{% extends "layout.html" %} {% block body %}
Visit the Landing page for Wikimetrics support, including Frequently Asked Questions.
Subscribe to the Analytics mailing list.
Read the training wiki Wikimetrics Training.
If you discover a bug, please file a bug report in Phabricator.


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×
Create Plant
Quantity:
Breeder:
Strain:
Name:
(Optional)
Group:
(Optional)
Methodology:
Hydro
Soil
Stage:
Seed
Seedling
Vegetative
Flowering
Flush
Drying
Curing


qm sendkey
Send QEMU monitor encoding key event to a virtual machine.
More information: home page
- Send the specified key event to a specific virtual machine:
qm sendkey vm_id key
- Allow root user to send key event and ignore locks:
qm sendkey --skiplock true vm_id key


At this point your setup should be completed and we’re going to start working in the Python shell via IDLE.
IDLE is python’s integrated development environment that comes standard with Python distributions.


UIImage
class UIImage : NSObject, NSSecureCoding
-
Tint, Colorize image with given tint color
This is similar to Photoshop’sColor
layer blend mode
This is perfect for non-greyscale source images, and images that have both highlights and shadows that should be preserved
white will stay white and black will stay black as the lightness of the image is preservedTo
Declaration
Swift
public func tintPhoto(tintColor: UIColor) -> UIImage
Parameters
tintColor
UIColor
Return Value
UIImage
-
Tint Picto to color
Declaration
Swift
public func tintPicto(fillColor: UIColor) -> UIImage
Parameters
fillColor
UIColor
Return Value
UIImage


Actor Emmanuel Armah Speaks For The First Time After Suffering From Stroke (+Video)
by vanessa nordziVeteran Ghanaian actor, Emmanuel Armah who has been battling stroke for sometime now has finally spoken.
Emmanuel Armah was reportedly struck with a stroke which affected his movement leading to him going off the screen for some time now.
The illness reportedly left Mr Armah speech impaired, a condition in which the ability to produce speech sounds becomes difficult.
In his first interview with One Ghana TV, the ailing actor eulogized Mr Samuel Nyamekye, CEO of Miracle Film Production for his pivotal role played in Ghana Movie industry,
In a video available to GhBase.com the veteran actor is seen struggling to talk indicating that he is still gradually recuperating.
Known for his exceptional performance in movies, Emmanuel Armah has featured in movies like Every Woman Wants Me, Fresh Trouble, Beyond Love, THINGS MEN DO, Shadows from the Past among others.
Watch video below;


--- title: Fasterai keywords: fastai sidebar: home_sidebar summary: "A library to make neural networks lighter and smaller" description: "A library to make neural networks lighter and smaller" nb_path: "nbs/index.ipynb" ---
fasterai
is a library created to make neural network smaller and faster. It essentially relies on common compression techniques for networks such as pruning, knowledge distillation, ...
You can use fasterai
to remove useless parameters in your PyTorch neural network
pruner = Sparsifier(model, granularity, method, criteria)
pruner.prune(sparsitiy)
But you can also use it as a fastai Callback
to prune your model while it is training
sparsifier = SparsifyCallback(sparsity, granularity, method, criteria, sched_func)
learn.fit_one_cycle(epochs, cbs=sparsifier)


Nalishebo Meebelo
Cyrus Au Yeing
Daniel Da Sobral
Lauren Prins
Brett Wallis
Regional Price Index (RPI)
RPI measures price changes experienced by households in Regional Western Australia. With Perth as the index, the RPI provides a comparison between
various consumer goods and services such as food, alcohol and housing.
This is an interactive chart to show the correlation of the RPI index. The data set is based on findings from 7 years, ranging from 2000 - 2019.
Interactive plot shows different data views. To see the relationship between different factors chose the particular axis.
Please click below for further information on the Visualizations


Assists in processing reflectance data, developing empirical models using stepwise regression and a generalized linear modeling approach, cross- validation, and analysis of trends in water quality conditions (specifically chl-a) and climate conditions using the Theil-Sen estimator.
| Version: | 1.0.0 |
| Depends: | R (≥ 2.10) |
| Imports: | plyr, lubridate, ggplot2, hydroGOF, stats, cvTools, mblm, graphics, utils |
| Published: | 2018-04-10 |
| Author: | Carly Hansen |
| Maintainer: | Carly Hansen <carlyahansen at gmail.com> |
| BugReports: | https://github.com/cahhansen/RSAlgae/issues |
| License: | GPL-2 |
| URL: | http://github.com/cahhansen/RSAlgae |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | RSAlgaeR results |
| Reference manual: | RSAlgaeR.pdf |
| Package source: | RSAlgaeR_1.0.0.tar.gz |
| Windows binaries: | r-devel: RSAlgaeR_1.0.0.zip, r-release: RSAlgaeR_1.0.0.zip, r-oldrel: RSAlgaeR_1.0.0.zip |
| OS X binaries: | r-release: RSAlgaeR_1.0.0.tgz, r-oldrel: RSAlgaeR_1.0.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=RSAlgaeR to link to this page.


O CAPTAIN! my Captain! our fearful trip is done,
The ship has weather’d every rack, the prize we sought is won,
The port is near, the bells I hear, the people all exulting,
While follow eyes the steady keel, the vessel grim and daring;
But O heart! heart! heart!
O the bleeding drops of red,
Where on the deck my Captain lies,
Fallen cold and dead.

