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How to Build a Cheap Storage Shed | The Family Handyman
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How to build a garden shed - free DIY plans
This DIY shed plan is a typical backyard design. It is designed as a garden shed . Make sure you have a flat site for your shed. You can place it in the corner of .
http://www.bettaliving.org/DIY/Detailed_plans/gardenshed/p1.html
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Over the years, Popular Mechanics has published several articles about designing and building backyard storage sheds, many of which I'm proud to have .
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How to Build a Solar Garden Shed Ron Hazelton Online
Nov 30, 2012 . How to Make a Bird Sanctuary in the Backyard. Learn how to create a bird sanctuary; includes details on landscaping to attract birds, building .
http://www.ronhazelton.com/projects/how_to_build_a_gardening_shed
5 Secrets to Building a Better Shed: DIY Guy - Popular Mechanics
Jun 27, 2008 . I'm something of an expert on building backyard sheds. I've constructed more than a dozen sheds, written a magazine article or two about .
http://www.popularmechanics.com/home/how-to-plans/sheds/4270714
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How to Build a Storage Shed : How-To : DIY Network
. DIY Network to build a stand-alone shed and declutter an overcrowded garage . . One solution for an overcrowded garage is a backyard storage shed where .
http://www.diynetwork.com/how-to/how-to-create-a-storage-shed/index.html
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A Firm Foundation for a Backyard Shed - Fine Homebuilding Article
Apr 1, 2003 . Carpenter Joseph Truini offers construction details for four types of basic, on- grade foundations suitable for backyard buildings.
http://www.finehomebuilding.com/how-to/articles/firm-foundation-backyard-shed.aspx
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Great ideas for backyard sheds, cottages, workspaces, and retreats ...
Get ideas for more living space and storage in your backyard from great pictures of backyard sheds, cottages, workspaces, and garden retreats.
http://www.sunset.com/home/outdoor-living/great-backyard-sheds-and-cottages-00400000058405/
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Backyard Sheds Plans: Make a Garden Shed - Video Dailymotion
http://bit.ly/RGwdLOBuilding A Shed SHOULD Be Fun & Enjoyable, NOT FrustratingGet Instant Access to Over 12000 Shed Plans and Woodworking Projects .
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How to Build a Backyard Shed - DIY - MOTHER EARTH NEWS
Finally, an easy-to-follow set of shed directions that anyone can use.
http://www.motherearthnews.com/do-it-yourself/how-to-build-a-shed-zmaz93asztak.aspx
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15 Free Shed Plans
You'll impress everyone in your neighborhood by using this free shed plan to build a backyard shed that you can drive your riding lawn mower right into.
http://freebies.about.com/od/homegardenfreebies/tp/free-shed-plans.htm
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How to Build a BackYard Shed in one afternoon. - YouTube
Apr 12, 2012 . http://tinyurl.com/sheplanspreview 12500 Shed & Woodworking Plans [ Discounted Price] Shed Plans, Lessons, Manuals, Drawings, .
http://www.youtube.com/watch?v=BX34brL3pIY
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Building a Back Yard Shed
Building a shed is fun and easy, really it is! My husband and father built this shed for our back yard with a shed kit from Home Depot. They completed the project .
http://www.squidoo.com/shed
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How to Build a Back Yard Shed | eHow.com
How to Build a Back Yard Shed. There are many options for back yard sheds, from simple four-sided boxes with one door, used for storage, to multifloored, fully .
http://www.ehow.com/how_5250204_build-back-yard-shed.html
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How to build a shed, backyard sheds, small storage buildings
Aug 20, 2010 . could it be possible that your next backyard activity may involve the building or the remodeling of a backyard structure.
http://greenhousesandsunrooms.com/shed-plans-easy-for-anyone.html
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Building a Backyard Shed - YouTube
Jun 16, 2011 . How I built my 8'x12' backyard shed with a gambrel roof.
http://www.youtube.com/watch?v=25WzQpDYnl4
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Building a backyard shed
Building a backyard shed. This article is about building a shed, which I built in a sort of "modular" way. The need for this shed started with a not-unusual problem: .
http://woodgears.ca/shed/build1.html
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Build a Great Garden Shed - Mitre 10
Build a Great Garden Shed. Print. Build a Great Garden Shed Image 1 Fullsize. Build a great garden shed - with a little help from Mitre 10. Every backyard needs .
http://www.mitre10.com.au/Planning-and-DIY/Resources/DIY/MitrePlans/view/2878/
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Pretty & Practical: Garden Sheds & Backyard Retreats! - The ...
May 13, 2012 . That shed is basically a guest house in your backyard. . Until you get to build your own little backyard hideaway, it sure is fun to dream, isn't it?
http://theinspiredroom.net/2012/05/13/pretty-and-practical-garden-sheds/
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Home - Backyard Storage Sheds
We are a small and dynamic company specializing in construcing and building backyard storage sheds on your site. High Quality craftsmanship and attenttion to .
http://backyardsheds.webs.com/
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Storage Shed Barn Kits, Buy a prefab storage shed kit in ...
These backyard storage shed and garage kits can be fully customized just as in each of our buildings. To make it easier for you, the "do-it-yourself" storage shed .
http://www.shedsunlimited.net/storage-shed-barn-kits.html
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HowStuffWorks "How to Build a Storage Shed"
Check out HowStuffWorks for great tips on how to build a storage shed. . And if designed properly, a backyard shed can even serve as a potting shed, home .
http://home.howstuffworks.com/home-improvement/how-to-build/build-storage-shed.htm
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Storage Buildings
Gable (Backyard Style). Modern styling and many available options make our Welcome Home Backyard shed a great choice. Details. Gambrel. Gable .
http://84lumber.com/Home/BuildItYourselfers/StorageBuildings.aspx
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TUFF SHED: Storage Sheds, Installed Garages, Custom Buildings ...
TUFF SHED leading supplier of storage sheds, installed garages, custom buildings, backyard . Backyard Chicken Coop. 1-Year Warranty 4" Foundation 3' 5" Int Wall Ht . Make your TUFF SHED building your own by adding great options.
http://www.tuffshed.com/
428 E Almar Dr
Amazon.com: The Tumbleweed DIY Book of Backyard Sheds and ...
The Tumbleweed DIY Book of Backyard Sheds and Tiny Houses: Build your own guest cottage, writing studio, home office, craft workshop, or personal retreat .
http://www.amazon.com/Tumbleweed-Book-Backyard-Sheds-Houses/dp/1565237048
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Watch Ron Hazelton's House Calls: How to Build a Backyard Solar ...
Ron Hazelton's House Calls - How to Build a Free-Standing Pergola (Excerpt) Ron Hazelton's House Calls - How to Build a Backyard Solar Gardening Shed .
http://www.hulu.com/watch/123183
Build a backyard shed - CONSTRUCTION PLANS | RONA
RONA carries supplies for your Build a backyard shed projects. Find how to help your home improvement project.
http://www.rona.ca/en/projects/Build-a-backyard-shed
How to Build a Shed - Popular Mechanics
Jun 11, 2002 . What you really need is more space, and one of the best ways to find it is to create it by building a backyard storage shed. At 10 x 16 ft., this .
http://www.popularmechanics.com/home/improvement/1276231
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#comment Copyright 2016 Erik Neumann. All Rights Reserved. #comment Use of this source code is governed by the Apache License, Version 2.0. #comment See the LICENSE file and http://www.apache.org/licenses/LICENSE-2.0. #define #TOP_DIR ../.. #define #APP_PATH sims.experimental.SimpleApp #include "#TOP_DIR/macros.html" #DOC_TYPE
#META_TAGS #HIDE_ALL #SITE_LOGOSimpleApp is a demonstration of using a minimal set of myPhysicsLab classes.
This displays something like the SingleSpringSim simulation, but it uses a very simple math routine to move the objects instead of a differential equation. There are no DiffEqSolver or Simulation classes involved. This sets up the LabCanvas and DisplayObjects and defines the callback that drives the animation.
#COMPILED_MENU #LOAD_APP #SHOW_ALL #PAGE_FOOTER


--- title: "Distributed Systems" excerpt: "Implementation of Some Distributed Systems" collection: portfolio --- In this repository, I keep all the work related to building scalable, robust and fault tolerant distriuted systems


