As an example of how file stream buffers and code conversion facets collaborate, we would now like to implement a code conversion facet that can translate text files encoded in EBCDIC into character streams encoded in ASCII. The conversion between ASCII characters and EBCDIC characters is a constant-size code conversion where each character is represented by one byte. Hence the conversion can be done on a character-by-character basis.
To implement and use an ASCII-EBCDIC code conversion facet, we:
Derive a new facet type from the standard code conversion facet type std::codecvt.
Specialize the new facet type for the character type char.
Implement the member functions that are used by the file buffer.
Imbue a file stream's buffer with a locale that carries an ASCII-EBCDIC code conversion facet.
The following sections explain these steps in detail.
Here is the new code conversion facet type AsciiEbcdicConversion:
template <class internT, class externT, class stateT> class AsciiEbcdicConversion : public std::codecvt<internT, externT, stateT> { };
It is empty because we will specialize the class template for the character type char.
Each code conversion facet has two main member functions, in() and out():
The public member function in() is responsible for the conversion done on reading from the external device.
The public member function out() is responsible for the conversion necessary for writing to the external device.
The other member functions of a code conversion facet used by a file stream buffer are:
The public member function always_noconv(), which returns true if no conversion is performed by the facet. This is because file stream buffers might want to bypass the code conversion facet when no conversion is necessary; for example, when the external encoding is identical to the internal. Our facet obviously will perform a conversion and does not want to be bypassed, so always_noconv() returns false in our example.
The public member function encoding(), which provides information about the type of conversion; that is, whether it is state-dependent or constant-size, etc. In our example, the conversion is constant-size. The function encoding() is supposed to return the size of the internal characters, which is 1 because the file buffer uses an ASCII encoding internally.
All public member functions of a facet call the respective, protected virtual member function, named do_...(). Here is the declaration of the specialized facet type:
template <> class AsciiEbcdicConversion<char, char, std::mbstate_t> : public std::codecvt<char, char, std::mbstate_t> { protected: virtual std::codecvt_base::result do_in (std::mbstate_t& state, const char* from, const char* from_end, const char*& from_next, char* to, char* to_limit, char*& to_next) const; virtual std::codecvt_base::result do_out(std::mbstate_t& state, const char* from, const char* from_end, const char*& from_next, char* to, char* to_limit, char*& to_next) const; virtual bool do_always_noconv() const throw(){ return false; } int do_encoding() const throw(){ return 1; } };
For the sake of brevity, we implement only those functions used by this implementation of file stream buffers. If you want to provide a code conversion facet that is more widely usable, you must also override the virtual member functions do_length() and do_max_length().
The implementation of the functions do_in() and do_out() is straightforward. Each of the functions translates a sequence of characters in the range [from,from_end) into the corresponding sequence [to,to_end). The pointers from_next and to_next point one beyond the last character successfully converted. In principle, you can do whatever you want, or whatever it takes, in these functions. However, for effective communication with the file stream buffer, it is important to indicate success or failure properly.
Here is an example of how the new code conversion facet can be used:
std::fstream inout("/tmp/fil"); //1 AsciiEbcdicConversion<char,char,std::mbstate_t> cvtfac; std::locale cvtloc(std::locale(),&cvtfac); inout.rdbuf()->pubimbue(cvtloc) //2 std::cout << inout.rdbuf(); //3
| //1 | When a file is created, a snapshot of the current global locale is attached as the default locale. Remember that a stream has two locale objects: one used for formatting numeric items, and a second used by the stream's buffer for code conversions. |
| //2 | Here the stream buffer's locale is replaced by a copy of the global locale that has an ASCII-EBCDIC code conversion facet. |
| //3 | The content of the EBCDIC file "/tmp/fil" is read, automatically converted to ASCII, and written to std::cout. |


The goal of arear is to …
You can install the released version of arear from CRAN with:
install.packages("arear")
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("terminological/arear")
There are a set of maps bundled:
devtools::load_all() #> Error in get(genname, envir = envir) : object 'testthat_print' not found #> Loading arear #> Registered S3 method overwritten by 'geojsonlint': #> method from #> print.location dplyr #> #> Attaching package: 'testthat' #> The following object is masked from 'package:dplyr': #> #> matches #> The following object is masked from 'package:purrr': #> #> is_null #> The following object is masked from 'package:tidyr': #> #> matches # library(arear) ## basic example code arear::listStandardMaps() #> [1] "WD11" "WD19" "LSOA11" "MSOA11" #> [5] "DZ11" "CA19" "HB19" "LHB19" #> [9] "CTYUA19" "LAD19" "LAD20" "CCG20" #> [13] "NHSER20" "PHEC16" "CTRY19" "LGD12" #> [17] "OUTCODE" "GBR_ISO3166_2" "GBR_ISO3166_3"
Getting and plotting a map for the UK is simple:
map = arear::getMap("CTRY19") #> caching item: /tmp/RtmpezoPiX/CTRY19-18d2521b53cf4da65ad099c6fcf78e0c-d97019db05610cf55de038f3acd898e9.rda #> Reading layer `Countries_(December_2019)_Boundaries_UK_BGC' from data source `/tmp/RtmpezoPiX/CTRY19' using driver `ESRI Shapefile' #> Simple feature collection with 4 features and 10 fields #> geometry type: MULTIPOLYGON #> dimension: XY #> bbox: xmin: -116.1928 ymin: 5342.7 xmax: 655653.8 ymax: 1220302 #> proj4string: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs #> Linking to GEOS 3.7.1, GDAL 2.2.2, PROJ 4.9.2 nhshospitals = arear::surgecapacity %>% dplyr::filter(sector == "NHS Sector") ggplot()+ geom_sf(data=map)+ geom_sf(data=nhshospitals,aes(colour=tier1))
But the main thing is creating a catchment area:
TODO:


| Class and Description |
|---|
| VisitorAppender
Vistor appender.
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| Class and Description |
|---|
| ContentDeliveryRuntime |
| ContentDeliveryRuntimeFactory |
| Class and Description |
|---|
| ContentDeliveryConfig
Content delivery configuration.
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| ContentDeliveryRuntime |
| ContentHandler
Smooks resource and a target of content delivery.
|
| ContentHandlerBinding
Mapping between a resource configuration and its corresponding resource
configuration.
|
| Filter |
| FilterBypass
Filter bypass interface.
|
| ReaderPool |
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentDeliveryConfig
Content delivery configuration.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| ContentHandlerBinding
Mapping between a resource configuration and its corresponding resource
configuration.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentDeliveryRuntime |
| ContentDeliveryRuntimeFactory |
| ContentHandlerBinding
Mapping between a resource configuration and its corresponding resource
configuration.
|
| Class and Description |
|---|
| ContentDeliveryConfig
Content delivery configuration.
|
| ContentDeliveryConfigBuilder |
| ContentDeliveryRuntime |
| ContentDeliveryRuntimeFactory |
| ContentHandler
Smooks resource and a target of content delivery.
|
| ContentHandlerBinding
Mapping between a resource configuration and its corresponding resource
configuration.
|
| ContentHandlerFactory
ContentHandler factory interface.
|
| Filter |
| FilterBypass
Filter bypass interface.
|
| FilterProvider
Produces a
ContentDeliveryConfig for a given sequence of visitor bindings. |
| ReaderPool |
| Class and Description |
|---|
| ContentDeliveryConfig
Content delivery configuration.
|
| ContentHandler
Smooks resource and a target of content delivery.
|
| ContentHandlerBinding
Mapping between a resource configuration and its corresponding resource
configuration.
|
| Filter |
| FilterBypass
Filter bypass interface.
|
| FilterProvider
Produces a
ContentDeliveryConfig for a given sequence of visitor bindings. |
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandlerBinding
Mapping between a resource configuration and its corresponding resource
configuration.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| ContentHandlerBinding
Mapping between a resource configuration and its corresponding resource
configuration.
|
| FilterBypass
Filter bypass interface.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| ContentHandlerBinding
Mapping between a resource configuration and its corresponding resource
configuration.
|
| Class and Description |
|---|
| ContentDeliveryConfig
Content delivery configuration.
|
| ContentHandler
Smooks resource and a target of content delivery.
|
| ContentHandlerBinding
Mapping between a resource configuration and its corresponding resource
configuration.
|
| Filter |
| FilterBypass
Filter bypass interface.
|
| FilterProvider
Produces a
ContentDeliveryConfig for a given sequence of visitor bindings. |
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandlerFactory
ContentHandler factory interface.
|
| Class and Description |
|---|
| ContentDeliveryConfig
Content delivery configuration.
|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
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| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
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| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
| Class and Description |
|---|
| ContentHandler
Smooks resource and a target of content delivery.
|
Copyright © 2023. All rights reserved.


Ocean [al33], ocean, in The Inner Sea
Last updated Turn #148
Ocean [al33], ocean, in The Inner Sea
------------------------------------------------------------------------
12: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails, arrived
12: from Ocean [al32], owner:
12: Dorothy [v1b], Pen, with one worker, 94 sailors
12: Apollo [b5e], Imperial Empire, with 11 workers, 63 lt. xbowmen,
12: accompanied by:
12: Lutra [a4w], Imperial Empire, with 11 paladins, 19 pikemen
12: Lungor [y4r], Imperial Empire
12: Merbit [e2m], Imperial Empire
12: Pink Blazer [n8e], Imperial Empire
12: Kiki Ghelfond [p2w], Imperial Empire
12: Hera [g3f], Imperial Empire, with three soldiers, 28 pikemen
12: Thor [r4g], Imperial Empire, with 31 pikemen
12: Eos [a2t], Imperial Empire, with ten workers, 48 lt. xbowmen
12: Khaki Blazer [w4j], Imperial Empire, with four soldiers,
12: three pikemen, 24 lt. foot
12: Banian [e7n], Imperial Empire, duke, the High Priest of the
12: Church of Eres [151], with one soldier, ten angels
12: Trader Joe [h5i], Mandor Confederacy, prisoner
12: Bang [g3i], Mandor Confederacy, prisoner
13: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails,
13: departed for Ocean [al34].
Routes leaving Ocean:
North, to Ocean [ak33], 6 days
East, to Ocean [al34], 6 days
South, to Ocean [am33], 6 days
West, to Ocean [al32], 6 days


Education
- Evergreen State College
- Western Oregon University
- Portland State University
- Epicodus
Hobbies
- Hike
- Walk my dog
- Visit wine country
- Fishing
Skills
- Decision Making
- Adaptability
- Creativity
- Conflict Resolution
This is my rendition of a Wikipedia page. It's in progress, but you can get an idea of my level of learning over the past 4 weeks at Epicodus.
Click here to view.
In week 2, I practiced branching. Here is an example of what I learned in this lesson.
Click here to view.
I practiced basic CSS utilization with added images in my version of a "Cupcakery".
Click here to view.
Feel free to contact ME, at your convenience email me if you need anything.


