Press release
While there has been some progress, further education and skills providers must do more to help the local economy.
FE and skills providers in Greater Manchester are making good progress, but must do more to support businesses and the local economy, an Ofsted report finds.
As part of the aim to improve education and learning, Ofsted worked with the Greater Manchester Chamber of Commerce and New Economy, an authority-owned company working to create a better local economy. In the process, examples of good practice were identified as well as the barriers providers face in meeting local skills needs.
The report, ‘Meeting local skills needs’ , highlights successes as well as areas for improvement for providers and employers in the Greater Manchester area.
Inspectors looked in detail at four sectors which were identified as being key to the Greater Manchester economy. These are:
Whilst some providers are working collaboratively to ensure they identify gaps in learning and develop courses to meet skills needs, there was no overview across the region to ensure that course duplication is avoided.
Although most of the providers looked at had developed links with individual employers, this was not enough to ensure that the curriculum was ultimately meeting local needs. The tracking of learners’ destinations after they complete their courses is also in early stages which made it hard to judge whether provision was leading to secure employment or further training.
Whilst good progress is being made by providers in a number of areas, including collaborative efforts to offer work experience, these projects are very new and the impact is yet to be seen.
Commenting on the findings, Ofsted’s Regional Director for the North West, Jo Morgan, said:
Greater Manchester is the second largest economic region in the UK and has the fastest growing population in the country. Despite this, the region still has a comparatively low skilled workforce, with 15 per cent of residents having no qualifications and an unemployment rate above the national average.
It is good to see that there are providers in Greater Manchester who are working hard to ensure that their learners are given the education and training they need to support them in the choices they make - whether that means moving into vocational education, higher education or the world of work.
However, it is clear that currently more progress needs to be made. Providers and employers need to work closely with one another to ensure that courses are equipping learners with expertise that businesses are looking for. This is vital if we are to give learners in Greater Manchester the best chance of achieving their goals.
Ofsted has set out a number of areas for improvement for all parties involved. This includes:
FE and skills providers working collaboratively with employers of all sizes to make sure they are meeting local employment needs
New Economy working with FE providers to identify reasons for varying success rates across the area in apprenticeships and classroom-based qualifications and improve the quality of careers advice so learners know the choice available to them
the Greater Manchester Colleges Group and Greater Manchester Learning Provider Network developing a coordinated approach to the curriculum across the area to ensure it meets the skills needed and avoids duplication
Notes to editors
The report ‘Meeting local skills needs – the responsiveness further education and skills providers in Greater Manchester’ is on GOV.UK.
The further education and skills providers visited as part of the research were: Alliance Learning; Bolton College; British Telecom PLC; Bury College; Economic Solutions Limited; Hopwood Hall College; Mantra Learning limited; Tameside College; The Manchester College; The Oldham College; Trafford College; Salford City College; and Wigan and Leigh College.
The Office for Standards in Education, Children’s Services and Skills (Ofsted) regulates and inspects to achieve excellence in the care of children and young people, and in education and skills for learners of all ages. It regulates and inspects childcare and children’s social care, and inspects the Children and Family Court Advisory and Support Service (Cafcass), schools, colleges, initial teacher training, work-based learning and skills training, adult and community learning, and education and training in prisons and other secure establishments. It assesses council children’s services, and inspects services for looked after children, safeguarding and child protection.
Media can contact the Ofsted Press Office through 0300 130 415 or via Ofsted’s enquiry line 0300 123 1231 between 8.30am to 6.00pm Monday to Friday. Out of these hours, during evenings and weekends, the duty press officer can be reached on 07919 057 359.


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Raspberry Cake
Created with Cupcake Ipsum
Donut cotton candy cake icing. Tiramisu oat cake powder cookie carrot cake chocolate bar. Cheesecake biscuit icing cake. Macaroon tootsie roll pastry toffee marzipan cotton candy biscuit sesame snaps pudding. Gummi bears jelly-o tart powder wafer sugar plum chocolate bar apple pie ice cream.
Danish wafer marshmallow. Pudding sweet tootsie roll cake lemon drops liquorice jelly-o cake icing. Pie muffin cheesecake donut marshmallow. Caramels jujubes gummi bears ice cream pudding marshmallow gummi bears gummi bears. Sesame snaps sesame snaps jelly candy canes fruitcake cake marshmallow croissant.


Events
State Race Double
May 27, 2017
- Registration Begins: 9:00 AM
- Registration Ends: 11:00 AM
- Entry Fee: 25
There is no description for this race.
Standings Top10
| 1. | Brandon Crain |
| 2. | Marshall Gehrke |
| 3. | Bryce Batten |
| 4. | Peter Choat |
| 5. | Cole Frederick |
| 6. | Jacob Trujillo |
| 7. | Braxton Miller |
| 8. | Braxton Elsen |
| 9. | Joshua White |
| 10. | Spencer Cole |
| 1. | Joris Daudet |
| 2. | Connor Fields |
| 3. | Corben Sharrah |
| 4. | Trent Jones |
| 5. | Sylvain Andre |
| 6. | Maris Strombergs |
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| 10. | Anthony Dean |
| 1. | Ava Corley |
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| 06/09/17 | East Coast Nationals |
| 06/09/17 | Great Salt Lake Nationals - Pro Series |
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| 06/23/17 | Tar Heel Nationals |
| 06/30/17 | Borderline Nationals |
| 07/07/17 | Stars & Stripes Nationals - Pro Series |
| 07/14/17 | Las Vegas Nationals |