This arm beneath your head!
It is some dream that on the deck,
You’ve fallen cold and dead.
My Captain does not answer, his lips are pale and still,
My father does not feel my arm, he has no pulse nor will,
The ship is anchor’d safe and sound, its voyage closed and done,
From fearful trip the victor ship comes in with object won;
Exult O shores, and ring O bells!
But I with mournful tread,
Walk the deck my Captain lies,
Fallen cold and dead.
WHEN lilacs last in the dooryard bloom’d,
And the great star early droop’d in the western sky in the night,
I mourn’d, and yet shall mourn with ever-returning spring.
Ever-returning spring, trinity sure to me you bring,
Lilac blooming perennial and drooping star in the west,
And thought of him I love.
O powerful western fallen star!
O shades of night—O moody, tearful night!
O great star disappear’d—O the black murk that hides the star!
O cruel hands that hold me powerless—O helpless soul of me!
O harsh surrounding cloud that will not free my soul.
In the dooryard fronting an old farm-house near the white-wash’d palings,
Stands the lilac-bush tall-growing with heart-shaped leaves of rich green,
With many a pointed blossom rising delicate, with the perfume strong I love,
With every leaf a miracle—and from this bush in the dooryard,
With delicate-color’d blossoms and heart-shaped leaves of rich green,
A sprig with its flower I break.
In the swamp in secluded recesses,
A shy and hidden bird is warbling a song.
Solitary the thrush,
The hermit withdrawn to himself, avoiding the settlements,
Sings by himself a song.
Song of the bleeding throat,
Death’s outlet song of life (for well dear brother I know,
If thou wast not granted to sing thou would’st surely die).
Over the breast of the spring, the land, amid cities,
Amid lanes and through old woods, where lately the violets peep’d from the ground, spotting the gray débris,
Amid the grass in the fields each side of the lanes, passing the endless grass,
Passing the yellow-spear’d wheat, every grain from its shroud in the dark-brown fields uprisen,
Passing the apple-tree blows of white and pink in the orchards,
Carrying a corpse to where it shall rest in the grave,
Night and day journeys a coffin.
Coffin that passes through lanes and streets,
Through day and night with the great cloud darkening the land,
With the pomp of the inloop’d flags with the cities draped in black,
With the show of the States themselves as of crape-veil’d women standing,
With processions long and winding and the flambeaus of the night,
With the countless torches lit, with the silent sea of faces and the unbared heads,
With the waiting depot, the arriving coffin, and the sombre faces,
With dirges through the night, with the thousand voices rising strong and solemn,
With all the mournful voices of the dirges pour’d around the coffin,
The dim-lit churches and the shuddering organs—where amid these you journey,
With the tolling tolling bells’ perpetual clang,
Here, coffin that slowly passes,
I give you my sprig of lilac.
(Nor for you, for one alone,
Blossoms and branches green to coffins all I bring,
For fresh as the morning, thus would I chant a song for you
O sane and sacred death.
All over bouquets of roses,
O death, I cover you over with roses and early lilies,
But mostly and now the lilac that blooms the first,
Copious I break, I break the sprigs from the bushes,
With loaded arms I come, pouring for you,
For you and the coffins all of you O death.)
O western orb sailing the heaven,
Now I know what you must have meant as a month since I walk’d,
As I walk’d in silence the transparent shadowy night,
As I saw you had something to tell as you bent to me night after night,
As you droop’d from the sky low down as if to my side (while the other stars all look’d on),
As we wander’d together the solemn night (for something I know not what kept me from sleep),
As the night advanced, and I saw on the rim of the west how full you were of woe,
As I stood on the rising ground in the breeze in the cool transparent night,
As I watch’d where you pass’d and was lost in the netherward black of the night,
As my soul in its trouble dissatisfied sank, as where you sad orb,
Concluded, dropt in the night, and was gone.
Sing on there in the swamp,
O singer bashful and tender, I hear your notes, I hear your call,
I hear, I come presently, I understand you,
But a moment I linger, for the lustrous star has detain’d me,
The star my departing comrade holds and detains me.
O how shall I warble myself for the dead one there I loved?
And how shall I deck my song for the large sweet soul that has gone?
And what shall my perfume be for the grave of him I love?
Sea-winds blown from east and west,
Blown from the Eastern sea and blown from the Western sea, till there on the prairies meeting,
These and with these and the breath of my chant,
I’ll perfume the grave of him I love.
O what shall I hang on the chamber walls?
And what shall the pictures be that I hang on the walls,
To adorn the burial-house of him I love?
Pictures of growing spring and farms and homes,
With the Fourth-month eve at sundown, and the gray smoke lucid and bright,
With floods of the yellow gold of the gorgeous, indolent, sinking sun, burning, expanding the air,
With the fresh sweet herbage under foot, and the pale green leaves of the trees prolific,
In the distance the flowing glaze, the breast of the river, with a wind-dapple here and there,
With ranging hills on the banks, with many a line against the sky, and shadows,
And the city at hand with dwellings so dense, and stacks of chimneys,
And all the scenes of life and the workshops, and the workmen homeward returning.
Lo, body and soul—this land,
My own Manhattan with spires, and the sparkling and hurrying tides, and the ships,
The varied and ample land, the South and the North in the light, Ohio’s shores and flashing Missouri,
And ever the far-spreading prairies cover’d with grass and corn.
Lo, the most excellent sun so calm and haughty,
The violet and purple morn with just-felt breezes,
The gentle soft-born measureless light,
The miracle spreading bathing all, the fulfill’d noon,
The coming eve delicious, the welcome night and the stars,
Over my cities shining all, enveloping man and land.
Sing on, sing on you gray-brown bird,
Sing from the swamps, the recesses, pour your chant from the bushes,
Limitless out of the dusk, out of the cedars and pines.
Sing on dearest brother, warble your reedy song,
Loud human song, with voice of uttermost woe.
O liquid and free and tender!
O wild and loose to my soul—O wondrous singer!
You only I hear—yet the star holds me (but will soon depart),
Yet the lilac with mastering odor holds me.
Now while I sat in the day and look’d forth,
In the close of the day with its light and the fields of spring, and the farmers preparing their crops,
In the large unconscious scenery of my land with its lakes and forests,
In the heavenly aerial beauty (after the perturb’d winds and the storms),
Under the arching heavens of the afternoon swift passing, and the voices of children and women,
The many-moving sea-tides, and I saw the ships how they sail’d,
And the summer approaching with richness, and the fields all busy with labor,
And the infinite separate houses, how they all went on, each with its meals and minutia of daily usages,
And the streets how their throbbings throbb’d, and the cities pent—lo, then and there,
Falling upon them all and among them all, enveloping me with the rest,
Appear’d the cloud, appear’d the long black trail,
And I knew death, its thought, and the sacred knowledge of death.
Then with the knowledge of death as walking one side of me,
And the thought of death close-walking the other side of me,
And I in the middle as with companions, and as holding the hands of companions,
I fled forth to the hiding receiving night that talks not,
Down to the shores of the water, the path by the swamp in the dimness,
To the solemn shadowy cedars and ghostly pines so still.
And the singer so shy to the rest receiv’d me,
The gray-brown bird I know receiv’d us comrades three,
And he sang the carol of death, and a verse for him I love.
From deep secluded recesses,
From the fragrant cedars and the ghostly pines so still,
Came the carol of the bird.
And the charm of the carol rapt me,
As I held as if by their hands my comrades in the night,
And the voice of my spirit tallied the song of the bird.
Come lovely and soothing death,
Undulate round the world, serenely arriving, arriving,
In the day, in the night, to all, to each,
Sooner or later delicate death.
Prais’d be the fathomless universe,
For life and joy, and for objects and knowledge curious,
And for love, sweet love—but praise! praise!
For the sure-enwinding arms of cool-enfolding death.
Dark mother always gliding near with soft feet,
Have none chanted for thee a chant of fullest welcome?
Then I chant it for thee, I glorify thee above all,
I bring thee a song that when thou must indeed come, come unfalteringly.
Approach strong deliveress,
When it is so, when thou hast taken them I joyously sing the dead,
Lost in the loving floating ocean of thee,
Laved in the flood of thy bliss O death.
From me to thee glad serenades,
Dances for thee I propose saluting thee, adornments and feastings for thee,
And the sights of the open landscape and the high-spread sky are fitting,
And life and the fields, and the huge and thoughtful night.
The night in silence under many a star,
The ocean shore and the husky whispering wave whose voice I know,
And the soul turning to thee O vast and well-veil’d death,
And the body gratefully nestling close to thee.
Over the tree-tops I float thee a song,
Over the rising and sinking waves, over the myriad fields and the prairies wide,
Over the dense-pack’d cities all and the teeming wharves and ways,
I float this carol with joy, with joy to thee O death.
To the tally of my soul,
Loud and strong kept up the gray-brown bird,
With pure deliberate notes spreading filling the night.
Loud in the pines and cedars dim,
Clear in the freshness moist and the swamp perfume,
And I with my comrades there in the night.
While my sight that was bound in my eyes unclosed,
As to long panoramas of visions.
And I saw askant the armies,
I saw as in noiseless dreams hundreds of battle-flags,
Borne through the smoke of the battles and pierc’d with missiles I saw them,
And carried hither and you through the smoke, and torn and bloody,
And at last but a few shreds left on the staffs (and all in silence),
And the staffs all splinter’d and broken.
I saw battle-corpses, myriads of them,
And the white skeletons of young men, I saw them,
I saw the débris and débris of all the slain soldiers of the war,
But I saw they were not as was thought,
They themselves were fully at rest, they suffer’d not,
The living remain’d and suffer’d, the mother suffer’d,
And the wife and the child and the musing comrade suffer’d,
And the armies that remain’d suffer’d.
Passing the visions, passing the night,
Passing, unloosing the hold of my comrades’ hands,
Passing the song of the hermit bird and the tallying song of my soul,
Victorious song, death’s outlet song, yet varying ever-altering song,
As low and wailing, yet clear the notes, rising and falling, flooding the night,
Sadly sinking and fainting, as warning and warning, and yet again bursting with joy,
Covering the earth and filling the spread of the heaven,
As that powerful psalm in the night I heard from recesses,
Passing, I leave thee lilac with heart-shaped leaves,
I leave thee there in the door-yard, blooming, returning with spring.
I cease from my song for thee,
From my gaze on thee in the west, fronting the west, communing with thee,
O comrade lustrous with silver face in the night.
Yet each to keep and all, retrievements out of the night,
The song, the wondrous chant of the gray-brown bird,
And the tallying chant, the echo arous’d in my soul,
With the lustrous and drooping star with the countenance full of woe,
With the holders holding my hand nearing the call of the bird,
Comrades mine and I in the midst, and their memory ever to keep, for the dead I loved so well,
For the sweetest, wisest soul of all my days and lands—and this for his dear sake,
Lilac and star and bird twined with the chant of my soul,
There in the fragrant pines and the cedars dusk and dim.


Node¶
Nodes are stops within each GameMap. Each turn your truck will be at a node. Below are the listed instance variables.
Name |
Type |
Description |
|---|---|---|
city_name |
String |
The name of the Node you are at |
roads |
Road [] |
A list of roads that you can take to travel to the next node |
next_node |
node |
The next node you will travel to. It will be the same regardless of the road you take |
gas_price |
decimal |
The current price of gas at this node |
repair_price |
decimal |
The current price of repairing at this node |


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The ultimate medicine, named after the goddess of universal remedy in Greek mythology, Panacea, was said to heal any and all ills, sicknesses, injuries, and maladies. Regardless of the specific case, one simply had to have a sip from her chalice and all their ailments would be removed and their health restored. This is a tantalizing concept to anyone, but especially to engineers.
The idea of a “catch-all” technology; one simple solution to a vast and complex problem-space, is appealing, but generally unrealistic. For example, we know that there is no one perfect technology for producing energy. We apply different methodologies for different circumstances; solar in sunny regions, windmills in windy regions, turbines for rivers and oceans, coal and oil while we had abundances of them, etc.
Water treatment is no exception. We do have a variety of technologies at our disposal including particulate filtration and chemical and bacterial treatments and we do our best to apply these technologies in an intelligent and informed manner to be as effective as possible. But we are only as good as the amount of information we have, and we don’t have enough.
You can log into google maps and check for an accident on your route home and it will be accurate to the minute, but no one can check to what the level of lead contamination is in their drinking water at home, or work, etc. There are only 350 automated groundwater testing sites in California and the available parameters they monitor (for quality) are very few with metal toxins like lead not being one of them. There is no onsite, real-time tracking of heavy metal contamination, radioactivity, bacteria, and viruses.
Large water treatment plants sample their own product before and after treatment, do manual testing and wait for results, but this is a process that can range from hours to days. And, as the nation clearly learned from Flint, just because the water is clean leaving the source doesn’t mean it is safe coming out of your tap. People have activated carbon filters to help improve taste of their water, but it does little for bacterial/viral contaminants and minerals, salts, and other inorganics.
The Final Frontier
Reverse osmosis systems are expensive and horribly inefficient, pushing 25% only in the best units. Throwing away 3 out of 4 gallons of your water down the drain when 4 billion people on earth won’t have access to fresh water 1 out of 12 months is pretty grossly wasteful .
TCurrently only lab testing is available for some of the critical contaminants that are significantly impacting over 60% of the US population . This means we have to be reactive; people drink water, if it is bad they get sick, they go see doctors, as quickly as they can (and if you are lucky) they trace it to a contaminant in the drinking water, and then remediation begins. This process takes a long time. Flint switched its water supply on April 21, 2014. It wasn’t until Feb. 26, 2015 that the EPA detected lead levels seven times higher than the EPA limit in homes (and this was only after people complained about the discolored water).
The dreams of yesterday are the hopes of today and the reality of tomorrow. Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.
We need to know what is in the water to make the right technological choice. We need the ability to remotely and automatically monitor water so that we can be proactive, and head-off potentially life-threatening problems before they take root.
Reaching for the Stars
Can we generate a wide-spread water quality grid, monitoring at tens of thousands, not hundreds, of places across California and many multiples more for the whole US/world? What could we do with a real-time map of Water metrics? Imagine see the effects of a fracking well, or a chemical spill, or a hurricane, a tsunami, a disease outbreak, etc. in real time as a function of location and time! The benefit to data science and fundamental research aside, such a network could save thousands of lives and untold amounts of damage. Until then, more and more scenarios like Flint will arise across the world
Every other person I tend to meet either is, or is aspiring to become, a data scientist. Everyone wants to use modern computing power to pull some game-changing conclusion out of what seems like innocuous data. But almost no-one seems to be willing to take on the real difficult challenge; generating new data. Those game-changing conclusions are only as good (and complete) as the data-set from which they were drawn and right now, that isn’t very good at all. We, at KETOS aim to tackle this crucial problem through disruptive technologies that can enable active monitoring of water metrics and help people, businesses and governments all obtain and understand actionable water metrics like never before.
Placeholder text by Space Ipsum. Photographs by NASA on The Commons.