|
MasterScan
Lab_Matlab_control Master Branch
|
A Matlab interface for the GraphViz graphing tool.
DataGrapher uses the GraphViz graphing tool to generate nice-looking graphs of arbitrary data. The input data are a Matlab struct array, each element of which defines a node to be plotted. User-supplied functions generate a name for each node as well as "edges" that connect nodes. Properties of DataGrapher control the general behavior of GraphViz.
Properties and Events | |
| Property | graphVizPath = '/usr/local/bin' |
| file path to the GraphViz executables, like "dot" and "neato" | |
| Property | graphVisAlgorithm = 'dot' |
| GraphViz algorithm name, "dot", "neato", "twopi", "circo", or "fdp". | |
| Property | workingPath = '~/Desktop/datagraph' |
| file path to put GraphViz data and image files | |
| Property | workingFileName = 'dataGraph' |
| file name base for GraphViz output files | |
| Property | imageType = 'png' |
| file extension for GraphViz output image files | |
| Property | inputData |
| struct array containing node data The graph will contain one node per element of inputData. More... | |
| Property | graphName = 'dataGraph' |
| string name for the graph | |
| Property | nodeNameFunction = @DataGrapher.nodeNameFromField |
| function to generate graph node names Should take inputData and an index into inputData and return a string name for the indexth node. More... | |
| Property | nodeDescriptionFunction |
| function to generate graph node descriptions (optional) Should take inputData and an index into inputData and return a cell array of strings describing the indexth node. More... | |
| Property | edgeFunction = @DataGrapher.edgeFromField |
| function to generate graph edges Should take inputData and an index into inputData and return an array of other indexes. More... | |
| Property | colors = spacedColors(10) |
| Matlab colormap for coloring in each node its edges. | |
| Property | graphIsDirected = true |
| true or false, whether the graph should be a directed "digraph" or an undirected "graph" | |
| Property | floatingEdgeNames = true |
| true or false whether to write edge names on the edges themselves. More... | |
| Property | listedEdgeNames = true |
| true or false whether to list edge names inside their originating nodes. More... | |
| Property | graphProperties |
| arbitrary struct of GraphViz properties to apply to the graph itself | |
| Property | nodeProperties |
| arbitrary struct of GraphViz properties to apply to all nodes | |
| Property | edgeProperties |
| arbitrary struct of GraphViz properties to apply to all edges | |
| Property | nodeAlpha = 1 |
| 0-255 opacity for nodes | |
| Property | edgeAlpha = .5 |
| 0-255 opacity for edges | |
| Property | nodes |
| internal accounting of node and edge data | |
Methods | |
| self | DataGrapher () |
| Constructor takes no arguments. | |
| nodes | parseNodes (self) |
| Apply nodeNameFunction and edgeFunction to the inputData. | |
| string | composeGraph (self) |
| Write a string that contains a whole GraphVis specification. | |
| string | composeGraphHeader (self) |
| Write a string that contains graph properties. | |
| string | composeNodeHeader (self) |
| Write a string that contains default node properties. | |
| string | composeEdgeHeader (self) |
| Write a string that contains default edge properties. | |
| string | composeNodes (self) |
| Write a string that specifies nodes. | |
| string | composeEdges (self) |
| Write a string that specifies edges. | |
| writeDotFile (self) | |
| Write a GraphViz specification to file. | |
| generateGraph (self) | |
| Feed the GraphViz specification to GraphViz and get a graph. | |
| string | composeProperties (self, propStruct, delimiter) |
| Write out property-value pairs from a struct. | |
| string | composeRGB (self, rgb, a) |
| Write out a hex string for an RGB[A] color. | |
Static Methods | |
| static nodeName | nodeNameFromField (inputData, index) |
| Default node names from the "name" field. | |
| static edgeIndexes edgeNames | edgeFromField (inputData, index) |
| Default edges from "edge" field, and trivial edge names. | |
| Property inputData |
struct array containing node data The graph will contain one node per element of inputData.
| Property nodeNameFunction = @DataGrapher.nodeNameFromField |
function to generate graph node names Should take inputData and an index into inputData and return a string name for the indexth node.
| Property nodeDescriptionFunction |
function to generate graph node descriptions (optional) Should take inputData and an index into inputData and return a cell array of strings describing the indexth node.
| Property edgeFunction = @DataGrapher.edgeFromField |
function to generate graph edges Should take inputData and an index into inputData and return an array of other indexes.
These specify edges from the given indexth node to the returned indexth nodes. Should also return as a second output a cell array with a string name for each edge.
| Property floatingEdgeNames = true |
true or false whether to write edge names on the edges themselves.
| Property listedEdgeNames = true |
true or false whether to list edge names inside their originating nodes.


| PReport |
|
This report is intended for printing table with variable number of columns. During report building, all off-bounds columns will be printed on new page (like in MS Excel). For creating this report, place "Detail" and "Vertical detail" bands on the page. Place object in the cross of these bands. This object will be printed as the cell of cross-table, for Detail band set UseVerticalBand property to true and for Vertical detail band set UseHorizontalBands property to true. That's all! If you want, place "Vertical detail header" and "Vertical detail footer" bands in your report. "Vertical detail footer” band is usable if you want to print sum of data in the row. Also you can use “Vertical group footer” band and others. |


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{% for r in rfolders %}
Folder: {{ r.name }}
State: {{ r.folder_state }}
Current size of Staging: {{ r.current_size_staging }} MB
Current size of Conflicts & Deleted: {{ r.current_size_conflicts }} MB
{% endfor %}
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{% endfor %}


