt
Variadic Arguments¶
The second most common version is variadic arguments where a specific (or
unlimited) number of arguments is accepted. This can be controlled with
the nargs
parameter. If it is set to -1
, then an unlimited number
of arguments is accepted.
The value is then passed as a tuple. Note that only one argument can be
set to nargs=-1
, as it will eat up all arguments.
Example:
@click.command()
@click.argument('src', nargs=-1)
@click.argument('dst', nargs=1)
def copy(src, dst):
for fn in src:
click.echo('move %s to folder %s' % (fn, dst))
And what it looks like:
$ copy foo.txt bar.txt my_folder
move foo.txt to folder my_folder
move bar.txt to folder my_folder
Note that this is not how you would write this application. The reason for this is that in this particular example the arguments are defined as strings. Filenames, however, are not strings! They might be on certain operating systems, but not necessarily on all. For better ways to write this, see the next sections.
Note on Non-Empty Variadic Arguments
If you come from argparse
, you might be missing support for setting
nargs
to +
to indicate that at least one argument is required.
This is supported by setting required=True
. However, this should
not be used if you can avoid it as we believe scripts should gracefully
degrade into becoming noops if a variadic argument is empty. The
reason for this is that very often, scripts are invoked with wildcard
inputs from the command line and they should not error out if the
wildcard is empty.
File Arguments¶
Since all the examples have already worked with filenames, it makes sense
to explain how to deal with files properly. Command line tools are more
fun if they work with files the Unix way, which is to accept -
as a
special file that refers to stdin/stdout.
Click supports this through the click.File
type which
intelligently handles files for you. It also deals with Unicode and bytes
correctly for all versions of Python so your script stays very portable.
Example:
@click.command()
@click.argument('input', type=click.File('rb'))
@click.argument('output', type=click.File('wb'))
def inout(input, output):
while True:
chunk = input.read(1024)
if not chunk:
break
output.write(chunk)
And what it does:
$ inout - hello.txt
hello
^D
$ inout hello.txt -
hello
File Path Arguments¶
In the previous example, the files were opened immediately. But what if we just want the filename? The naïve way is to use the default string argument type. However, remember that Click is Unicode-based, so the string will always be a Unicode value. Unfortunately, filenames can be Unicode or bytes depending on which operating system is being used. As such, the type is insufficient.
Instead, you should be using the Path
type, which automatically
handles this ambiguity. Not only will it return either bytes or Unicode
depending on what makes more sense, but it will also be able to do some
basic checks for you such as existence checks.
Example:
@click.command()
@click.argument('f', type=click.Path(exists=True))
def touch(f):
click.echo(click.format_filename(f))
And what it does:
$ touch hello.txt
hello.txt
$ touch missing.txt
Usage: touch [OPTIONS] F
Error: Invalid value for "f": Path "missing.txt" does not exist.
File Opening Safety¶
The FileType
type has one problem it needs to deal with, and that
is to decide when to open a file. The default behavior is to be
“intelligent” about it. What this means is that it will open stdin/stdout
and files opened for reading immediately. This will give the user direct
feedback when a file cannot be opened, but it will only open files
for writing the first time an IO operation is performed by automatically
wrapping the file in a special wrapper.
This behavior can be forced by passing lazy=True
or lazy=False
to
the constructor. If the file is opened lazily, it will fail its first IO
operation by raising an FileError
.
Since files opened for writing will typically immediately empty the file, the lazy mode should only be disabled if the developer is absolutely sure that this is intended behavior.
 If a file is opened in lazy mode, it will receive a
close_intelligently
method that can help figure out if the file
needs closing or not. This is not needed for parameters, but is
necessary for manually prompting with the prompt()
function as you
do not know if a stream like stdout was opened (which was already open
before) or a real file that needs closing.
Starting with Click 2.0, it is also possible to open files in atomic mode by
passing atomic=True
. In atomic mode, all writes go into a separate
file in the same folder, and upon completion, the file will be moved over to
the original location. This is useful if a file regularly read by other
users is modified.
Environment Variables¶
Like options, arguments can also grab values from an environment variable. Unlike options, however, this is only supported for explicitly named environment variables.
Example usage:
@click.command()
@click.argument('src', envvar='SRC', type=click.File('r'))
def echo(src):
click.echo(src.read())
And from the command line:
$ export SRC=hello.txt
$ echo
Hello World!
In that case, it can also be a list of different environment variables where the first one is picked.
Generally, this feature is not recommended because it can cause the user a lot of confusion.
Option-Like Arguments¶
Sometimes, you want to process arguments that look like options. For
instance, imagine you have a file named -foo.txt
. If you pass this as
an argument in this manner, Click will treat it as an option.
To solve this, Click does what any POSIX style command line script does,
and that is to accept the string --
as a separator for options and
arguments. After the --
marker, all further parameters are accepted as
arguments.
