WITNESS LIST
County Affairs Committee
November 16, 2016 - 10:00 AM
Child Protective Services
For:
Brown, Vanessa (Self; Texas Department of Family & Protective Services)
Estrada, Leinady (Self; Texas State Employees Union)
Garcia, Nora (Self; Texas State Employee Union)
Gonzalez, Liliana (Self; Texas State Employees Union)
Lopez, Stephanie (Self)
Patel, Dimple (TexProtects: The Texas Association for the Protection of Children)
Sanders, Kimberly (Texas State Employee Union)
Watson, Maleeta (Self; Tseu)
On:
Asbury, Cynthia (Self)
Benson, Shametta (Self; TSEU)
Cumberbatch, April (Texas State Employees Union)
Drozd, Marilyn (Self)
Edwards, Michael (TSEU)
Evens, Kristin (Self)
Francis, Will (National Association of Social Workers - Texas Chapter)
Griffin, Tamela (Health and Human Services Commission)
Guidry, Sarah (Self; Earl carl institute at Texas Southern University)
Irvin, Bradford (Self)
Jones, Aurora (Travis County Civil District Courts)
Lacefield lewis, Lauren (HHSC)
Margolin, Ann (Tex Protects)
McCown, Scott (Self)
Mendez, Lisa (TSEU)
Perez, Elena (Self)
Richards, Ellen (Austin Travis County Integral Care)
Votaw, Mary (Self; Texas State Employees Union)
Whitman, Hank (DFPS)
Wyatt, Toni (Self)
Registering, but not testifying:
For:
Rice, Rebecca (Self)
On:
Blackmore, Lynn (DFPS)
Blackstone, Kristene (CPS)
Carver, Roya (Cps centralized placement unit coordinator)
Cleveland, Tria (Self)
Eckstein, Deece (Travis County Commissioners Court -- Resource Witness Only)
Giardino, Timothy (DFPS)
Hillman, Michelle (Self)
1
WITNESS LIST
On:
Johnson, Sheniqua (DFPS)
Kanne, Lisa (DFPS)
Kimbrough, Michelle (Travis county Parenting in Recovery/ Family Drug Treatment Court)
Palomino, Irma (Self; Texas state employees union)
Sadler, Jerri (Self)
Subia, Lisa (DFPS)
Wilson, Columba (Self)
Woodruff, Trevor (DFPS)
DPS and Criminal Justice Reform
For:
Pardue, Craig (Self)
On:
Deitch, Michele (Self)
Fabelo, Tony (Council of State Governments, Justice Center)
Gnaedinger, Amanda (Texas Criminal Justice Coalition)
Hansch, Gregory (National Alliance on Mental Illness (NAMI) Texas)
McCraw, Steve (Texas Department of Public Safety)
Natarajan, Ranjana (Self)
Stamm, Alex (UT School of Law Civil Rights Clinic)
Washington, Craig A. (Self)
Woods, Ashton p. (Black Lives Matter: houston)
Registering, but not testifying:
For:
Mitchell, Kathy (Texas criminal justice coalition)
Moore, Chas (Self; Austin justice coalition)
2


Wellness coaching initially evolved out of need to help my children who were suffering with ADD and eczema. Conventional methods were not helping them, so I began searching for answers on my own. Through trial and error, I found the Chek Institute in 2013 and began taking courses in Holistic Lifestyle Coaching. What began as a way to find some answers for my family has since turned into a passion for overall health and wellness. I discovered that nutrition was a great start, but how you truly take care of your body through proper movement, hydration, relaxation, beneficial thoughts and sleep are very powerful tools for true health and longevity.
Because I knew this journey wasn’t complete I began taking a few classes with Dr. Guy Voyer and am now in the process of becoming a Soma Trainer. Soma (body) Training has taken Wellness Coaching to a new level and it has enabled me to help people relieve back pain, gain strength and teach individuals to learn how to take care of their own bodies.
I am constantly learning in an effort to truly help people become the best version of themselves and feel good so they can remain active for as long as possible.


{% extends "base.html" %} {% block content %} Office TEL: +886-2-2876-3030 EMAIL<EMAIL_ADDRESS>ADDRESS: 2F, No.1-5, Tianmu E. Rd., Shilin Dist. Taipei City 111, Taiwan Weekdays: M-F 1 p.m. - 9 p.m. Form powered by QuickSchools.com - School Management System Form powered by QuickSchools.com - Student Information System {% endblock %}
TEL: +886-2-2876-3030 EMAIL<EMAIL_ADDRESS>ADDRESS: 2F, No.1-5, Tianmu E. Rd., Shilin Dist. Taipei City 111, Taiwan Weekdays: M-F 1 p.m. - 9 p.m.


the neg price one changed how it showed in the orderbook btw
before I cancelled it:
{
"funcid": "s",
"txid": "396425cf7ff031e4c0263b3e379f40af4c1d742ace8a1a8283bbc06ce9aa4b5c",
"vout": 0,
"amount": "15000",
"askamount": "15000",
"origaddress": "RPbmcj1sJqh3K1RXSx6ftbvAMAiEoxNG1R",
"tokenid": "9eec77a3e02dec0ca60ead7e8cfb6cb6809c40fe54b804e51d5c6c2a445ffbf3"
},