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Here’s how much you need to earn to be considered rich
by Harry RobertsonA fiery debate was sparked last night about what makes a person “rich” after a man on BBC Question Time wrongly claimed he was not even in the top 50 per cent of earners – despite bringing home £80,000 a year.
The man laid into Labour candidate Richard Burgeon, who was on the show to defend Labour’s new manifesto. He attacked the party’s claim that it will only raise taxes on the richest five per cent in the country.
Read more: General Election 2019: Shadow chancellor John McDonnell defends Labour’s tax plans
“I am one of them people that he will tax more and I am nowhere near in the top five per cent,” the audience member said angrily – and incorrectly – to Burgeon. “I’m not even in the top 50 per cent,” he added later.
The man said he earned over £80,000 a year when asked by host Fiona Bruce, but again claimed: “I’m not in the top five per cent.”
Burgeon called the man “mistaken,” a response borne out in the latest data from HM Revenue & Customs from 2016-17. It shows that anyone earning above £75,300 is in the top five per cent of taxpayers.
In 2017, polling firm Yougov looked into what kind of salary Britons think makes a person “rich”.
The results showed that people in the UK think the top 10 per cent of earners – those bagging on average £60,500 a year – are wealthy. Almost 70 per cent of people called this group “rich”.
By this standard, Brits overwhelmingly think Question Time’s £80k earner is rich.
The UK population thought anyone one rung down the ladder was not poor, but not rich either. Over a third said those earning on average £39,800 – the 10 per cent of earners below the top 10 per cent – were “neither rich nor poor”.
Just over a third of Brits thought a person who earned this much was “rich” while practically none called them poor.
Read more: Labour manifesto: Capital gains hike to fund spending spree
Those polled by YouGov only started to mainly think a person was “poor” once they got down to those earning on average £15,300, which put them in the lowest 40 per cent of earners.
What income bracket do you fall into?
| Salary before tax | Income bracket in 2017 |
| £166,000 or more | Top 1 per cent |
| £75,300 or more | Top 5 per cent |
| £53,600 or more | Top 10 per cent |
| £40,000 or more | Top 20 per cent |
| £32,600 or more | Top 30 per cent |
| £27,500 or more | Top 40 per cent |
| £23,600 or more | Top 50 per cent |
| £20,500 or more | Bottom 50 per cent |
| £17,800 or more | Bottom 40 per cent |
| £15,500 or more | Bottom 30 per cent |
| £13,300 or more | Bottom 20 per cent |
| Below £13,300 | Bottom 10 per cent |
Source: HM Revenue & Customs. These figures are from 2016-17 and so are more recent than the figures YouGov used.
The argument over tax was not only on Question Time last night. Labour’s plans to tax the richest five per cent to fund a spending increase of around £80bn was criticised by the Institute for Fiscal Studies (IFS) think tank.
It said it was “not credible” to argue that only the very richest would shoulder the burden.


PATCH /orgs/:org (:write)
PATCH /orgs/:org
GET /orgs/:org/interaction-limits (:read)
GET /orgs/:org/interaction-limits
PUT /orgs/:org/interaction-limits (:write)
PUT /orgs/:org/interaction-limits
DELETE /orgs/:org/interaction-limits (:write)
DELETE /orgs/:org/interaction-limits