Example usage:
@click.command()
@click.argument('files', nargs=-1, type=click.Path())
def touch(files):
for filename in files:
click.echo(filename)
And from the command line:
$ touch -- -foo.txt bar.txt
-foo.txt
bar.txt


WITNESS LIST Ways & Means Committee February 17, 2005-10:30A Alcohol & Cigarette Taxes On: Haire, Jim (Self) Alcohol Tax on Wine On: Constable, Les (Texas Wine and Grape Growers Association) Alcohol Taxes On: Jackson, Richie (Texas Restaurant Association) McKinney, Mike (Self and Wholesale Beer Distributors of Texas) Wolfe, Tracey (Self) Alcohol Bev. Tax On: Szyndrowski, Dale (Distilled Spirits Council of U.S.) Alcohol Taxes On: Steen, Allan (TABC - Administrator) Cigarette and OTP Tax On: Ross, Shon (Self and National Association of Tobacco Outlets) Cigarette Tax On: Byram, Anderson (Citigroup Global Markets) Haddon, James F. (Citigroup) Cigarette Tax Increase On: Bacchi, MD, Donna (American Heart Association) Cigarette Taxation On: Orzechowski, Bill (R.J. Reynolds Tobacco Company) GasTax On: Lehman, Mark (Texas Association of Realtors) Motor Fuels Tax On: Nichols, Robert (Texas Transportation Commission) Olsen, Lawrence (Texas Good Roads/Trans. Assn.) State Excise Taxes On: New, Alvin (Texas Petroleum Marketers and Convenience Store Assoc. and Town and Country Food Stores, Inc.) Tobacco Tax On: McGoldrick, Daniel (Campaign for Tobacco-Free Kids) Ratcliff, Joe (Texas Grocery & Convenience Association) Shepherd, Robert L. (UST Public Affairs Inc.) Woody, Bob (ESSCA Club & Rest. Industry) Tobacco Tax Increase For: Hendrick, Kelly (American Cancer Society - Texas Division) On: Huang, MD, MPH, Philip (Texas Department of State Health Services) Tobacco Taxation On: Campbell, Tim (Philip Morris USA) Marosko, Fred (Texas Package Stores Association) Tobacco Taxes On: Tully, Ronald (Council of Independent Tobacco Manufacturers of America) Various Taxes On: Heleman, John (Texas Comptroller of Public Accounts) Reissig, Mike (Comptroller of Public Accounts)


EXCESS BAGGAGE 1997 A film review by Timothy Voon Copyright 1997 Timothy Voon 3 :-( :-( :-( for a lug of heavy baggage
Cast: Alicia Silverstone, Benicio Del Toro, Christopher Walken, Jack Thompson, Harry Connick Jr., Nicholas Turturro, Michael Bowen Director: Marco Brambilla Screenplay: Max D. Adams and Dick Clement & Ian La Frenais
Ever gone on a holiday and suddenly realised that you took only enough underwear for three days, but you really needed a weeks worth. Fortunately most holiday retreats have a customer laundry and shops for the accidental tourist with missing underwear in action. However, in a movie like EXCESS BAGGAGE where do you turn when the most important contents, like underwear, suddenly go missing? Missing elements like passionate romance, witty dialogue, suspenseful action or light hearted comedy? Surely there must be something worth wearing in this Alicia Silverstone package. I remain sadly silent. It's like I suddenly woke up one morning and discovered the world was naked, and I was the only one wearing clothes and when I took my clothes off, there was nothing to see underneath.
Like most young men who first saw Ms Silverstone in her starring role as "Cher" in CLUELESS, I soon fell in love with her lips and the rest of her body. I know sadly admit that the grand delusion of puppy love is finally shattered by a ton of excess baggage. The basis of the story is not too uncommon as wealthy rich girl tries to get attention from Papa by staging her own kidnapping. She has nothing to complain about except for the fact that her father has not given her the attention she needed as a child, but then neither has half the human race. So I do not sympathise entirely with her situation. However, this is not the problem. The problem like most bad movies, is what you actually decide to do with the material.
With romantic comedies, I'm not sure whether it is more difficult to generate romance or comedy. Having both is like striking gold. Unfortunately the romance factor in this movie doesn't take off at all. It becomes hard to believe that this poor little rich girl falls in love with a scruffy car thief who accident steals the car with her in the boot. I haven't seen Mr Benicio Del Toro (car thief) act before, but I think "subzero personality" best describes his character. He carries that dark fazed look of a drug junky, and mumbles his lines with the slurred, droll manner of a stoned alcoholic. My question is - is it all an act or is it really him? If so, is this the sort of character that attracts girls nowadays? The only reason I'm asking is because this movie wants us to get all warm and fuzzy when he breathes down Ms Silverstones neck after downing a bottle of Jack Daniels; and Ms Silverstone's character isn't much better.
As for the comedy, I don't remember laughing once or hearing anybody else laugh for that matter. The saddest factor is that Ms Silverstone is out of her prime material. Gone is that innocent sweet girl that stole the hearts of a generation of men. So perhaps it will take another movie, or several more, before I fall in love again.
Timothy Voon e-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


DenseTorch: PyTorch Wrapper for Smooth Workflow with Dense Per-Pixel Tasks¶
This library aims to ease typical workflows involving dense per-pixel tasks in PyTorch. The progress in such tasks as semantic image segmentation and depth estimation have been significant over the last years, and in this library we provide an easy-to-setup environment for experimenting with given (or your own) models that reliably solve these tasks.