I am specialised in Website Designing, Branding, UI/UX Designing and Video Editing. I have 4 years' of experience in the field and committed to deliver you high-quality work in the least possible turnaround time. I'm passionate about beautiful Web design and with every project I take on I will make it my business to deliver exceptional and individual solutions. I am experienced in working different freelancing platforms with an excellent track record.
High-quality graphic assets are guaranteed. I use my skills and expertise in the field of designing like meticulous approach, perfection in designing.
 But who knows, maybe that would be the best
I ought to just try that with my boss; I'd get kicked out on the spot. But who knows, maybe that would be the best
I ought to just try that with my boss; I'd get kicked out on the spot. But who knows, maybe that would be the best


DFES issue Watch and Act for Cascade bushfire
by Sarah MakseUPDATE: The Cascade bushfire has been downgraded to a bushfire advice for parts of Cascade in the Shire of Esperance.
The Department of Fire and Emergency Services issued a bushfire advice for people in an area bounded by West Point Road, Clare Road and River Road in Cascade in the Shire of Esperance.
The alert level for this fire has been downgraded as conditions have eased and the fire is under control.
There is no threat to lives or homes.
Although there is no immediate danger you need to be aware and keep up to date in case the situation changes.
Firefighters are actively fighting the fire and strengthening containment lines with 40 Pastoralists and 30 Bush Fire Service firefighters in attendance.
The bushfire is moving slowly in a north easterly direction and is controlled but not contained.
People in the area are advised to stay alert, monitor their surroundings and watch for signs of a bushfire, especially smoke and flames.
The Department of Fire and Emergency Services issued a Bushfire Watch and Act for parts of Cascade in the Shire of Esperance after a bushfire was reported at 12.18pm on Sunday, November 17.
A bushfire Watch and Act has been issued for people in an area bounded by West Point Road, Clare Road and River Road in Cascade in Shire of Esperance.

Seventy Bush Fire Service and Pastoralists firefighters are attending and aerial support has been sent to assist ground crews.
DFES have advised there is a possible threat to lives and homes as a fire is approaching in the area and conditions are changing.
The bushfire is moving fast in a north easterly direction and is out of control and unpredictable.
The fire started near the intersection of River Road and Cascade Road in Cascade.
People in the area are advised to leave or prepare to actively defend.
Those who plan to stay and actively defend, are advised to not rely on mains water pressure as it may be affected. Those who have access to a water tank and plan to defend their home, are advised to start patrolling with their hose and put out spot fires.
Residents who are not prepared or plan to leave are advised to leave now if the way is clear.
Those who are well prepared and plan to actively defend their home are advised to make final preparations now.
If residents are not home they are advised to not return, as conditions in the area could be very dangerous.
People are advised to close all doors and windows and turn off evaporative air conditioners, but keep water running through the system if possible.
Roads have not been closed.
Motorists are asked to avoid the area, reduce speed and drive carefully due to smoke.
Road information may also be available from Main Roads WA by calling 138 138 or visiting www.mainroads.wa.gov.au or Shire of Esperance.
DFES advises to never fly a drone, model aircraft or multirotor near bushfires. Doing so poses a major safety risk to firefighting personnel in the air and on the ground, and forces aircraft to be grounded or redirected, hindering firefighting efforts.
The Shire of Esperance is managing the fire.
Visit www.emergency.wa.gov.au, call 13 DFES (13 3337), follow DFES on Twitter: https://twitter.com/dfes_wa, Facebook: https://facebook.com/dfeswa/, listen to ABC Local Radio or listen to news bulletins.
Updates will be issued every two hours unless the situation changes.


Talks
2016
Jackline: A secure instant messaging application, functional from the ground up
in Bob Konferenz (BobKonf16)
Knock, knock: Unikernels Calling!
in the 14th Annual Southern California Linux Expo (Scale14X)
2015
Not-So-Broken TLS: Re-engineering TLS in a Purely Functional Setting
in Lambda World (Lambda15)
Git Your Own Immutable Infrastructure via Unikernels
in Software Circus (SoftwareCircus15)
MirageOS: Robust Operating System Reengineering from the Ground Up
in Das ist Netzpolitik! (Netzpl15)
Persistent Networking with Irmin and MirageOS
in The OCaml Users and Developers Workshop (OCaml15)
Immutable Distributed Infrastructure with Unikernels
in European Conference on Object-Oriented Programming (ECOOP15)
MirageOS: Leaving Legacy Behind: A Clean-Slate Approach to Operating Systems
in EnthusiastiCon (ECon15)
Immutable Distributed Infrastructure for Unikernels
in International Federation for Information Processing Working Group 2.8 (IFIPWG15)
Not-Quite-So-Broken TLS: Lessons in Re-engineering a Security Protocol Specification and Implementation
in High Confidence Software and Systems Conference (HCSS15)
Unikernels: A Principled Foundation for Networked Services: Student Seminar
in Imperial College London (ICL)
2014
Trustworthy Secure Modular Operating System Engineering
in the 31st Chaos Communication Congress (CCC14)
Jitsu: Just-In-Time Summoning of Unikernels
in New Directions in Operating Systems (OSIO14)
MirageOS 2.0: Branch Consistency for Xen Stub Domains
in Xen Developer Summit (XDS14)
Build Your Own Functional Operating System
in European Conference on Object-Oriented Programming (ECOOP14)
Mirage Cloud Operating System and the OCaml Language
in Software Engineering Radio: The Podcast for Professional Software Developers (SERadio)
Branch Consistency, or How to Design a Version Controlled Database
in the Systems Research Group Seminar (SRG)
MirageOS: Compiling Functional Library Operating Systems
in Free and Open Source Software Developers' European Meeting (FOSDEM14)
2013
MirageOS: Modular Operating System Programming
in Computer Laboratory Wednesday Seminar Series (CLWS13)
MirageOS: Tomorrow's Cloud, Today
in Functional Programming Lab at the University of Nottingham (FPLab13)
Compiling Functional Library Operating Systems with MirageOS
in Tech Talk at Facebook HQ (TTFB13)


Core
Interfaces, Classes and Traits
- App
- Base class for apps.
- Autoloader
- Responsable for autoloading other classes
Based on the PSR-4 Autoloader class example
- CLIApp
- Base class for apps invoked via CLI.
- DependencyInjector
- Class responsable for automatically setting object properties
- HTTPApp
- Base class for apps the handle the HTTP protocol.
- Logger
- Class responsable for the logging system.
- NamespaceFactory
- Any class that extends this class becomes a factory for all it's namespace subclasses
- Polyfills
- Class responsable for defining global functions.
- ResponseStorage
- Abstract base class for response content storage.
- Router
- Responsable for routing (mapping) request URIs to Resources
- Session
- Class used for session management.
- Settings
- Class for app settings storage.
- AppletException
- Base exception for applets
- ApplicationException
- Exception for problems related to the application logic
- CoreException
- Base exception for Core namespace
- FactoryException
- Exception for problems related to the class factories
- FileSystemException
- Exception for problems related to file system
- RequestException
- Exception for problems related to requests
- ResourceException
- Exception for problems related to the resources
- ResponseException
- Exception for problems related to the responses
- RouteException
- Exception for problems related to the routing system
- Directory
- Class for operations with directories in a OO way.
- File
- Class for operations with files in a OO way.
- FileSystemObject
- Base class for file system objects.
- UploadedFile
- Class for operations with files in a OO way with some security checks and specific methods for uploaded files.
- CLIRequest
- Class for command line (CLI) requests.
- HTTPRequest
- A request received by the server.
- Request
- Request base class
- CacheableResource
- Resource class with cache related methods.
- CLIResource
- Base class for resources that work with the CLI.
- GeneratedHTMLResource
- Resource class intended to respond with dynamically generated HTML pages.
- HTTPResource
- Base class for resources that work with the HTTP protocol.
- InvalidCLIResource
- Resource class for invalid (not found) CLI resources.
- NotFoundResource
- Resource class that noly returns NotFound Responses.
- Resource
- Resource base class
- StaticResource
- Resource class intended for sending contents of a file.
- StoredHTMLResource
- Resource class intended to respond with stored (server cached) HTML pages.
- BadRequestResponse
- Response class for bad requests
- CacheableResponse
- Base class for HTTP derived responses with cache settings.
- CLIResponse
- Response class for the CLI.
- DownloadResponse
- Response class for downloadable files. Intended to be used for downloads that trigger the browser download dialog.
- DynamicResponse
- Response class for dynamically generated content.
- HTTPResponse
- Base class for responses that work with the HTTP protocol.
- JSONResponse
- Response class for JSON content.
- NotFoundResponse
- Response class for not found resources.
- NotModifiedResponse
- Response class for not modified resources.
- RedirectResponse
- Response class for HTTP redirection.
- Response
- Response base class.
- ServerErrorResponse
- Response class for server errors.
- StaticResponse
- Response class that outputs file contents.
- UnsucessfulResponse
- Response class for unsucessful requests.
- CryptUtils
- Class with methods for hashing and encryption.
- HTTPUtils
- Class with utility methods related to the HTTP protocol.
- Mail
- Class used to send e-mails. You shouldn't rely on this class for sending a large amount of emails as it is not
optimized for that. If you want something with more features (SMTP support for example) take a look at PHPMailer.
- MagicInjection
- Trait used for dependency injection of non public properties.