UIScrollContainer
class UIScrollContainer
extends
UIContainer
Represents a UI component that contains other components and allows scrolling horizontally and/or vertically, and emits throttled scroll events (Scroll
and ScrollEnd
, see UIScrollEvent
).
Constructor
(...content:
UIRenderable
[]): UIScrollContainer
.verticalSnap
"start" | "end" | "center"
Vertical scroll snap mode: start
(snap to start of first ‘mostly’ visible component), center
(snap container center to center of component closest to the center), or end
(snap to end of last ‘mostly’ visible component).
.horizontalSnap
"start" | "end" | "center"
Horizontal scroll snap mode: start
(snap to start of first ‘mostly’ visible component), center
(snap container center to center of component closest to the center), or end
(snap to end of last ‘mostly’ visible component).
.topThreshold
number
Vertical threshold (in pixels) until which UIScrollEvent.atTop
is set, defaults to 0.
.bottomThreshold
number
Vertical threshold (in pixels) until which UIScrollEvent.atBottom
is set, defaults to 0.
.leftThreshold
number
Horizontal threshold (in pixels) until which UIScrollEvent.atLeft
is set, defaults to 0.
.rightThreshold
number
Horizontal threshold (in pixels) until which UIScrollEvent.atRight
is set, defaults to 0.
.verticalScrollEnabled
boolean
True if vertical scrolling should be enabled, defaults to true.
.horizontalScrollEnabled
boolean
True if horizontal scrolling should be enabled, defaults to true.
.handleScrollSnap()
(e:
UIScrollEvent
): void
Handle given scroll event to snap scroll position to content components; called automatically when the container is scrolled, but may be overridden.
.applyStyle() protected
(style?:
UIStyle
): void
Inherited from UIContainer.applyStyle
.
.isFocusable()
(): boolean
Inherited from UIContainer.isFocusable
.
.isKeyboardFocusable()
(): boolean
Inherited from UIContainer.isKeyboardFocusable
.
.allowFocus
boolean
Inherited from UIContainer.allowFocus
.
.allowKeyboardFocus
boolean
Inherited from UIContainer.allowKeyboardFocus
.
.layout
Readonly<ContainerLayout>
Inherited from UIContainer.layout
.
.asyncContentRendering
boolean
Inherited from UIContainer.asyncContentRendering
.
.animatedContentRenderingDuration
number
Inherited from UIContainer.animatedContentRenderingDuration
.
.animatedContentRenderingVelocity
number
Inherited from UIContainer.animatedContentRenderingVelocity
.
.content
Inherited from UIContainer.content
.
.propagateComponentEvent() deprecated
(name: string, inner?:
ManagedEvent
, event?: any): void
Inherited from UIComponent.propagateComponentEvent
.
.delegateEvent() protected
(e:
ManagedEvent
, propertyName: string): true
Inherited from UIComponent.delegateEvent
.
.render()
(callback: RenderCallback<Output<
UIRenderable
, any>>): void
Inherited from UIComponent.render
.
.requestFocus()
(): void
Inherited from UIComponent.requestFocus
.
.requestFocusNext()
(): void
Inherited from UIComponent.requestFocusNext
.
.requestFocusPrevious()
(): void
Inherited from UIComponent.requestFocusPrevious
.
.style
Inherited from UIComponent.style
.
.hidden
boolean
Inherited from UIComponent.hidden
.
.dimensions
Readonly<Dimensions>
Inherited from UIComponent.dimensions
.
.position
Readonly<Position>
Inherited from UIComponent.position
.
.accessibleRole
string
Inherited from UIComponent.accessibleRole
.
.accessibleLabel
string
Inherited from UIComponent.accessibleLabel
.
.lastRenderOutput
Output<this, any>
Inherited from UIComponent.lastRenderOutput
.
.isPresetComponent() protected
(): boolean
Inherited from Component.isPresetComponent
.
.getParentComponent()
<TParent extends Component = Component>(ParentClass?:
ComponentConstructor
<TParent>): TParent
Inherited from Component.getParentComponent
.
.getBoundParentComponent()
<TParent extends Component>(ParentClass?:
ComponentConstructor
<TParent>): TParent
Inherited from Component.getBoundParentComponent
.
.emitAction()
(name: string, inner?:
ManagedEvent
, context?:
ManagedObject
): void
Inherited from Component.emitAction
.
.managedId
number
Inherited from ManagedObject.managedId
.
.managedState
Inherited from ManagedObject.managedState
.
.getReferenceCount() protected
(): number
Inherited from ManagedObject.getReferenceCount
.
.getManagedReferrers() protected
():
ManagedObject
[]
Inherited from ManagedObject.getManagedReferrers
.
.getManagedParent() protected
<TParent extends ManagedObject = ManagedObject>(ParentClass?:
ManagedObjectConstructor
<TParent>): TParent
Inherited from ManagedObject.getManagedParent
.
.emit()
<TEvent extends ManagedEvent = ManagedEvent, TConstructorArgs extends any[] = any[]>(e: string | TEvent | (new (...args: TConstructorArgs) => TEvent), ...constructorArgs: TConstructorArgs): this
Inherited from ManagedObject.emit
.
.emitChange()
(name?: string): void
Inherited from ManagedObject.emitChange
.
.propagateChildEvents() protected deprecated
(...types: ((new (...args: any[]) =>
ManagedEvent
) | ((e:
ManagedEvent
) => any))[]): this
Inherited from ManagedObject.propagateChildEvents
.
.activateManagedAsync() protected
(): Promise<any>
Inherited from ManagedObject.activateManagedAsync
.
.deactivateManagedAsync() protected
(): Promise<void>
Inherited from ManagedObject.deactivateManagedAsync
.
.destroyManagedAsync() protected
(): Promise<void>
Inherited from ManagedObject.destroyManagedAsync
.
.onManagedStateActivatingAsync() protected
(): Promise<void>
Inherited from ManagedObject.onManagedStateActivatingAsync
.
.onManagedStateActiveAsync() protected
(): Promise<void>
Inherited from ManagedObject.onManagedStateActiveAsync
.
.onManagedStateDeactivatingAsync() protected
(): Promise<void>
Inherited from ManagedObject.onManagedStateDeactivatingAsync
.
.onManagedStateInactiveAsync() protected
(): Promise<void>
Inherited from ManagedObject.onManagedStateInactiveAsync
.
.onManagedStateDestroyingAsync() protected
(): Promise<void>
Inherited from ManagedObject.onManagedStateDestroyingAsync
.
UIScrollContainer.Presets
extends
UIContainer.Presets
UIScrollContainer presets type, for use with Component.with
.
.verticalScrollEnabled
boolean
True if vertical scrolling should be enabled, defaults to true.
.horizontalScrollEnabled
boolean
True if horizontal scrolling should be enabled, defaults to true.
.verticalSnap
"start" | "end" | "center"
Vertical scroll snap mode: start
(snap to start of first ‘mostly’ visible component), center
(snap container center to center of component closest to the center), or end
(snap to end of last ‘mostly’ visible component).
.horizontalSnap
"start" | "end" | "center"
Horizontal scroll snap mode: start
(snap to start of first ‘mostly’ visible component), center
(snap container center to center of component closest to the center), or end
(snap to end of last ‘mostly’ visible component).
.topThreshold
number
Vertical threshold (in pixels) until which UIScrollEvent.atTop
is set, defaults to 0.
.bottomThreshold
number
Vertical threshold (in pixels) until which UIScrollEvent.atBottom
is set, defaults to 0.
.leftThreshold
number
Horizontal threshold (in pixels) until which UIScrollEvent.atLeft
is set, defaults to 0.
.rightThreshold
number
Horizontal threshold (in pixels) until which UIScrollEvent.atRight
is set, defaults to 0.
.onScroll
UIComponentEventHandler
<
UIComponent
,
UIComponentEvent
<
UIComponent
>>
Event handler for Scroll events.
.onScrollEnd
UIComponentEventHandler
<
UIComponent
,
UIComponentEvent
<
UIComponent
>>
Event handler for ScrollEnd events.
.content
Iterable<
UIRenderable
>
Inherited from UIContainer.Presets.content
.
.layout
Partial<{} | ContainerLayout>
Inherited from UIContainer.Presets.layout
.
.allowFocus
boolean
Inherited from UIContainer.Presets.allowFocus
.
.allowKeyboardFocus
boolean
Inherited from UIContainer.Presets.allowKeyboardFocus
.
.asyncContentRendering
boolean
Inherited from UIContainer.Presets.asyncContentRendering
.
.animatedContentRenderingDuration
number
Inherited from UIContainer.Presets.animatedContentRenderingDuration
.
.animatedContentRenderingVelocity
number
Inherited from UIContainer.Presets.animatedContentRenderingVelocity
.
.style
string |
UIStyle
Inherited from UIComponent.Presets.style
.
.hidden
boolean
Inherited from UIComponent.Presets.hidden
.
.dimensions
Partial<{} | Dimensions>
Inherited from UIComponent.Presets.dimensions
.
.position
Partial<{} | Position>
Inherited from UIComponent.Presets.position
.
.accessibleRole
string
Inherited from UIComponent.Presets.accessibleRole
.
.accessibleLabel
string
Inherited from UIComponent.Presets.accessibleLabel
.
.requestFocus
boolean
Inherited from UIComponent.Presets.requestFocus
.


|
|
| From version: 3 to: 4 |
Watch out! Format of logging tables was changed. Also plpgsql
function dom_add was changed, dom_log_add was added. To upgrade your
system do following steps (if this list is not accurate please notify
me):
-
Install libpqxx (use the newest package available).
-
Install Sablotron, at least version 1.0. It will compile
with older versions but won't work properly.
-
Do backup of your all files!!! Especially database.
-
cd docs/pgsql
-
Drop your old version of dom_add (DROP FUNCTION dom_add(text)).
Following command will not drop it (new function is dom_add(text,text)).
-
psql -Umail < dom_add.sql
-
psql -Umail < dom_log_add.sql
-
Run psql, drop *_log tables and recreate them with dom_log_add.
Drop log table, recreate it using log.sql file.
-
Install new software and test your system. Remember that
programs from contrib may not be compatible with new
version so you need to upgrade them also.
|
|
|