jmlr jmlr2009 jmlr2009-lda knowledge-graph by maker-knowledge-mining
topicId: wordWeight*wordName (topN-words)
topic #0: 0.006*vt + 0.001*sin + 0.001*dasgupta + 0.001*sphere + 0.001*px + 0.001*alai + 0.001*onteleoni + 0.001*erceptron + 0.001*separators + 0.001*realizable
topic #1: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #2: 0.002*trans + 0.002*kcentroid + 0.002*blas + 0.002*libraries + 0.001*toolkits + 0.001*engel + 0.001*dlib + 0.001*igel + 0.001*trainer + 0.001*algebra
topic #3: 0.004*knn + 0.003*sn + 0.003*esn + 0.003*glue + 0.002*ual + 0.002*overlap + 0.002*localizable + 0.002*dn + 0.002*wcm + 0.002*tg
topic #4: 0.000*psms + 0.000*fms + 0.000*pso + 0.000*particle + 0.000*swarm + 0.000*tness + 0.000*patsms + 0.000*guyon + 0.000*ber + 0.000*eberhart
topic #5: 0.004*chunk + 0.002*recoded + 0.002*discretization + 0.002*disk + 0.002*preprocessing + 0.002*boull + 0.001*chunks + 0.001*modl + 0.001*chunking + 0.001*cale
topic #6: 0.005*nonparanormal + 0.004*wire + 0.003*wires + 0.003*injection + 0.003*fj + 0.003*glasso + 0.003*circuit + 0.002*gate + 0.002*en + 0.002*circuits
topic #7: 0.004*rkbs + 0.003*banach + 0.003*nn + 0.002*hang + 0.002*eproducing + 0.002*reproducing + 0.001*gx + 0.001*chet + 0.001*normed + 0.001*achine
topic #8: 0.004*sm + 0.004*ers + 0.004*distribution + 0.003*mi + 0.003*mn + 0.003*dm + 0.003*shifts + 0.002*performance + 0.002*measures + 0.002*user
topic #9: 0.006*hash + 0.003*hashing + 0.002*rp + 0.001*collision + 0.001*etterson + 0.001*ash + 0.001*traintest + 0.001*ror + 0.001*ishwanathan + 0.001*dmoz
topic #10: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #11: 0.005*path + 0.004*rai + 0.004*directed + 0.004*representer + 0.003*likelihood + 0.003*nodes + 0.003*projectron + 0.003*covariance + 0.003*edges + 0.003*ricf
topic #12: 0.003*dynamics + 0.002*corl + 0.001*nat + 0.001*dvar + 0.001*mk + 0.001*runskill + 0.001*effler + 0.001*lef + 0.001*rovably + 0.001*yped
topic #13: 0.003*ih + 0.003*ph + 0.002*cpsd + 0.002*umlp + 0.002*softplus + 0.001*gridpoints + 0.001*nadeau + 0.001*gh + 0.001*lisle + 0.001*ugas
topic #14: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #15: 0.004*xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx + 0.004*xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx + 0.002*xxxxxxxxxxxxxxxxxxxxxxxxxxxxx + 0.002*robustness + 0.002*disturbance + 0.001*aramanis + 0.001*bhattacharyya + 0.001*robust + 0.001*tc + 0.001*christmann
topic #16: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #17: 0.000*espsi + 0.000*nl + 0.000*pan + 0.000*shen + 0.000*semisupervised + 0.000*esvm + 0.000*ev + 0.000*unlabeled + 0.000*wang + 0.000*etsvm
topic #18: 0.004*jq + 0.003*vk + 0.002*monotonic + 0.002*backdoor + 0.002*blocked + 0.001*collider + 0.001*paths + 0.001*robins + 0.001*pay + 0.001*qualitative
topic #19: 0.004*sgd + 0.003*wt + 0.003*sampling + 0.003*coef + 0.003*nite + 0.003*cn + 0.003*dfa + 0.002*iteration + 0.002*regular + 0.002*pn
topic #20: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #21: 0.005*kernels + 0.003*hilbert + 0.003*spectral + 0.003*attributes + 0.002*vi + 0.002*norm + 0.002*cautious + 0.002*inference + 0.002*cf + 0.002*singular
topic #22: 0.006*pebl + 0.002*python + 0.001*yes + 0.001*script + 0.001*bns + 0.001*interventional + 0.001*apple + 0.001*import + 0.001*packages + 0.000*ascher
topic #23: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #24: 0.003*parallel + 0.003*mpi + 0.002*processor + 0.002*liblinear + 0.001*cores + 0.001*pascal + 0.001*processors + 0.001*parallelization + 0.001*gondzio + 0.001*alpha
topic #25: 0.002*qn + 0.002*ec + 0.002*vb + 0.002*corrections + 0.002*ep + 0.002*nference + 0.001*paquet + 0.001*inther + 0.001*pper + 0.001*orrections
topic #26: 0.004*owl + 0.003*variables + 0.003*selection + 0.003*response + 0.003*masking + 0.003*variable + 0.003*tree + 0.003*importance + 0.002*web + 0.002*ace
topic #27: 0.000*estimator + 0.000*entropy + 0.000*ml + 0.000*shrinkage + 0.000*nsb + 0.000*cell + 0.000*mutual + 0.000*aracne + 0.000*mse + 0.000*estimators
topic #28: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #29: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #30: 0.006*settable + 0.003*pcm + 0.001*endogenous + 0.001*equilibrium + 0.001*nash + 0.001*interventions + 0.001*ettable + 0.001*responses + 0.001*ystems + 0.001*equilibria
topic #31: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #32: 0.005*fdr + 0.003*pcfdr + 0.001*pmax + 0.001*pc + 0.001*etrue + 0.001*aetrue + 0.001*estop + 0.001*skeleton + 0.001*xb + 0.001*ate
topic #33: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #34: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #35: 0.005*mdp + 0.004*logdet + 0.003*policy + 0.003*action + 0.002*delayed + 0.002*neumann + 0.002*divergence + 0.002*bregman + 0.002*vmax + 0.002*strehl
topic #36: 0.000*agnes + 0.000*petri + 0.000*ns + 0.000*event + 0.000*miner + 0.000*dispatch + 0.000*activity + 0.000*logs + 0.000*events + 0.000*vanthienen
topic #37: 0.005*fourier + 0.002*irreducible + 0.001*irreducibles + 0.001*tracks + 0.001*sn + 0.001*ourier + 0.001*uibas + 0.001*uestrin + 0.001*uang + 0.001*marginals
topic #38: 0.007*agent + 0.005*xy + 0.004*ieme + 0.004*reinforcement + 0.003*software + 0.003*environment + 0.003*architecture + 0.003*interface + 0.002*labels + 0.002*learner
topic #39: 0.005*skewing + 0.003*clean + 0.003*immune + 0.002*dirty + 0.002*boolean + 0.002*isotropic + 0.002*sdirty + 0.002*parity + 0.002*sclean + 0.002*ex
topic #40: 0.007*beam + 0.001*len + 0.001*planning + 0.001*width + 0.001*yoon + 0.001*plan + 0.001*lr + 0.001*bfs + 0.001*ern + 0.001*blocksworld
topic #41: 0.005*xf + 0.002*greedy + 0.002*irrepresentable + 0.002*lasso + 0.001*ey + 0.001*zhao + 0.001*squares + 0.001*tropp + 0.001*reedy + 0.001*quares
topic #42: 0.003*rounding + 0.002*sparsi + 0.002*auc + 0.002*truncated + 0.001*vowpal + 0.001*runcated + 0.001*radient + 0.001*wabbit + 0.001*duchi + 0.001*angford
topic #43: 0.006*nondeterministic + 0.001*lr + 0.001*brier + 0.001*fit + 0.001*lung + 0.001*ncc + 0.001*credal + 0.001*bahamonde + 0.001*del + 0.001*ondeterministic
topic #44: 0.004*sis + 0.003*isis + 0.002*fan + 0.002*screening + 0.001*lasso + 0.001*lv + 0.001*jr + 0.001*scad + 0.001*amworth + 0.001*ltrahigh
topic #45: 0.004*ni + 0.003*event + 0.003*dag + 0.003*ns + 0.003*dn + 0.002*petri + 0.002*agnes + 0.002*scoreb + 0.002*activity + 0.002*dispatch
topic #46: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #47: 0.004*lim + 0.004*estimator + 0.003*shrinkage + 0.003*mf + 0.003*rmse + 0.003*game + 0.003*ml + 0.002*brismf + 0.002*qi + 0.002*rc
topic #48: 0.004*wendland + 0.002*nk + 0.002*native + 0.001*hx + 0.001*rieger + 0.001*wicknagl + 0.001*ieger + 0.001*ll + 0.001*sv + 0.001*eterministic
topic #49: 0.003*nement + 0.003*hk + 0.002*rd + 0.001*hg + 0.001*nontrivial + 0.001*borel + 0.001*zd + 0.001*hgc + 0.001*gc + 0.001*translation
topic #50: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #51: 0.004*ranking + 0.004*fs + 0.003*fsi + 0.002*stability + 0.001*agarwal + 0.001*iyogi + 0.001*eneralization + 0.001*mcdiarmid + 0.001*elisseeff + 0.001*tability
topic #52: 0.008*kernel + 0.006*classi + 0.006*training + 0.005*test + 0.005*xi + 0.004*features + 0.004*earning + 0.004*feature + 0.003*update + 0.003*er
topic #53: 0.004*rankboost + 0.003*jt + 0.003*adaboost + 0.002*mik + 0.001*ranking + 0.001*oost + 0.001*chapire + 0.001*botd + 0.001*dt + 0.001*tcorrected
topic #54: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #55: 0.010*xt + 0.005*kt + 0.004*vt + 0.004*bayesian + 0.004*tr + 0.004*sup + 0.004*rd + 0.003*bi + 0.003*proposition + 0.003*noise
topic #56: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #57: 0.003*pag + 0.003*disga + 0.002*preg + 0.002*admg + 0.002*mb + 0.002*spg + 0.001*admgs + 0.001*lmp + 0.001*rlmp + 0.001*disg
topic #58: 0.006*svm + 0.004*wt + 0.004*node + 0.004*xn + 0.003*independence + 0.003*ocas + 0.003*samples + 0.003*similarity + 0.003*gene + 0.002*similarities
topic #59: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #60: 0.000*banach + 0.000*rkbs + 0.000*nn + 0.000*reproducing + 0.000*gx + 0.000*normed + 0.000*hang + 0.000*iy + 0.000*paces + 0.000*chet
topic #61: 0.003*tk + 0.003*rpr + 0.002*zk + 0.002*episodes + 0.002*qtk + 0.002*mtrl + 0.002*ok + 0.001*ak + 0.001*pomdp + 0.001*dm
topic #62: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #63: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #64: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #65: 0.003*subgroups + 0.002*subgroup + 0.002*trt + 0.002*treatment + 0.002*categorical + 0.001*terminal + 0.001*tb + 0.001*interaction + 0.001*women + 0.001*logworth
topic #66: 0.005*ln + 0.005*log + 0.005*yt + 0.004*matrix + 0.004*graph + 0.003*yi + 0.003*theorem + 0.003*loss + 0.003*rn + 0.003*regularization
topic #67: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #68: 0.006*st + 0.005*wt + 0.004*gradient + 0.004*obos + 0.004*gt + 0.004*lmnn + 0.003*knn + 0.003*active + 0.003*neigh + 0.003*nearest
topic #69: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #70: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #71: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #72: 0.003*za + 0.002*av + 0.002*uv + 0.002*polytope + 0.001*dirichlet + 0.001*integration + 0.001*zonotope + 0.001*integral + 0.001*integrals + 0.001*algebraic
topic #73: 0.004*ratings + 0.004*items + 0.003*recommender + 0.003*recommendation + 0.003*user + 0.002*item + 0.002*recommendations + 0.002*rating + 0.002*users + 0.001*utility
topic #74: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #75: 0.003*dpmnl + 0.003*dirichlet + 0.003*mnl + 0.002*covariates + 0.001*qmnl + 0.001*parkinson + 0.001*lsvm + 0.001*hahbaba + 0.001*ding + 0.001*irichlet
topic #76: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #77: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #78: 0.004*neurosvm + 0.003*mlp + 0.002*module + 0.001*ensemble + 0.001*mlps + 0.001*pal + 0.001*sfms + 0.001*wdbc + 0.001*mvoting + 0.001*optdigits
topic #79: 0.004*lda + 0.003*hdp + 0.002*perplexity + 0.002*topics + 0.002*processor + 0.002*jp + 0.002*topic + 0.002*processors + 0.001*nwkp + 0.001*nwk
topic #80: 0.005*un + 0.002*vovk + 0.002*wdaa + 0.002*football + 0.001*superprediction + 0.001*bookmakers + 0.001*brier + 0.001*tennis + 0.001*bookmaker + 0.001*xpert
topic #81: 0.001*im + 0.001*kc + 0.001*characterizations + 0.000*nq + 0.000*wavelets + 0.000*schoenberg + 0.000*munkres + 0.000*fitzgerald + 0.000*surjective + 0.000*sh
topic #82: 0.005*psms + 0.003*sq + 0.002*tsallis + 0.002*pso + 0.002*jt + 0.002*particle + 0.002*nonextensive + 0.002*tq + 0.002*fms + 0.002*swarm
topic #83: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #84: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #85: 0.004*subgroup + 0.002*emerging + 0.002*approved + 0.002*visualization + 0.002*lavra + 0.001*descriptive + 0.001*csm + 0.001*maritalstatus + 0.001*epm + 0.001*yes
topic #86: 0.002*kriging + 0.002*evolutionary + 0.002*ep + 0.001*genetic + 0.001*surrogate + 0.001*gorissen + 0.001*ensemble + 0.001*rational + 0.001*migration + 0.001*ann
topic #87: 0.004*ait + 0.003*deep + 0.003*sit + 0.003*hidden + 0.002*argumentative + 0.002*layer + 0.002*layers + 0.002*unsupervised + 0.002*units + 0.002*aitt
topic #88: 0.003*nsp + 0.002*dmg + 0.002*dag + 0.002*odels + 0.002*wishart + 0.001*richardson + 0.001*parameterization + 0.001*latent + 0.001*hahramani + 0.001*ilva
topic #89: 0.004*lsh + 0.002*subsequences + 0.002*motif + 0.002*lshcs + 0.002*patterns + 0.001*hamming + 0.001*locality + 0.001*nucleotide + 0.001*npc + 0.001*exhsearch
topic #90: 0.004*ik + 0.003*table + 0.002*distance + 0.002*lipschitz + 0.002*eer + 0.002*clustering + 0.002*scores + 0.002*qik + 0.002*xk + 0.002*metric
topic #91: 0.006*library + 0.003*interfaces + 0.003*clustering + 0.002*clusters + 0.002*dataset + 0.002*kmeans + 0.002*developers + 0.002*load + 0.002*yvan + 0.002*abeel
topic #92: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #93: 0.004*agnostic + 0.004*ntr + 0.003*membership + 0.002*ulsif + 0.002*lsif + 0.002*nte + 0.002*queries + 0.002*pac + 0.002*pseudorandom + 0.002*xtr
topic #94: 0.004*fk + 0.003*cmle + 0.003*fn + 0.002*eb + 0.002*qn + 0.002*ir + 0.002*greenshtein + 0.002*fair + 0.002*vol + 0.002*fan
topic #95: 0.003*socp + 0.003*xa + 0.003*xb + 0.003*xab + 0.002*lp + 0.001*relaxation + 0.001*mrf + 0.001*ms + 0.001*ab + 0.001*soc
topic #96: 0.005*ft + 0.005*reward + 0.004*online + 0.004*rank + 0.004*actions + 0.003*transfer + 0.003*target + 0.003*regression + 0.003*tasks + 0.003*source
topic #97: 0.000*sublevel + 0.000*preexisting + 0.000*formulas + 0.000*forcing + 0.000*force + 0.000*fk + 0.000*fisher + 0.000*ferrer + 0.000*female + 0.000*featurelevel
topic #98: 0.004*batchpsv + 0.003*batchsv + 0.002*batchprob + 0.002*cad + 0.001*batch + 0.001*psvm + 0.001*colon + 0.001*ung + 0.001*candidates + 0.001*ao
topic #99: 0.005*fdr + 0.003*knot + 0.003*quantile + 0.003*knots + 0.002*ph + 0.002*lsu + 0.002*crossing + 0.002*benjamini + 0.001*validation + 0.001*procedures
Latent Dirichlet allocation
In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003.
Topics in LDA
In LDA, each document may be viewed as a mixture of various topics. This is similar to probabilistic latent semantic analysis (pLSA), except that in LDA the topic distribution is assumed to have a Dirichlet prior. In practice, this results in more reasonable mixtures of topics in a document. It has been noted, however, that the pLSA model is equivalent to the LDA model under a uniform Dirichlet prior distribution.
For example, an LDA model might have topics that can be classified as CAT_related and DOG_related. A topic has probabilities of generating various words, such as milk, meow, and kitten, which can be classified and interpreted by the viewer as "CAT_related". Naturally, the word cat itself will have high probability given this topic. The DOG_related topic likewise has probabilities of generating each word: puppy, bark, and bone might have high probability. Words without special relevance, such as the (see function word), will have roughly even probability between classes (or can be placed into a separate category). A topic is not strongly defined, neither semantically nor epistemologically. It is identified on the basis of supervised labeling and (manual) pruning on the basis of their likelihood of co-occurrence. A lexical word may occur in several topics with a different probability, however, with a different typical set of neighboring words in each topic.
Each document is assumed to be characterized by a particular set of topics. This is akin to the standard bag of words model assumption, and makes the individual words exchangeable.
Model
With plate notation, the dependencies among the many variables can be captured concisely. The boxes are “plates” representing replicates. The outer plate represents documents, while the inner plate represents the repeated choice of topics and words within a document. M denotes the number of documents, N the number of words in a document. Thus:
α is the parameter of the Dirichlet prior on the per-document topic distributions,
β is the parameter of the Dirichlet prior on the per-topic word distribution,
\theta_i is the topic distribution for document i,
\phi_k is the word distribution for topic k,
z_{ij} is the topic for the jth word in document i, and
w_{ij} is the specific word.
The w_{ij} are the only observable variables, and the other variables are latent variables. Mostly, the basic LDA model will be extended to a smoothed version to gain better results .[citation needed] The plate notation is shown on the right, where K denotes the number of topics considered in the model and:
\phi is a K*V (V is the dimension of the vocabulary) Markov matrix each row of which denotes the word distribution of a topic.
The generative process behind is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words. LDA assumes the following generative process for a corpus D consisting of M documents each of length N_i:
1. Choose \theta_i \, \sim \, \mathrm{Dir}(\alpha) , where i \in \{ 1,\dots,M \} and \mathrm{Dir}(\alpha) is the Dirichlet distribution for parameter \alpha
2. Choose \phi_k \, \sim \, \mathrm{Dir}(\beta) , where k \in \{ 1,\dots,K \}
3. For each of the word positions i, j, where j \in \{ 1,\dots,N_i \} , and i \in \{ 1,\dots,M \}
(a) Choose a topic z_{i,j} \,\sim\, \mathrm{Multinomial}(\theta_i).
(b) Choose a word w_{i,j} \,\sim\, \mathrm{Multinomial}( \phi_{z_{i,j}}) .
(Note that the Multinomial distribution here refers to the Multinomial with only one trial. It is formally equivalent to the categorical distribution.)
The lengths N_i are treated as independent of all the other data generating variables (q and z). The subscript is often dropped, as in the plate diagrams shown here.
Inference
Learning the various distributions (the set of topics, their associated word probabilities, the topic of each word, and the particular topic mixture of each document) is a problem of Bayesian inference. The original paper used a variational Bayes approximation of the posterior distribution;[1] alternative inference techniques use Gibbs sampling and expectation propagation.
Following is the derivation of the equations for collapsed Gibbs sampling, which means \varphis and \thetas will be integrated out. For simplicity, in this derivation the documents are all assumed to have the same length N_{}. The derivation is equally valid if the document lengths vary.
Applications, extensions and similar techniques
Topic modeling is a classic problem in information retrieval. Related models and techniques are, among others, latent semantic indexing, independent component analysis, probabilistic latent semantic indexing, non-negative matrix factorization, and Gamma-Poisson distribution.
The LDA model is highly modular and can therefore be easily extended. The main field of interest is modeling relations between topics. This is achieved by using another distribution on the simplex instead of the Dirichlet. The Correlated Topic Model follows this approach, inducing a correlation structure between topics by using the logistic normal distribution instead of the Dirichlet. Another extension is the hierarchical LDA (hLDA), where topics are joined together in a hierarchy by using the nested Chinese restaurant process. LDA can also be extended to a corpus in which a document includes two types of information (e.g., words and names), as in the LDA-dual model. Nonparametric extensions of LDA include the Hierarchical Dirichlet process mixture model, which allows the number of topics to be unbounded and learnt from data and the Nested Chinese Restaurant Process which allows topics to be arranged in a hierarchy whose structure is learnt from data.
As noted earlier, PLSA is similar to LDA. The LDA model is essentially the Bayesian version of PLSA model. The Bayesian formulation tends to perform better on small datasets because Bayesian methods can avoid overfitting the data. In a very large dataset, the results are probably the same. One difference is that PLSA uses a variable d to represent a document in the training set. So in PLSA, when presented with a document the model hasn't seen before, we fix \Pr(w \mid z)—the probability of words under topics—to be that learned from the training set and use the same EM algorithm to infer \Pr(z \mid d)—the topic distribution under d. Blei argues that this step is cheating because you are essentially refitting the model to the new data.
Variations on LDA have been used to automatically put natural images into categories, such as "bedroom" or "forest", by treating an image as a document, and small patches of the image as words; one of the variations is called Spatial Latent Dirichlet Allocation.
from wiki http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation


ANSI escape sequence to clear the terminal
Clear the terminal and reset the cursor to 1,1
CLICommander constructor.
CLICommander is a set of advanced tools for working with PHP scripts on the linux/unix command line.
Convert an RGB hex color to the closest xterm equivilent
Convert a hex octet to the closest grey octet used for xterm colors
Get the closest xterm color octet for the octet supplied


Otto Porter Out At Least Two More Weeks With Foot Sprain
by Austin KentThe Chicago Bulls have been without Otto Porter for five games as he recovers from a left foot sprain. Now the team says that the forward will continue to miss time as a second MRI has revealed bone bruising that they didn’t know about initially.
Porter will be re-assessed in two weeks, pushing his return back to the first week of December at the earliest.
In the nine games that he has played this season, Porter has averaged 11.2 points and 3.2 rebounds. Since he’s been sidelined, sophomore forward Chandler Hutchinson has slid into the starting lineup.
Porter originally suffered the injury during the middle of a game against the Atlanta Hawks on Nov. 6.
The Bulls are currently 4-10 on the season and have gone 1-4 in the five games that Porter has missed.


| 1 | /* =========================================================== | |
| 2 | * JFreeChart : a free chart library for the Java(tm) platform | |
| 3 | * =========================================================== | |
| 4 | * | |
| 5 | * (C) Copyright 2000-2008, by Object Refinery Limited and Contributors. | |
| 6 | * | |
| 7 | * Project Info: http://www.jfree.org/jfreechart/index.html | |
| 8 | * | |
| 9 | * This library is free software; you can redistribute it and/or modify it | |
| 10 | * under the terms of the GNU Lesser General Public License as published by | |
| 11 | * the Free Software Foundation; either version 2.1 of the License, or | |
| 12 | * (at your option) any later version. | |
| 13 | * | |
| 14 | * This library is distributed in the hope that it will be useful, but | |
| 15 | * WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY | |
| 16 | * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public | |
| 17 | * License for more details. | |
| 18 | * | |
| 19 | * You should have received a copy of the GNU Lesser General Public | |
| 20 | * License along with this library; if not, write to the Free Software | |
| 21 | * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, | |
| 22 | * USA. | |
| 23 | * | |
| 24 | * [Java is a trademark or registered trademark of Sun Microsystems, Inc. | |
| 25 | * in the United States and other countries.] | |
| 26 | * | |
| 27 | * ------------------------ | |
| 28 | * JFreeChartResources.java | |
| 29 | * ------------------------ | |
| 30 | * (C) Copyright 2002-2008, by Object Refinery Limited. | |
| 31 | * | |
| 32 | * Original Author: David Gilbert (for Object Refinery Limited); | |
| 33 | * Contributor(s): -; | |
| 34 | * | |
| 35 | */ | |
| 36 | ||
| 37 | package org.jfree.chart.resources; | |
| 38 | ||
| 39 | import java.util.ListResourceBundle; | |
| 40 | ||
| 41 | /** | |
| 42 | * Localised resources for JFreeChart. | |
| 43 | */ | |
| 44 | public class JFreeChartResources extends ListResourceBundle { | |
| 45 | ||
| 46 | /** | |
| 47 | * Returns the array of strings in the resource bundle. | |
| 48 | * | |
| 49 | * @return The resources. | |
| 50 | */ | |
| 51 | public Object[][] getContents() { | |
| 52 |
1
1. getContents : mutated return of Object value for org/jfree/chart/resources/JFreeChartResources::getContents to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return CONTENTS; |
| 53 | } | |
| 54 | ||
| 55 | /** The resources to be localised. */ | |
| 56 | private static final Object[][] CONTENTS = { | |
| 57 | {"project.name", "JFreeChart"}, | |
| 58 | {"project.version", "1.2.0-pre"}, | |
| 59 | {"project.info", "http://www.jfree.org/jfreechart/index.html"}, | |
| 60 | {"project.copyright", | |
| 61 | "(C)opyright 2000-2008, by Object Refinery Limited and Contributors"} | |
| 62 | }; | |
| 63 | ||
| 64 | } | |
Mutations | ||
| 52 |
1.1 |


{%- include head.html -%}
Experience counts and we have 50 years of it. Those years of experience combined with our innovative and driven management team, have propelled Hurricane Hydrovac to be one of the fastest-growing hydro excavation companies in Southwestern Ontario. No matter what the job is, our clients are provided exceptional service and value through our versatility and full range of hydro excavation, utility and sewer services.
Our team at Hurricane Hydrovac includes team members with backgrounds in all facets of construction. Locally based in Windsor/London the team brings forward many years of experience, knowledge and diverse skill sets; which allows our company to provide exceptional services for our clients, delivered in a clean, safe and precise fashion.
{%- include stats.html -%}Shawn is responsible for overseeing Hurricane Hydrovac. Shawn graduated from the University of Windsor with a civil engineering degree. After graduation Shawn become President and shareholder of Michael O'Keefe Ltd. Shawn has over 25 years' experience in the civil construction industry. While Shawn's primary focus was excavation, his background, reputation, and in-depth knowledge of technology has allowed him to drive success into other facets of the business.
Max is responsible for overseeing Hurricane Hydrovac. Max started his career in construction over two decades ago as the President of De Angelis Construction Inc. Max is an innovative and results-oriented construction professional with extensive and diverse experience in business and development spanning across Ontario and Canada. Through his commitment to excellence and a team-oriented approach, Max has been a catalyst in establishing the trusted Hurricane name.
Don became the President of Heaton Sanitation Ltd. when his father Wilfred Heaton retired. Don continued to grow the business through the acquisition of hydrovacs and combination units and as a result, became one of the biggest locally owned hydrovac companies in Windsor Essex. Don is also well-known for his expertise in septic systems.
Geoff is responsible for overseeing the London operations. Geoff has over 25 years of experience in the hydrovac, sewer flushing, and CCTV industries which he combines with his customer service experience to drive the local team to success. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Cras sit amet eleifend quam, vitae porttitor elit.
Hurricane Hydrovac Services was established in April of 2015 when Hurricane Hydrovac acquired Heaton Sanitation Ltd. Heaton Sanitation Ltd. has been serving Windsor, Essex County and Chatham-Kent for well over 50 years in septic and liquid waste removal, along with disposal, sewer and catch basin cleaning, video inspections, and hydro excavation services. The combined enterprise now operates a fleet of over 35 trucks. Although the name may have changed what hasn't changed is the exceptional service provided to our many valued clients and the company's vision to drive for excellence continually.
Our Windsor and London teams are equipped with top of the line hydro vac, dryvac and combination units along with all the supporting equipment required for restoration, and sewer maintenance. We also have access to our sister companies fleet, Hurricane SMS, as required. We’re equipped and experienced to handle any size project.