|
Biomedical Image Analysis Library
The Biomedical Image Analysis Library is a poweful tool for developers, physicians, researchers, engineers, and so on.
|
#include <OrientedInternPathFunction.hpp>
Public Member Functions | |
| OrientedInternPathFunction (const Image< D > &handicap, const Image< D > &new_intensity, Image< int > *restriction=nullptr, double new_alpha=0.0) | |
| Basic constructor. More... | |
| OrientedInternPathFunction (const OrientedInternPathFunction< D > &pf) | |
| Copy constructor. More... | |
| ~OrientedInternPathFunction () | |
| Destructor. More... | |
| OrientedInternPathFunction< D > | operator= (const OrientedInternPathFunction< D > &pf) |
| Assignment operator. More... | |
| void | Initialize (Image< D > &init_value, Image< int > *init_label, Image< int > *init_predecessor, bool sequential_label) |
| Initializes object attributes. More... | |
| bool | RemoveSimple (size_t index, BucketState state) |
| Sets initial value for root pixel of index 'index'. More... | |
| bool | RemoveLabel (size_t index, BucketState state) |
| Sets initial value for root pixel of index 'index'. Also sets its label value. More... | |
| bool | Capable (int index, int adj_index, BucketState adj_state) |
| Checks if index can offer better value to adj_index, prior to computing it by Propagate function. This function makes IFT faster. More... | |
| bool | Propagate (int index, int adj_index) |
| Updates adjacent pixel values and returns true if path_function is propagated. More... | |
| bool | Increasing () |
| Returns whether this path function uses increasing or decreasing values. More... | |
| Public Member Functions inherited from Bial::PathFunction< Image, D > | |
| PathFunction () | |
| Basic constructor. More... | |
| PathFunction (const PathFunction< Image, D > &pf) | |
| Copy constructor. More... | |
| virtual | ~PathFunction () |
| Virtual destructor required for virtual classes. More... | |
| void | UpdateCompleteData (size_t index, size_t adj_index) |
| Updates value, label, and predecessor images of index adj_index according to 'index' and value. One of these functions will be used depending on the chosen resultant images. More... | |
| void | UpdatePredecessorData (size_t index, size_t adj_index) |
| void | UpdateLabelData (size_t index, size_t adj_index) |
| void | UpdateSimpleData (size_t index, size_t adj_index) |
| PathFunction< Image, D >::RemoveFn | RemoveFunction () |
| Returns a root initialization function that will be used depending on used maps. More... | |
| PathFunction< Image, D >::UpdateFn | UpdateFunction () |
| Returns an update function that will be used depending on used maps. More... | |
Protected Attributes | |
| const Image< D > & | intensity |
| Reference to source image. More... | |
| Image< D > | handicap |
| Handicap image. More... | |
| Image< int > * | geodesic_restriction |
| Restrictions to geodesic star. Generated by GeodesicRestriction IFT. More... | |
| double | alpha |
| Orientation weight. 0.0 to 1.0. The higher is the value, the higher is the orientation weight. Default value is 0.0. More... | |
| Protected Attributes inherited from Bial::PathFunction< Image, D > | |
| Image< D > * | value |
| Pointer to value container (Vector, Matrix, Image, etc). More... | |
| Image< int > * | label |
| Pointer to label container (Vector, Matrix, Image, etc). More... | |
| Image< int > * | predecessor |
| Pointer to predecessor container (Vector, Matrix, Image, etc). More... | |
| int | next_label |
Additional Inherited Members | |
| Public Types inherited from Bial::PathFunction< Image, D > | |
| typedef bool(PathFunction< Image, D >::* | RemoveFn) (size_t index, BucketState state) |
| Initialization functions of IFT. It will operate on valid maps among value, label, predecessor, and. More... | |
| typedef void(PathFunction< Image, D >::* | UpdateFn) (size_t index, size_t adj_index) |
| Update functions of IFT. It will operate on valid maps among value, label, and predecessor. More... | |
Definition at line 23 of file OrientedInternPathFunction.hpp.
| Bial::OrientedInternPathFunction< D >::OrientedInternPathFunction | ( | const Image< D > & | handicap, |
| const Image< D > & | new_intensity, | ||
| Image< int > * | restriction = nullptr , |
||
| double | new_alpha = 0.0 |
||
| ) |
Basic constructor.
| new_intensity | Source image intensity. Value image is a gradient. |
| restriction | Geodesic path restrictions for data propagation. It is the predecessor map of the Computed GeodesicRestrictionPathFunction IFT. |
| new_alpha | Alpha weight to orient the edges. |
| Bial::OrientedInternPathFunction< D >::OrientedInternPathFunction | ( | const OrientedInternPathFunction< D > & | pf | ) |
Copy constructor.
| pf | path-function to be copied. |
| Bial::OrientedInternPathFunction< D >::~OrientedInternPathFunction | ( | ) |
Destructor.
| none. |
|
virtual |
Checks if index can offer better value to adj_index, prior to computing it by Propagate function. This function makes IFT faster.
| source | Source pixel index. |
| target | Adjacent pixel index. |
Implements Bial::PathFunction< Image, D >.
|
virtual |
Returns whether this path function uses increasing or decreasing values.
| none. |
Implements Bial::PathFunction< Image, D >.
|
virtual |
Initializes object attributes.
| init_value | Reference for initial value container. |
| init_label | Reference for initial label container. |
| init_predecessor | Reference for predecessor container. |
| sequential_label | Sets labeling sequentially. |
Reimplemented from Bial::PathFunction< Image, D >.
| OrientedInternPathFunction< D > Bial::OrientedInternPathFunction< D >::operator= | ( | const OrientedInternPathFunction< D > & | pf | ) |
Assignment operator.
| pf | path-function to be assigned. |
|
virtual |
Updates adjacent pixel values and returns true if path_function is propagated.
| source | Source pixel index. |
| target | Adjacent pixel index. |
Implements Bial::PathFunction< Image, D >.
|
virtual |
Sets initial value for root pixel of index 'index'. Also sets its label value.
| index | The index of the pixel to be initalized. |
Implements Bial::PathFunction< Image, D >.
|
virtual |
Sets initial value for root pixel of index 'index'.
| index | The index of the pixel to be initalized. |
Implements Bial::PathFunction< Image, D >.
|
protected |
Orientation weight. 0.0 to 1.0. The higher is the value, the higher is the orientation weight.
Default value is 0.0.
Definition at line 43 of file OrientedInternPathFunction.hpp.
|
protected |
Restrictions to geodesic star. Generated by GeodesicRestriction IFT.
Definition at line 38 of file OrientedInternPathFunction.hpp.
|
protected |
Handicap image.
Definition at line 34 of file OrientedInternPathFunction.hpp.
|
protected |
Reference to source image.
Definition at line 30 of file OrientedInternPathFunction.hpp.


Key Bindings (control+keys)
| control+d | forward delete |
| control+h | delete |
| control+i | tab |
| control+[ | escape |
| control+m | return |
| control+bfnp | arrow keys |
| control+v | page down |
| control+a (Microsoft Office) | home |
| control+e (Microsoft Office) | end |
| control+k (Microsoft Office) | shift+end, forward delete |
Key Bindings (option+keys)
| option+v | page up |
| option+bf | option+arrow keys |
| option+d | option+forward delete |
Key Bindings (C-x key strokes)
| C-x C-c | Quit application (post command-q) |
| C-x C-f | Open file (post command-o) |
| C-x C-s | Save file (post command-s) |
Bash Style Key Bindings
| control+w | Delete the word behind point (option+delete) |
| control+u | Delete backward from point to the beginning of the line. |


Thank you for your purchase!
Your order is confirmed
Products:
{{sub_total}}
Delivery:
{{shipment_price}}
Total:
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I am a research engineer in robotics and autonomous systems with a track record of contributions to highly challenging research and engineering projects. In particular, I have expertise and hands-on experience in classical and optimal control systems, path planning and state estimation.
I have recently graduated with a Master's degree in Mechanical Engineering from ETH Zurich, focusing on classical/optimal control systems and state estimation. I performed my master's thesis under Dr. Roland Brockers in the Robotic Aerial Mobility Group at the NASA Jet Propulsion Laboratory, co-supervised by ETH's Autonomous Systems Lab under Prof. Roland Siegwart.
In parallel to my studies, I interned in NASA JPL's Computer Vision Group (2017) and worked as an engineer (2015) and team leader (2016) at AMZ Racing, ETH's world-record-breaking Formula Student Electric team. After graduating, I continued to work as a research assistant in ETH's Autonomous Systems Lab.
I strongly believe that advanced control and planning algorithms, combined with rapid advances in perception and machine learning, will enable the next moonshots of our time, significantly improving the safety and efficiency of many of our daily activities. Yet, the field of robotics is still relatively young, and many hurdles remain to be cleared until fully autonomous systems can be deployed on a large scale with sufficient safety and robustness guarantees. I am excited to help tackle these important challenges.
In this 6-month master's thesis project I designed, implemented and tested different classical (PID) and optimal (MPC) flight control and state estimation (EKF, UKF) algorithms from scratch, with the goal of improving the position control accuracy of small unmanned aerial vehicles in the presence of strong and dynamic disturbances such as heavy wind gusts or ground effect.
The developed nonlinear model predictive position controller takes into account the disturbance state, estimated online using an Extended Kalman Filter, to consistently achieve a reduction in position control error in heavy winds of up to around 80% compared to common PID controllers currently used in most industrial UAV applications.
IROS 2019 PaperIn a three-person team in JPL's Computer Vision Group, we developed and implemented a complete guidance, navigation and control framework for a fully autonomous Unmanned Aerial Vehicle, capable of performing repeated flights for long-term observation missions without any human intervention. We addressed two key technologies that are critical for such a system: full platform autonomy to enable mission execution independently from human operators and the ability of vision‐based precision landing on a platform for automated recharging.
My responsibilities included the development of robust flight control and autonomy algorithms, integration of RTK GPS state estimation into the control system and the evaluation of the landing pad pose estimation accuracy. In April 2018, we achieved an 11 hour fully-autonomous indoor flight, as well as a 4 hour outdoor flight without the need for human intervention.
JFR Paper IROS 2018 PaperIn this project, I delved into web development for the first time. My goal was to develop an intuitive user interface to a ROS-based search-and-rescue drone without the need for a native ROS installation or the use of a Linux OS (hence the choice to go browser-based). The UI is used by first responders to visualize two live camera streams, the humans detected by the onboard neural network (in the form of bounding boxes overlaid on the image streams as well as GPS markers on a map), as well as the current position of the drone.
The frontend (browser) is implemented in vue.js and communicates with the backend (running onboard the drone) over an axios http client and a private LTE connection. The frontend subscribes to live messages from the ROS nodes running onboard the drone using roslib.js. It also reads in the live camera feeds using a websocket transport layer. The backend is implemented in node.js and has CRUD access to a sqlite database storing GPS coordinates of detected humans.
As a student researcher under Prof. Maryam Kamgarpour, I developed a novel data modeling and path planning framework to enable safety-optimal path planning for commercial aircraft flying in the presence of hazardous thunderstorms.
The thunderstorm obstacles are modeled stochastically, based on large meteorological datasets. The stochastic obstacle model is then used within an optimal path planning algorithm solved with dynamic programming. The resulting aircraft trajectories maximize the probability of reaching a given waypoint while avoiding multiple thunderstorms, taking into account uncertainties in both the system dynamics (due to wind disturbance) and the obstacles. The resulting trajectories are validated against actual realizations of thunderstorms.
AESCTE PaperAMZ Racing is one of the world's leading Formula Student teams, and the team behind the world's fastest accelerating electric vehicle. Each year, the team of undergrad students sets out to design, manufacture, test and race the fastest electric racecar in a field of more than a hundred international teams, in the timeframe of merely ten months. After having worked at AMZ as a battery engineer in 2015, I had the pleasure of leading the team as team captain in 2016. Vital aspects of the project management included cost and risk assessment, efficient knowledge transfer and communication as well as competitive yet flexible milestone scheduling.
Our tireless work resulted in an electric racecar sprinting from 0 to 100 km/h in only 1.9s. This is made possible by high power (four wheelhub motors delivering 4x39 kW and a peak torque of 375 Nm per wheel), excellent total powertrain efficiency (81%) and a lightweight, carbon-fibre chassis (181 kg total vehicle weight). The organization, quality and level of innovation of our engineering approach was rewarded with glowing feedback from leading automotive and motorsports experts at the 2016 Formula Student competitions, three out of four wins in the Engineering Design category, one overall win and a number of special awards.
Picture Credits: Christian Kreutzmann