{% extends "account_keeping/base.html" %} {% load account_keeping_tags humanize i18n libs_tags tz %} {% block main %}
| {% trans "Month" %} | {% trans "Income" %} | {% trans "Expenses" %} | {% trans "Profit" %} | {% trans "New invoiced income" %} | {% trans "Total outstanding profit" %} | {% trans "Bank balance" %} | {% trans "Total equity" %} |
|---|---|---|---|---|---|---|---|
| {{ month|date:"M" }} | {% endlocaltime %}{% call income_total "get" month as income_total_value %}{{ income_total_value|default:0|currency:branch.currency }} | {% call expenses_total "get" month as expenses_total_value %}{{ expenses_total_value|default:0|currency:branch.currency }} | {% call profit_total "get" month as profit_total_value %}{{ profit_total_value|default:0|currency:branch.currency }} | {% call new_total "get" month as new_total_value %}{{ new_total_value|default:0|currency:branch.currency }} | {% call outstanding_total "get" month as outstanding_total_value %}{{ outstanding_total_value|default:0|currency:branch.currency }} | {% call balance_total "get" month as balance_total_value %}{{ balance_total_value|default:0|currency:branch.currency }} | {% call equity_total "get" month as equity_total_value %}{{ equity_total_value|default:0|currency:branch.currency }} |
| {% trans "Total" %} | {{ income_total_total|currency:branch.currency }} | {{ expenses_total_total|currency:branch.currency }} | {{ profit_total_total|currency:branch.currency }} | {{ new_total_total|currency:branch.currency }} | |||
| {% trans "Average" %} | {{ income_average|currency:branch.currency }} | {{ expenses_average|currency:branch.currency }} | {{ profit_average|currency:branch.currency }} | {{ new_average|currency:branch.currency }} | {{ outstanding_average|currency:branch.currency }} | {{ balance_average|currency}} | {{ equity_average|currency:branch.currency }} |


Give a brief description of this waste site and it's surroundings. Tell us about your expirience(s) with this location.
Food Groups Found
Volume Estimate
Length
0
Width
0
Height
0
vol:
0
ft^3
Condition
Usable
Unusuable
Sun
Mon
Tue
Wed
Thr
Fri
Sat
0:00-3:00
4:00-7:00
8:00-11:00
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This is a simple Tennis Game app entirely made using Vanilla JavaScript. Your goal is to beat your opponent by scoring 15 points. Every time you catch the ball with your paddle and your opponent doesn't, you score 1 point. Otherwise, your opponent does. To move your paddle, simply use the mouse up and down or the arrow keys. Good luck!


Autobahn: Simple RPCs with WAMP
You need to turn on JavaScript.
Open development console (press F12) to watch and press
Run


I am not afraid to experiment and dug for new knowledge. Specificity of teamwork is based on continuous dialogue that helps to achieve the perfect interface. We think globally, that enables us to operate effectively at the local level and contribute the changes in the sphere of market web technologies.
Get ready to dive headfirst into my world of graphic design. I am a web specialist with big ideas and creative mind. I develop exclusive designs, unique, easy and clear functionality of the products. I know what it means to be a specialist and how to run effective and successful business. I'm here to turn your ideas into a profitable business.
My main requirement is to create products of flexible functionality. I feel the trends of the present, that's why I introduce the latest web building technologies. I never wait for ideal conditions, so I work hard to train and develop my workers. Choosing the best, you get the best.


Terms & conditions:
-
People under 18 years old are not allowed to participate in the
event. Please, provide the
employee at the entrance with your ID for an age check!
-
Visitors cannot buy any items (food, drinks, souvenirs, etc.) with cash. Every visitor must use their event
account to buy and loan items at the event. (“How to use my event account?” – Instructions provided in My
Account
page)
-
For each loan item a deposit of 50% of the price of the item is taken. In case of damaged or lost products, we
do not return the deposit.
-
No refund if for any reason a person is not available to visit the event.
-
Buying a ticket or reserving a camping spot for a day is not possible.
-
People are allowed to leave during the event and return later through special gates. Ask our employees for
more
information.


Documentation
With M2H, you can generate a static website easily.
- Write your content with Markdown Language.
- Organize website tree using the directory structure.
- Adapt the design by changing the configuration files.
- let Md2html do the job
Upload the result on your web server


NGO is a grassroot movement that majors in urban slums by providing very wanting services for all.We are committed in helping the society by providing education for the needy,skills development for our youths and care for the elderly and the homeless.
"Building everyone to be a better person all round."
The NGO is a non-governmental organization that is based Nairobi,Kenya along Argwings Kodhek road near Yaya Center.This NGO was founded by some members who are Domnic Mugo, Japheth Nyaranga,Mercy Vugutsa,Laurine Praise and Brian Obiri in the year 2020.
We run several major projects that work in handy to lead to the promotion of education,skills development,care for the elderly and moreso for the homeless.
For our work to be a success in years to come,forming a good rapport with various partners and relationship with various partners and donors is the major issues that we always want to achieve and maintain it for a successful goal.
Seeing many children,youths,aged people and the homeless out there who are struggling with life and some social evils were increasing among the youths due to idleness we decided to come up with this project of helping out the needy so as to help communities out there. We decided to look for donors and partners to help us manage some things in terms of financial aid.We planned this for a very long time and we thank everyone who made all this a success in every way.This NGO was formed in the year 20202 during to this corona pandemic.
Achieving all this could not have been easy without some help from various donors.A big THANK YOU to the below;


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SAP Tables¶
Functions and constants implementing various SAP tables
Hide the implementation details of the tables inside this package
Import the relevant functions as needed by other parts of saptk. When you add, modify, or remove functions from the tables modules, you must make the corresponding change in this file.
Aim to reduce the API surface of tables over time by encapsulating functionality inside it, and reducing the number of imports into this file.
-
sap.tables.sap_tables.
system_efficiency
(system_data, fuel)[source]¶ Try to get the efficiency of the given heating sustem
Parameters: - system_data – HeatingSystem
- fuel – fuel of this heating system.
- todo (.) – why can’t we just get the fuel from the system?
Returns:
-
sap.tables.sap_tables.
table_1b_daily_hot_water
(low_water_use, Nocc)[source]¶ Table 1b part 2: Domestic hot water usage
Annual average hot water usage in litres per day Vd,average = (25 × N) + 36
Reduce the annual average hot water usage by 5% if the dwelling is designed to achieve a water use target of not more that 125 litres per person per day (all water use, hot and cold)
For each month, multiply Vd,average by the factor from Table 1c to obtain the daily volume in the month Vd,m
- The energy content of water used is
4.190 × Vd,m × nm × ∆Tm / 3600 kWh/month
where ∆Tm is the temperature rise for month m from Table 1d.
Distribution loss is 0.15 times energy content calculated in (d).
This function calculates parts a) and b)
Parameters: - Nocc – Number of Occupants (from Table 1b part 1)
- low_water_use (bool) – Whether the dwelling is designed to save water (see point b)
Returns: Average daily hot water use
-
sap.tables.sap_tables.
table_1b_occupancy
(TFA)[source]¶ Table 1b Part 1: Occupancy
if TFA > 13.9: N = 1 + 1.76 × [1-exp (-0.000349 × (TFA-13.9)2 )] + 0.0013 × (TFA-13.9)
if TFA ≤ 13.9: N=1
Parameters: TFA – Total Floor Area Returns: Assumed number of occupants Return type: int
-
sap.tables.sap_tables.
table_2_hot_water_store_loss_factor
(hw_cylinder_insulation_type, hw_cylinder_insulation)[source]¶ Table 2
Calculate Hot water storage loss factor according to equation in Note 1. of Table 2 (rather than using the tabulated values)
Parameters: - hw_cylinder_insulation_type (CylinderInsulationTypes) –
- hw_cylinder_insulation – thickness of cylinder insulation in mm
Returns: hot water storage loss factor in kWh/litre/day
-
sap.tables.sap_tables.
table_2a_hot_water_vol_factor
(cylinder_volume)[source]¶ Table 2a
Calculate the volume factor according to equation in Note 2. of Table 2a When using Table 2, the loss is to be multiplied by the volume factor.
Parameters: cylinder_volume – volume of hot water cylinder Returns: hot water volume factor