Belarus threatens to pull out of Russia integration deal over subsidy row
MINSK (Reuters) - Belarus President Alexander Lukashenko on Sunday threatened to pull out of signing an integration deal with Russia next month if Moscow failed to resolve their dispute over energy subsidies.
Russia has propped up its traditional ally with loans and subsidies to keep Belarus in its political orbit but now plans to phase these out to lessen the burden on its economy.
That has strained ties at the same time as Lukashenko has sought to improve relations with the West.
Belarus previously said that it stands to lose hundreds of millions of dollars a year from changes to Russian tax policy and has tried to negotiate compensation. Russia says the subsidies cost its exchequer billions of dollars.
The two countries are due to sign a roadmap in December to bring their economies closer together.
That includes a unified tax code, closely aligned banking supervision, and a single regulator of oil, gas and electricity markets, according to details of the plan revealed by Russia’s Kommersant newspaper in September.
“If our fundamental issues are not resolved: on the supply of hydrocarbons, on the opening of markets, no roadmaps can be signed,” Lukashenko told reporters on the same day as Belarus held parliamentary elections.
Lukashenko previously accused Russia of trying to strong-arm his country into fully merging with its neighbor and has accused Moscow of falling into “hysterics” over his moves to balance ties between east and west.


Inserting the keys into hash table
Open addressing is the hashing technique in which for inserting an element in the table firstly we search for the elements if it is already contained in it, the search returns either the element or the free space in the array to fill that element.
In uniform hashing technique sequence of elements to be search can be any of the m! permutation inthat is this technique gives the method of searching which search in the full list for the single element in the generalized way.
When the first key inserted, it has m choices out of m, the second key has choices out of m so on, key n has choices out of m. The insertion of these keys is independent of each other,
Thus probability:
……(1)
Hence from equation (1) the probability is as:
By theorem for the n keys to be inserted in a table where then the probability of occurring a match in the table is less than, it is very efficient to find the hash function for which there is no collisions in a static set K containing n keys.
When the number of key values n exceeds, that is a hash table of size that is is excessive. Hence in this case we use the hashing technique at the two levels, in the first level or at the outer level the hash function to hash the keys into slots. Then, if the keys hash to slot j. we use a secondary hash table of size it gives a set having invariant or constant time without any collisions in the table.
Hence, in a hash table of capacity m for inserting n keys by open addressing and uniform hashing techniques then the probability of collisions to occur is where.


Kubernetes Orchestration
By the end of this exercise, you should be able to:
- Define and launch basic pods, replicaSets and deployments using
kubectl
- Get metadata, configuration and state information about a kubernetes object using
kubectl describe
- Update an image for a pod in a running kubernetes deployment
Creating Pods
On your master node, create a yaml file
pod.yaml
to describe a simple pod with the following content:apiVersion: v1 kind: Pod metadata: name: demo spec: containers: - name: nginx image: nginx:1.7.9
Deploy your pod:
[centos@node-0 ~]$ kubectl create -f pod.yaml
Confirm your pod is running:
[centos@node-0 ~]$ kubectl get pod demo
Get some metadata about your pod:
[centos@node-0 ~]$ kubectl describe pod demo
Delete your pod:
[centos@node-0 ~]$ kubectl delete pod demo
Modify
pod.yaml
to create a second container inside your pod:apiVersion: v1 kind: Pod metadata: name: demo spec: containers: - name: nginx image: nginx:1.7.9 - name: sidecar image: centos:7 command: ["ping"] args: ["<IP_ADDRESS>"]
Deploy this new pod, and create a bash shell inside the container named
sidecar
:[centos@node-0 ~]$ kubectl create -f pod.yaml [centos@node-0 ~]$ kubectl exec -c=sidecar -it demo -- /bin/bash
From within the
sidecar
container, fetch the nginx landing page on the default port 80 usinglocalhost
:[root@demo /]# curl localhost:80
You should see the html of the nginx landing page. Note these containers can reach each other on localhost, meaning they are sharing a network namespace. Now list the processes in your
sidecar
container:[root@demo /]# ps -aux
You should see the
ping
process we containerized, the shell we created to explore this container usingkubectl exec
, and theps
process itself - but nonginx
. While a network namespace is shared between the containers, they still have their own PID namespace (for example).Finally, remember to exit out of this pod, and delete it:
[root@demo /]# exit [centos@node-0 ~]$ kubectl delete pod demo
Creating ReplicaSets
On your master
node-0
, create a yaml filereplicaset.yaml
to describe a simple replicaSet with the following content:apiVersion: apps/v1 kind: ReplicaSet metadata: name: rs-demo spec: replicas: 3 selector: matchLabels: component: reverse-proxy template: metadata: labels: component: reverse-proxy spec: containers: - name: nginx image: nginx:1.7.9
Notice especially the
replicas
key, which defines how many copies of this pod to create, and thetemplate
section; this defines the pod to replicate, and is described almost exactly like the first pod definition we created above. The difference here is the required presence of thelabels
key in the pod's metadata, which must match theselector -> matchLabels
item in the specification of the replicaSet.Deploy your replicaSet, and get some state information about it:
[centos@node-0 ~]$ kubectl create -f replicaset.yaml [centos@node-0 ~]$ kubectl describe replicaset rs-demo
After a few moments, you should see something like
Name: rs-demo Namespace: default Selector: component=reverse-proxy Labels: component=reverse-proxy Annotations: <none> Replicas: 3 current / 3 desired Pods Status: 3 Running / 0 Waiting / 0 Succeeded / 0 Failed Pod Template: Labels: component=reverse-proxy Containers: nginx: Image: nginx:1.7.9 Port: <none> Host Port: <none> Environment: <none> Mounts: <none> Volumes: <none> Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal SuccessfulCreate 35s replicaset-controller Created pod: rs-demo-jxmjj Normal SuccessfulCreate 35s replicaset-controller Created pod: rs-demo-dmdtf Normal SuccessfulCreate 35s replicaset-controller Created pod: rs-demo-j62fx
Note the replicaSet has created three pods as requested, and will reschedule them if they exit.
Try killing off one of your pods, and reexamining the output of the above
describe
command. The<pod name>
comes from the last three lines in the output above, such asrs-demo-jxmjj
:[centos@node-0 ~]$ kubectl delete pod <pod name> [centos@node-0 ~]$ kubectl describe replicaset rs-demo
The dead pod gets rescheduled by the replicaSet, similar to a failed task in Docker Swarm.
 Deployments create a replicaSet of pods, but add some deployment management functionality on top of them, such as rolling updates and rollback.Spin up your deployment, and get some state information:
[centos@node-0 ~]$ kubectl create -f deployment.yaml [centos@node-0 ~]$ kubectl describe deployment nginx-deployment
The
describe
command should return something like:Name: nginx-deployment Namespace: default CreationTimestamp: Thu, 24 May 2018 04:29:18 +0000 Labels: <none> Annotations: deployment.kubernetes.io/revision=1 Selector: app=nginx Replicas: 3 desired | 3 updated | 3 total | 3 available | 0 unavailable StrategyType: RollingUpdate MinReadySeconds: 0 RollingUpdateStrategy: 25% max unavailable, 25% max surge Pod Template: Labels: app=nginx Containers: nginx: Image: nginx:1.7.9 Port: <none> Host Port: <none> Environment: <none> Mounts: <none> Volumes: <none> Conditions: Type Status Reason ---- ------ ------ Available True MinimumReplicasAvailable Progressing True NewReplicaSetAvailable OldReplicaSets: <none> NewReplicaSet: nginx-deployment-85f7784776 (3/3 replicas created) Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal ScalingReplicaSet 10s deployment-controller Scaled up replica set nginx-deployment-85f7784776 to 3
Note the very last line, indicating this deployment actually created a replicaSet which it used to scale up to three pods.
List your replicaSets and pods:
[centos@node-0 ~]$ kubectl get replicaSet NAME DESIRED CURRENT READY AGE nginx-deployment-79d4c74645 3 3 3 17s [centos@node-0 ~]$ kubectl get pod NAME READY STATUS RESTARTS AGE nginx-deployment-79d4c74645-9mtzm 1/1 Running 0 22s nginx-deployment-79d4c74645-k7wml 1/1 Running 0 22s nginx-deployment-79d4c74645-rrfrf 1/1 Running 0 22s
You should see one replicaSet and three pods created by your deployment, similar to the above.
Upgrade the nginx image from
1.7.9
to1.9.1
:[centos@node-0 ~]$ kubectl set image deployment/nginx-deployment nginx=nginx:1.9.1
After a few seconds,
kubectl describe
your deployment as above again. You should see that the image has been updated, and that the old replicaSet has been scaled down to 0 replicas, while a new replicaSet (with your updated image) has been scaled up to 3 pods. List your replicaSets one more time:[centos@node-0 ~]$ kubectl get replicaSets
You should see something like
NAME DESIRED CURRENT READY AGE nginx-deployment-69df9ccbf8 3 3 3 4m nginx-deployment-85f7784776 0 0 0 9m
Do a
kubectl describe replicaSet <replicaSet scaled down to 0>
; you should see that while no pods are running for this replicaSet, the old replicaSet's definition is still around so we can easily roll back to this version of the app if we need to.Clean up your cluster:
[centos@node-0 ~]$ kubectl delete deployment nginx-deployment
Conclusion
In this exercise, you explored the basic scheduling objects of pods, replicaSets, and deployments. Each object is responsible for a different part of the orchestration stack; pods are the basic unit of scheduling, replicaSets do keep-alive and scaling, and deployments provide update and rollback functionality. In a sense, these objects all 'nest' one inside the next; by creating a deployment, you implicitly created a replicaSet which in turn created the corresponding pods. In most cases, you're better off creating deployments rather than replicaSets or pods directly; this way, you get all the orchestrating scheduling features you would expect in analogy to a Docker Swarm service.