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Traditional Shanghai flavor, just for you
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There are several available model types:
If you intend to build a model on your own, look at the FreeTrack project pages - it contains the "model" creation hints, tutorials and more.
There are several predefined models - "NP TrackClip", "NP TrackClip Pro", "Face", "Single point reflective" and "Single LED" - if you use one of those, just select the one of your choice in the Model Name combo box and you can continue to the next pane.
If none of the predefined models suits you, you can define your own.
Type your model's name to the Model Name box and then select the appropriate model type. Available model types are these:
Now fill-in the model dimensions (where applicable) according to the diagram (all dimensions are in millimeters). The last thing to do is to select, if the model uses LEDs (obviously with the exception of face model type) - if it does, check the Model with LEDs check-box. After pressing the Create button, the new model should become available in the Model Name combo-box for you to select it.
For example, the first slider in the 3 point Clip model tweaking panel allows you to minimize the back and forth head movement while looking left and right. To find the "sweet spot", employ the "halving of the interval" method, utilizing the fact, that at that point the movement is smallest and the movement direction reverses.
Move the slider to the left extreme and observe the Z axis (back/forth movement), while panning left and right - note the direction of the movement of the cross; for example, when looking left, the cross moves right and vice versa. Now move the slider fully to the right and see that the response is reversed (e.g. look left, move left).
The spot you are looking for is somewhere in between the two. Move the slider to the middle and see what the response is in there; for example the look left moves the cross to the right - which is the same direction as the leftmost slider extreme. That means, that the sweet spot lies in the second half of the interval (in this example). Now you can continue by repeating the halving it, or just try moving the slider around in small increments to minimize the movement.
Repeat the above steps for both (3-point Cap)/all three (3-point Clip) sliders to reduce the amount of the unwanted movements; if the result doesn't satisfy you, you can remove the rest of it by increasing the dead-zone for the axis of interest.


{% extends 'django_openstack/syspanel/base.html' %}
{% block sidebar %}
{% with current_sidebar="flavors" %}
{{block.super}}
{% endwith %}
{% endblock %}
{% block page_header %}
{% url syspanel_flavors as refresh_link %}
{# to make searchable false, just remove it from the include statement #}
{% include "django_openstack/common/_page_header.html" with title="Flavors" refresh_link=refresh_link searchable="true" %}
{% endblock page_header %}
{% block syspanel_main %}
{% include "django_openstack/syspanel/flavors/_list.html" %}
Create New Flavor
{% endblock %}


CodeStart?
I created CodeStart because as a young developer, I noticed that the hampstead/manchester area had a shortage of teenagers in technology, specifically programming. I want to teach kids the valuble skill of programming, and maybe even more valuble, the ability to think pragmatically and to be intuitive.
Who Am I?
Hi! My name is Brady Redding and I've been programming for about five years now - I'm 15 with a passion for technology, learning, and music, and I'm excited about this years programming camps!


Retro & New
The house of Dr. Marsh being fully occupied, we made our beds in a shed, a short distance from it. Suspended from one of the poles »
The house of Dr. Marsh being fully occupied, we made our beds in a shed, a short distance from it. Suspended from one of the poles »
I noticed near the house a vegetable garden, with the usual variety of vegetables. In another inclosure was the commencement of an extensive vineyard, the fruit »
Having determined to deviate from our direct course, in order to visit the rancho of Dr. Marsh, we parted from Messrs. McKee and Pickett about noon. »
We arrived at the rancho of Dr. Marsh about 5 o'clock P.M., greatly fatigued with the day's ride. The residence of Dr. M. »
Our horses were frightened last night by bears, and this morning, with the exception of those which were picketed, had strayed so far that we did »
We commenced to-day our journey from Tlajomulco de Zuniga to Guadalajara. Our party consisted, including myself, of Colonel Russell, Dr. McKee of Monterey, Mr. Pickett, a »
Next day Marcolf proves all his statements. Thus, he places a pan of milk in a dark closet, and suddenly calls the king. Solomon steps into »
Joseph Zabara has only in recent times received the consideration justly due to him. Yet his "Book of Delight," finished about the year 1200, is more »
You must try to understand that when I finished school I was as raw as raw could be. I had never travelled anywhere on my own, »


<dependency> <groupId>org.testng</groupId> <artifactId>testng</artifactId> <version>7.6.1</version> <scope>test</scope> </dependency>
dependencies { testCompile 'org.testng:testng:7.6.1' }
repositories { maven { url 'https://oss.sonatype.org/content/repositories/snapshots' } }
Java 1.7+ is required for running the TestNG for Eclipse plugin.
Eclipse 4.2 and above is required. Eclipse 3.x is NOT supported any more, please update your Eclipse to 4.2 or above.
You can use either the Eclipse Marketplace or the update site:
You can also install older versions of the plug-ins here. Note that the URL's on this page are update sites as well, not direct download links.
TestNG is also hosted on GitHub, where you can download the source and build the distribution yourself:
$ git clone git://github.com/cbeust/testng.git $ cd testng $ ./gradlew build
You will then find the jar file in the testng/build/libs directory
TestNG Eclipse Plugin is hosted on GitHub, you can download the source code and build by ourself.


Get-ChildItem
Use Get-ChildItem with Inlcude (or Filter) and Recurse
#ex, look recurse for files with case-insensitive string
find . -type f -iname "azure"
Get-ChildItem -Filter "*azure*" -Recurse -File


Sitcaruser
Activar GPS
This is some text in a paragraph.
3D
Buscar
Pdf
This is some text in a paragraph.
Porfavor espera se estan activando los satelites gps...
This is some text in a paragraph.
<--


VolumeBarStyle
public struct VolumeBarStyle
A value type wrapping parameters used to customize the appearance of VolumeBar.
-
The height of the bar that will be displayed on screen.
Declaration
Swift
public var height: CGFloat = 10
-
Insets from the top edge of the device screen.
If
respectsSafeAreaInsets
isfalse
, VolumeBar will be inset from screen edges by exactly these insets.If
respectsSafeAreaInsets
istrue
, VolumeBar will be inset by the sum of the safe area insets of the device andedgeInsets
.Seealso
respectsSafeAreaInsets
Declaration
Swift
public var edgeInsets: UIEdgeInsets = UIEdgeInsets(top: 5, left: 5, bottom: 5, right: 5)
-
Whether or not VolumeBar should respect
safeAreaInsets
when displaying.Seealso
edgeInsets
Declaration
Swift
public var respectsSafeAreaInsets: Bool = false
-
The number of segments that the VolumeBar is made up of. Use this with
segmentSpacing
to give VolumeBar a segmented appearance.The default value, 16, is equal to the number of volume steps on iOS devices. (When the volume is 0%, pressing volume up exactly 16 times will cause the volume to reach 100%)
Seealso
segmentSpacing
Declaration
Swift
public var segmentCount: UInt = 16
-
The number of points between individual segments in the VolumeBar.
Seealso
segmentCount
Declaration
Swift
public var segmentSpacing: CGFloat = 0
-
The corner radius of the VolumeBar.
Declaration
Swift
public var cornerRadius: CGFloat = 0
-
The color of the progress displayed on the bar.
Declaration
Swift
public var progressTintColor: UIColor = .black
-
The background color of the track.
Declaration
Swift
public var trackTintColor: UIColor = UIColor.black.withAlphaComponent(0.5)
-
The background color behind the track view. This should match the color of the view behind the VolumeBar, which might be the color of your navigation bar.
Declaration
Swift
public var backgroundColor: UIColor = .white
-
A volume bar style like Instagram, where the bar is a continuous progress view that displays to the left of the notch on iPhone X and covers the full width of the iOS status bar on all other iOS devices.
Declaration
Swift
public static let likeInstagram: VolumeBarStyle = UIDevice.current.volumeBar_isiPhoneX ? .leftOfNotch : .fullWidthContinuous
-
A volume bar style like Snapchat, where the bar is a segmented progress view that displays under the notch and status bar on iPhone X (respecting the device’s safe area insets) and covers the iOS status bar on all other iOS devices.
Declaration
Swift
public static let likeSnapchat: VolumeBarStyle =
-
A volume bar style that displays a continuous progress view and has minimal insets.
Declaration
Swift
public static let fullWidthContinuous: VolumeBarStyle =
-
A volume bar style that displays to the left of the notch on iPhone X.
Declaration
Swift
public static let leftOfNotch: VolumeBarStyle =
-
A volume bar style that displays to the right of the notch on iPhone X.
Declaration
Swift
public static let rightOfNotch: VolumeBarStyle =


Toggle navigation
Home
Speakers
Sponsors
Team
Contact
Sponsors
Name of sponsor
-Type of sponsorship
Website Link!
Name of sponsor
-Type of sponsorship
Website Link!
Name of sponsor
-Type of sponsorship
Website Link!
Become a Sponsor!


Bass Product Details
Martin BCPA4 Acoustic Bass.
A bass for the performing artist. The BCPA4 features a sapele back and sides and a solid spruce top, a J-style cutaway body, and Fishman Matrix VT Enhance NT2 electronics for deep, rich tone plugged-in or unplugged. A perfect marriage of upright and electric tone.
Specs: J-14 Fret Cutaway Finish: SatinPlug in or Play acoustic
Features: Solid Spruce Top
Number of strings: 4
Price: $1800.00
Similar Featured Basses


Enumeration
SwiftSymbol.Contents
public enum Contents
Relationships
Member Of
SwiftSymbol
Enumeration Cases
none
case none
index
case index(: UInt64)
name
case name(: String)


Puparazzi
Share your pictures of your four legged best friend
submit your dog
Breed
English Bulldog
American Pit Bull Terrier
Golden-Doodle
Labrador
Maltese
West Highland Terrier
Submit
Name
API dog information


{% extends "bootstrap_theme/base.html" %} {# Load the tag library #} {% load bootstrap3 %} {% block content %}
We have a long list of things we need and want, both large and small, so we’ve put together a wedding registry. But not everything we want can be bought at a store – we want to fill our lives with art and would love for yours to be a part of it, whatever form that takes!
{% endblock %}


\[W_{\overline{x}} = (B_x^2 + P_{\overline{x}^2}^{1/2})\]
def.: comparison of measured value with a standard
Primary Standards
- Mass - international prototype kilogram, etc.
- Time - radiation frequency
- Length - length of a proton travel path
Secondary Standards
- Mass - weights
- Gauge Blocks - precise machined metal
- Sesnors of known accuracy
- Take data
- Plot calibration curve
- Deviation Plot (true value - best fit line)


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https://plnkr.co/edit/7F8wipXu5kUne7T0Wj4n?p=preview
https://api.jquery.com/click/
Avoid excessive use of document or document.body for delegated events on large documents.
static button


Summoner Celestiel:
You are allowed the first attack and must never touch your opponent's creature. Are you ready? Let's make this a good battle!
Begin the battle


Ballad’s documentation!#
Ballad is a package designed to handle dependency installation when Poetry might be unavailable.
Ballad is mainly used for (and was created for) Github actions. This was because Poetry failed to register packages, thus commands were raising errors.