JURASSIC PARK III A film review by David N. Butterworth Copyright 2001 David N. Butterworth
** (out of ****)
I lost track of how many times someone yells "Run!" in "Jurassic Park III."
As Dr. Ian Malcolm so succinctly put it in "The Lost World: Jurassic Park" (the second go-round of the trilogy-to-date ... and there's absolutely no reason why they should call it quits with this one), "Oooh! Ahhh! That's how it always starts. Then later there's running and screaming."
There's a lot of running and screaming in "Jurassic Park III." What there isn't is the afore-referenced Dr. Malcolm (a sorely-missed Jeff Goldblum, who perked up the first two installments no end with his Chaos theorizing, cool attire, and utterly sarcastic humor) and the sure hand of Steven Spielberg who, perhaps wisely, has abdicated directorial duties to "Jumanji" man Joe Johnston.
For the third time running (and running, and screaming) the dinosaurs alone are worth the price of admission--this time we get big nasty spinosaurs with long snouts and huge finned backs, flying pteranodons with vicious little babies, club-tailed ankylosaurs, noble brachiosaurs, galloping herds of nanosaurs and, of course, a lot more razor sharp velociraptors (although this time out *communicative* ones that can vocalize better than their human prey). Each time out the huge reptiles get more and more convincing as technological advances bring them closer into contact with the prix-fix beauzeaus that either wittingly or unwittingly stumble onto their Isla Sorna.
There seems to be some unwritten rule that future chapters of Michael Crichton's dino-saga must feature cast-offs from a P.T. Anderson picture. In 1997 it was Julianne Moore; in "Jurassic Park III" it's the turn of William H. Macy (who, in the film's scariest realization, is the spitting image of Homer Simpson's good neighbor Ned Flanders). Moore is "replaced" by Téa Leoni, naturally enough, which at first consideration seems to be a sound casting decision ... until you witness Ms. Leoni trying to out act the special effects. Sam Neill is back as paleontologist Dr. Alan Grant as is Laura Dern in a brief bit as Dr. Ellie Sattler; she does the only smart thing and stays the heck off the island.
The plot? "Good dinosaurs, bad people." OK, the real plot: Mr. and Mrs. Kirby (Macy and Leoni) con Dr. Grant into helping them find their son Eric (Trevor Morgan), who has accidentally parasailed onto a Costa Rican island teeming with large, hungry carnivores.
The "Jurassic Park" franchise will always be worth seeing for Stan Winston's mesmerizing creature effects (if you like that sort of thing I suppose--I almost hated "America's Sweethearts" and would have were it not for the lovely Julia Roberts and her genuinely fetching smile, so I guess it's all a matter of taste).
-- David N. Butterworth<EMAIL_ADDRESS>Got beef? Visit "La Movie Boeuf" online at http://members.dca.net/dnb
========== X-RAMR-ID: 28948 X-Language: en X-RT-ReviewID: 237325 X-RT-TitleID: 1108705 X-RT-SourceID: 878 X-RT-AuthorID: 1393 X-RT-RatingText: 2/4
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


class ValueDateCalculator
A calculator for FX value dates with sensible defaults.
Unless necessary, ValueDateCalculator
requires very little configuration:
it assumes Saturdays and Sundays as the standard workends (codified by
settler.WorkWeek.STANDARD_WORKWEEK) and
T+2 as the standard spot
settlement day. Use the method setWorkWeek to support currencies with
different work weeks; use setSpotLag to support pairs with different
spot settlements.
All currencies and currency pairs should be in lower- or upper-case
consistently, i.e., ValueDateCalculator
does not defensively normalize
them. Currency pairs should also be in the format of "xxxyyy" where "xxx"
is the base currency and "yyy" is the term currency, e.g., "USDSGD",
"USDJPY" are acceptable, but not "USD/CAD" and "AUD-SGD".
ValueDateCalculator
also does not handle automatic trade date rollover.
The calling application should track such rollover and pass the
trade date to ValueDateCalculator
for determining the value date.
This deliberate omission allows using ValueDateCalculator
to calculate
value dates with historical and future dates.
ccyHolidays
- the registry of holidays for all currencies
<init> |
Creates a ValueDateCalculator with the given currency holiday registry. ValueDateCalculator(ccyHolidays: CurrencyHolidays) |
setSpotLag |
Sets the spot settlement (spot lag) for the specified currency pair. fun setSpotLag(pair: String, spotLag: Long): ValueDateCalculator |
setWorkWeek |
Sets the work week for the specified currency. fun setWorkWeek(ccy: String, workweek: WorkWeek): ValueDateCalculator |
spotFor |
Calculates the spot settlement date of the given currency pair and trade date. fun spotFor(pair: String, tradeDate: LocalDate): LocalDate |


Man arrested after charging officer with shovel, resisting arrest, police say
by Logan RudeMADISON, Wis. - A Madison man was arrested Monday after police got a call from a Denton Place couple who said they woke up to "really heavy pounding" around 1:20 a.m. from a stranger at their front door, according to an incident report.
When police arrived, they found 31-year-old Jonah Decorah still in the area, according to the report. Police said he was yelling and acting erratically. Decorah came at one of the officers with a shovel while saying something about the officer shooting him, but the officer backed up, according to the report.
Police said it took four officers to restrain Decorah as he bucked, screamed and spit. He was taken to a hospital then to jail.
One officer suffered a hand sprain during the incident, according to the report.
Decorah was arrested on suspicion of disorderly conduct, resisting/obstructing, discharging bodily fluids at public safety worker and felony bail jumping.


Assembly for the Google HTTP Client Library for Java 1.17.0-rc
Overview
High-level details about this library can be found at http://code.google.com/p/google-http-java-client
Dependencies and Licenses
The license can be found
here.
Dependent jars can be found in the
libs folder and the corresponding source jars can be found
in the
libs-sources folder.
The dependency structure and licenses for the different libraries can
be found here:
Maven Usage
For information on how to add these libraries to your Maven project please see
http://code.google.com/p/google-http-java-client/wiki/Setup#Maven.
Eclipse
A .classpath file snippet that can be included in your project's .classpath
has been provided
here. Please only use the classpathentry's you
actually need (see below for details).
ProGuard
A ProGuard configuration file proguard-google-http-client.txt
is included for common settings for using the library. On Android projects,
you may want to add a reference to
proguard-google-http-client.txt
in the
project.properties
file under the
proguard.config
property.
Please read Setup
ProGuard for more details.
Dependencies for all Platforms
The following are the jars from the
libs folder needed for applications on all platform:
- google-http-client-1.17.0-rc.jar
- jsr305-1.3.9.jar
- google-http-client-protobuf-1.17.0-rc.jar (when using
protobuf-java)
- google-http-client-gson-1.17.0-rc.jar (when using GSON)
- google-http-client-jackson2-1.17.0-rc.jar (when using
Jackson 2)
- google-http-client-jackson-1.17.0-rc.jar (for backwards
compatability when using Jackson 1)
- jackson-core-asl-1.9.11.jar
- google-http-client-jdo-1.17.0-rc.jar (when using JDO)
- google-http-client-xml-1.17.0-rc.jar (when using XML)
- xpp3-1.1.4c.jar (when NOT on Android)
Android Dependencies
The following are the jars from the
libs folder required for Android applications or a newer
compatible version:
- google-http-client-android-1.17.0-rc.jar
The
libs folder also contains properties files that specify the
location of source jars for Android projects in Eclipse.
Please see the
Android
wiki for the Android Developer's Guide.
Google App Engine Dependencies
The following are the jars from the
libs folder required for Google App Engine applications or
a newer compatible version:
- google-http-client-appengine-1.17.0-rc.jar
Please see the
GoogleAppEngine
wiki for the Google App Engine Developer's Guide.
General Purpose Java 5 Environment Dependencies
The following are the jars from the
libs folder required for general purpose Java 5
applications or a newer compatible version:
- commons-logging-1.1.1.jar
- httpclient-4.0.1.jar
- httpcore-4.0.1.jar


Default pagination:
No direction links:
With boundary links:
Current page: {{ page }}
Restricted size, no rotation:
Restricted size with rotation:
Restricted size with rotation and no ellipses:
Current page: {{ page2 }}
Pagination control can be disabled:
A pagination with customized pages:
Current page: {{ page5 }}


Ed Sheeran 'related to Mafia mobster Frank Sheeran' from Scorsese's The Irishman
Ed Sheeran has discovered that his family tree connects him to ruthless murdering mobster Frank Sheeran, who is portrayed by Robert De Niro on the big screen
by James Brinsford, https://www.facebook.com/james.brinsfordEd Sheeran has found that he is connected to a ruthless mobster, who is the subject of Martin Scorsese’s new film.
The 28-year-old singer's family tree has revealed that he is related to Mafia hitman Frank Sheeran, who is portrayed by Robert De Niro in The Irishman.
The murderous gangster was infamous for "painting houses" with his victims' blood, during vicious slayings that were ordered by his Mafia bosses.
Ed has reportedly revealed his connection to Frank in a conversation with De Niro's co-star in the movie, Stephen Graham.
The 46-year-old actor, who recently starred in the latest series of Line of Duty, said: "They’re related, you know.
“That’s what Ed said, he says it’s his distant uncle.”
While the events in The Irishman are deeply unsavoury, they are based upon real events according to Graham.
He said about the film: "It’s all based on true events, on Frank Sheeran. Before he passed away, he gave all this information to a journalist who turned it into a book.”
This biography was called I Heard You Paint Houses, and it was written by Charles Brandt.
He wrote that Frank, who was 6ft 4in, was working for Mafia bosses Russell Bufalino and Angelo Bruno.
They are depicted in Scorcese's movie by Joe Pesci and Harvey Keitel.
Sheeran, the gangster - not the singer, also worked as muscle and security for union boss Jimmy Hoffa, who is played by Al Pacino in the movie.
Brandt has claimed that Frank Sheeran killed Hoffa, after he went missing in 1975.
Ed himself confirmed the family connection on social media, as The Irishman opened in cinemas before heading to Netflix on November 27.
The chart-topping singer wrote: "Robert De Niro is playing a top Irish gangster in a new Martin Scorsese film. The gangster he is playing is called Frank Sheeran. True story.”
Frank died of cancer in 2003, aged 83.
Do you have a story to sell? Get in touch with us at<EMAIL_ADDRESS>or call us direct 0207 29 33033.