Installation¶
Python >= 3.6.7 is supported.
git clone https://github.com/drsleep/densetorch.git
cd densetorch
pip install -e .
Examples¶
Currently, we provide several models for single-task and multi-task setups:
resnet
ResNet-18/34/50/101/152.mobilenet-v2
MobileNet-v2.xception-65
Xception-65.deeplab-v3+
DeepLab-v3+.lwrf
Light-Weight RefineNet.mtlwrf
Multi-Task Light-Weight RefineNet.
Examples are given in the examples/
directory. Note that the provided examples do not necessarily reproduce the results achieved in corresponding papers, rather their goal is to demonstrate what can be done using this library.
Motivation behind the library¶
As my everyday research is concerned with dense per-pixel tasks, I found myself oftentimes re-writing and updating (occassionally improving upon) my own code for each project. With the number of projects being on the rise recently, such an approach was no longer easy to manage. Hence, I decided to create a simple to use and simple to extend upon backbone (pun is not intended) structure, which I would be able to share with the community and, hopefully, ease the experience for others in the field.
Future Work¶
This library is still work-in-progress. More examples and more models will be added. Contributions are welcome.
Citation¶
If you found this library useful in your research, please consider citing
@misc{Nekrasov19,
author = {Nekrasov, Vladimir},
title = {DenseTorch},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/drsleep/densetorch}}
}
Multi-Task Training Example¶
In this example, we are going to train Multi-Task Light-Weight RefineNet for joint semantic segmentation and depth estimation. Note that inference examples together with pre-trained weights can be found in the official repository.
The hyperparameters set here are not the same as used in the corresponding paper, hence the results will differ. Please refer to the paper below for more information on the model and the training regime.
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
Vladimir Nekrasov, Thanuja Dharmasiri, Andrew Spek, Tom Drummond, Chunhua Shen, Ian Reid
In ICRA 2019
Prepare Data¶
Considering that you successfully installed the DenseTorch
package, the next step is to download the NYUDv2 dataset with segmentation and depth masks. The dataset can be downloaded by following the link.
After downloading and unpacking the archive, create the datasets
folder and link the nyudv2
directory in the archive
to the just created folder:
mkdir datasets
ln -s /path/to/nyudv2 ./datasets/
Training¶
Now you are ready to run the example script. To do so, simply execute python train.py
. After it is finished, the
best model will be stored in the corresponding pth.tar
file. Note that it would the model that improves upon the
previous checkpoint both in terms of mean IoU (for segmentation) and linear RMSE (for depth estimation).
Single-Task Training Example¶
In this example, we are going to train DeepLab-v3+ with the Xception-65 backbone for the task of semantic segmentation on NYUDv2.
Prepare Data¶
Considering that you successfully installed the DenseTorch
package, the next step is to download the NYUDv2 dataset with segmentation and depth masks. The dataset can be downloaded by following the link.
After downloading and unpacking the archive, create the datasets
folder and link the nyudv2
directory in the archive
to the just created folder:
mkdir datasets
ln -s /path/to/nyudv2 ./datasets/
Training¶
Now you are ready to run the example script. To do so, simply execute python train.py
. After it is finished, the
best model will be stored in the corresponding pth.tar
file.
Code Documentation¶
densetorch.nn¶
The nn module implements a range of well-established encoders and decoders.
-
class
densetorch.nn.decoders.
DLv3plus
(input_sizes, num_classes, skip_size=48, agg_size=256, rates=(6, 12, 18))¶ DeepLab-v3+ for Semantic Image Segmentation.
ASPP with decoder. Allows to have multiple skip-connections. More information about the model: https://arxiv.org/abs/1802.02611
- Parameters
input_sizes (int, or list) – number of channels for each input. Last value represents the input to ASPP, other values are for skip-connections.
num_classes (int) – number of output channels.
skip_size (int) – common filter size for skip-connections.
agg_size (int) – common filter size.
rates (list of ints) – dilation rates in the ASPP module.
-
forward
(xs)¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
class
densetorch.nn.decoders.
LWRefineNet
(input_sizes, collapse_ind, num_classes, agg_size=256, n_crp=4)¶ Light-Weight RefineNet for Semantic Image Segmentation.
More information about the model: https://arxiv.org/abs/1810.03272
- Parameters
input_sizes (int, or list) – number of channels for each input.
collapse_ind (list) – which input layers should be united together (via element-wise summation) before CRP.
num_classes (int) – number of output channels.
agg_size (int) – common filter size.
n_crp (int) – number of CRP layers in a single CRP block.
-
forward
(xs)¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
static
make_crp
(in_planes, out_planes, stages)¶ Creating Light-Weight Chained Residual Pooling (CRP) block.
- Parameters
in_planes (int) – number of input channels.
out_planes (int) – number of output channels.
stages (int) – number of times the design is repeated (with new weights)
- Returns
nn.Sequential of CRP layers.
-
class
densetorch.nn.decoders.