Extracts a slice from a tensor.
tf.compat.v1.slice
tf.compat.v2.slice
tf.slice( input_, begin, size, name=None )
This operation extracts a slice of size size
from a tensor input_
starting
at the location specified by begin
. The slice size
is represented as a
tensor shape, where size[i]
is the number of elements of the 'i'th dimension
of input_
that you want to slice. The starting location (begin
) for the
slice is represented as an offset in each dimension of input_
. In other
words, begin[i]
is the offset into the i'th dimension of input_
that you
want to slice from.
Note that tf.Tensor.getitem
is typically a more pythonic way to
perform slices, as it allows you to write foo[3:7, :-2]
instead of
tf.slice(foo, [3, 0], [4, foo.get_shape()[1]-2])
.
begin
is zero-based; size
is one-based. If size[i]
is -1,
all remaining elements in dimension i are included in the
slice. In other words, this is equivalent to setting:
size[i] = input_.dim_size(i) - begin[i]
This operation requires that:
0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]
t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]) tf.slice(t, [1, 0, 0], [1, 1, 3]) # [[[3, 3, 3]]] tf.slice(t, [1, 0, 0], [1, 2, 3]) # [[[3, 3, 3], # [4, 4, 4]]] tf.slice(t, [1, 0, 0], [2, 1, 3]) # [[[3, 3, 3]], # [[5, 5, 5]]]
input_
: A Tensor
.begin
: An int32
or int64
Tensor
.size
: An int32
or int64
Tensor
.name
: A name for the operation (optional).A Tensor
the same type as input_
.


| cm_foldcode {codemirrorR} | R Documentation |
Add code folding for your codemirror
htmlwidget. This is designed
for easy piping with magrittr
or pipeR
.
cm_foldcode(cm_widget = NULL, fold_mode = NULL)
cm_widget |
|
fold_mode |
|
codemirror
htmlwidget


Source code for torch.distributed.fsdp.api
"""
This file includes public APIs for FSDP such as the classes used for the
constructor arguments.
"""
from dataclasses import dataclass
from enum import auto, Enum
from typing import Optional
import torch
__all__ = [
"ShardingStrategy",
"BackwardPrefetch",
"MixedPrecision",
"CPUOffload",
"StateDictType",
"StateDictConfig",
"FullStateDictConfig",
"LocalStateDictConfig",
"ShardedStateDictConfig",
"OptimStateDictConfig",
"FullOptimStateDictConfig",
"LocalOptimStateDictConfig",
"ShardedOptimStateDictConfig",
"StateDictSettings",
]
[docs]class ShardingStrategy(Enum):
"""
This specifies the sharding strategy to be used for distributed training by
:class:`FullyShardedDataParallel`.
- ``FULL_SHARD``: Parameters, gradients, and optimizer states are sharded.
For the parameters, this strategy unshards (via all-gather) before the
forward, reshards after the forward, unshards before the backward
computation, and reshards after the backward computation. For gradients,
it synchronizes and shards them (via reduce-scatter) after the backward
computation. The sharded optimizer states are updated locally per rank.
- ``SHARD_GRAD_OP``: Gradients and optimizer states are sharded during
computation, and additionally, parameters are sharded outside
computation. For the parameters, this strategy unshards before the
forward, does not reshard them after the forward, and only reshards them
after the backward computation. The sharded optimizer states are updated
locally per rank. Inside ``no_sync()``, the parameters are not resharded
after the backward computation.
- ``NO_SHARD``: Parameters, gradients, and optimizer states are not sharded
but instead replicated across ranks similar to PyTorch's
:class:`DistributedDataParallel` API. For gradients, this strategy
synchronizes them (via all-reduce) after the backward computation. The
unsharded optimizer states are updated locally per rank.
- ``HYBRID_SHARD``: Apply ``FULL_SHARD`` within a node, and replicate parameters across
nodes. This results in reduced communication volume as expensive all-gathers and
reduce-scatters are only done within a node, which can be more performant for medium
-sized models.
- ``_HYBRID_SHARD_ZERO2``: Apply ``SHARD_GRAD_OP`` within a node, and replicate parameters across
nodes. This is like ``HYBRID_SHARD``, except this may provide even higher throughput
since the unsharded parameters are not freed after the forward pass, saving the
all-gathers in the pre-backward.
"""
FULL_SHARD = auto()
SHARD_GRAD_OP = auto()
NO_SHARD = auto()
HYBRID_SHARD = auto()
_HYBRID_SHARD_ZERO2 = auto()
[docs]class BackwardPrefetch(Enum):
"""
This configures explicit backward prefetching, which can improve throughput
but may slightly increase peak memory usage.
For a single process group using NCCL backend, any collectives, even if
issued in different streams, contend for the same per-device NCCL stream,
which is why the relative order in which the collectives are issued matters
for overlapping. The different backward prefetching settings correspond to
different orderings.
- ``BACKWARD_PRE``: This prefetches the next set of parameters before the
current set of parameter's gradient computation. This improves backward
pass throughput by overlapping communication (next all-gather) and
computation (current gradient computation).
- ``BACKWARD_POST``: This prefetches the next set of parameters after the
current set of parameter's gradient computation. This may improve
backward pass throughput by overlapping communication (current
reduce-scatter) and computation (next gradient computation).
Specifically, the next all-gather is reordered to be before the current
reduce-scatter.
.. note:: If the increase in peak memory usage from prefetching is an
issue, you may consider passing ``limit_all_gathers=True`` to the FSDP
constructor, which may help reduce peak memory usage in some cases.
"""
# NOTE: For both modes, the ordering that defines "current" and "next" is
# not always correct in the current implementation, so this may cause some
# performance regression for some models.
BACKWARD_PRE = auto()
BACKWARD_POST = auto()
[docs]@dataclass
class MixedPrecision:
"""
This configures FSDP-native mixed precision training.
Attributes:
param_dtype (torch.dtype): This specifies the dtype for model
parameters, inputs (when ``cast_forward_inputs`` or
``cast_root_forward_inputs``is set to
``True``), and therefore the dtype for computation.
However, outside the forward and backward passes, parameters are in
full precision. Model checkpointing always happens in full
precision.
reduce_dtype (torch.dtype): This specifies the dtype for gradient
reduction, which is permitted to differ from ``param_dtype``.
buffer_dtype (torch.dtype): This specifies the dtype for buffers. FSDP
does not shard buffers, casts them to ``buffer_dtype`` in the first
forward pass, and keeps them in that dtype thereafter. Model
checkpointing always happens in full precision.
keep_low_precision_grads (bool): This specifies whether to upcast
gradients back to the full parameter precision after the backward
pass. This may be set to ``False`` to save memory if using custom
optimizers that can perform the optimizer step in ``reduce_dtype``.
(Default: ``False``)
cast_forward_inputs (bool): Cast floating point tensors in the forward
arguments and keyword arguments to ``param_dtype``.
(Default: ``False``)
cast_root_forward_inputs (bool): Cast floating point tensors in the forward
arguments and keyword arguments to ``param_dtype`` for the root FSDP instance.
It takes precedence over ``cast_forward_inputs`` for the root FSDP instance.
(Default: ``True``)
.. note:: This API is experimental and subject to change.
.. note:: Only floating point tensors are cast to their specified dtypes.
.. note:: In ``summon_full_params``, parameters are forced to full
precision, but buffers are not.
.. note:: ``state_dict`` checkpoints parameters and buffers in full
precision. For buffers, this is only supported for
``StateDictType.FULL_STATE_DICT``.
.. note:: Each low precision dtype must be specified explicitly. For
example, ``MixedPrecision(reduce_dtype=torch.float16)`` only specifies
the reduction dtype to be low precision, and FSDP will not cast
parameters or buffers.
.. note:: If a ``reduce_dtype`` is not specified, then gradient reduction
happens in ``param_dtype`` if specified or the original parameter dtype
otherwise.
.. note:: If the user passes a model with ``BatchNorm`` modules and an
``auto_wrap_policy`` to the FSDP constructor, then FSDP will disable
mixed precision for ``BatchNorm`` modules by wrapping them separately
in their own FSDP instance with mixed precision disabled. This is due
to some missing low precision ``BatchNorm`` kernels. If the user does
not use an ``auto_wrap_policy``, then the user must take care to not
use mixed precision for FSDP instances containing ``BatchNorm``
modules.
.. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
``cast_forward_inputs=False`` by default. For the root FSDP instance,
its ``cast_root_forward_inputs`` takes precedence over its
``cast_forward_inputs``. For non-root FSDP instances, their
``cast_root_forward_inputs`` values are ignored. The default setting is
sufficient for the typical case where each FSDP instance has the same
``MixedPrecision`` configuration and only needs to cast inputs to the
``param_dtype`` at the beginning of the model's forward pass.
.. note:: For nested FSDP instances with different ``MixedPrecision``
configurations, we recommend setting individual ``cast_forward_inputs``
values to configure casting inputs or not before each instance's
forward. In such a case, since the casts happen before each FSDP
instance's forward, a parent FSDP instance should have its non-FSDP
submodules run before its FSDP submodules to avoid the activation dtype
being changed due to a different ``MixedPrecision`` configuration.
Example::
>>> # xdoctest: +SKIP("undefined variables")
>>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
>>> model[1] = FSDP(
>>> model[1],
>>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
>>> )
>>> model = FSDP(
>>> model,
>>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
>>> )
The above shows a working example. On the other hand, if ``model[1]``
were replaced with ``model[0]``, meaning that the submodule using
different ``MixedPrecision`` ran its forward first, then ``model[1]``
would incorrectly see ``float16`` activations instead of ``bfloat16``
ones.
"""
param_dtype: Optional[torch.dtype] = None
reduce_dtype: Optional[torch.dtype] = None
buffer_dtype: Optional[torch.dtype] = None
keep_low_precision_grads: bool = False
cast_forward_inputs: bool = False
cast_root_forward_inputs: bool = True
[docs]@dataclass
class CPUOffload:
"""
This configures CPU offloading.
Attributes:
offload_params (bool): This specifies whether to offload parameters to
CPU when not involved in computation. If enabled, this implicitly
offloads gradients to CPU as well. This is to support the optimizer
step, which requires parameters and gradients to be on the same
device.
"""
offload_params: bool = False
class StateDictType(Enum):
"""
This enum indicates that which type of ``state_dict`` the FSDP module is
currently processing (returning or loading).
The default value is FULL_STATE_DICT to comply the PyTorch convention.
..note::
FSDP currently supports three types of ``state_dict``:
1. ``state_dict/load_state_dict`: this pair of APIs return and load
the non-sharded, unflattened parameters. The semantics is the
same as using DDP.
2. ``_local_state_dict/_load_local_state_dict``: this pair of APIs return
and load local sharded, flattened parameters. The values returned
by ``_local_state_dict`` can be directly used by FSDP and is only
meaningful to FSDP (because parameters are flattened). Note that
these APIs are meant for use via the :func:`state_dict_type`
context manager as follows:
>>> # xdoctest: +SKIP("undefined variables")
>>> with fsdp.state_dict_type(StateDictType.LOCAL_STATE_DICT):
... state = fsdp.state_dict() # loads local state dict
3. ``_sharded_state_dict/_load_sharded_state_dict``: this pair of APIs
return and load sharded, unflattened parameters. The ``state_dict``
return by ``sharded_state_dict`` can be used by all other parallel
schemes (resharding may be required).
"""
FULL_STATE_DICT = auto()
LOCAL_STATE_DICT = auto()
SHARDED_STATE_DICT = auto()
@dataclass
class StateDictConfig:
"""
``StateDictConfig`` is the base class for all state_dict configuration classes.
Users should instantiate a child version (i.e. ``FullStateDictConfig``) in
order to configure settings for the particular type of ``state_dict``
implementation FSDP will use.
"""
offload_to_cpu: bool = False
@dataclass
class FullStateDictConfig(StateDictConfig):
"""
``FullStateDictConfig`` is a config class meant to be used with
``StateDictType.FULL_STATE_DICT``. Currently, it accepts two parameters,
``offload_to_cpu`` and ``rank0_only`` which can be configured to offload
the full ``state_dict`` to CPU and to materialize the ``state_dict`` on
rank 0 only. When used, it is recommended to enable both of these flags
together to optimize memory savings when taking checkpoints. Note that
this config class is meant for user via the :func:`state_dict_type`
context manager as follows:
>>> # xdoctest: +SKIP("undefined variables")
>>> fsdp = FSDP(model, auto_wrap_policy=...)
>>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
>>> with FullyShardedDataParallel.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
>>> state = fsdp.state_dict()
>>> # state will be empty on non rank 0 and contain CPU tensors on rank 0.
>>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
>>> model = model_fn() # Initialize model on CPU in preparation for wrapping with FSDP
>>> if dist.get_rank() == 0:
>>> # Load checkpoint only on rank 0 to avoid memory redundancy
>>> state_dict = torch.load("my_checkpoint.pt")
>>> model.load_state_dict(state_dict)
>>> # All ranks initialize FSDP module as usual. ``sync_module_states`` argument
>>> # communicates loaded checkpoint states from rank 0 to rest of the world.
>>> fsdp = FSDP(model, device_id=torch.cuda.current_device(), auto_wrap_policy=..., sync_module_states=True)
>>> # After this point, all ranks have FSDP model with loaded checkpoint.
"""
rank0_only: bool = False
@dataclass
class LocalStateDictConfig(StateDictConfig):
pass
@dataclass
class ShardedStateDictConfig(StateDictConfig):
pass
@dataclass
class OptimStateDictConfig:
"""
``OptimStateDictConfig`` is the base class for all optimizer state_dict
configuration classes. Users should instantiate a child version
(i.e. ``FullOptimStateDictConfig``) in order to configure settings for the
particular type of ``optim_state_dict`` implementation FSDP will use.
"""
# TODO: actually use this flag in the _optim_utils.py
offload_to_cpu: bool = True
@dataclass
class FullOptimStateDictConfig(OptimStateDictConfig):
rank0_only: bool = False
@dataclass
class LocalOptimStateDictConfig(OptimStateDictConfig):
offload_to_cpu: bool = False
@dataclass
class ShardedOptimStateDictConfig(OptimStateDictConfig):
pass
@dataclass
class StateDictSettings:
state_dict_type: StateDictType
state_dict_config: StateDictConfig
optim_state_dict_config: OptimStateDictConfig