A major hurdle for automated approaches is the training and validation of machine learning models. These models use massive amounts of data to find patterns that can be used to make predictions or classifications. Image classification models, such as convolutional neural networks, need even more data than "traditional" machine learning approaches. Datasets such as MNIST and CIFAR are used to train these models, and contain 10s of thousands of images. At the moment, there is no comparable dataset for storm morphology. Initially, we are going to provide a limited dataset of labels on which to test your down machine learning models. We hope that with crowd-sourcing the classification process, we can greatly expand the size of the dataset.
The initial sample data are available below. We use a train/validation/test split of ~70% / ~10% / ~20%, respectively, and the data are organized as follows:
You can browse image thumbnails for each class at the following links:
These will be posted shortly, along with examples.
Radar images are centered on SPC severe weather reports and extracted from the closest hourly data in GridRad which can be downloaded from the Research Data Archive. The original ~2x2km 3D data are converted to 2D by calculating the column maximum reflectivity. These values are then converted to 8-bit integers and interpolated to a 3.75 km Lambert conformal conic grid using nearest neighbor. The 136 x 136 dimensions result in a region approximately 512 x 512 km.
Each image is assigned to one of six classes. These classes and their descriptions are as follows:
| Class Name | Class Description |
|---|---|
| Cellular | Circular areas of red and orange near the center of the image.
|
| QLCS | Continuous red and orange lines that intersect the center of the image.
|
| Tropical | Green and yellow lines that appear to circle around the bottom or left edge of the image.
|
| Other | Morphologies that do not obviously fit into one of the previous three classes.
|
| Noise | Low intensity rings, spikes, or pixelation that does not look natural.
|
| Missing | The entire image or the majority of the image is blue (i.e., missing intensity).
|
The data are provided at no cost, as-is, with no warranty of any kind. No modification of either the SPC reports or the GridRad data (beyond interpolation) is done before these data are hosted on the website. The process is completely repeatable from start to finish, assuming you have patience or access to a supercomputer cluster. Please examine the GridRad and the SPC severe weather reports pages to read about the caveats and issues with those data before using these data. See the data page for more information.
We are generating these data solely because we think they would be of interest to the meteorology and climatology community. That being said, we would like to get some credit if you find them useful!
Haberlie, A. M., W. S. Ashley, and M. Karpinski, 2020: Mean storms: Composites of radar reflectivity images during two decades of severe thunderstorm events. International Journal of Climatology, In Press.
Bowman, K. P., and C. R. Homeyer. 2017. GridRad - Three-Dimensional Gridded NEXRAD WSR-88D Radar Data. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory.


It has already been more than two weeks since the second Taming the BEAST workshop took place on Waiheke island in New Zealand. Thanks to the group from the University of Auckland for putting up an amazing workshop in a stunning location! Thanks also to all the speakers for agreeing to take part and making the long journey to New Zealand!
The workshop was the first test of the website as a workshop tool. Although there were a few issues and misunderstandings, the website seems to be working. I’ll be going over the feedback from the workshop in the next month and integrating any suggestions to the website. We also identified some issues with a few of the tutorials during the workshop and I’ll also be updating them soon as well. If there are any further issues, please raise them on the Github repository of the tutorial in question. Finally, all of the lecture slides from the workshop have been uploaded and are available from the workshops page.
We are currently discussing and planning the next workshop in the series and it will be posted here as soon as the dates have been finalised!


SemPlan.Spiral provides common services for RDF parsing and manipulation.
See the Spiral Wiki for full information.


Spring MVC - jQuery File Upload
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--- layout: post title: K&RIP! date: 2011-10-14 13:30:00.000000000 +11:00 type: post published: true status: publish categories: [] tags: - c - programming - unix meta: lj_itemid: '44' lj_permalink: http://jus4kix.livejournal.com/11485.html lj_current_location: Australia, Melbourne lj_current_mood: sad lj_picture_keyword: pic#76252655 author: login: ajaysusarla email<EMAIL_ADDRESS>display_name: partha first_name: '' last_name: '' ---
Dennis Ritchie passed away.
It is indeed a sad day.
I started using computers when in university (I was 18 then). And in the last 14 years, I have been using both C and Unix (and its variants, Linux mostly). And if I look around, most of the devices that I have, including but not limited to, my laptop, my phone, my desktop at work, use C and Unix (Linux in this case). I earn my 'daily-bread' as a C programmer, I have enjoyed being a C programmer. I just love the flexibility that it provides and its sheer simplicity. I've _always_ had a copy of The C programming language with me. And that is the book that I always keep going back to, between jobs (for interviews) and occasional reference.
I owe it to Dennis Ritchie big time.
My heartfelt condolences to his family and friends. May his soul rest in peace.


open var manuallyRun: Boolean
Sometimes you may want your user to be in charge of when a task is run. Setting manuallyRun to true will assert that this task does not get run automatically by the Wendy task runner. You will need to manually run the task yourself via Wendy.runTask.
manuallyRun
- Sometimes you may want your user to be in charge of when a task is run. Setting manuallyRun to true will assert that this task does not get run automatically by the Wendy task runner. You will need to manually run the task yourself via Wendy.runTask.