Larry's Pizza Place
Our Prices
Our pizzas include a single topping. Additional toppings $1.50 each.
SIZES
Large: $15
Medium: $11
Small: $9
PLACE YOUR ORDER
Customer Name
Pizza Size
Small
Medium
Large
Toppings
Pepperoni
Extra Cheese
Sausage
Mushrooms
Jalapenos
Onions
Bacon
PLACE ORDER
MORE TOPPINGS
Order Details:
Name on Order:
Size:
Toppings:
TOTAL COST:


Reports SLF4J logging calls where the number of {}-placeholders in the string constant argument does not match the number of other arguments to the logging call.
New in 12.1, Powered by InspectionGadgets


secretcodes.js demo
Type out 'qwerty' or the konami code. hint: start = return/enter key
Your input as you type:


PowerMockitoRulePlugin()
MockspressoPowerMockitoPluginsJavaSupport#mockByPowerMockitoRule()
This class will be removed in a future release
Plugin that applies PowerMockitoConfig
AND applies a PowerMockRule as an outer rule to Mockspresso. Use this plugin if you want to use PowerMock without applying the PowerMockRunner


You already use it!
More stuff has been moving to the client in recent years: AJAX, etc.
Limitations have been language support and performance
These are related issues: JavaScript used to be "slow"
Before we go into them, remember:
Performance != JavaScript
The DOM used to be "slow"
Graphics used to be "slow"
I won't focus on these, but:
DOM access is now very fast
Hardware acceleration is now common
WebGL support ships in Firefox and Chrome, will ship in Internet Explorer and Safari
(Unless you need to support old browsers...)
JavaScript is the only standards-based language in web browsers
If we want to run other languages on the web we need to go through JavaScript
Might seem like an odd solution
But replacing JavaScript with anything else would slow down the web
Adding another virtual machine (VM) alongside JavaScript is complicated (cross-VM GC, etc.)
Consensus to standardize any such change would be very hard
But it turns out to be not so odd!
Running other languages in JavaScript requires no standardization
Turns out performance can be good in many cases
No new VM means no new attack surface for exploits
Java (GWT) used in Blogger, enterprise apps
C# (Script#) used in Microsoft Office Web Apps
float array[5000]; // C++
int main() {
for (int i = 0; i < 5000; ++i) {
array[i] += 1.0f;
}
}
var g = Float32Array(32768); // JavaScript
function main() {
var a = 0, b = 0;
do {
a = 8 + (b << 2) | 0;
g[a >> 2] = +g[a >> 2] + 1.0;
b = b + 1 | 0;
} while ((b | 0) < 5000);
}
This is a subset of JS called asm.js
An optimizable subset of JavaScript, intended primary as a compiler target
Avoids potential slowdowns (types changing, GC pauses)
Low-level and easy to optimize
That asm.js example from before:
var g = Float32Array(32768); // JavaScript
function main() {
var a = 0, b = 0;
do {
a = 8 + (b << 2) | 0;
g[a >> 2] = +g[a >> 2] + 1.0;
b = b + 1 | 0;
} while ((b | 0) < 5000);
}
Integers remain integers
"Memory" is a fast typed array
Every compiler - GWT, CoffeeScript, etc. - generates a particular subset of JavaScript
Emscripten and Mandreel converged on a useful subset for C/C++
Added to a popular benchmark, Google Octane
asm.js formalizes that subset
(VMs and Emscripten from July 10th 2013, run on 64-bit linux)
source: @jgw, Box2D Addendum
asm.js is faster than "typical" JavaScript on Internet Explorer, Chrome and Firefox
asm.js on Firefox and Chrome is comparable to Java
Ensures that types are not mixed up
Makes it possible to reason about global program structure
Helps in existing JavaScript engines by making it simpler to detect types and avoid them changing later
Makes ahead of time (AOT) compilation possible with asm.js-specific optimizations
source: arewefastyet.com
AOT (red/orange) still faster, but lead is decreasing
C/C++ compiled to JavaScript can run close to native speed
No need to standardize (asm.js is just like GWT, CoffeeScript, etc.), so improves at the speed that JS engines improve: FAST
Already fast enough even for demanding use cases like 3D games
Not just for games, but for anything that needs raw computation
Emscripten can emit asm.js output
asm.js code already works in all modern browsers: IE10, Chrome, Firefox, Safari
Requires nothing more than normal JavaScript plus typed arrays
Early adopters are mainly in the games industry (Epic, etc.) but not only (Prezi, etc.)
Write performance-sensitive code in C or C++
Compile to asm.js using Emscripten
Interact with other JavaScript
emcc is a drop-in replacement for gcc/clang/etc.
$ cat hello.c #include "stdio.h" int main() { printf("hello world!\n"); } $ emcc hello.c -o hello.js $ node hello.js hello world!
Can use C APIs like libc, SDL, OpenGL, etc.
What do other platforms/environments do?
Main project is written in C/C++
VM for a scripting language (e.g., Python) is also written in C/C++
VM has a C/C++ API to bridge the two "worlds"
Python makes it easy to run code in its VM:
// C code
PyRun_SimpleString("print 'hello from python!'");
Other APIs let you create Python objects and operate on them from C
Script language is just another module in the C/C++ project
(it happens to be a VM)
// C++ with Boost.Python
#include "boost/python.hpp"
char const* greet() {
return "hello from C!";
}
BOOST_PYTHON_MODULE(greeter) {
boost::python::def("greet", greet);
}
# Python
>>> import greeter
>>> greeter.greet()
hello from C!
Boost.Python can also wrap C++ classes
// C++ class
class Cat {
string name;
public:
Cat(string name_) : name(name_) {}
void meow() { printf("%s meows\n", name.c_str());
};
BOOST_PYTHON_MODULE(Cats) {
class <Cat>("Cat").def("meow", &Cat::meow);
}
// Python
>>>> import Cats
>>>> my_cat = Cats.Cat('Fluffy')
>>>> print my_cat.meow()
Fluffy meows
But can we do this in JavaScript?
YES!
Emscripten provides APIs on the C side
#include "emscripten.h"
int main() {
// Call JS from C
emscripten_run_script("alert('hello!')");
return 0;
}
Similar to how we called Python from C...
Difference doesn't matter though,
API is the same ;)
Implement a C API in JS:
// myLib.js
mergeInto(LibraryManager.library, {
js_func: function(x) { alert('you sent ' + x) }
});
// main.c (emcc main.c --js-library myLib.js)
void js_func(int x);
int main() {
js_func(3); // will cause alert('you sent 3')
return 0;
}
Very efficient:
// JavaScript output from last slide's files
function _js_func(x) { // from JavaScript library
alert('you sent ' + x);
}
function _main() { // compiled from C
_js_func(3);
return 0;
}
This is how emscripten implements C APIs (like SDL) using HTML5 APIs
Emscripten provides APIs on the JS side too
// C file that was compiled to JS
int c_add(int x, int y) { return x+y; }
// Call C from JS
var result = ccall('c_add', // name
'number', // return type
['number', 'number'], // argument types
[10, 20]); // arguments
// returns 30
cwrap is useful for multiple calls
// Call C from JS
var c_add = cwrap('c_add', // name
'number', // return type
['number', 'number']); // argument types
console.log(c_add(10, 20)); // 30
console.log(c_add(20, 30)); // 50
Wrapper behaves just like a normal JS function
Embind, written by IMVU, glues together C++ and JavaScript
// Access JS objects in C++ like C++ objects
#include "emscripten/val.h"
using namespace emscripten;
int main() {
val Math = val::global("Math");
return Math.call("abs", -10); // returns 10
}
Embind can also go the other way
// C++ library
#include "emscripten/bind.h"
using namespace emscripten;
int c_add(int a, int b) { return a+b; }
EMSCRIPTEN_BINDINGS(my_module) {
function("c_add", &c_add);
}
// Use it in JS
alert(Module.c_add(10, 20)); // shows 30
Classes as well:
// C++ class wrapped by Embind
class Cat {
string name;
public:
Cat(string name_) : name(name_) {}
void meow() { printf("%s meows\n", name.c_str());
};
EMSCRIPTEN_BINDINGS(Cats) { // much like Boost.Python...
class_<Cat>("Cat").constructor()
.function("meow", &Cat::meow);
}
// JavaScript
var myCat = new Module.Cat("Fluffy");
alert(myCat.meow()); // shows "Fluffy meows"
No way to know when a JS object is garbage-collected, must call myCat.delete() to clean it up
Weak refs in JavaScript (ES7) might help fix that
But overall, surprisingly easy despite big differences in languages
In modern browsers we can run code at near-native speed
Write in C/C++, compile to asm.js
Why spend money on servers? ;)
That's it! Questions?


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{% extends "control-panel_template.htm" %} {% block title %} VOWR Librarian Setup {% endblock %} {% block content %} {% include 'includes/_messages.htm' %}
It looks like your database is all set up.
{% else %}It looks like your database is needs a little setup.
We tried to create the necessary tables but MySQL is being particularly difficult to get along with today. The error we got is printed below.
{{ params['setupErr'] }}{% endif %}


This project is the joining of two worlds; my artistic world and my development world.
From creating the png images of the mountain silhouettes above, to coding the parallax effect on them, I have coded and drawn everything you see on this page. I could not have done this without the help of Hamish Macpherson, Bootstrap, and Adobe Illustrator. Thank you, and enjoy!
Check back every day during the month of October for a new drawing!


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I'm a Celeb's new campmate Cliff Parisi joined jungle due to financial woes
EXCLUSIVE: Cliff Parisi, who played Minty on EastEnders, has revealed he said yes to going into the jungle because of money worries
by Irishmirror.ieCliff Parisi, known to millions as Minty from EastEnders , is not minted, and says he is going into the jungle because he needs the money.
The actor, 59, has admitted that he initially turned down the job until he was shown a huge tax bill by his accountant, which changed his mind.
Cliff said: “What made me sign up? The taxman. I have got a bill I need to pay.
"Like everyone at this time of year, you go, ‘Oh no. Again...' You would think I earn a lot of money but I don’t.
"I work for the BBC, so it ain’t that big.
“I had a meeting about this earlier on in the year, and I thought about it long and hard and said no.
"And then as I got an estimate for my tax, I phoned them up and said, is it too late to say yes?
“So here I am. But it could be an amazing experience and I hope it is.