{%extends 'dashboard.html'%} {%block activeswap01%} {%endblock%} {%block activeswap} {%block activeswap03%} class="active" {%endblock%} {%block col10swap%}


CSPICE_DAFAC adds comments from a buffer of character strings to the
comment area of a binary DAF file, appending them to any comments which
are already present in the file's comment area.
Given:
handle file handle referring to a DAF.
[1,1] = size(handle); int32 = class(handle)
buffer vector containing comments which to write into
the comment area of the binary DAF attached to 'handle'.
Each element of 'buffer' should contain one comment line.
[n,c1] = size(buffer); char = class(buffer)
or
[1,n] = size(buffer); cell = class(buffer)
the call:
cspice_dafac( handle, buffer )
returns:
The call adds the contents of 'buffer' to the DAF referred
to by 'handle'.
returns:
None.
Any numerical results shown for this example may differ between
platforms as the results depend on the SPICE kernels used as input
and the machine specific arithmetic implementation.
%
% Create a comment text block. Use the list
% of bodies in an SPK for this example.
%
comments = { ...
'-9', 'DEIMOS (402)', 'TITANIA (703)', ...
'MERCURY BARYCENTER (1)', 'MARS (499)', 'OBERON (704)', ...
'VENUS BARYCENTER (2)', 'IO (501)', 'MIRANDA (705)', ...
'EARTH BARYCENTER (3)', 'EUROPA (502)', 'URANUS (799)', ...
'MARS BARYCENTER (4)', 'GANYMEDE (503)', 'TRITON (801)', ...
'JUPITER BARYCENTER (5)', 'CALLISTO (504)', 'NEREID (802)', ...
'SATURN BARYCENTER (6)', 'JUPITER (599)', 'NEPTUNE (899)', ...
'URANUS BARYCENTER (7)', 'TETHYS (603)', 'CHARON (901)', ...
'NEPTUNE BARYCENTER (8)', 'DIONE (604)', 'PLUTO (999)', ...
'PLUTO BARYCENTER (9)', 'RHEA (605)', '301001*', ...
'SUN (10)', 'TITAN (606)', 'GOLDSTONE (399001)*', ...
'MERCURY (199)', 'HYPERION (607)', 'CANBERRA (399002)*', ...
'VENUS (299)', 'IAPETUS (608)', 'MADRID (399003)*', ...
'MOON (301)', 'SATURN (699)', '401001*', ...
'EARTH (399)', 'ARIEL (701)', ...
'PHOBOS (401)', 'UMBRIEL (702)' };
%
% Define the SPK file to which to add the 'comments' text.
%
SPK = 'test.spk';
%
% Open the 'SPK' for writing; return the corresponding
% file handle to 'handle'.
%
handle = cspice_dafopw( SPK );
%
% Add the comments to the 'SPK', use a default line length
% of 80 characters.
%
cspice_dafac( handle, comments )
%
% SAFELY close the file.
%
cspice_dafcls( handle )
Matlab outputs:
None.
Assuming 'SPK' originally lacked comments, the file now
contains the comments:
-9
DEIMOS (402)
TITANIA (703)
MERCURY BARYCENTER (1)
MARS (499)
OBERON (704)
VENUS BARYCENTER (2)
IO (501)
MIRANDA (705)
EARTH BARYCENTER (3)
EUROPA (502)
URANUS (799)
MARS BARYCENTER (4)
GANYMEDE (503)
TRITON (801)
JUPITER BARYCENTER (5)
CALLISTO (504)
NEREID (802)
SATURN BARYCENTER (6)
JUPITER (599)
NEPTUNE (899)
URANUS BARYCENTER (7)
TETHYS (603)
CHARON (901)
NEPTUNE BARYCENTER (8)
DIONE (604)
PLUTO (999)
PLUTO BARYCENTER (9)
RHEA (605)
301001*
SUN (10)
TITAN (606)
GOLDSTONE (399001)*
MERCURY (199)
HYPERION (607)
CANBERRA (399002)*
VENUS (299)
IAPETUS (608)
MADRID (399003)*
MOON (301)
SATURN (699)
401001*
EARTH (399)
ARIEL (701)
PHOBOS (401)
UMBRIEL (702)
If 'SPK' contained comments before running the program, the comments
defined in 'comments' are appended to the existing comments.
A binary DAF contains a data area which is reserved for storing
annotations or descriptive textual information about the data
contained in a file. This area is referred to as the ``comment
area'' of the file. The comment area of a DAF is a line oriented
medium for storing textual information. The comment area preserves
leading or embedded white space in the line(s) of text which are
stored so that the appearance of the information will be unchanged
when it is retrieved (extracted) at some other time. Trailing
blanks, however, are NOT preserved, due to the way that character
strings are represented in standard Fortran 77.
This routine will take a buffer of text lines and add (append) them
to the comment area of a binary DAF. If there are no comments in the
comment area of the file, then space will be allocated and the text
lines in `buffer' will be placed into the comment area. The text lines
may contain only printable ASCII characters (decimal values 32 -
126).
There is NO maximum length imposed on the significant portion of a
text line that may be placed into the comment area of a DAF. The
maximum length of a line stored in the comment area should be
reasonable, however, so that they may be easily extracted. A good
maximum value for this would be 255 characters, as this can easily
accommodate ``screen width'' lines as well as long lines which may
contain some other form of information.
For important details concerning this module's function, please refer to
the CSPICE routine dafac_c.
MICE.REQ
DAF.REQ
-Mice Version 1.0.0, 20-JUL-2012, EDW (JPL)
add comments to a binary DAF
append comments to a DAF comment area
|


Flax Seeds $0.59 / lb
French Green Lentils $0.99 / lb
Green Split Peas $0.59 / lb
Lentils $0.79 / lb
Pinto Beans $0.79 / lb
White Navy Beans $0.79 / lb
Yellow Split Peas $0.59 / lb
Peanuts $1.89 / lb
Pistachios $9.99 / lb
Roasted Cashews $12.89 / lb
Sunflower Seeds $1.49 / lb
Toasted Pumpkin Seeds $2.99 / lb
Egg Noodles $0.79 / lb
Jasmine Rice $0.79 / lb
Macaroni $0.49 / lb
Mini Shells $0.49 / lb
Penne $0.49 / lb
Rotini $0.49 / lb
Short Grain Brown Rice $0.79 / lb
Avocados $1.29 / each
Bell Peppers $1.49 / each
Brussels Sprouts $2.99 / lb
Concord Grapes $2.49 / each
Cranberries $2.49 / lb
Eggplant $1.49 / each
Gala Apples $1.49 / lb
Granny Smith Apples $1.69 / lb
Grapefruit $0.99 / lb
Green Tomatoes $2.69 / lb
Limes $0.59 / each
Mushrooms $3.99 / lb
Onions $0.79 / lb
Oranges $0.89 / lb
Peaches $1.29 / each
Potatoes $0.79 / lb
Red Apples $1.69 / lb
Red Cherries $7.99 / lb
Spinach $2.49 / lb
Tangerines $2.89 / lb
Tomatoes $1.89 / lb
Vine-Ripened Tomatoes $2.89 / lb
Yams $0.99 / lb
Yukon Gold Potatoes $1.49 / lb


avahi-browse
Displays services and hosts exposed on the local network via mDNS/DNS-SD.
Avahi is compatible with Bonjour (Zeroconf) found in Apple devices.
More information: home page
- List all services available on the local network along with their addresses and ports while ignoring local ones:
avahi-browse --all --resolve --ignore-local
- List all domains:
avahi-browse --browse-domains
- Limit the search to a particular domain:
avahi-browse --all --domain=domain


About Me
I'm just mostly a guy who develops, not much to say.
Organizations I am associated with
TotalCore
I am the Lead Developer currently of the Total-Core organization (other than rovertdude). You can find TotalCore at here
Repositories I am working on
Music-Pug
The Music-Pug is located here The Music-Pug was created by rovertdude, the creator of the Total Core organization that I am a contributor of. You can get the Music-Pug to your server by using this link
Core
The Core is located here The Core was again made by rovertdude.
adroition.github.io
WolfFreedom-Core (inactive)
The WFC (or WolfFreedom-Core) is a TotalFreedomMod that was created by rovertdude for the WolfFreedom server.