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Head of Guerrilla Games promoted to head of Sony Worldwide Studios
Source: Sony Interactive Entertainment - 2017 interview with Neil Druckmann
What you need to know
- Sony Interactive Entertainment has been undergoing big leadership changes in 2019.
- Shuhei Yoshida, President of Sony Worldwide Studios, is stepping down and taking a new role managing independent developers.
- Hermen Hulst, co-founder of Guerrilla Games, has been promoted as the new head of Sony Worldwide Studios.
The past year has seen some major changes PlayStation and with the PlayStation 5 set to release in holiday 2020, the changes to Sony Interactive Entertainment leadership keep on coming. Today, Sony announced that President of Sony Worldwide Studios Shuhei Yoshida is stepping down from his current role and will be moving to a job that has him oversee relationships with independent developers.
Taking Yoshida's place as the head of Sony Worldwide Studios is Hermen Hulst, the former managing director and co-founder of Guerrilla Games. Hulst will be taking on this job managing Sony's 14 studios immediately. Guerrilla Games has been responsible for the Killzone franchise, as well the as 2017 game Horizon Zero Dawn. Guerrilla Games also contributed to the development of Death Stranding.
With Hulst being promoted, Angie Smets, Jan-Bart van Beek and Michiel van der Leeuw will all share a role as co-directors at Guerrilla Games. These changes come a little over a month after Shawn Layden, Chairman of Sony Worldwide Studios, left his role in an unorthodox announcement through the PlayStation Twitter account.
Jim Ryan told GamesIndustry.biz that "Hermen is a European who will be taking one of the major offices at PlayStation." He continued by saying "I think everybody in Europe should be thrilled and happy and proud that that is happening."
"I have worked closely with PlayStation and the entire Worldwide Studios family since 2001, and I have the utmost respect and admiration for the creative talent and ambitious ideas within the network of studios across the US, Europe, and Japan," said Hulst, Head of Worldwide Studios. "I am beyond excited to have the opportunity now to lead such an inspired and talented team whose singular mission is to build amazing games for PlayStation fans."
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--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/learn.20about.20macros.html ---
Hi everyone,
I've heard about macros in Lean, but I don't exactly know what they are or how they work, except that you can assimilate them with various trust levels. They are not mentioned in the lean tutorial, so what would be a good place to read about them?
To build macros, you really have to work inside Lean itself. It's not really exposed to meta programming
What application do you have in mind?
I wrote a tactic working by reflexivity, inspired from the ring tactic in Coq.
but it's using rationals as constants and can be quite slow.
@Rob Lewis mentioned that macros could be used to do the computation more efficiently, running the tactic in the kernel only if that's required, depending on the level of trust.
of course a more direct solution would be to first write a more efficient definition of rat, but he mentioned the macros and I wanted to learn more about them
I've had some limited rational programming experience, and there were huge speedups by locally inlining the rat computation functions if I recall
@Keeley Hoek how can I do this? I've never used inlining
local attribute [inline] <idents>
is all there is too it, that has to appear before the functions using the functions you are inlining are defined. It is useful to look at the generated bytecode to see if more should be inlined after. To do that, use set_option trace.compiler.code_gen true
thanks!
Rob Lewis mentioned that macros could be used to do the computation more efficiently, running the tactic in the kernel only if that's required, depending on the level of trust.
Could possibly be used. I don't actually know if there's any way to do this in practice.
I've had some limited rational programming experience, and there were huge speedups by locally inlining the rat computation functions if I recall
Inlining will only affect computation in the VM though, right? This is good to know for future reference, but unfortunately we're dealing with kernel computation here.
Rob Lewis mentioned that macros could be used to do the computation more efficiently, running the tactic in the kernel only if that's required, depending on the level of trust.
Could possibly be used. I don't actually know if there's any way to do this in practice.
yes, sorry that's what I meant.
I meant to use the past as conditional, but in this case I guess it doesn't work
Last updated: Apr 24 2020 at 19:02 UTC


class: center, middle, inverse, title-slide # ETC1010: Data Modelling and Computing ## Lecture 10B: Exam Review ### Dr. Nicholas Tierney & Professor Di Cook ### EBS, Monash U. ### 2019-10-11 --- class: bg-main1 # Announcements .huge[ - Some of the lecture slides don't load on a web browser - Current alternative for viewing: Download the HTML or the RMD for the moment and view locally. - I'm in the process of sorting it out ] --- class: bg-main1 # The Practical Exam .huge[ - Time: 1 Hour - Task: Complete a data analysis and answer questions (like the assignments, but short - similar to the in class exercises) - Conditions: - Open book - Internet access - No contacting / messaging / communicating friends during exam (penalty will be the same as cheating in exam) ] --- class: bg-main1 # The Project Data Milestone: What do Submit .huge[ - An Rmd file + - The rendered HTML file - The data - Zipped / compressed together - In the document: - Write about the data - what is it, what are the variables, what sort of questions are you asking, what did you need to do clean the data, what cleaning is remaining? - (About 1-2 paragraphs of text.) ] --- class: bg-main1 # The Project: More general questions .huge[ - "How many questions should we ask?" - About 3 - roughly one per person in your group - "How will the project be marked?" - You will all mark the projects as you watch them - You will need to present for 5 minutes max - use this to help frame how long your analysis should be. - 5 minutes is short - but it is actually a pretty serious challenge! - But also consider that the project will be marked by me as well - you won't need to cover everything ] --- class: bg-main1 # The project: What should we present? .huge[ Many options! - You can submit an Rmarkdown document + HTML file - You present with a [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) (demo) - You could even present as a slide show (like the lecture slides) - How to share - Ideally you can use netlify drag and drop (demo) ] --- class: bg-main1 # Exam details .huge[ - Worth 50% of your final grade - NOT Open Book - Calculator: Yes - Allowed to take in "Only HP 10bII+ or Casio FX82 (any suffix) calculator permitted" - One A4 page of notes, printed on both sides. This will be collected at the end of the examination. - **Hurdle requirement**: You must get 40% on the exam to pass the course (also true of the final project). ] --- class: bg-main1 # Exam Details .huge[ - I'll now talk about the questions in the exam, and some of the concepts you need to be familiar with - These concepts will help guide what you focus on in the lectures - Disclaimer: This list is not exhaustive - these are to help give you a sense of what I'm thinking about for each of the questions in the exam. ] --- class: bg-main1 # Tidy data Concepts: .huge[ - Variables - Observations - Tidy data ] --- class: bg-main1 # Data Wrangling Concepts: .huge[ - Converting "messy" data to tidy data - Code / key functions to use to convert data into "tidy" data - e.g, `gather`, `spread`, `separate`, etc. ] --- class: bg-main1 # Relational data concepts: .huge[ - Left Join - Why do joins of data - Predict output of a join - Sketching out code to summarise data from a join ] --- class: bg-main1 # Data visualisation concepts: .huge[ - How the grammar of graphics produces a plot - identify plots produced by which code - Understand the focus of a given graphic on the data - questions like: - "What and how does this graphic make us focus on what feature of the data?" - "What do you learn from a graphic?" ] --- class: bg-main1 # Missing Values concepts: .huge[ - Principles of tidy missing data - Interpreting graphics of missing data - Predict output of function on data with missing values - Imputation - what is it, which methods are good / bad / better ] --- class: bg-main1 # Linear Models Concepts: .huge[ - Write down an equation of a model from code output ] -- .huge[ - The formula `\(y = 3x + 5\)` is a function with input `\(x\)`, and output `\(y\)`, when x is `___` , the output is `___` ] --- class: bg-main1 # Linear Models Concepts: .huge[ `$$\widehat{height_{in}} \sim 3.62 + 0.78 Width_{in}$$` - **slope**: For each additional inch the painting is wider, the height is expected to be higher, on average, by 0.78 inches. - **Intercept**: Paintings that are 0 inches wide are expected to be 3.62 inches high, on average. - If a paining is 5 Inches wide, what is it's estimated height? ] --- class: bg-main1 # Linear Models Concepts: .huge[ - How to make predictions from a fitted model - Understand what makes predictions good and bad - Measurements of model fit: - R2 - what it is - what values mean good/bad fit? - **A good idea to bring in an equation of R2** - Residuals - What do we expect to see - Centering variables - Think about how you can improve fit of models to your data ] --- class: bg-main1 # Programming concepts: .huge[ - Why write functions - How to write a function in R - How to take existing code and turn it into a function - Identify potential mistakes in provided code - Understand what `map` does ] --- class: bg-main1 # Networks Concepts: .huge[ - From an association / correlation matrix, which are most or least related? - Understand how you can convert a numeric matrix into a binary association matrix - Understand how a correlation matrix (or other association) of data relates to a provided network (let's do an example) ] --- class: bg-main1 # How to study for the exam .vlarge[ These concepts guide what to focus on While the readings aren't assessed, **they provide great information that will certainly help improve your understanding** Remember that these are to help you focus, I can't give you the exam, but I can help tell you what is important. Disclaimer: This list is not exhaustive - these are to help give you a sense of what I'm thinking about for each of the questions in the exam. ] --- class: bg-main1 # On a more personal note .huge[ I've had a great time this semester I've been **seriously impressed** with you all - you are all so bright! Thanks for being engaged and curious, I'm really excited to see your projects! ] -- .huge[ Also a special thanks to everyone for dealing with the 8am lecture! ]