So I give myself a bit of a talking to, and then I spoke to the accountant and he made my mind up.
What is the worse-case scenario?  Which do you want?”
Cliff, who quit EastEnders in 2010 and has since starred regularly in BBC drama Call The Midwife, will go into the jungle as a late entry tomorrow, alongside Coronation Street actor Andy Whyment, who plays Kirk Sutherland on the soap.
Cliff started off as a stand-up comic,, but soon appeared as an actor in The Darling Buds of May and Drop the Dead Donkey.
He then landed bigger parts in Kavanagh QC, Bramwell and A Prince Among Men, before being cast as Minty in EastEnders in 2002, a role he played for eight years.
Cliff admitted he has not watched the show since he left.
He said: “I loved working there, but I think that in life you’ve got to move forward and do different things.
"Although I do miss the people there, I’m happy doing what I’m doing. I love Call the Midwife. It’s a new family.”
Asked if he would ever return, he said: “Never say never. If there was a time that financially I needed to do it and they offered it to me and it was an interesting enough storyline, I’d consider it.”
Cliff admitted that he had some concerns about the famously meagre jungle rations.
He said: “I obviously like my food, so this might be a bit of a struggle for me.
"But, I like challenges, so let’s just see. I’d love to lose a couple of stone – that’d be incredible.”
The star also said he would miss his family during his time Down Under. Cliff has son Arthur, nine, with wife Tara Wyer, a BBC production assistant.
He said he told Arthur: “I said, Daddy’s going away for a while and it’s going to be an adventure. I’m going to miss you and I love you.”
Cliff also has daughter Mandy, 37, and sons Dean, 31, and Jack 27, from previous relationships.
And he said Tara offered some helpful hints for his time in the jungle.
He said: “My wife’s written a long list out of things I mustn’t do.

"I’m sure I’ll forget from time to time and I’ll have to have manners.”


Pizza Time!
Established in 1975 by Joe Pozzuoli, who is originally from Naples, Italy, the birthplace of pizza, Joe's Pizza is a "Greenwich Village institution" offering the classic New York slice for over 37 years. First, we served our customers from our corner location at Bleecker and Carmine Street and now three doors down at 7 Carmine Street. At 75 years of age, Joe Pozzuoli still owns and operates the restaurant.
After 38 years, Joe's is still an independently owned business at only one location, personally supervised by founder Joe himself. We still are what we've always been: a classic NY slice joint.  We are tried and true - the real deal.


--- template: example.html title: Custom tooltips example shortdesc: This example shows how to customize the buttons tooltips with Bootstrap. docs: > This example shows how to customize the buttons tooltips with Bootstrap. tags: "custom, tooltip" ---


{% load mezzanine_tags i18n future %} {% load comment_tags userProfile_tags activity_tags %} {% if object %} {% comment_count_on_object object as total_comments %} {% comments_subset_url object 3 8 as feed %} {% get_comments_subset object 0 3 as comments %} {% if comments|length and comments|length == 3 and comments|length < total_comments %}


Special notes for Android
Any cross platform developer knows that Android is a very special beast. Unfortunately, Forms9Patch doesn't mitigate this. In fact, until I get the time to remove some warts, some would argue Forms9Patch.Clipboard
isn't doing most developers any favors. That being said, here are some notes for your Android platform projects:
AS OF VERSION 1.4, THE FOLLOWING IS NO LONGER NECESSARY. Because of Forms9Patch's more comprehensive
Forms9Patch.Clipboard
functionality, you will have to add the following code to your Android platform project'sResources/Values/string.xml
file, before you can build it:<string name="forms9patch_copy_paste_authority">your_Android_app_package_name_here.f9pcopypaste</string>
I know - it's a pain. At some point of time, I'll have to see if I can use some of the trickery used by the Xamarin.Facebook NuGet Package to get rid of this requirement.
I've made too many mistakes in building the Forms9Patch NuGet package - and Android seems to bear the brunt of those mistakes.
/Users/ben/Projects/PizzaTruck/PizzaTruck.Mobile/Droid/Renderers/BottomBarPageRenderer.cs(42,42): Error CS0012: The type 'FormsViewGroup' is defined in an assembly that is not referenced. You must add a reference to assembly 'FormsViewGroup, Version=<IP_ADDRESS>, Culture=neutral, PublicKeyToken=null'. (CS0012) /Users/ben/Projects/PizzaTruck/PizzaTruck.Mobile/Droid/MainActivity.cs(33,33): Error CS0012: The type 'FormsViewGroup' is defined in an assembly that is not referenced. You must add a reference to assembly 'FormsViewGroup, Version=<IP_ADDRESS>, Culture=neutral, PublicKeyToken=null'. (CS0012)
... then I'm the one you need to blame. To fix this you'll need to do the following in any Android platform projects that use Forms9Patch:
- Remove the Forms9Patch NuGet package
- Remove the Xamarin.Forms NuGet package
- If present, remove any SkiaSharp NuGet packages
- If present in the References folder, remove any references to
FormsViewGroup
- Reinstall the Xamarin.Forms NuGet package
- Reinstall the Forms9Patch NuGet package
If the above doesn't work, PLEASE LET ME KNOW by emailing me at: forms9patch(AT)buildcalc.com
This isn't a Forms9Patch issue but just good advice: Do yourself a favor and never use any Android ARM emulators. This means you'd be better off just deleting the
Android_ARM7a_Nouget (API 25)
emulator that comes pre-installed with VisualStudio 2017.


URL SHORTENER MICROSERVICE
Please see the below instructions!
Example calls for google.com:
-
https://url-microservice-2.glitch.me/new/http://google.com
-
https://url-microservice-2.glitch.me/new/https://google.com
Which will return:
{"original_url": "http(s)://google.com", "shortened_url": "https://url-microservice-2.glitch.me/short/#"}
Whereas the shortened url:
https://url-microservice-2.glitch.me/short/#
Will redirect to the original:
http(s)://google.com


Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.
| Version: | 1.0-8 |
| Depends: | R (≥ 2.10) |
| Imports: | brglm, gtools, lme4 (≥ 1.0), qvcalc, stats |
| Suggests: | prefmod |
| Enhances: | gnm |
| Published: | 2017-09-20 |
| Author: | Heather Turner [aut, cre], David Firth [aut] |
| Maintainer: | Heather Turner <ht at heatherturner.net> |
| BugReports: | https://github.com/hturner/BradleyTerry2/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/hturner/BradleyTerry2 |
| NeedsCompilation: | no |
| Citation: | BradleyTerry2 citation info |
| Materials: | README NEWS |
| In views: | Psychometrics |
| CRAN checks: | BradleyTerry2 results |
| Reference manual: | BradleyTerry2.pdf |
| Vignettes: |
Bradley-Terry models in R |
| Package source: | BradleyTerry2_1.0-8.tar.gz |
| Windows binaries: | r-devel: BradleyTerry2_1.0-8.zip, r-release: BradleyTerry2_1.0-8.zip, r-oldrel: BradleyTerry2_1.0-8.zip |
| OS X binaries: | r-release: BradleyTerry2_1.0-8.tgz, r-oldrel: BradleyTerry2_1.0-8.tgz |
| Old sources: | BradleyTerry2 archive |
| Reverse suggests: | caret, fastR, fastR2, gap, glmmsr, ordBTL, PlackettLuce |
Please use the canonical form https://CRAN.R-project.org/package=BradleyTerry2 to link to this page.


Installing scikit-learn¶
Note
If you wish to contribute to the project, it’s recommended you install the latest development version.
Installing the latest release¶
Scikit-learn requires:
Python (>= 3.5)
NumPy (>= 1.11.0)
SciPy (>= 0.17.0)
joblib (>= 0.11)
Scikit-learn plotting capabilities (i.e., functions start with “plot_” and classes end with “Display”) require Matplotlib (>= 1.5.1). For running the examples Matplotlib >= 1.5.1 is required. A few examples require scikit-image >= 0.12.3, a few examples require pandas >= 0.18.0.
Warning
Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. Scikit-learn now requires Python 3.5 or newer.
If you already have a working installation of numpy and scipy,
the easiest way to install scikit-learn is using pip
pip install -U scikit-learn
or conda
:
conda install scikit-learn
If you have not installed NumPy or SciPy yet, you can also install these using conda or pip. When using pip, please ensure that binary wheels are used, and NumPy and SciPy are not recompiled from source, which can happen when using particular configurations of operating system and hardware (such as Linux on a Raspberry Pi). Building numpy and scipy from source can be complex (especially on Windows) and requires careful configuration to ensure that they link against an optimized implementation of linear algebra routines. Instead, use a third-party distribution as described below.
If you must install scikit-learn and its dependencies with pip, you can install
it as scikit-learn[alldeps]
. The most common use case for this is in a
requirements.txt
file used as part of an automated build process for a PaaS
application or a Docker image. This option is not intended for manual
installation from the command line.
Note
For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ are required.
For installation instructions for more distributions see other distributions. For compiling the development version from source, or building the package if no distribution is available for your architecture, see the Advanced installation instructions.
Third-party Distributions¶
If you don’t already have a python installation with numpy and scipy, we recommend to install either via your package manager or via a python bundle. These come with numpy, scipy, scikit-learn, matplotlib and many other helpful scientific and data processing libraries.
Available options are:
Canopy and Anaconda for all supported platforms¶
Canopy and Anaconda both ship a recent version of scikit-learn, in addition to a large set of scientific python library for Windows, Mac OSX and Linux.
Anaconda offers scikit-learn as part of its free distribution.
Warning
To upgrade or uninstall scikit-learn installed with Anaconda
or conda
you should not use the pip command. Instead:
To upgrade scikit-learn
:
conda update scikit-learn
To uninstall scikit-learn
:
conda remove scikit-learn
Upgrading with pip install -U scikit-learn
or uninstalling
pip uninstall scikit-learn
is likely fail to properly remove files
installed by the conda
command.
pip upgrade and uninstall operations only work on packages installed
via pip install
.