#include <DOMLSParserFilter.hpp>
Public Types | |
Public Contants | |
| enum | FilterAction { FILTER_ACCEPT = 1, FILTER_REJECT = 2, FILTER_SKIP = 3, FILTER_INTERRUPT = 4 } |
Constants returned by acceptNode. More... | |
Public Member Functions | |
Destructor | |
| virtual | ~DOMLSParserFilter () |
| Destructor. | |
Functions introduced in DOM Level 3 | |
| virtual FilterAction | acceptNode (DOMNode *node)=0 |
| This method will be called by the parser at the completion of the parsing of each node. | |
| virtual FilterAction | startElement (DOMElement *node)=0 |
The parser will call this method after each DOMElement start tag has been scanned, but before the remainder of the DOMElement is processed. | |
| virtual DOMNodeFilter::ShowType | getWhatToShow () const =0 |
Tells the DOMLSParser what types of nodes to show to the method DOMLSParserFilter::acceptNode . | |
Protected Member Functions | |
Hidden constructors | |
| DOMLSParserFilter () |
Constants returned by acceptNode.
FILTER_ACCEPT:
Accept the node.
FILTER_REJECT:
Reject the node and its children.
FILTER_SKIP:
Skip this single node. The children of this node will still be considered.
FILTER_INTERRUPT:
Interrupt the normal processing of the document.
| xercesc::DOMLSParserFilter::DOMLSParserFilter | ( | ) | [inline, protected] |
| virtual xercesc::DOMLSParserFilter::~DOMLSParserFilter | ( | ) | [inline, virtual] |
Destructor.
| virtual FilterAction xercesc::DOMLSParserFilter::acceptNode | ( | DOMNode * | node | ) | [pure virtual] |
This method will be called by the parser at the completion of the parsing of each node.
The node and all of its descendants will exist and be complete. The parent node will also exist, although it may be incomplete, i.e. it may have additional children that have not yet been parsed. Attribute nodes are never passed to this function. From within this method, the new node may be freely modified - children may be added or removed, text nodes modified, etc. The state of the rest of the document outside this node is not defined, and the affect of any attempt to navigate to, or to modify any other part of the document is undefined. For validating parsers, the checks are made on the original document, before any modification by the filter. No validity checks are made on any document modifications made by the filter. If this new node is rejected, the parser might reuse the new node and any of its descendants.
| node | The newly constructed element. At the time this method is called, the element is complete - it has all of its children (and their children, recursively) and attributes, and is attached as a child to its parent. |
| virtual DOMNodeFilter::ShowType xercesc::DOMLSParserFilter::getWhatToShow | ( | ) | const [pure virtual] |
Tells the DOMLSParser
what types of nodes to show to the method DOMLSParserFilter::acceptNode
.
If a node is not shown to the filter using this attribute, it is automatically included in the DOM document being built. See DOMNodeFilter
for definition of the constants. The constants SHOW_ATTRIBUTE, SHOW_DOCUMENT, SHOW_DOCUMENT_TYPE, SHOW_NOTATION, SHOW_ENTITY, and SHOW_DOCUMENT_FRAGMENT are meaningless here. Those nodes will never be passed to DOMLSParserFilter::acceptNode.
| virtual FilterAction xercesc::DOMLSParserFilter::startElement | ( | DOMElement * | node | ) | [pure virtual] |
The parser will call this method after each DOMElement
start tag has been scanned, but before the remainder of the DOMElement
is processed.
The intent is to allow the element, including any children, to be efficiently skipped. Note that only element nodes are passed to the startElement function. The element node passed to startElement for filtering will include all of the attributes, but none of the children nodes. The DOMElement
may not yet be in place in the document being constructed (it may not have a parent node.) A startElement filter function may access or change the attributes for the DOMElement
. Changing namespace declarations will have no effect on namespace resolution by the parser.
| node | The newly encountered element. At the time this method is called, the element is incomplete - it will have its attributes, but no children. |


After the Taiwanese film, Cape No. 7, successfully swept audiences off their feet in 2008 and set the highest box office, the director, Wei Te-Sheng, with his newest film, 52Hz, I Love You, will be in North America this year.
WHY THE MOVIE
“52Hz” refers to a very unusual frequency sung by the loneliest whale in the Pacific Ocean. The mysterious pitch is like no other; it sounds like the whale is crying out for companionship that never comes…
However, this Taiwanese film, 52Hz, I Love You, captures both the loneliness and warmth in a metropolitan city--Taipei City, and suggests that even in the hustle and bustle of metropolis, millions of lonely people crying out for LOVE.
WHO
After the Taiwanese film, Cape No. 7, successfully swept audiences off their feet in 2008 and set the highest box office, the director, Wei Te-Sheng, with his newest film, 52Hz, I Love You, will be in North America this year.
WHEN & HOW
Date:   11/9/2016 (Wed)
Venue:   Independence Hall 100, Ohio State University.
              1923 Neil Ave Mall, Columbus, Ohio, 43210
Admission Information: TWSA members – free; General admission – $10
(For more details, please visit event page.）
WHAT'S MORE
Remarkably, the movie hits another record on “the first Taiwanese musical film,” and again, the director found new appealing actors to present a series of romantically comedic stories with beautiful original songs. It is a story that take place in modern Taipei City, Taiwan, on Valentine’s Day. It contrasts crowd and loneliness, love and being loved. It will bring a brand-new impression about the city and Taiwan to you.
On the way home from the screen, you might find yourself humming those bittersweet songs as those characters do with happiness.