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Calculate genotype posterior likelihoods given panel data
Given a VCF with genotype likelihoods from the HaplotypeCaller, UnifiedGenotyper, or another source which provides unbiased genotype likelihoods, calculate the posterior genotype state and likelihood given allele frequency information from both the samples themselves and input VCFs describing allele frequencies in related populations.
The AF field will not be used in this calculation as it does not provide a way to estimate the confidence interval or uncertainty around the allele frequency, while AN provides this necessary information. This uncertainty is modeled by a Dirichlet distribution: that is, the frequency is known up to a Dirichlet distribution with parameters AC1+q,AC2+q,...,(AN-AC1-AC2-...)+q, where "q" is the global frequency prior (typically q << 1). The genotype priors applied then follow a Dirichlet-Multinomial distribution, where 2 alleles per sample are drawn independently. This assumption of independent draws is the assumption Hardy-Weinberg Equilibrium. Thus, HWE is imposed on the likelihoods as a result of CalculateGenotypePosteriors.
A collection of VCFs to use for informing allele frequency priors. Each VCF must have one of
A new VCF with:
Using the default behavior, priors will only be applied for each variants (provided each variant has at least 10 called samples.) SNP sites in the input callset that have a SNP at the matching site in the supporting VCF will have priors applied based on the AC from the supporting samples and the input callset (unless the --ignoreInputSamples flag is used). If the site is not called in the supporting VCF, priors will be applied using the discovered AC from the input samples (unless the --discoveredACpriorsOff flag is used).
All tools inherit arguments from the GATK Engine' "CommandLineGATK" argument collection, which can be used to modify various aspects of the tool's function. For example, the -L argument directs the GATK engine to restrict processing to specific genomic intervals; or the -rf argument allows you to apply certain read filters to exclude some of the data from the analysis.
This table summarizes the command-line arguments that are specific to this tool. For more details on each argument, see the list further down below the table or click on an argument name to jump directly to that entry in the list.
| Argument name(s) | Default value | Summary | |
|---|---|---|---|
| Required Inputs | |||
| --variant -V |
NA | Input VCF file | |
| Optional Inputs | |||
| --supporting |
[] | Other callsets to use in generating genotype posteriors | |
| Optional Outputs | |||
| --out -o |
stdout | File to which variants should be written | |
| Optional Parameters | |||
| --deNovoPrior -DNP |
1.0E-6 | The de novo mutation prior | |
| --globalPrior -G |
0.001 | The global Dirichlet prior parameters for the allele frequency | |
| --numRefSamplesIfNoCall -nrs |
0 | The number of homozygous reference to infer were seen at a position where an "other callset" contains no site or genotype information | |
| Optional Flags | |||
| --defaultToAC -useAC |
false | Use the AC field as opposed to MLEAC. Does nothing if VCF lacks MLEAC field | |
| --discoveredACpriorsOff -useACoff |
false | Do not use discovered allele count in the input callset for variants that do not appear in the external callset. | |
| --ignoreInputSamples -ext |
false | Use external information only; do not inform genotype priors by the discovered allele frequency in the callset whose posteriors are being calculated. Useful for callsets containing related individuals. | |
| --skipFamilyPriors -skipFam |
false | Skip application of family-based priors | |
| --skipPopulationPriors -skipPop |
false | Skip application of population-based priors |
Arguments in this list are specific to this tool. Keep in mind that other arguments are available that are shared with other tools (e.g. command-line GATK arguments); see Inherited arguments above.
Use the AC field as opposed to MLEAC. Does nothing if VCF lacks MLEAC field
Rather than looking for the MLEAC field first, and then falling back to AC; first look for the AC field and then
fall back to MLEAC or raw genotypes
boolean false
The de novo mutation prior
The mutation prior -- i.e. the probability that a new mutation occurs. Sensitivity analysis on known de novo
mutations suggests a default value of 10^-6.
double 1.0E-6 [ [ -∞ ∞ ] ]
Do not use discovered allele count in the input callset for variants that do not appear in the external callset.
Calculate priors for missing external variants from sample data -- default behavior is to apply flat priors
boolean false
The global Dirichlet prior parameters for the allele frequency
The global prior of a variant site -- i.e. the expected allele frequency distribution knowing only that N alleles
exist, and having observed none of them. This is the "typical" 1/x trend, modeled here as not varying
across alleles. The calculation for this parameter is (Effective population size) * (steady state mutation rate)
double 0.001 [ [ -∞ ∞ ] ]
Use external information only; do not inform genotype priors by the discovered allele frequency in the callset whose posteriors are being calculated. Useful for callsets containing related individuals.
Do not use the [MLE] allele count from the input samples (the ones for which you're calculating posteriors)
in the site frequency distribution; only use the AC and AN calculated from external sources.
boolean false
The number of homozygous reference to infer were seen at a position where an "other callset" contains no site or genotype information
When a variant is not seen in a panel, whether to infer (and with what effective strength) that only reference
alleles were ascertained at that site. E.g. "If not seen in 1000Genomes, treat it as AC=0, AN=2000". This is
applied across all external panels, so if numRefIsMissing = 10, and the variant is absent in two panels, this
confers evidence of AC=0,AN=20
int 0 [ [ -∞ ∞ ] ]
File to which variants should be written
VariantContextWriter stdout
Skip application of family-based priors
Skip application of family-based priors. Note: if pedigree file is absent, family-based priors will be skipped.
boolean false
Skip application of population-based priors
Skip application of population-based priors
boolean false
Other callsets to use in generating genotype posteriors
Supporting external panels. Allele counts from these panels (taken from AC,AN or MLEAC,AN or raw genotypes) will
be used to inform the frequency distribution underying the genotype priors. These files must be VCF 4.2 spec or later.
This argument supports reference-ordered data (ROD) files in the following formats: BCF2, VCF, VCF3
List[RodBinding[VariantContext]] []
Input VCF file
Variants from this VCF file are used by this tool as input.
The file must at least contain the standard VCF header lines, but
can be empty (i.e., no variants are contained in the file).
This argument supports reference-ordered data (ROD) files in the following formats: BCF2, VCF, VCF3
R RodBinding[VariantContext] NA
See also GATK Documentation Index | Tool Docs Index | Support Forum
GATK version 3.6-0-g89b7209 built at 2017/02/09 12:52:48.