COBB A film review by Michael John Legeros Copyright 1995 Michael John Legeros
(WB) Directed by Ron Shelton Written by Ron Shelton, based on the book "Cobb: A Biography" by Al Stump Cast Tommy Lee Jones, Robert Wuhl, Lolita Davidovich MPAA Rating "R" (presumably for language, violence, and sexual references) Running Time 128 minutes Reviewed at The Carolina Theatre, Chapel Hill (9MAR95)
==
Tommy Lee Jones is the best thing behind COBB, director Ron Shelton's broadly played "biography" of baseball great Ty Cobb. By all accounts, the immortal Cobb broke more than just records. He was everything from a racist, to a wife beater, to a man who attacked a handicapped heckler during a stadium game. Jones plays the part wide-open, and you can't take your eyes off of him.
Costar Robert Wuhl is another matter entirely--he's *all* wrong for the part of the sportswriter-turned-biographer. The actor may have a greater range, but here he acts like the dopey sidekick. He shows about five facial expressions total and, since he's in nearly every scene, he basically kills the tone. The overblown music suggests that COBB is a drama. You'd never know it from watching Wuhl.
If this trip's worth taking it's because of the script. Shelton gives Jones a few great lines, and the actor hits each one right over the fence. On rude behavior: "life's too short to be a diplomat."  The gems are few and far-between, thanks to the poor pacing, but at least they're there.
When COBB finally wraps, it wraps thirty minutes too late.
Grade: C+
-- Michael J. Legeros Raleigh, North Carolina
.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- permalink: google7f8debac56a66e22.html eleventyExcludeFromCollections: true --- google-site-verification: google7f8debac56a66e22.html


| MPI_CART_SUB(comm, remain_dims, newcomm) | |
| IN comm | communicator with Cartesian structure (handle) |
| IN remain_dims | the i-th entry of remain_dims specifies whether the i-th dimension is kept in the subgrid ( true) or is dropped ( false) (logical vector) |
| OUT newcomm | communicator containing the subgrid that includes the calling process (handle) |
int MPI_Cart_sub(MPI_Comm comm, const int remain_dims[], MPI_Comm *newcomm)
MPI_Cart_sub(comm, remain_dims, newcomm, ierror)
TYPE(MPI_Comm), INTENT(IN) :: comm
LOGICAL, INTENT(IN) :: remain_dims(*)
TYPE(MPI_Comm), INTENT(OUT) :: newcomm
INTEGER, OPTIONAL, INTENT(OUT) :: ierror
MPI_CART_SUB(COMM, REMAIN_DIMS, NEWCOMM, IERROR)
INTEGER COMM, NEWCOMM, IERROR
LOGICAL REMAIN_DIMS(*)
If a Cartesian topology has been created with MPI_CART_CREATE, the function MPI_CART_SUB can be used to partition the communicator group into subgroups that form lower-dimensional Cartesian subgrids, and to build for each subgroup a communicator with the associated subgrid Cartesian topology. If all entries in remain_dims are false or comm is already associated with a zero-dimensional Cartesian topology then newcomm is associated with a zero-dimensional Cartesian topology. (This function is closely related to MPI_COMM_SPLIT.)
Example
Assume that MPI_CART_CREATE (..., comm) has defined a
(2 × 3 × 4) grid. Let remain_dims = (true, false, true).
Then a call to
MPI_CART_SUB(comm, remain_dims, comm_new);will create three communicators each with eight processes in a 2 × 4 Cartesian topology. If remain_dims = (false, false, true) then the call to MPI_CART_SUB(comm, remain_dims, comm_new) will create six non-overlapping communicators, each with four processes, in a one-dimensional Cartesian topology.


Preparation time
30 mins to 1 hour
Cooking time
1 to 2 hours
Serves
Serves 2
The rich, creamy walnuts complement the crisp and crunchy side salad in this delicious steak supper.
Equipment: You will need a food processor.
Preheat the oven to 220C/200C Fan/Gas 7. Mix the egg whites and salt into a paste. Put the paste around the celeriac to form a salt crust and place on a baking tray. Bake for 1½ hours, then set aside to cool completely. (This can be done the night before, if preferred.).
Drain the pickled walnuts, reserving the liquid, and blend in a food processor. Slowly add the liquid, blending all the time, until a thick, smooth purée forms. Sieve the mixture into a clean bowl and set aside.
For the celeriac chips, break up the salt crust and discard. Cut the celeriac into chip-sized pieces and dust in the tapioca flour. Heat the oil in a deep-fat fryer to 190C (CAUTION: hot oil can be dangerous. Do not leave unattended.). Fry the celariac chips until golden, then drain on kitchen paper.
Heat a little olive oil in a frying pan over a high heat. Season the beef, then fry for 2 minutes on each side, or until done to your liking. Set aside to rest somewhere warm.
Cut the base off the chicory and lettuce and remove the outer leaves. Separate the inner leaves and place in a large bowl. Sprinkle over the blue cheese and chives, season and drizzle with a little walnut oil.
To serve, heat the jus until hot. Remove the bone from the beef fillet and slice the meat in half, then place it on two warmed plates and spoon the jus over it. Add the chips and salad with a spoonful of walnut ketchup on the side. Sprinkle with chopped walnuts.


They call him Flipper Flipper faster than lightning no one you see is smarter than he well we are moving on up to the east side to a deluxe apartment in the sky love exciting and new come and dance on our floor take a step that is new we have a loveable space that needs your face threes company too never heard the word impossible this time there's no stopping us here is the story of a man named Brady who was busy with three boys of his own.


Voice Recognition is implemented as a directive. Therefore, it can extend any input or textarea to allow voice input whenever text is accepted.
To use it click on the mic icon and talk.
Language must be configured to improve recognition accuracy.
<label for="searchField">Click the mic to talk:</label>
<input id="searchField" luxVoiceRecognition language="es-ES">


Lens #0: no lens
Lens #1: bi-convex lens 1
Lens #2: plano-convex lens 2,
flat side facing beam
Lens #3: plano-convex lens 3,
flat side facing beam
Lens #4: plano-convex lens 3,
curved side facing beam
major scale units: 1cm,
1 cm hole spacings
Use the mouse to move the lens
and zoom and rotate the view.


Configure your server and client processes and data regions to run your client/server system.
Prerequisites - Configure your server system using locators for member discovery. See Configuring Peer-to-Peer Discovery and Managing a Peer or Server Cache. - Configure your clients as standalone applications. See Managing a Client Cache. - Be familiar with cache region configuration. See Data Regions. - Be familiar with server and client configuration properties. See cache.xml.
Procedure
Configure servers to listen for clients by completing one or both of the following tasks.
Configure each application server as a server by specifying the
<cache-server>
element in the application’scache.xml
and optionally specifying a non-default port to listen on for client connections.For example:
<cache-server port="40404" ... />
Optional. Configure each
cacheserver
process with a non-default port to listen on for client connections.For example:
prompt> cacheserver start -port="44454"
Configure clients to connect to servers. In the client
cache.xml
, use the server system’s locator list to configure your client server pools and configure your client regions to use the pools. For example:<client-cache> <pool name="publisher" subscription-enabled="true"> <locator host="lucy" port="41111"/> <locator host="lucy" port="41111"/> </pool> ... <region name="clientRegion" ... <region-attributes pool-name="publisher" ...
You do not need to provide the complete list of locators to the clients at startup, but you should provide as complete a list as possible. The locators maintain a dynamic list of locators and servers and provide the information to the clients as needed.
Configure the server data regions for client/server work, following these guidelines. These do not need to be performed in this order.
Configure your server regions as partitioned or replicated, to provide a coherent cache view of server data to all clients. Note: If you do not configure your server regions as partitioned or replicated, you can get unexpected results with calls that check server region contents, such as
keySetOnServer
andcontainsKeyOnServer
. You might get only partial results, and you might also get inconsistent responses from two consecutive calls. These results occur because the servers report only on their local cache content and, without partitioned or replicated regions, they might not have a complete view of the data in their local caches.When you define your replicated server regions, use any of the
REPLICATE
RegionShortcut
settings except forREPLICATE_PROXY
. Replicated server regions must havedistributed-ack
orglobal
scope
, and every server that defines the region must store data. The region shortcuts usedistributed-ack
scope and all non-proxy settings store data.When you define your partitioned server regions, use the
PARTITION
RegionShortcut
options. You can have local data storage in some servers and no local storage in others.
When you start the server and client systems, the client regions will use the server regions for cache misses, event subscriptions, querying, and other caching activities.
What to do next
Configure your clients to use the cache and to subscribe to events from the servers as needed by your application. See Configuring Client/Server Event Messaging.