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By borrowing things — rather than buying or renting — community groups in Kirklees have saved more than £40,000, while also making new connections with other groups
To Kirklees Council, these shares are more than transactions. They’re part of building community strength at a time when severe cutbacks in government funding to localities is forcing the council to slash local services
Over 2,600 sharable resources are now managed on the platform and available to communities in Kirklees
Over half of these resources are owned by local community groups, rather than the council
17% of resources shared on the platform enabled activities to go ahead which otherwise wouldn't have happened
Over a quarter of the activities benefit 250 local people or more
The project approach inspired the council to adopt human-centred design and Agile working practices in other departments
I developed the design and delivery strategy and pitched this to Kirklees Council through a competitive tender process
I made sure the approach was based on ongoing user research to ensure an Agile, human-centred approach
I attended project board meetings to get buy-in from senior stakeholders and report on progress
I pulled together and managed an Agile delivery team using a collaborative, dynamic planning process – prioritising the work against capacity and capability of multi-disciplinary teams
I facilitated several rounds of usability testing and optimisation sprints and ran discovery and design workshops
I delivered training to the council in service design and Agile working


Activations module¶
-
activations.
relu
(Z)¶ Implement the
RELU
function.- Arguments:
Z
– Output of the linear layer, of any shape
- Returns:
A
– Post-activation parameter, of the same shape as Zcache
– a python dictionary containing “A” ; stored for computing the backward pass efficiently
\[ReLU(Z) = max(0, Z)\]
-
activations.
sigmoid
(Z)¶ Implements the sigmoid activation in numpy
- Args:
Z
– numpy array of any shape
- Returns:
A
– output ofsigmoid(z)
, same shape asZ
cache
– returnsZ
as well, useful during backpropagation