- Aerialbot Formation
- Ancient Wisdom
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- Cooling Vents
- Cornered!
- Defensive Driving
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- Enemy Combat Analysis
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Source code for torchtraining.accumulators
"""Accumulate results from `iterations` or `epochs`
.. note::
**IMPORTANT**: This module is one of core features
so be sure to understand how it works.
.. note::
**IMPORTANT**: Accumulators should be applied to `iteration`
objects. This way those can efficiently accumulate value later passed
to other operations.
Example::
iteration
** tt.Select(predictions=1, labels=2)
** tt.metrics.classification.multiclass.Accuracy()
** tt.accumulators.Mean()
** tt.Split(
tt.callbacks.Log(f"{name} Accuracy"),
tt.callbacks.tensorboard.Scalar(writer, f"{name}/Accuracy"),
)
Code above will accumulate `accuracy` from each step and after `iteration`
ends it will be send to `tt.Split`.
.. note::
**IMPORTANT**: If users wish to implement their own `accumulators`
`forward` shouldn't return anything but accumulate data in `self.data`
variable. No argument `calculate` should return `self.data` after
calculating accumulated value (e.g. for `mean` it would be division
by number of samples).
"""
import abc
import typing
import torch
from ._base import Accumulator
[docs]class Sum(Accumulator):
"""Sum data coming into this object.
`data` should have `+=` operator implemented between it's instances
and Python integers.
.. note::
**IMPORTANT**: This is one of memory efficient accumulators
and can be safely used.
Returns
-------
torch.Tensor | Any
Sum of values after accumulation. At each step proper
summation up to this point is returned nonetheless.
`torch.Tensor` usually, but can be anything "summable".
"""
def __init__(self):
super().__init__()
self.data = 0
[docs] def reset(self) -> None:
"""Assign 0 to `self.data` clearing `saver`."""
self.data = 0
[docs] def forward(self, data) -> None:
"""
Arguments
---------
data: Any
Anything which has `__iadd__`/`__add__` operator implemented between it's instances
and Python integers.
"""
self.data += data
[docs] def calculate(self) -> typing.Any:
"""Calculate final value.
Returns
-------
torch.Tensor
Data accumulated via addition.
"""
return self.data
[docs]class Mean(Accumulator):
"""Take mean of the data coming into this object.
`data` should have `+=` operator implemented between it's instances
and Python integers.
.. note::
**IMPORTANT**: This is one of memory efficient accumulators
and can be safely used. Should be preferred over accumulating
data via `torchtraining.accumulators.List`
Returns
-------
torch.Tensor | Any
Mean of values after accumulation. At each step proper
mean up to this point is returned nonetheless.
`torch.Tensor` usually, but can be anything implementing concept above.
"""
def __init__(self):
super().__init__()
self.data = 0
self._counter = 0
[docs] def reset(self) -> None:
"""Assign `0` to `self.data` and zero out counter clearing `saver`"""
self.data = 0
self._counter = 0
[docs] def forward(self, data: typing.Any) -> None:
"""
Arguments
---------
data: Any
Anything which has `__iadd__`/`__add__` operator implemented between it's instances
and Python integers. It should also have `__div__` operator implemented
for proper mean calculation.
"""
self._counter += 1
self.data += data
[docs] def calculate(self) -> typing.Any:
"""Calculate final value.
Returns
-------
torch.Tensor
Accumulated data after summation and division by number of samples.
"""
return self.data / self._counter
[docs]class List(Accumulator):
"""Sum data coming into this object.
.. note::
**IMPORTANT**: It is advised **NOT TO USE** this accumulator
due to memory inefficiencies. Prefer `torchtraining.accumulators.Sum`
or `torchtraining.accumulators.Mean` instead.
List containing data received up to this moment.
`data` **does not** have to implement any concept
(as it is only appended to `list`).
Returns
-------
List
List of values after accumulation. At each step proper
`list` up to this point is returned nonetheless.
"""
def __init__(self):
super().__init__()
self.data = []
[docs] def reset(self) -> None:
"""Assign empty `list` to `self.data clearing `saver`"""
self.data = []
[docs] def forward(self) -> typing.List[typing.Any]:
"""
Arguments
---------
data: Any
Anything which can be added to `list`. So anything I guess
"""
return self.data
[docs] def accumulate(self, data) -> None:
"""Calculate final value.
Returns
-------
torch.Tensor
Return `List` with gathered data.
"""
self.data.append()
[docs]class Except(Accumulator):
"""Special modifier of accumulators accumulating every value except specified.
.. note::
**IMPORTANT**: One of the `begin`, `end` has to be specified.
.. note::
**IMPORTANT**: This accumulators is useful in conjunction
with `torchtraining.iterations.Multi` (e.g. for GANs and other irregular
type of training).
User can effectively choose which data coming from step should be accumulated
and can divide accumulation based on that.
Parameters
----------
accumulator: tt.Accumulator
Instance of accumulator to use for `data` accumulation.
begin: int | torch.Tensor[int], optional
If `int`, it should specify beginning of incoming values stream which
will not be taken into accumulation. If `torch.Tensor` containing integers,
it should specify consecutive beginnings of streams which are not taken into account.
If left unspecified (`None`), `begin` is assumed to be `0`th step.
Every modulo element of stream matching [begin, end] range will not be
forwarded to accumulator.
end: int | torch.Tensor[int], optional
If `int`, it should specify end of incoming values stream which
will not be taken into accumulation. If `torch.Tensor` containing integers,
it should specify consecutive ends of stream which will not be taken into account.
If left unspecified (`None`), `end` is assumed to be the same as `begin`.
This effectively excludes every `begin` element coming from value stream.
Every modulo element of stream matching [begin, end] range will not be
forwarded to accumulator.
Returns
-------
Any
Whatever `accumulator` returns after accumulation. At each step proper
value up to this point is returned nonetheless. Usually `torch.Tensor`
or `list`.
"""
def __init__(
self, accumulator: Accumulator, begin=None, end=None,
):
def _validate_argument(arg, name: str):
if torch.is_tensor(arg):
if not arg.dtype in (
torch.uint8,
torch.int8,
torch.int16,
torch.int32,
torch.int64,
):
raise ValueError(
"{} as torch.Tensor should be of integer-like type, got: {}".format(
name, arg.dtype
)
)
if not isinstance(arg, int):
raise ValueError(
"{} should be torch.Tensor/int, got : {}".format(name, type(arg))
)
return arg
def _same_type(arg1, arg2):
if not isinstance(arg1, type(arg2)):
raise ValueError(
"begin and end should be of the same type. Got {} for begin and {} for end".format(
type(arg1), type(arg2)
)
)
def _same_shape(arg1, arg2):
if arg1.shape != arg2.shape:
raise ValueError(
"begin and end should have the same shape. Got {} for begin and {} for end".format(
arg1.shape, arg2.shape
)
)
def _one_of_specified(arg1, arg2):
if arg1 is None and arg2 is None:
raise ValueError("One of begin/end has to be specified.")
super().__init__()
_one_of_specified(begin, end)
self.begin = _validate_argument(begin, "begin")
if end is None:
self.end = self.begin
else:
_same_type(self.begin, end)
self.end = _validate_argument(end, "end")
_same_shape(begin, end)
self.accumulator = accumulator
if isinstance(self.begin, int):
self._in = lambda i: (self.begin <= (i % self.end + 1) <= self.end) and (
i != self.end + 1
)
else:
maximum = torch.max(self.end)
self._in = lambda i: (
(self.begin <= (i % maximum + 1)) & ((i % maximum + 1) <= self.end)
).any() & (i != maximum + 1)
self._counter = -1
[docs] def reset(self) -> None:
"""Reset internal `accumulator`."""
self._counter = -1
self.accumulator.reset()
[docs] def forward(self, data) -> None:
"""
Arguments
---------
data: Any
Anything which `accumulator` can consume
"""
self._counter += 1
if not self._in(self._counter):
self.accumulator(data)
[docs] def calculate(self) -> typing.Any:
"""Calculate final value.
Returns
-------
Any
Returns anything `accumulator` accumulated.
"""
return self.accumulator.calculate()