MTLWRefineNet
(input_sizes, collapse_ind, num_classes, agg_size=256, n_crp=4)¶ Multi-Task Light-Weight RefineNet for Dense per-pixel tasks.
More information about the model: https://arxiv.org/abs/1809.04766
- Parameters
input_sizes (int, or list) – number of channels for each input.
collapse_ind (list) – which input layers should be united together (via element-wise summation) before CRP.
num_classes (int or list) – number of output channels per each head.
agg_size (int) – common filter size.
n_crp (int) – number of CRP layers in a single CRP block.
-
forward
(xs)¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
densetorch.nn.mobilenetv2.
mobilenetv2
(pretrained=True, **kwargs)¶ Constructs the mobilenet-v2 network.
- Parameters
pretrained (bool) – whether to load pre-trained weights.
- Returns
nn.Module instance.
-
densetorch.nn.resnet.
resnet18
(pretrained=False, **kwargs)¶ Constructs the ResNet-18 model.
- Parameters
pretrained (bool) – If True, returns a model pre-trained on ImageNet.
- Returns
`nn.Module’ instance.
-
densetorch.nn.resnet.
resnet34
(pretrained=False, **kwargs)¶ Constructs the ResNet-34 model.
- Parameters
pretrained (bool) – If True, returns a model pre-trained on ImageNet.
- Returns
`nn.Module’ instance.
-
densetorch.nn.resnet.
resnet50
(pretrained=False, **kwargs)¶ Constructs the ResNet-50 model.
- Parameters
pretrained (bool) – If True, returns a model pre-trained on ImageNet.
- Returns
`nn.Module’ instance.
-
densetorch.nn.resnet.
resnet101
(pretrained=False, **kwargs)¶ Constructs the ResNet-101 model.
- Parameters
pretrained (bool) – If True, returns a model pre-trained on ImageNet.
- Returns
`nn.Module’ instance.
-
densetorch.nn.resnet.
resnet152
(pretrained=False, **kwargs)¶ Constructs the ResNet-152 model.
- Parameters
pretrained (bool) – If True, returns a model pre-trained on ImageNet.
- Returns
`nn.Module’ instance.
-
densetorch.nn.xception.
xception65
(pretrained=False, **kwargs)¶ Constructs the Xception-65 network.
- Parameters
pretrained (bool) – whether to load pre-trained weights.
- Returns
nn.Module instance.
densetorch.engine¶
The engine module contains metrics and losses typically used for tasks of semantic segmenation and depth estimation. Also contains training and validation functions.
-
class
densetorch.engine.losses.
InvHuberLoss
(ignore_index=0)¶ Inverse Huber Loss for depth estimation.
The setup is taken from https://arxiv.org/abs/1606.00373
- Parameters
ignore_index (float) – value to ignore in the target when computing the loss.
-
forward
(x, target)¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
-
class
densetorch.engine.metrics.
MeanIoU
(num_classes)¶ Mean-IoU computational block for semantic segmentation.
- Parameters
num_classes (int) – number of classes to evaluate.
-
name
¶ descriptor of the estimator.
- Type
str
-
class
densetorch.engine.metrics.
RMSE
(ignore_val=0)¶ Root Mean Squared Error computational block for depth estimation.
- Parameters
ignore_val (float) – value to ignore in the target when computing the metric.
-
name
¶ descriptor of the estimator.
- Type
str
-
densetorch.engine.trainval.
train
(model, opts, crits, dataloader, loss_coeffs)¶ Full Training Pipeline.
Supports multiple optimisers, multiple criteria, multiple losses, multiple outputs. Assumes that the model.eval() property has been set up properly before the function call, that the dataloader outputs have the correct type, that the model outputs do not require any post-processing bar the upsampling to the target size. Criteria, loss_coeff, and model’s outputs all must have the same length, and correspond to the same keys as in the ordered dict of dataloader’s sample.
- Parameters
model – PyTorch model object.
opts – list of optimisers.
crits – list of criterions.
dataloader – iterable over samples. Each sample must contain image key and >= 1 optional keys.
loss_coeffs – list of coefficients for each loss term.
-
densetorch.engine.trainval.
trainbal
(model, dataloader)¶ Full Training Pipeline with balanced model.
Assumes that the model.eval() property has been set up properly before the function call, that the dataloader outputs have the correct type, that the model outputs do not require any post-processing bar the upsampling to the target size.
- Parameters
model – PyTorch model object.
dataloader – iterable over samples. Each sample must contain image key and >= 1 optional keys.
-
densetorch.engine.trainval.
validate
(model, metrics, dataloader)¶ Full Validation Pipeline.
Support multiple metrics (but 1 per modality), multiple outputs. Assumes that the dataloader outputs have the correct type, that the model outputs do not require any post-processing bar the upsampling to the target size. Metrics and model’s outputs must have the same length, and correspond to the same keys as in the ordered dict of dataloader’s sample.
- Parameters
model – PyTorch model object.
metrics – list of metric classes. Each metric class must have update and val functions, and must have ‘name’ attribute.
dataloader – iterable over samples. Each sample must contain image key and >= 1 optional keys.
densetorch.data¶
The data module implements datasets and relevant utilities used for data pre-processing. It supports multi-modal data.