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Table of Contents
CNNs in CV
CNNs in NLP
CNNs Architectures
Convnet Ch.9 Summary (blog)
Introduction
- CNNs:
In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery.
In general, it works on data that have grid-like topology.E.g. Time-series data (1-d grid w/ samples at regular time intervals), image data (2-d grid of pixels).
Convolutional networks are simply neural networks that use convolution in place of general matrix multiplication in at least one of their layers.
- The Big Idea:
CNNs use a variation of multilayer Perceptrons designed to require minimal preprocessing. In particular, they use the Convolution Operation.
The Convolution leverage three important ideas that can help improve a machine learning system:- Sparse Interactions/Connectivity/Weights:
Unlike FNNs, where every input unit is connected to every output unit, CNNs have sparse interactions. This is accomplished by making the kernel smaller than the input.
Benefits:- This means that we need to store fewer parameters, which both,
- Reduces the memory requirements of the model and
- Improves its statistical efficiency
- Also, Computing the output requires fewer operations
- In deep CNNs, the units in the deeper layers interact indirectly with large subsets of the input which allows modelling of complex interactions through sparse connections.
These improvements in efficiency are usually quite large.
If there are \(m\) inputs and \(n\) outputs, then matrix multiplication requires \(m \times n\) parameters, and the algorithms used in practice have \(\mathcal{O}(m \times n)\) runtime (per example). If we limit the number of connections each output may have to \(k\), then the sparsely connected approach requires only \(k \times n\) parameters and \(\mathcal{O}(k \times n)\) runtime.
- This means that we need to store fewer parameters, which both,
-
Parameter Sharing:
refers to using the same parameter for more than one function in a model.Benefits:
- This means that rather than learning a separate set of parameters for every location, we learn only one set of parameters.
- This does not affect the runtime of forward propagation—it is still \(\mathcal{O}(k \times n)\)
- But it does further reduce the storage requirements of the model to \(k\) parameters (\(k\) is usually several orders of magnitude smaller than \(m\))
Convolution is thus dramatically more efficient than dense matrix multiplication in terms of the memory requirements and statistical efficiency.
- This means that rather than learning a separate set of parameters for every location, we learn only one set of parameters.
- Equivariant Representations:
For convolutions, the particular form of parameter sharing causes the layer to have a property called equivariance to translation.A function is equivariant means that if the input changes, the output changes in the same way.
Specifically, a function \(f(x)\) is equivariant to a function \(g\) if \(f(g(x)) = g(f(x))\).Thus, if we move the object in the input, its representation will move the same amount in the output.
Benefits:
- It is most useful when we know that some function of a small number of neighboring pixels is useful when applied to multiple input locations (e.g. edge detection)
- Shifting the position of an object in the input doesn’t confuse the NN
- Robustness against translated inputs/images
Note: Convolution is not naturally equivariant to some other transformations, such as changes in the scale or rotation of an image.
Finally, the convolution provides a means for working with inputs of variable sizes (i.e. data that cannot be processed by neural networks defined by matrix multiplication with a fixed-shape matrix).
- Sparse Interactions/Connectivity/Weights:
- Inspiration Model:
Convolutional networks were inspired by biological processes in which the connectivity pattern between neurons is inspired by the organization of the animal visual cortex.
Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.
Architecture and Design
-
Design:
A CNN consists of an input and an output layer, as well as multiple hidden layers.
The hidden layers of a CNN typically consist of convolutional layers, pooling layers, fully connected layers and normalization layers. - Volumes of Neurons:
Unlike neurons in traditional Feed-Forward networks, the layers of a ConvNet have neurons arranged in 3-dimensions: width, height, depth.Note: Depth here refers to the third dimension of an activation volume, not to the depth of a full Neural Network, which can refer to the total number of layers in a network.
-
Connectivity:
The neurons in a layer will only be connected to a small region of the layer before it, instead of all of the neurons in a fully-connected manner.
Every Layer has a simple API: It transforms an input 3D volume to an output 3D volume with some differentiable function that may or may not have parameters. - Layers:
We use three main types of layers to build ConvNet architectures:- Convolutional Layer:
- Convolution (Linear Transformation)
- Activation (Non-Linear Transformation; e.g. ReLU)
Known as Detector Stage
- Pooling Layer
- Fully-Connected Layer
- Convolutional Layer:
-
- Process:
- ConvNets transform the original image layer by layer from the original pixel values to the final class scores.
-
- Example Architecture (CIFAR-10):
- Model: [INPUT - CONV - RELU - POOL - FC]
-
- INPUT: [32x32x3] will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
- CONV-Layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume.
This may result in volume such as [\(32\times32\times12\)] if we decided to use 12 filters. - RELU-Layer: will apply an element-wise activation function, thresholding at zero. This leaves the size of the volume unchanged ([\(32\times32\times12\)]).
- POOL-Layer: will perform a down-sampling operation along the spatial dimensions (width, height), resulting in volume such as [\(16\times16\times12\)].
- Fully-Connected: will compute the class scores, resulting in volume of size [\(1\times1\times10\)], where each of the 10 numbers correspond to a class score, such as among the 10 categories of CIFAR-10.
As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
-
- Fixed Functions VS Hyper-Parameters:
- Some layers contain parameters and other don’t.
-
- CONV/FC layers perform transformations that are a function of not only the activations in the input volume, but also of the parameters (the weights and biases of the neurons).
-
- RELU/POOL layers will implement a fixed function.
-
The parameters in the CONV/FC layers will be trained with gradient descent so that the class scores that the ConvNet computes are consistent with the labels in the training set for each image.
- Summary:
- A ConvNet architecture is in the simplest case a list of Layers that transform the image volume into an output volume (e.g. holding the class scores)
- There are a few distinct types of Layers (e.g. CONV/FC/RELU/POOL are by far the most popular)
- Each Layer accepts an input 3D volume and transforms it to an output 3D volume through a differentiable function
- Each Layer may or may not have parameters (e.g. CONV/FC do, RELU/POOL don’t)
- Each Layer may or may not have additional hyperparameters (e.g. CONV/FC/POOL do, RELU doesn’t)
The Convolutional Layer
N.B.: Blue maps are inputs, and cyan maps are outputs.
| No padding, no strides | Arbitrary padding, no strides | Half padding, no strides | Full padding, no strides |
| No padding, strides | Padding, strides | Padding, strides (odd) | |
| No padding, no strides, transposed | Arbitrary padding, no strides, transposed | Half padding, no strides, transposed | Full padding, no strides, transposed |
| No padding, strides, transposed | Padding, strides, transposed | Padding, strides, transposed (odd) | |
| No padding, no stride, dilation |
-
Convolutions:
In its most general form, the convolution is a Linear Operation on two functions of real-valued arguments.Mathematically, a Convolution is a mathematical operation on two functions (\(f\) and \(g\)) to produce a third function, that is typically viewed as a modified version of one of the original functions, giving the integral of the point-wise multiplication of the two functions as a function of the amount that one of the original functions is translated.
The convolution could be thought of as a weighting function (e.g. for taking the weighted average of a series of numbers/function-outputs).The convolution of the continuous functions \(f\) and \(g\):
$${\displaystyle {\begin{aligned}(f * g)(t)&\,{\stackrel {\mathrm {def} }{=}}\ \int _{-\infty }^{\infty }f(\tau )g(t-\tau )\,d\tau \\&=\int_{-\infty }^{\infty }f(t-\tau )g(\tau )\,d\tau .\end{aligned}}}$$
The convolution of the discreet functions f and g:
$${\displaystyle {\begin{aligned}(f * g)[n]&=\sum_{m=-\infty }^{\infty }f[m]g[n-m]\\&=\sum_{m=-\infty }^{\infty }f[n-m]g[m].\end{aligned}}} (commutativity)$$
In this notation, we refer to:
- The function \(f\) as the Input
- The function \(g\) as the Kernel/Filter
- The output of the convolution as the Feature Map
Commutativity:
Can be achieved by flipping the kernel with respect to the input; in the sense that as increases, the index into the \(m\) input increases, but the index into the kernel decreases.
While the commutative property is useful for writing proofs, it is not usually an important property of a neural network implementation.
Moreover, in a CNN, the convolution is used simultaneously with other functions, and the combination of these functions does not commute regardless of whether the convolution operation flips its kernel or not.
Because Convolutional networks usually use multichannel convolution, the linear operations they are based on are not guaranteed to be commutative, even if kernel flipping is used. These multichannel operations are only commutative if each operation has the same number of output channels as input channels.Motivation:
The convolution operation can be used to compute many results in different domains. It originally arose in pure mathematics and probability theory as a way to combine probability distributions.Understanding the Convolution Operation:
-
It can be seen as a sliding window of multiplying the values from one array with the other array. It can also be seen as a matrix generated by the outer product of the two “vectors” and then summing the diagonals.
-
The \(n\)th element of the convolution is the sum of the product of the elements in the two arrays such that the indices of the arrays sum up to \(n\):
The formula for the convolution of \(a\) and \(b\):
$$(a * b)_n=\sum_{\substack{i, j \\ i+j=n}} a_i \cdot b_j$$
Where does it apply?
- Image Processing: different kernels give us different image processing effects like the examples below.
- Image Blurring: calculating a 2d moving average of the image results in a form of blurring.
- Vertical Edge Detection:
- Horizontal Edge Detection:
- Image Sharpening:
- Image Blurring: calculating a 2d moving average of the image results in a form of blurring.
- Probability:
- Sum of two Probability Distributions: the convolution of the probabilities of each event corresponds to adding two probability distributions together \(P_{X+Y}\):
- Calculating a moving average (equivalently, data smoothing):
- Sum of two Probability Distributions: the convolution of the probabilities of each event corresponds to adding two probability distributions together \(P_{X+Y}\):
- Differential Equations: solving DEs
- Polynomials: in multiplying two polynomials, the coeffecients are the convolution of the coeffecients of the original polynomials (which are the sums of the diagonals of the convolution matrix):
- Multiplication of two numbers:
Computing the Convolution Operation:
We can compute the convolution of two arrays much faster by utilizing the FFT algorithm as implemented by thescipy.signal.fftconvolve
function.Deriving the faster algorithm for computing the convolution operation:
- We utilize the connection between multiplication and convolutions to come up with a faster algorithm for computing the convolution
- New Algorithm for computing the convolution of two arrays \(a, b\) (\(\mathcal{O}(N^2)\)):
- Assume the Arrays are coeffecients of two polynomials:
- Sample the polynomials at len(\(a\)), len(\(b\)) points respectively:
- Multiply the samples pointwise
- Solve the new system to recover the coeffecients which will be the convolution of \(a, b\)
Problems with this approach:
- Multiplying two polynomials by expanding the products is an \(\mathcal{O}(n^2)\) algorithm.
Key Ideas for the solution:
- Recovering a polynomial of order \(n\) only requires \(n+1\) samples
- Utilize the connection between multiplication of polynomials and convolutions I.E. we can translate one problem into the other.
-
Utilize the Discrete Fourier Transform (DFT) of the coeffecients to compute the samples of each constructed polynomial to reduce the runtime from \(\mathcal{O}(N^2)\) to \(\mathcal{O}(N \log(N))\)
Fast Convolution Algorithm using the Fast Fourier Transform (FFT) \(\mathcal{O}(N \log(N))\):
- Compute the FFT of each array (as coeffecients)
I.E. Treat them as polynomials and evaluate them at the roots of unity
- Multiply the FFT of each array, pointwise
- Compute the inverse FFT of the new result of multiplication
Key Results:
The connection between all these applications of Convolutions, and its’ connection to the FFT implies that we can compute all the results above in \(\mathcal{O}(N \log(N))\) time (e.g. sum of probabilities, image processing, etc.). -
- Cross-Correlation:
- Cross-Correlation is a measure of similarity of two series as a function of the displacement of one relative to the other.
- The continuous cross-correlation on continuous functions f and g:
- \[(f\star g)(\tau )\ {\stackrel {\mathrm {def} }{=}}\int_{-\infty }^{\infty }f^{*}(t)\ g(t+\tau )\,dt,\]
- The discrete cross-correlation on discreet functions f and g:
-
$$(f\star g)[n]\ {\stackrel {\mathrm {def} }{=}}\sum _{m=-\infty }^{\infty }f^{*}[m]\ g[m+n].$$
- Convolutions and Cross-Correlation:
- Convolution is similar to cross-correlation.
- For discrete real valued signals, they differ only in a time reversal in one of the signals.
- For continuous signals, the cross-correlation operator is the adjoint operator of the convolution operator.
-
CNNs, Convolutions, and Cross-Correlation:
The term Convolution in the name “Convolution Neural Network” is unfortunately a misnomer.
CNNs actually use Cross-Correlation instead as their similarity operator.
The term ‘convolution’ has stuck in the name by convention.
- Convolution in DL:
The Convolution operation:$$s(t)=(x * w)(t)=\sum_{a=-\infty}^{\infty} x(a) w(t-a)$$
we usually assume that these functions are zero everywhere but in the finite set of points for which we store the values.
- Convolution Over Two Axis:
If we use a 2D image \(I\) as our input, we probably also want to use a two-dimensional kernel \(K\):$$S(i, j)=(I * K)(i, j)=\sum_{m} \sum_{n} I(m, n) K(i-m, j-n)$$
In practice we use the following formula instead (commutativity):
$$S(i, j)=(K * I)(i, j)=\sum_{m} \sum_{n} I(i-m, j-n) K(m, n)$$
Usually the latter formula is more straightforward to implement in a machine learning library, because there is less variation in the range of valid values of \(m\) and \(n\).
The Cross-Correlation is usually implemented by ML-libs:
$$S(i, j)=(K * I)(i, j)=\sum_{m} \sum_{n} I(i+m, j+n) K(m, n)$$
- The Mathematics of the Convolution Operation:
- The operation can be broken into matrix multiplications using the Toeplitz matrix representation for 1D and block-circulant matrix for 2D convolution:
- Discrete convolution can be viewed as multiplication by a matrix, but the matrix has several entries constrained to be equal to other entries.
For example, for univariate discrete convolution, each row of the matrix is constrained to be equal to the row above shifted by one element. This is known as a Toeplitz matrix.
A Toeplitz matrix has the property that values along all diagonals are constant. - In two dimensions, a doubly block circulant matrix corresponds to convolution.
A matrix which is circulant with respect to its sub-matrices is called a block circulant matrix. If each of the submatrices is itself circulant, the matrix is called doubly block-circulant matrix.
- Discrete convolution can be viewed as multiplication by a matrix, but the matrix has several entries constrained to be equal to other entries.
- Convolution usually corresponds to a very sparse matrix (a matrix whose entries are mostly equal to zero).
This is because the kernel is usually much smaller than the input image. - Any neural network algorithm that works with matrix multiplication and does not depend on specific properties of the matrix structure should work with convolution, without requiring any further changes to the neural network.
- Typical convolutional neural networks do make use of further specializations in order to deal with large inputs efficiently, but these are not strictly necessary from a theoretical perspective.
- The operation can be broken into matrix multiplications using the Toeplitz matrix representation for 1D and block-circulant matrix for 2D convolution:
- The Convolution operation in a CONV Layer:
- The CONV layer’s parameters consist of a set of learnable filters.
- Every filter is small spatially (along width and height), but extends through the full depth of the input volume.
For example, a typical filter on a first layer of a ConvNet might have size 5x5x3 (i.e. 5 pixels width and height, and 3 because images have depth 3, the color channels).
- Every filter is small spatially (along width and height), but extends through the full depth of the input volume.
- In the forward pass, we slide (convolve) each filter across the width and height of the input volume and compute dot products between the entries of the filter and the input at any position.
- As we slide the filter over the width and height of the input volume we will produce a 2-dimensional activation map that gives the responses of that filter at every spatial position.
Intuitively, the network will learn filters that activate when they see some type of visual feature such as an edge of some orientation or a blotch of some color on the first layer, or eventually entire honeycomb or wheel-like patterns on higher layers of the network.
- Now, we will have an entire set of filters in each CONV layer (e.g. 12 filters), and each of them will produce a separate 2-dimensional activation map.
- We will stack these activation maps along the depth dimension and produce the output volume.
- As we slide the filter over the width and height of the input volume we will produce a 2-dimensional activation map that gives the responses of that filter at every spatial position.
As a result (of what?), the network learns filters that activate when it detects some specific type of feature at some spatial position in the input.
- The CONV layer’s parameters consist of a set of learnable filters.
-
The Brain Perspective:
Every entry in the 3D output volume can also be interpreted as an output of a neuron that looks at only a small region in the input and shares parameters with all neurons to the left and right spatially.
- Local Connectivity:
- Convolutional networks exploit spatially local correlation by enforcing a local connectivity pattern between neurons of adjacent layers:
- Each neuron is connected to only a small region of the input volume.
- The Receptive Field of the neuron defines the extent of this connectivity as a hyperparameter.
For example, suppose the input volume has size \([32\times32\times3]\) and the receptive field (or the filter size) is \(5\times5\), then each neuron in the Conv Layer will have weights to a \([5\times5\times3]\) region in the input volume, for a total of \(5*5*3 = 75\) weights (and \(+1\) bias parameter).
Such an architecture ensures that the learnt filters produce the strongest response to a spatially local input pattern.
- Convolutional networks exploit spatially local correlation by enforcing a local connectivity pattern between neurons of adjacent layers:
- Spatial Arrangement:
There are three hyperparameters control the size of the output volume:- The Depth of the output volume is a hyperparameter that corresponds to the number of filters we would like to use (each learning to look for something different in the input).
- The Stride controls how depth columns around the spatial dimensions (width and height) are allocated.
e.g. When the stride is 1 then we move the filters one pixel at a time.
- The Smaller the stride, the more overlapping regions exist and the bigger the volume.
- The bigger the stride, the less overlapping regions exist and the smaller the volume.
- The Padding is a hyperparameter whereby we pad the input volume with zeros around the border.
This allows to control the spatial size of the output volumes.
- The Spatial Size of the Output Volume:
We compute the spatial size of the output volume as a function of:- \(W\): The input volume size.
- \(F\): \(\:\)The receptive field size of the Conv Layer neurons.
- \(S\): \(\:\)The stride with which they are applied.
- \(P\): \(\:\)The amount of zero padding used on the border.
Thus, the Total Size of the Output:
$$\dfrac{W−F+2P}{S} + 1$$
Potential Issue: If this number is not an integer, then the strides are set incorrectly and the neurons cannot be tiled to fit across the input volume in a symmetric way.
- Fix: In general, setting zero padding to be \({\displaystyle P = \dfrac{K-1}{2}}\) when the stride is \({\displaystyle S = 1}\) ensures that the input volume and output volume will have the same size spatially.
- Calculating the Number of Parameters:
Given:- Input Volume: \(32\times32\times3\)
- Filters: \(10\:\:\: (5\times5)\)
- Stride: \(1\)
- Pad: \(2\)
The number of parameters equals the number of parameters in each filter \(= 5*5*3 + 1 = 76\) (+1 for bias) times the number of filters \(76 * 10 = 760\).
-
The Convolution Layer:
__
__The Conv Layer and the Brain:
__
__
__
-
From FC-layers to Conv-layers:
**
**
*** -
\(1\times1\) Convolutions:
- Notes:
- Summary:
- ConvNets stack CONV,POOL,FC layers
- Trend towards smaller filters and deeper architectures
- Trend towards getting rid of POOL/FC layers (just CONV)
- Typical architectures look like [(CONV-RELU) * N-POOL?] * M-(FC-RELU) * K, SOFTMAX
where \(N\) is usually up to ~5, \(M\) is large, \(0 <= K <= 2\).
But recent advances such as ResNet/GoogLeNet challenge this paradigm
- Effect of Different Biases:
Separating the biases may slightly reduce the statistical efficiency of the model, but it allows the model to correct for differences in the image statistics at different locations. For example, when using implicit zero padding, detector units at the edge of the image receive less total input and may need larger biases. - In the kinds of architectures typically used for classification of a single object in an image, the greatest reduction in the spatial dimensions of the network comes from using pooling layers with large stride.
- Summary:
The Pooling Layer
-
The Pooling Operation/Function:
The pooling function calculates a summary statistic of the nearby pixels at the point of operation.
Some common statistics are max, mean, weighted average and \(L^2\) norm of a surrounding rectangular window.
- The Key Ideas/Properties:
In all cases, pooling helps to make the representation approximately invariant to small translations of the input.Invariance to translation means that if we translate the input by a small amount, the values of most of the pooled outputs do not change.
Invariance to local translation can be a useful property if we care more about whether some feature is present than exactly where it is.(Learned) Invariance to other transformations:
Pooling over spatial regions produces invariance to translation, but if we pool over the outputs of separately parametrized convolutions, the features can learn which transformations to become invariant to.
This property has been used in Maxout networks.
For many tasks, pooling is essential for handling inputs of varying size.
This is usually accomplished by varying the size of an offset between pooling regions so that the classification layer always receives the same number of summary statistics regardless of the input size. For example, the final pooling layer of the network may be defined to output four sets of summary statistics, one for each quadrant of an image, regardless of the image size.
One can use fewer pooling units than detector units, since they provide a summary; thus, by reporting summary statistics for pooling regions spaced \(k\) pixels apart rather than \(1\) pixel apart, we can improve the computational efficiency of the network because the next layer has roughly \(k\) times fewer inputs to process.
This reduction in the input size can also result in improved statistical efficiency and reduced memory requirements for storing the parameters.
-
Theoretical Guidelines for choosing the pooling function:
Link
-
Variations:
Dynamical Pooling:
It is also possible to dynamically pool features together, for example, by running a clustering algorithm on the locations of interesting features (Boureau et al., 2011) link. This approach yields a different set of pooling regions for each image.Learned Pooling:
Another approach is to learn a single pooling structure that is then applied to all images (Jia et al., 2012).
-
Pooling and Top-Down Architectures:
Pooling can complicate some kinds of neural network architectures that use top-down information, such as Boltzmann machines and autoencoders.
- The Pooling Layer (summary):
Notes:
- Pooling Layer:
- Makes the representations smaller and more manageable
- Operates over each activation map independently (i.e. preserves depth)
- You can use the stride instead of the pooling to downsample
Convolution and Pooling as an Infinitely Strong Prior
-
A Prior Probability Distribution:
This is a probability distribution over the parameters of a model that encodes our beliefs about what models are reasonable, before we have seen any data.
- What is a weight prior?:
Assumptions about the weights (before learning) in terms of acceptable values and range are encoded into the prior distribution of the weights.- A Weak Prior: has a high entropy, and thus, variance and shows that there is low confidence in the initial value of the weight.
- A Strong Prior: in turn has low entropy/variance, and shows a narrow range of values about which we are confident before learning begins.
- A Infinitely Strong Prior: demarkets certain values as forbidden completely, assigning them zero probability.
- Convolutional Layer as a FC Layer:
If we view the conv-layer as a FC-layer, the:- Convolution: operation imposes an infinitely strong prior by making the following restrictions on the weights:
- Adjacent units must have the same weight but shifted in space.
- Except for a small spatially connected region, all other weights must be zero.
- Pooling: operation imposes an infinitely strong prior by:
- Requiring features to be Translation Invariant.
- Requiring features to be Translation Invariant.
- Convolution: operation imposes an infinitely strong prior by making the following restrictions on the weights:
- Key Insights/Takeaways:
- Convolution and pooling can cause underfitting if the priors imposed are not suitable for the task. When a task involves incorporating information from very distant locations in the input, then the prior imposed by convolution may be inappropriate.
As an example, consider this scenario. We may want to learn different features for different parts of an input. But the compulsion to used tied weights (enforced by standard convolution) on all parts of an image, forces us to either compromise or use more kernels (extract more features).
- Convolutional models should only be compared with other convolutional models. This is because other models which are permutation invariant can learn even when input features are permuted (thus loosing spatial relationships). Such models need to learn these spatial relationships (which are hard-coded in CNNs).
- Convolution and pooling can cause underfitting if the priors imposed are not suitable for the task. When a task involves incorporating information from very distant locations in the input, then the prior imposed by convolution may be inappropriate.
Variants of the Basic Convolution Function and Structured Outputs
- Practical Considerations for Implementing the Convolution Function:
- In general a convolution layer consists of application of several different kernels to the input. This allows the extraction of several different features at all locations in the input. This means that in each layer, a single kernel (filter) isn’t applied. Multiple kernels (filters), usually a power of 2, are used as different feature detectors.
- The input is generally not real-valued but instead vector valued (e.g. RGB values at each pixel or the feature values computed by the previous layer at each pixel position). Multi-channel convolutions are commutative only if number of output and input channels is the same.
- Strided Convolutions are a means to do DownSampling; they are used to reduce computational cost, by calculating features at a coarser level. The effect of strided convolution is the same as that of a convolution followed by a downsampling stage. This can be used to reduce the representation size.
$$Z_{i, j, k}=c(\mathrm{K}, \mathrm{V}, s)_{i, j, k}=\sum_{l, m, n}\left[V_{l,(j-1) \times s+m,(k-1) \times s+n} K_{i, l, m, n}\right] \tag{9.8}$$
- Zero Padding is used to make output dimensions and kernel size independent (i.e. to control the output dimension regardless of the size of the kernel). There are three types:
- Valid: The output is computed only at places where the entire kernel lies inside the input. Essentially, no zero padding is performed. For a kernel of size \(k\) in any dimension, the input shape of \(m\) in the direction will become \(m-k+1\) in the output. This shrinkage restricts architecture depth.
- Same: The input is zero padded such that the spatial size of the input and output is same. Essentially, for a dimension where kernel size is \(k\), the input is padded by \(k-1\) zeros in that dimension. Since the number of output units connected to border pixels is less than that for center pixels, it may under-represent border pixels.
- Full: The input is padded by enough zeros such that each input pixel is connected to the same number of output units.
The optimal amount of Zero-Padding usually lies between “valid” and “same” convolution.
- Locally Connected Layers/Unshared Convolution: has the same connectivity graph as a convolution operation, but without parameter sharing (i.e. each output unit performs a linear operation on its neighbourhood but the parameters are not shared across output units.).
This allows models to capture local connectivity while allowing different features to be computed at different spatial locations; at the expense of having a lot more parameters.$$Z_{i, j, k}=\sum_{l, m, n}\left[V_{l, j+m-1, k+n-1} w_{i, j, k, l, m, n}\right] \tag{9.9}$$
They’re useful when we know that each feature should be a function of a small part of space, but there is no reason to think that the same feature should occur across all of space.
For example, if we want to tell if an image is a picture of a face, we only need to look for the mouth in the bottom half of the image. - Tiled Convolution: offers a middle ground between Convolution and locally-connected layers. Rather than learning a separate set of weights at every spatial location, it learns/uses a set of kernels that are cycled through as we move through space.
This means that immediately neighboring locations will have different filters, as in a locally connected layer, but the memory requirements for storing the parameters will increase only by a factor of the size of this set of kernels, rather than by the size of the entire output feature map.$$Z_{i, j, k}=\sum_{l, m, n} V_{l, j+m-1, k+n-1} K_{i, l, m, n, j \% t+1, k \% t+1} \tag{9.10}$$
- Max-Pooling, and Locally Connected Layers and Tiled Layers: When max pooling operation is applied to locally connected layer or tiled convolution, the model has the ability to become transformation invariant because adjacent filters have the freedom to learn a transformed version of the same feature.
This essentially similar to the property leveraged by pooling over channels rather than spatially.
- Different Connections: Besides locally-connected layers and tiled convolution, another extension can be to restrict the kernels to operate on certain input channels. One way to implement this is to connect the first m input channels to the first n output channels, the next m input channels to the next n output channels and so on. This method decreases the number of parameters in the model without decreasing the number of output units.
- Other Operations: The following three operations—convolution, backprop from output to weights, and backprop from output to inputs—are sufficient to compute all the gradients needed to train any depth of feedforward convolutional network, as well as to train convolutional networks with reconstruction functions based on the transpose of convolution.
See Goodfellow (2010) for a full derivation of the equations in the fully general multidimensional, multiexample case.
- Bias: Bias terms can be used in different ways in the convolution stage.
- For locally connected layer and tiled convolution, we can use a bias per output unit and kernel respectively.
- In case of traditional convolution, a single bias term per output channel is used.
- If the input size is fixed, a bias per output unit may be used to counter the effect of regional image statistics and smaller activations at the boundary due to zero padding.
- Structured Outputs:
Convolutional networks can be trained to output high-dimensional structured output rather than just a classification score.
A good example is the task of image segmentation where each pixel needs to be associated with an object class.
Here the output is the same size (spatially) as the input. The model outputs a tensor \(S\) where \(S_{i, j, k}\) is the probability that pixel \((j,k)\) belongs to class \(i\).- Problem: One issue that often comes up is that the output plane can be smaller than the input plane.
- Solutions:
- To produce an output map as the same size as the input map, only same-padded convolutions can be stacked.
- Avoid Pooling Completely (Jain et al. 2007)
- Emit a lower-Resolution grid of labels (Pinheiro and Collobert, 2014, 2015)
- Recurrent-Convolutional Models: The output of the first labelling stage can be refined successively by another convolutional model. If the models use tied parameters, this gives rise to a type of recursive model.
- Another model that has gained popularity for segmentation tasks (especially in the medical imaging community) is the U-Net. The up-convolution mentioned is just a direct upsampling by repetition followed by a convolution with same padding.
The output can be further processed under the assumption that contiguous regions of pixels will tend to belong to the same label. Graphical models can describe this relationship. Alternately, CNNs can learn to optimize the graphical models training objective.
Extra
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- Image Features:
- are certain quantities that are calculated from the image to better describe the information in the image, and to reduce the size of the input vectors.
-
- Examples:
- Color Histogram: Compute a (bucket-based) vector of colors with their respective amounts in the image.
- Histogram of Oriented Gradients (HOG): we count the occurrences of gradient orientation in localized portions of the image.
- Bag of Words: a bag of visual words is a vector of occurrence counts of a vocabulary of local image features.
The visual words can be extracted using a clustering algorithm; K-Means.
- Examples:


{% extends "layout.html" %} {% block main_body %}
Your friendly Jekyll blogging client for Android.
MrHyde was developed as part of the Android Challenge: one app every month for one year (think of it as a new years resolution).
MrHyde was our project for April.
If you want to know more be sure to check out our website.
Our apps are open source and you can find everything on GitHub.
Philipp Eichhorn
Simon Hofmann
Julia Schottenhamml
Richard Baumann


Double click on a track to display the notation editor.
Use pencil to enter notes. The icon tools at left allow changing time and flat/sharp.
Use pointer to select many notes.
Add slur by selecting multiple notes, then Phrase/Add Slur
If you add a note to wrong place, it can be dragged to other place
Add a measure
In the main screen where the tracks are displayed:
Use click and drag to copy a track.
Click at the end of a track and drag to add measures. The cursor changes to
a double arrow cursor if the cursor is at the right part of where the track ends.
Click on the "T" button, then click on the note where the dynamics should be placed below.
Save Rosegarden to Sound File
Start Audacity
Select menu Edit Preferences
Select MIDI Devices
Select Host: ALSA
Start recording (Red Circle)
Press Play in Rosegarden
Trumpet Example
The trumpets sound in fields/game/doll-data/trumpets.ogg was created by copying some public domain music.
The music is 3 Jazz Duets from Rondeau and is from
https://musopen.org/music/21683-3-jazz-duets/.
It was entered by hand into Rosegarden. It was saved to a sound that can be played using Audacity.


Celia Wong is an undergraduate Computer Science and Chemistry major currently in her last year of study. She is currently looking for a full-time job upon graduation (June 2015).