REFERENCES
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- Batjes NH (2004) SOTER-based soil parameter estimates for Southern Africa (ver. 1.0). Report 2004/04, ISRIC - World Soil Information, Wageningen, 27 p. https://www.isric.org/sites/default/files/isric_report_2004_04.pdf
- BirdLife International and Handbook of the Birds of the World (2017) Bird species distribution maps of the world. Version 2017.2. URL: http://datazone.birdlife.org/species/requestdis
- Cadastral Spatial Data (2018) Chief Surveyor - General Property Search Web Application. Department of rural development and land reform, Republic of South Africa. URL: https://csg.esri-southafrica.com/portal/sharing/rest/content/items/6f449851fa8b4364830b892cac8fbd7f/data
- ?u?kovi? Z (2016) Advanced viewshed analysis: a Quantum GIS plug-in for the analysis of visual landscapes. The Journal of Open Source Software 4(1). doi: 10.21105/joss.00032
- Dijkshoorn JA (2003) Soil and Terrain digital database for Southern Africa (SOTERSAF). ISRIC: International Soil Reference and Information Centre, Wageningen. URL: http://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/6eb4dafe-a184-44e3-9ed1-d2f73020725d
- E-GIS: Environmental Geographical Information Systems () Department of environmental affairs, Republic of South Africa. URL: https://egis.environment.gov.za/data_egis/data_download/current
- Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, et al. (2017) SoilGrids250m: global gridded soil information based on Machine Learning. PLOS One 12(2). doi:10.13
- DJ, Driver A, Hill L, Van Deventer H, Funke N, Swartz ER, Smith-Adao LB, Mbona N, Downsborough L and Nienaber S (2011) Technical Report for the National Freshwater Ecosystem Priority Areas project. WRC Report No. K5/1801. URL: http://bgis.sanbi.org/nfepa/project.asp
- QGIS Development Team (2018) QGIS Geographic Information System version 3.2.3-Bonn. Open Source Geospatial Foundation Project. URL: http://qgis.osgeo.org
- RCMRD: Regional centre for mapping of resources for development (2016) Sentinel-2 Land Use Land Cover map for South Africa. URL: http://opendata.rcmrd.org/datasets/southafrica-sentinel2-lulc2016
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- South African National Land-Cover Dataset 2013 - 2014 (2017) Implementation of Land-use maps for South Africa. DEA/CARDNO Project SCPF002, GEOTERRAIMAGE. URL: https://egis.environment.gov.za/data_egis/data_download/current
- USGS (2014) Shuttle Radar Topography Mission, 1 Arc-Second Global, Global Land Cover Facility, University of Maryland, College Park, Maryland. URL: http://dwtkns.com/srtm30m
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- Zvoleff A (2016) glcm: Calculate Textures from Grey-Level Co-Occurrence Matrices (GLCMs). R package version 1.6.1. Available from https://CRAN.R-project.org/package=glcm (accessed 01 December 2017)


A Reviews Site through Spring Boot + MVC app that displays a list of reviews allowing the user to click on an individual review to see the details.
Skills Used: Java, JavaScript, TDD, JUnit,HTML,CSS,Spring MVC,Thymeleaf
This was the start of a 3 part project. The virtual pet is named Pauly. Pauly will be able to inform its owner when it needs various tasks to be completed. For example, when Pauly is either hungry or thirsty. It is up to the Owner of Pauly to keep their virtual pet happy
Skills Used: TDD, JUnit, Java
This was one of the first programs I wrote using Java. The goal is to write a program where the User will enter there First & Last name along with their Age, Birth Month, Favorite ROYGBIV color and Number of Siblings. Once the data is collected the program will greet the User and tell them how many years they have left to retire, how much money they have in their bank account, the location that he or she will live and their mode of transportation.
Skills Used: TDD, JUnit, Java


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