-
class
densetorch.data.datasets.
MMDataset
(data_file, data_dir, line_to_paths_fn, masks_names, transform_trn=None, transform_val=None, stage='train')¶ Multi-Modality dataset.
Works with any datasets that contain image and any number of 2D-annotations.
- Parameters
data_file (string) – Path to the data file with annotations.
data_dir (string) – Directory with all the images.
line_to_paths_fn (callable) – function to convert a line of data_file into paths (img_relpath, msk_relpath, …).
masks_names (list of strings) – keys for each annotation mask (e.g., ‘segm’, ‘depth’).
transform_trn (callable, optional) – Optional transform to be applied on a sample during the training stage.
transform_val (callable, optional) – Optional transform to be applied on a sample during the validation stage.
stage (str) – initial stage of dataset - either ‘train’ or ‘val’.
-
static
read_image
(x)¶ Simple image reader
- Parameters
x (str) – path to image.
Returns image as np.array.
-
set_stage
(stage)¶ Define which set of transformation to use.
- Parameters
stage (str) – either ‘train’ or ‘val’
-
class
densetorch.data.utils.
Normalise
(scale, mean, std, depth_scale=1.0)¶ Normalise a tensor image with mean and standard deviation. Given mean: (R, G, B) and std: (R, G, B), will normalise each channel of the torch.*Tensor, i.e. channel = (scale * channel - mean) / std
- Parameters
scale (float) – Scaling constant.
mean (sequence) – Sequence of means for R,G,B channels respecitvely.
std (sequence) – Sequence of standard deviations for R,G,B channels respecitvely.
depth_scale (float) – Depth divisor for depth annotations.
-
class
densetorch.data.utils.
Pad
(size, img_val, msk_vals)¶ Pad image and mask to the desired size.
- Parameters
size (int) – minimum length/width.
img_val (array) – image padding value.
msk_vals (list of ints) – masks padding value.
-
class
densetorch.data.utils.
RandomCrop
(crop_size)¶ Crop randomly the image in a sample.
- Parameters
crop_size (int) – Desired output size.
-
class
densetorch.data.utils.
RandomMirror
¶ Randomly flip the image and the mask
-
class
densetorch.data.utils.
ResizeAndScale
(side, low_scale, high_scale, shorter=True)¶ Resize shorter/longer side to a given value and randomly scale.
- Parameters
side (int) – shorter / longer side value.
low_scale (float) – lower scaling bound.
high_scale (float) – upper scaling bound.
shorter (bool) – whether to resize shorter / longer side.
-
class
densetorch.data.utils.
ToTensor
¶ Convert ndarrays in sample to Tensors.
densetorch.misc¶
The misc module has various useful utilities.
-
class
densetorch.misc.utils.
AverageMeter
(momentum=0.99)¶ Simple running average estimator.
- Parameters
momentum (float) – running average decay.
-
update
(val)¶ Update running average given a new value.
The new running average estimate is given as a weighted combination of the previous estimate and the current value.
- Parameters
val (float) – new value
-
class
densetorch.misc.utils.
Balancer
(model, opts, crits, loss_coeffs)¶ Wrapper for balanced multi-GPU training.
When forward and backward passes are fused into a single nn.Module object, the multi-GPU consumption is distributed more equally across the GPUs.
- Parameters
model (nn.Module) – PyTorch module.
opts (list or single instance of torch.optim) – optimisers.
crits (list or single instance of torch.nn or nn.Module) – criterions.
loss_coeffs (list of single instance of float) – loss coefficients.
-
forward
(inp, targets=None)¶ Forward and (optionally) backward pass.
When targets are provided, the backward pass is performed. Otherwise only the forward pass is done.
- Parameters
inp (torch.tensor) – input batch.
targets (None or torch.tensor) – targets batch.
- Returns
Forward output if targets=None, else returns the loss value.
-
class
densetorch.misc.utils.
Saver
(init_vals, comp_fns)¶ Saver class for monitoring the training progress.
Given initial values and comparison functions, Saver keeps track of newly added values and updates them in case all of the new values satisfy the corresponding comparison functions.
- Parameters
init_vals (list) – initial values. Represent lower bounds for performance of each task.
comp_fns (list) – list of comparison functions. Each function takes two inputs and produces one boolean output. Each newly provided value will be compared against the initial value using the corresponding comparison function.
-
save
(new_vals)¶ Saving criterion.
Checks whether the saving criterion is trigerred. The saving occurs when all newly added values satisfy their corresponding comparison functions.
- Parameters
new_vals (list) – new values for comparison.
- Returns
True if all comparison functions return True. Otherwise, returns False.
-
densetorch.misc.utils.
compute_params
(model)¶ Compute the total number of parameters.
- Parameters
model (nn.Module) – PyTorch model.
- Returns
Total number of parameters - both trainable and non-trainable (int).
-
densetorch.misc.utils.
create_optim
(enc, parameters, **kwargs)¶ Initialise optimisers.
- Parameters
enc (string) – type of optimiser - either ‘SGD’ or ‘Adam’.
parameters (iterable) – parameters to be optimised.