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
x_inputs = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels])
z_inputs = tf.placeholder(tf.float32, [None, z_dim])
learn_rate = tf.placeholder(tf.float32)
return (x_inputs, z_inputs, learn_rate)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
alpha = 0.2
with tf.variable_scope('discriminator', reuse=reuse):
# images: 28x28xdepth
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='SAME')
x1 = tf.maximum(alpha*x1, x1)
# x1: 14x14x64
x2 = tf.layers.conv2d(x1, 128, 5, strides=2, padding='SAME')
x2 = tf.layers.batch_normalization(x2, training=True)
x2 = tf.maximum(alpha*x2, x2)
# x2: 7x7x128
x3 = tf.layers.conv2d(x2, 256, 5, strides=2, padding='SAME')
x3 = tf.layers.batch_normalization(x3, training=True)
x3 = tf.maximum(alpha*x3, x3)
# x3: 4x4x256
flat = tf.reshape(x3, [-1, 4*4*256])
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
alpha = 0.2
with tf.variable_scope('generator', reuse=not is_train):
x1 = tf.layers.dense(z, 4*4*512)
x1 = tf.reshape(x1, [-1,4,4,512])
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha*x1, x1)
# x1: 4x4x512
x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='SAME')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha*x2, x2)
# x2: 8x8x256
x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='SAME')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha*x3, x3)
# x3: 16x16x128
x4 = tf.layers.conv2d_transpose(x3, 64, 5, strides=2, padding='SAME')
x4 = tf.layers.batch_normalization(x4, training=is_train)
x4 = tf.maximum(alpha*x4, x4)
# x4: 32x32x64
logits = tf.layers.conv2d(x4, out_channel_dim, 5, strides=1, padding='VALID')
return tf.tanh(logits)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
g_out = generator(input_z, out_channel_dim)
d_out_fake, d_logits_fake = discriminator(g_out)
d_out_real, d_logits_real = discriminator(input_real, reuse=True)
d_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.zeros_like(d_logits_fake)))
d_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_logits_real)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.ones_like(d_logits_fake)))
d_loss = d_fake_loss + d_real_loss
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
g_vars = [var for var in t_vars if var.name.startswith('generator')]
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_vars)
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(d_loss, var_list=d_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
n_color_channels = 3 if data_image_mode == 'RGB' else 1
x_inputs, z_inputs, learn_rate = model_inputs(28, 28, n_color_channels, z_dim)
d_loss, g_loss = model_loss(x_inputs, z_inputs, n_color_channels)
d_optimizer, g_optimizer = model_opt(d_loss, g_loss, learn_rate, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
batch_i = 0
for batch_images in get_batches(batch_size):
# TODO: Train Model
batch_images *= 2
batch_i += 1
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
sess.run(d_optimizer, feed_dict={x_inputs:batch_images, z_inputs:batch_z, learn_rate:learning_rate})
sess.run(g_optimizer, feed_dict={x_inputs:batch_images, z_inputs:batch_z, learn_rate:learning_rate})
print('batch: ', batch_i)
if batch_i % 100 == 0:
show_generator_output(sess, 9, z_inputs, n_color_channels, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 3
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Welcome to Sky Bike! Start renting a bike by loging in or signing up.
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Hello World
Your Quiver Router seems to work properly
Also try following routes:
/api/table
: shows your entire route table.
/api/multistack
: shows you the work of middleware.
/api/param/simple/@value/
: here a parameter is parsed.
/api/param/multi/@valueN/@valueN+1/
: here four parameters are parsed.
/api/inherits/url
and
/api/inherits/id
here middleware is inherited by url / id.
/api/reverse/@id
: this shows how to route reversing works.
/api/cookie/single/@name/@value/
: Cookie api included!
/api/cookie/multi/…names/
$
/…values
$
/@maxAge
: Comfortable API indeed!
/static/path/to/file
: static file serving works too.
Templating support is also included. See the readme for more info.


Welcome To Template
Welcome to a template for emails with support for Gmail, Yahoo and Outlook, with responsive design for desktop, tablet and mobile
Reload
From
by Jonathan Araujo


a recent graduate from UC Berkeley, interested in software engineering and machine learning! I am currently actively looking for a full-time software engineering role. In my free time, I am working on weinvest.live, an application that is designed to highlight trends and abnormalities to long-term investors.
When I'm not coding or working on side projects, I like to go fishing in California's many great national parks or beachs. Feel free to talk to me about machine learning, SWE, my project or any job opportunities!
Contact me at<EMAIL_ADDRESS>- or take a look at my Resume or my Github.


rlp.cr
a native library implementing rlp
purely for the crystal language. rlp
is ethereum's recursive length prefix used to encode arbitray data structures.
this library allows for rlp-encoding of:
- binary data (assumed encoded)
- boolean values (true, false)
- scalars (positive integers)
- string literals and characters
- arrays containing any of the above
- nested arrays containing any of the above
this library allows for decoding of:
- rlp-encoded data in binary format
- rlp-encoded data in hexadecimal string format
note, that decoded data is always binary as per ethereum's design rationale:
"RLP does not attempt to define any specific data types such as booleans, floats, doubles or even integers; instead, it simply exists to store structure, in the form of nested arrays, and leaves it up to the protocol to determine the meaning of the arrays"
installation
add the rlp
library to your shard.yml
dependencies:
rlp:
github: q9f/rlp.cr
version: "~> 0.1"
usage
# import rlp
require "rlp"
this library exposes the following modules (in logical order):
Rlp
: core library exposingencode
anddecode
logicRlp::Util
: a collection of utilities to ease the conversion between data typesRlp::RecursiveArray
: is a data type alias allowing for arrays of unknown nesting depth
basic usage:
# rlp-encode a string
rlp = Rlp.encode "A cat with a short string."
# => Bytes[154, 65, 32, 99, 97, 116, 32, 119, 105, 116, 104, 32, 97, 32, 115, 104, 111, 114, 116, 32, 115, 116, 114, 105, 110, 103, 46]
# (optionally) get a hex representation of the rlp-encoded data
hex = Rlp::Util.bin_to_hex rlp
# => "9a4120636174207769746820612073686f727420737472696e672e"
# decode the rlp data
bin = Rlp.decode hex
# => Bytes[65, 32, 99, 97, 116, 32, 119, 105, 116, 104, 32, 97, 32, 115, 104, 111, 114, 116, 32, 115, 116, 114, 105, 110, 103, 46]
# we expect a string, so we can try to convert it here
str = Rlp::Util.bin_to_str bin
# => "A cat with a short string."
documentation
the full library documentation can be found here: q9f.github.io/rlp.cr
generate a local copy with:
crystal docs
testing
the library is entirely specified through tests in ./spec
; run:
crystal spec --verbose
understand
recursive length prefixes are used by the ethereum protocol to store arbitrary data structures, e.g., signed transactions, and is a fundamental serialization used by ethereum's networking protocol devp2p
which implements rlpx
, the recursive length prefix transfer protocol.
rlp
can encode any data and data structure. the resulting data is a serialized byte-stream containing prefix bytes, header data, and actual data depending on the type and size of the encoded data.
Rlp.encode [42, "eth"]
# => Bytes[197, 42, 131, 101, 116, 104]
deserialization of rlp
-encoded byte-streams allows for recovering the underlying data structure. however, rlp
is kept minimalistic in its specification and is therefore agnostic to the data types used in the structures.
Rlp.decode Bytes[197, 42, 131, 101, 116, 104]
# => [Bytes[42], Bytes[101, 116, 104]]
It's up to applications using rlp
to further specify protocols for decoding the actual data.
decoded = Rlp.decode Bytes[197, 42, 131, 101, 116, 104]
protocol = [] of String | Int32 | BigInt
protocol << Rlp::Util.bin_to_int decoded[0]
protocol << Rlp::Util.bin_to_str decoded[1]
protocol
# => [42, "eth"]
contribute
create a pull request, and make sure tests and linter passes.
this library with built with the help of the blog post by the mana team implementing rlp
in elixir and coinmonks' annotated version of the rlp
specification. ethereum classic's rlp
article allows for some sweet test cases.
license: apache license v2.0


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- syndicate front-end systems
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- transform rich infrastructures
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Conditional processing blocks provide the ability to include or exclude parts of a template file based on the presence or absence of identifiers that can be defined using the -define command line option. Unlike expression tokens the evaluation of conditional processing expressions a does not change based on the current context within a template file, rather it remains consistent throughout the entire code generation job.
Conditional processing is based on identifiers that can be defined on the command line. For example:
codegen -s CUSTOMER -t FILEIO_CLASS -define ATTACH_IO_HOOKS -r
And is used to include or exclude parts of the code in a template file by evaluating the presence or absence of the identifier via special <IF DEFINED_identifier> and <IF NOT_DEFINED_identifier> tokens. For example:
(IO_OPEN_UPD),
begin
open(a_channel=0,u:i,"<FILE_NAME>") [ERR=openError]
<IF DEFINED_ATTACH_IO_HOOKS>
new <StructureName>Hooks(a_channel)
</IF>
end
There are two types of conditional processing block:
Multi-line conditional processing blocks cause one or more lines of code to be included or excluded based on the presence or absence of the defined identifier and occur when the opening and closing conditional tags appear on separate lines in a template file. They delimit one or more entire lines of template code, like this:
<IF [NOT_]DEFINED_identifier>
code
</IF>
In-line conditional processing blocks cause part of a line to be included or excluded and occur when the opening and closing conditional tags appear on the same line in a template file. They delimit part of a line of template code, like this:
[code] <IF [NOT_]DEFINED_identifier> code </IF> [code]
Conditional Processing Block Else Clauses
Else clauses are supported in multi-line conditional processing blocks, like this:
<IF [NOT_]DEFINED_identifier>
code
<ELSE>
code
</IF>
And in in-line conditional processing blocks, like this:
[code] <IF [NOT_]DEFINED_identifier> code <ELSE> code </IF> [code]
Nesting Conditional Processing Blocks
Multi-line conditional processing blocks may be nested within one another. For example:
<IF DEFINED_identifier1>
[code]
<IF DEFINED_identifier2>
code
</IF>
[code]
</IF>
In-line conditional processing blocks may be nested within multi-line conditional processing blocks.
<IF DEFINED_identifier1>
[code]
[code] <IF DEFINED_identifier2> code </IF> [code]
[code]
</IF>
Multiple in-line conditional processing blocks may be used within the same line:
[code] <IF DEFINED_identifier1> code </IF> [code] <IF DEFINED_identifier2> code </IF> [code]
In-line conditional processing blocks may be nested within one another:
[code] <IF DEFINED_identifier1> [code] <IF DEFINED_identifier2> code </IF></IF> [code]
Copyright © 2021 Synergex International, Inc.


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