Threads: Artificial Intelligence and Information Internetworks
Relevant Coursework:
- Software Development (Java)
- Systems Programming (C++)
- Data Structures & Algorithms
- Objects & Design (Android Development, Agile Development Process)
- Intro to Database Systems (SQL)
- Computer Organization (Assembly and C)
- Systems and Netowrks (Computer Architecture)
- Intro to AI
- Junior Design (Team based I2P Implementation)
- Linear Algebra
- Algorithms
Presidential Scholar, Dean's List, ACM Corporate Outreach Officer, Student Toastmasters, Club Rugby.
- Working as a backend engineer on Commece API's for the Adobe Commerce Platform
- Created an application to automatically create and upload datasets into Salesforce orgs based on configurable parameters such as users, user roles, opportunities, products, etc.
- Empowered engineers to increase the accuracy and reliability of the Sales Cloud Enterprise Forecasting feature by utilizing the DataGen tool in software testing
- Worked with the Sales Cloud Performance Engineering team utilizing tools such as Python, Apache XMLBeans, and Salesforce DataLoader to implement backend software engineering principles
- Created a web app search tool to easily query and visualize articles from the CNN/NBA database
- Utilized AWS Elasticsearch and GraphQL in the backend, ReactJS and MaterialUI in the frontend
- Helped migrate the CNN and NBA Digital databases from REST to GraphQL
- Worked w/ Sales Cloud Performance Engineering team on running automation testing scripts in Soleil (platform that runs automation scripts for Sales Cloud) w/ Python to be displayed in Perf Framework (internal frontend web platform for the test runs).
- Created a report quantifying and measuring the data differences between the Aura and LWC frameworks that were tested
- Built a release page to compare any two features or frameworks in Sales Cloud and Lightning utilizing HTML, JavaScript, NodeJS, Heroku, and MongoDB
- Worked closely with Google Cloud Platform teams, tests products and provides feedback on the products and features.
- Hosted and organized events with hundreds of people to bring awareness to GCP.
- Demoed DialogueFlow,
BigQuery, Cloud Datastore, and other technologies to hundreds of students.
- Aided the College of Computing faculty in the handling of lecture material, office equipment, and technology.
- Managed over $10,000 worth of computing hardware on a daily basis.
- Received a 4.9-star customer satisfaction rating by curating gourmet shaved ice/drinks and handling hundreds of customers
- Managed over $300 in cash on a daily basis, improved speed and efficiency of shaved ice by modifying
a step from the creation process.
- Supervised financial transactions, recorded weekly profits, and instructed two new employees on customer interaction.
- An example of creative and clean UX/UI design. This website was built from the ground up, and exhibits proficiency with web development and HTML/CSS/JS, as well as offering an understanding of personal style, taste, and design sensibilities.
- Hosted on GitHub Pages w’ a custom URL domain, utilized Bootstrap for responsive web design.
- Google Home application that interprets the menus on the UGA’s Build Your Plate website and parses for matching keywords based on type of food and phrases said. Tells you what’s for breakfast or if a food you want is at the dining hall
- Built and collaborated with at UGAHacks with Actions on Google, DialogueFlow, and JSON.
- iOS app that outputs scenes of random fun facts and images about myself with each press of a button.
- Solo project built with Swift and XCode to learn functional programming and iOS development.


Judge rules against family in Missouri vaccination lawsuit
KANSAS CITY, Mo. (AP) - A judge has ruled that it isn’t unconstitutional for Missouri health officials to encourage families to vaccinate their children in a form they must complete to get a religious exemption.
KCUR reports that Senior U.S. District Judge Howard Sachs wrote in Friday’s ruling that the state’s freedom to advocate vaccination as governmental policy is “well settled.”
The lawsuit was filed by Linus Baker, who is the grandfather of a student at Crossroads Academy in Kansas City. He also is representing families from Bates, Christian and Miller counties.
Baker says he disagrees with the ruling. Sachs deferred the ruling until Dec. 16 to give the Bakers a chance to secure an exemption.
Baker also has sued in Kansas to keep from having to vaccinate his son. That case is pending.
Information from: KCUR-FM, http://www.kcur.org
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Camden Dedicated schools grant (DSG) for 2019 to 2020
Total allocation for financial year 2019 to 2020
£161,322,000
Download this DSG for 2019 to 2020 (ODS, 206KB)
Published March 2019
This file is in an OpenDocument format
If you need this in a different format, you can request another.
Contents
Total funding factors for schools block after recoupment
£108,135,000
| Current total | |
|---|---|
| Unit of funding | £5,389.25 |
| Pupil number | 10,751 |
| Current total | |
|---|---|
| Unit of funding | £6,928.20 |
| Pupil number | 7,957 |
| Current total | |
|---|---|
| Funding through the premises and mobility factors | £3,410,000 |
| Funding for growth | £458,000 |
| Total schools block before recoupment | £116,935,000 |
|---|
How is this total calculated?
The primary unit of funding (PUF) and secondary unit of funding (SUF) for each local authority are multiplied by:
- PUF from the October 2018 school census
- SUF from the October 2018 school census
We then add:
- the actual 2019 to 2020 funding for the premises and mobility factors
- growth funding using the differences between the primary and secondary numbers on roll in the local authority on the October 2017 and October 2018 school censuses
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
Funding deductions
| Deductions to schools block for recoupment | £8,800,000 |
|---|
| Total schools block after recoupment | £108,136,000 |
|---|
How is this total calculated?
We will calculate amounts for recoupment using data provided on the authority proforma tool (APT) submitted in January 2019.
This is then subtracted from the total initial schools block allocation to get a total schools block allocation after deductions.
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
View previous in-year versions of this funding allocation
Guidance
Total funding factors for central school services block (CSSB)
£1,430,000
| Current total | |
|---|---|
| Unit of funding | £38.57 |
| Total pupil number | 18,708 |
| Current total | |
|---|---|
| Funding for historic commitments | £708,000 |
| Total CSSB block | £1,430,000 |
|---|
How is this total calculated?
We multiply the CSSB units of funding by the total pupil number.
We then add the resulting allocation to the actual funding for historic commitments.
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
View previous in-year versions of this funding allocation
Guidance
Total funding factors for high needs block after deductions
£34,023,000
| Current total | |
|---|---|
| Factor unit rate | £4,822.53 |
| Pupil number in special schools/academies | 342 |
| Current total | |
|---|---|
| NFF allocations, excluding basic entitlement factor and import/export adjustments | £32,692,000 |
| Import/export adjustments (based on January 2018 school census and February R06 2017 to 2018 ILR) | £1,549,000 |
| Mid 2019 age 2 to 18 ONS population projection | 48,568 |
| Additional high needs funding | £534,000 |
| Total high needs block before deductions | £36,424,000 |
|---|
How is this total calculated?
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
Funding deductions
| Deductions to high needs for direct funding of places by the ESFA | £2,401,000 |
|---|
| Total high needs block after deductions | £34,023,000 |
|---|
How is this total calculated?
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
View previous in-year versions of this funding allocation
Guidance
Total high needs block deductions for direct funding of places by the ESFA
£2,401,000
Recoupment academies (SEN units and resourced provision)
| April 2019 to August 2019 | September 2019 to March 2020 | SEN places deduction | ||
|---|---|---|---|---|
| Total | 0 | 0 | £0 |
| April 2019 to August 2019 | September 2019 to March 2020 | SEN places deduction | ||
|---|---|---|---|---|
| Total | 0 | 0 | £0 |
| April 2019 to July 2019 | August 2019 to March 2020 | SEN places allocation | ||
|---|---|---|---|---|
| Total | 0 | 0 | £0 |
| April 2019 to August 2019 | September 2019 to March 2020 | AP places allocation | ||
|---|---|---|---|---|
| Total | 0 | 0 | £0 |
Special academies
| April 2019 to August 2019 | September 2019 to March 2020 | SEN places allocation | ||
|---|---|---|---|---|
| Total | 0 | 0 | £0 |
| April 2019 to July 2019 | August 2019 to March 2020 | SEN places allocation | ||
|---|---|---|---|---|
| Total | 0 | 0 | £0 |
| April 2019 to August 2019 | September 2019 to March 2020 | AP places allocation | ||
|---|---|---|---|---|
| Total | 0 | 0 | £0 |
AP academies
| April 2019 to August 2019 | September 2019 to March 2020 | SEN places allocation | ||
|---|---|---|---|---|
| Total | 0 | 0 | £0 |
| April 2019 to August 2019 | September 2019 to March 2020 | AP places allocation | ||
|---|---|---|---|---|
| Total | 2 | 2 | £20,000 |
Maintained special schools
| April 2019 to July 2019 | August 2019 to March 2020 | SEN places deduction | ||
|---|---|---|---|---|
| Total | 16 | 0 | £53,000 |
Maintained mainstream schools
| April 2019 to July 2019 | August 2019 to March 2020 | SEN places deduction | ||
|---|---|---|---|---|
| Total | 30 | 0 | £60,000 |
Hospital academies
| April 2019 to August 2019 | September 2019 to March 2020 | Total Hospital Education | ||
|---|---|---|---|---|
| Total | £0 | £0 | £0 |
16 to 19 academies and free schools
| April 2019 to August 2019 | September 2019 to March 2020 | Total 16 to 19 places allocation | ||
|---|---|---|---|---|
| Total | £0 | £0 | £0 |
FE and ILP
| April 2019 to July 2019 | August 2019 to September 2020 | Total FE and ILP | ||
|---|---|---|---|---|
| Total | 298 | 418 | £2,268,000 |
| Total deduction to high needs block | £2,401,000 |
|---|
Find out more about high needs places allocations
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
View previous in-year versions of this funding allocation
Guidance
Total funding factors for early years block
£17,734,000
| Current total | |
|---|---|
| Early Years National Funding Formula (EYNFF) LA hourly rate for 3 and 4 year olds (£ / hr) | £8.51 |
| Part time equivalent (PTE) 3 and 4 year old child numbers | 2,701.07 |
| Initial funding allocation for universal entitlement for 3 and 4 year olds | £13,102,000 |
How is this total calculated?
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
| Current total | |
|---|---|
| Early Years National Funding Formula (EYNFF) LA hourly rate for 3 and 4 year olds (£ / hr) | £8.51 |
| Estimated PTE 3 and 4 year old child number | 546 |
| Initial funding allocation for universal entitlement for 3 and 4 year olds | £2,649,000 |
How is this total calculated?
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
| Current total | |
|---|---|
| LA hourly rate for 2 year old entitlement (£ / hr) | £6.50 |
| PTE 2 year old child numbers | 492 |
| Initial funding allocation for universal entitlement for 3 and 4 year olds | £1,822,000 |
How is this total calculated?
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
| Current total | |
|---|---|
| Initial funding allocation for Early Years Pupil Premium | £110,000 |
| Initial funding allocation for Disability Access Fund | £51,000 |
| Initial allocation for maintained nursery school supplementary funding | 0 |
| Total early years block | £17,734,000 |
|---|
How is this total calculated?
For more information on these calculations, you can visit the dedicated schools grant: technical note 2019 to 2020.
View previous in-year versions of this funding allocation


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