- Returns
An instance of torch.optim.
- Raises
ValueError if enc is not either of 'SGD' or 'Adam'. –
-
densetorch.misc.utils.
ctime
()¶ Returns current timestamp in the format of hours-minutes-seconds.
-
densetorch.misc.utils.
get_args
(func)¶ Get function’s arguments.
- Parameters
func (callable) – input function.
- Returns
List of positional and keyword arguments.
-
densetorch.misc.utils.
make_list
(x)¶ Returns the given input as a list.
-
densetorch.misc.utils.
set_seed
(seed)¶ Setting the random seed across torch, numpy and random libraries.
- Parameters
seed (int) – random seed value.


I'm Jason, a soon to be fifth-year Computer Science undergraduate student at the University of California, San Diego. With a background in software engineering and graphic design, my interest lies in using the power of computers to create great content that can bedazzle people. I'm estatic about creating great applications and new experiences for people that not only work great, but are beautiful to see.


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--- layout: post title: ASP.NET Support in ReSharper 5 - Hadi Hariri - Devlicio.us - Just the Tasty Bits date: '2010-01-21T01:43:00.001Z' author: solrevdev tags: modified_time: '2010-01-21T01:43:14.350Z' blogger_id: tag:blogger.com,1999:blog-31446756.post-5855801557235662754 blogger_orig_url: http://www.solrevdev.com/2010/01/aspnet-support-in-resharper-5-hadi.html ---


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List of terms
Adaptive Behaviour
- System behaviour that appears to be (partially) coordinated by previously ‘experienced events’
Analytic solution
- The solution to a difference or differential equation allows one to find any state of the system without the need to iterate the model starting from some initial condition. There are very few (systems of) equations for which analytical solutions exist
Attractor
- The status that a dynamic system eventually “settles down to.” An attractor is a set of values in the phase space to which a system migrates over time, or iterations. Attractors can have as many dimensions as the number of variables that influence its system
Basin of attraction
- A region in phase space associated with a given attractor. The basin of attraction of an attractor is the set of all (initial) points that eventually end up in that attractor
Behaviour (of a dynamic system)
- The temporal evolution of states of a system according to one or more rules (also known as state propagation rules, or, iterative processes). Models of the behaviour of dynamic systems use difference or differential equations to describe the iterative processes hypothesized to underlie the temporal evolution
Bifurcation
- A clearly observable qualitative change in the behavioural mode (attractor state) of a dynamic system associated with continuous change in one or more control parameters (also known as Phase-, State-, or Order- Transition). The value of a control parameter at which a bifurcation occurs is often non-specific, or trivial
Bifurcation diagram
- Visual summary of the succession of period-doubling bifurcations produced by gradual changes in the control parameter(s)
Catastrophe flags
- Markers indicative for a physical system that is described by a catastrophe. There are 5 ‘classical’ flags and 3 ‘diagnostic’ flags. Classical: bimodality, sudden jumps, inaccessibility, sensitivity & hysteresis. Diagnostic: divergence from linear response, critical slowing down and critical fluctuations. Diagnostic flags can be used as early-warning signals.
Catastrophe theory
- Mathematical research program describing how gradual change in some parameters can lead to disproportionately large changes in another parameter, called catastrophes (similar to bifurcations, Phase-, State-, or Order-transitions). ‘This kind of behaviour has been summarized succinctly in the phrase “the straw that broke the camel’s back.”’ (Gilmore, 1992).
Complex Network
- A network with of many nodes and likely many substructures depending on that nature and distribution of connections between nodes
Complex system
- Spatially and/or temporally extended nonlinear systems characterized by emergent properties and self-organised behavioural modes at a global, or, macro-level (the system as a whole), that is often different from the characteristic behaviour at a local, or, micro-level (behaviour of the individual parts that constitute the whole)
Complexity science
- Complexity science studies how systems that consist of many components can generate relatively simple and stable (non-random) behaviour. Important behavioural phenomena studied in Complexity Science are synchronisation, adaptation and coordination of behaviour across many different temporal and spatial scales, emergent properties and collective behaviour, holism and self-organisation
Component dominant dynamics
- A causal ontology in which observed behaviour is explained by assuming it is the result of a chain of independent efficient causes (components)
Control parameter
- A variable that controls the global behaviour of a dynamic system. For certain values of the parameter, transitions between qualitatively different behavioural modes (orders) can occur.
Critical fluctuations
- An early warning signal for a phase transition that is characterised by an increase in fluctuations (variability) of the behaviour of the system. The increase occurs because the self-organised transition from one state to another relaxes the constraints on the degrees of freedom a system has available to generate its behaviour, allowing states and behavioural modes to appear that were previously inaccessible.
Critical slowing down
- An early warning signal for a phase transition that is characterised by an increase in the duration of relaxation times. If it takes longer for the system to return to the state it was perturbed from, this implies the emergence of a new stable state is imminent
Deterministic Chaos
- Behaviour of a dynamic system that “looks random, but is not” (Lorenz, 1973). The dynamics can be characterised as follows: 1) A-periodic, no point or trajectory in state space will exactly recur; 2) Sensitive dependence in initial conditions; 3) Bounded, not all theoretically possible degrees of freedom are available to the system; 4) The origin of this behaviour is deterministic, not stochastic
Difference equation
- A function specifying the underlying change process in a variable from one discrete point in time to another
Differential equation
- A function specifying the underlying change process of a variable in continuous time
Dimension
- See embedding dimension, box-counting dimension, correlation dimension, information dimension, dimension of a system
Dimensions of a system
- The set of variables that define a system. Iterative processes operate on the dimensions of a system
Dynamic system
- A set of equations specifying how certain variables change over time. The equations specify how to determine (compute) the new values as a function of their current values and control parameters. The functions, when explicit, are either difference equations or differential equations. Dynamic systems may be stochastic or deterministic. In a stochastic system, new values come from a probability distribution. In a deterministic system, a single new value is associated with any current value
Early warning signals
- Critical slowing down and critical fluctuations. Early-warning signals indicate instability in the existing state which may result in a qualitative shift towards a new state (phase transition / catastrophe). Early-warning signals are similar to diagnostic catastrophe flags.
Effective Complexity
- “The effective complexity of an entity is the length of a highly compressed description of its regularities.” (Gell-man & Lloyd, 2004)
Embedding Dimension
- Successive N-tuples of points in a time series are treated as points in N dimensional space. The points are said to reside in embedding dimensions of size N, for N = 1, 2, 3, 4, … etc.
Emergence
- A complex system can generate emergent behaviour or display emergent properties that are novel and unexpected, that is, they are not predictable from the behaviour and properties of the components of the system
Entropy
- Relative absence of order/redundancy in a system. The degrees of freedom a system has available for generating its behaviour: Possibility
Epigenetic landscape (potential landscape)
- A hypothetical landscape describing the relative stability of behavioural modes of a system over time
Experienced event
- An interaction of a system with its environment that changed the internal structure/organization of the system such that it can be said to display adaptive behaviour. “Interaction with after-effects.” Random behaviour is “Interaction without after-effects.”
Fractal
- An irregular shape with self-similarity. It has infinite detail, and cannot be differentiated. “Wherever chaos, turbulence, and disorder are found, fractal geometry is at play” (Briggs and Peat, 1989).
Fractal Dimension
- A measure of a geometric object that can take on fractional values. At first used as a synonym to Hausdorff dimension, fractal dimension is currently used as a more general term for a measure of how fast length, area, or volume increases with decrease in scale. (Peitgen, Jurgens, & Saupe, 1992a).
Graph theory
- Models in which associations between mathematical objects are defined as edges (connections) between vertices (nodes)
Hausdorff Dimension
- A measure of a geometric object that can take on fractional values. (see fractal dimension).
Holism (epistemic)
- “some property of a whole would be holistic if, according to the theory in question, there is no way we can find out about it using only local means, i.e., by using only all possible non-holistic resources available to an agent.” (Seevinck, 2002)
Idiographic approach
- Scientific explanation in which the goal is to generate knowledge about specific facts, events or entities. The goal is not to generalize to universal laws and first principles.
Information (quantity)
- A measurable quantity that resolves uncertainty about the state of a system by assigning a value to the uncertainty.
Initial condition
- The starting point of a dynamic system, the initial state of a system from which it evolved to the current state.
Interaction dominant dynamics
- A causal ontology in which observed behaviour is explained by assuming it is the result of interactions between processes across many temporal and spatial scales
Iteration
- The repeated application of a function, using its output from one application as its input for the next.
Iterative function
- A function used to calculate the new state of a dynamic system.
Iterative system
- A system in which one or more functions are iterated to define the system.
Largest Lyapunov exponent
- The value of the largest exponent in a spectrum of exponents (the Lyapunov spectrum), coefficients of time, that reflect the rate of departure (divergence) of dynamic orbits of a system. The largest exponent indicates the extent to which the behaviour of a system is sensitive to initial conditions.
Limit cycle
- An attractor that is periodic in time, that is, that cycles periodically through an ordered sequence of states.
Limit points
- Points in phase space. There are three kinds: attractors, repellors, and saddle points. A system moves away from repellors and towards attractors. A saddle point is both an attractor and a repellor, it attracts a system in certain regions, and repels the system to other regions.
Linear function of predictors
- A linear equation is of predictors is of the form y=a*x(i)+b, in which variable y varies ‘linearly’ with other variables x(i). In this equation, ‘a’ determines the slope of the line and ‘b’ reflects the y-intercept, the value y obtains when all x(i) equal zero.
Linear function of time
- A linear function of time is of the form ŷ(t) = a*y(t) + b, in which variable y varies ‘linearly’ with time ‘t,’ that is, with itself at an earlier moment in time. In this equation ‘a’ determines the rate with which ‘y’ will change as time passes, ‘b’ reflects the initial condition, the value y obtains when t equals zero.
Nonlinear dynamics
- The study of dynamic systems whose functions specify that change is not a linear function of time.
Orbit (trajectory)
- A sequence of coordinates (a path) through the phase space of a system.
Order
- “order is essentially the arrival of redundancy in a system, a reduction of possibilities”(Von Förster, 2003). Any form of non-random association or dependency that exists between parts of a system, its behaviour over time and/or its environment is a form of order. In scientific explanation of behaviour, the presence of order in non-artificial systems must be explained and should not be (implicitly) assumed.
Order Parameter
- A nominal variable that indexes qualitatively different behavioural modes of a system, for example the phases of matter (gas, liquid, solid, plasma)
Period-doubling
- The change in dynamics in which a N-point attractor is replaced by a 2N-point attractor.
Phase portrait
- The collection of all trajectories from all possible starting points in the phase space of a dynamic system.
Phase space
- An abstract space used to represent the behaviour of a system. Its dimensions are the variables of the system. Thus a point in the phase space defines a potential state of the system. The points actually achieved by a system depend on its iterative function and initial condition (starting point).
Phase transition
- A transition between qualitatively different behavioural modes
Potential function
- A function that describes the order parameter of a system, that is, it describes the relative stability of the potential end-states (attractor states) a system can settle into. The parameters of the potential function include the control parameter.
Power-law scaling
- A relationship between two variables that is linear on doubly logarithmic coordinates, meaning the law is expressed in increments that represent ‘power’
Recursive process
- For our purposes, “recursive” and “iterative” are synonyms. Thus recursive processes are iterative processes, and recursive functions are iterative functions.
Relaxation time
- The time it takes for a system to return to a stable state after it was perturbed enough to leave that state. A characteristic warning signal of an imminent phase transition is an increase relaxation times, also known as critical slowing down.
Repellors
- One type of limit point. A point in phase space that a system moves away from.
Return map
- Plot of time series values vs. a delayed copy of itself. A return plot can be used to get an idea about the functional form of the iterative process, it is a simple variant of delay embedding.
Saddle point
- A point, usually in three dimensional state space, that both attracts and repels, attracting in one dimension and repelling to another.
Scale free network
- A network in which the distribution of the number of connections of a node and their frequency of occurrence follows a power-law in which there are just a few nodes with many connections and many nodes with just a few connections
Self-affinity
- An infinite nesting of characteristic structure on all scales. Strict self-affinity refers to a form of which all substructures are affine transformation, which means the different dimensions of the system can be scaled by their own exponent. Statistical self-affinity refers to an approximate equivalence of form at all scales.
Self-similarity
- An infinite nesting of characteristic structure on all scales. Strict self-similarity refers to a form of which all substructures can be considered scaling transformations, larger or smaller copies scaled by a single exponent for all dimensions of the structure. Statistical self-similarity refers to an approximate equivalence of scaled structure.
Sensitive dependence on initial conditions
- A property of chaotic systems. A dynamic system has sensitivity to initial conditions when very small differences in starting values result in very different behaviour. If the orbits of nearby starting points diverge, the system has sensitivity to initial conditions.
Small world network
- Many real-world networks have a small average shortest path length, but also a clustering coefficient that is significantly higher than expected by chance. These networks are extremely efficient, each node in a very large network can still be reach in just a few steps (the ’six degrees of separation` phenomenon).
State
- A coordinate in state space designating the current status of a dynamic system. The elements of the coordinates are values on the dimensions of the system that span the state space.
State space
- A hypothetical space spanned by the dimensions of the system. Each combination of values of variables that represent the dimension is a state of the system, it is a coordinate in state space.
State space (phase space)
- An abstract space used to represent the behaviour of a system. Its dimensions are the variables of the system. Thus a point in the phase space defines a potential state of the system.
Strange attractor
- An attractor state representing chaotic dynamics: a-periodic, bounded, and sensitive dependence on initial conditions
System
- An entity that can be described as a composition of components according to some organising principle. Organising principles describe how parts of the system relate to the whole and.
Time series
- A record of observations (data points) of behaviour over time.
Trajectory (orbit)
- A sequence of positions (path) of a system in its phase space. The path from its starting point (initial condition) to and within its attractor.
Transient time (transient behaviour)
- The time it takes for a system to transition from one stable state (behavioural mode, attractor state) into another, during which the system displays transient behaviour


James Holzhauer to face Ken Jennings in ‘Jeopardy!’ tournament
by Christopher LawrenceJames Holzhauer is coming to prime time.
Fresh off of winning the “Jeopardy!” Tournament of Champions, the Las Vegan will take on Ken Jennings and Brad Rutter, the only contestants who’ve won more money on the game show, in an ABC special event.
“Jeopardy! The Greatest of All Time,” hosted by Alex Trebek, will debut at 8 p.m. Jan. 7. In a series of hourlong specials, the first contestant to win three matches will take home the $1 million grand prize. The two runners-up each will receive $250,000.
The matches will air Jan. 7 to 9, with additional contests Jan. 10 and Jan. 14 to 16 if necessary.
So far, Holzhauer’s winnings total $2,712,216.
Jennings’ record 74-game winning streak earned him $3,370,700.
Rutter, whose total winnings have reached $4,688,436, has never lost a game of “Jeopardy!” to a human opponent.


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