12 restaurant openings we're excited about
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| |
The eight-unit Terrace is situated on two lots in historic Old Town Lafayette, and was built in 1900. It is a unique example of a "terrace" or row house, built of wood frame and clapboard rather than the usual masonry. The building is designed in the Colonial Revival style which is unique for these humble, three room units. The Terrace was used to house coal miners from 1900 through the 1930's. At the rear there was originally a circular bathhouse, where miners washed before going into their homes. There were also outhouses for the units along the alley. In the 1960s, the bathhouse and outhouses were torn down and bathrooms were built on the back porches of the units. One of its original owners was W. P. Cannon, who sank the first coal shaft on the Miller farm in 1887. Several units were damaged by fire in 2000, and much of The Terrace is currently under restoration. |
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--- title: Build your custom image classifier using FastAI and WML-CE keywords: fastai sidebar: home_sidebar ---
This notebook was copied and modified for this lab from this source https://github.com/fastai/course-v3
Welcome to lesson 1! For those of you who are using a Jupyter Notebook for the first time, you can learn about this useful tool in a tutorial we prepared specially for you; click File
->Open
now and click 00_notebook_tutorial.ipynb
.
In this lesson we will build our first image classifier from scratch, and see if we can achieve world-class results. Let's dive in!
Every notebook starts with the following three lines; they ensure that any edits to libraries you make are reloaded here automatically, and also that any charts or images displayed are shown in this notebook.
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import os
# For Using GPU's
os.environ["CUDA_VISIBLE_DEVICES"]="0"
We import all the necessary packages. We are going to work with the fastai V1 library which sits on top of Pytorch 1.3. The fastai library provides many useful functions that enable us to quickly and easily build neural networks and train our models.
For this class, we are using WML-CE running in the Worldwide Client Experience Center Cloud, or CECC for short. To access and environment, simply browse to this website and request a Power8 or Power9 environment.
https://www.ibm.com/it-infrastructure/services/cecc-portal/web/Catalog
https://github.ibm.com/vanstee/aicoc-ai-immersion/blob/master/FastAI/setup_fastai.sh
def getconfig(cfg_in={}):
cfg = {}
cfg["bs"] = 16
cfg["image_dir"] = "/home/cecuser/FastAI/images"
cfg["classes"] = ["cars","busses","trucks"]
cfg["num_images"] = {"train":200,"valid":20,"test":20}
cfg["d_partitions"] = list(cfg["num_images"].keys())
cfg["jpeginfo"] ="/home/cecuser/aicoc-ai-immersion/FastAI/jpeginfo"
cfg["repo_dir"] = "/home/cecuser/aicoc-ai-immersion/FastAI"
# overwrite configs if passed
for (k,v) in cfg_in.items() :
nprint("Overriding Config {}:{} with {}".format(k,cfg[k],v))
cfg[k] = v
return cfg
# bs = 16 # uncomment this line if you run out of memory even after clicking Kernel->Restart
Here we are going to build our own dataset !! Think of 3 categories you would like to classify images. In this example, we will use
We will use an open source tool called googliser to download our images from google images.
For a Covid-19 based example you could make your classes something like
#################################################################################################
# @@ Students : Customize this cell with your custom classes for image classification
################################################################################################
# Overrides for lab
mycfg = {
"classes":["people wearing masks","people posing street","people skateboarding","people on bikes"], ## <<- CLASS Enter your search terms here
"d_partitions":["train"],
}
cfg=getconfig(mycfg)
FastAI is very flexible and help you label your image data in all sorts of ways.  You might have your images organized by folder with the folder name being the labels.
To see all the supported methods check out this link.. https://docs.fast.ai/vision.data.html#ImageDataBunch.from_folder
For our class, we are going to organize our data by folder.
TODO : insert image here ...
# Helpers to make directories
def class_folder_name(base,d_part,cls) :
return base+"/"+d_part+"/"+ cls.replace(" ","_")
def makeDirIfNotExist(directory) :
if not os.path.exists(directory):
nprint("Making directory {}".format(directory))
os.makedirs(directory)
else :
nprint("Directory {} already exists .. ".format(directory))
# Build directory hierarchy
# [train|valid|test ]
# -----------------> [class1 | class2 | class...]
for d_part in cfg["d_partitions"] :
for cls in cfg["classes"] :
directory=class_folder_name(cfg['image_dir'],d_part,cls)
makeDirIfNotExist(directory)
# install googliser
def install_googliser():
googliser_directory = cfg['repo_dir']+"/googliser"
if not os.path.exists(googliser_directory):
nprint("Installing Googliser here : {} ".format(googliser_directory))
os.chdir(cfg['repo_dir'])
!git clone https://github.com/teracow/googliser
else :
nprint("Googliser already installed here : {} ".format(googliser_directory))
googliser = cfg['repo_dir']+"/googliser/googliser.sh"
return googliser
googliser = install_googliser()
!ls {googliser}
Luckily not too many.. it always depends on the project, but we are going to use a pre-training deep learning network when we perform training. Since that network was already trained on over 1 million images, we don't need to supply too many for our task. This is why transfer learning is so powerful!
cfg['num_images']
# The code below will download files to train folder only to avoid duplicate downloads.
# We then move a few files over. This can be done manually or programatically. For our example
# we will let FastAI do the work for us!
def download_images(cfg):
utility_dir = cfg['repo_dir']
for d_p in cfg["d_partitions"] : # train only for now ..
for cls in cfg["classes"] :
current_dir =class_folder_name(cfg['image_dir'],d_p,cls)
#os.chdir(current_dir)
os.chdir(utility_dir)
command = googliser + \
" --o {}".format(current_dir) +\
" --phrase \"{}\"".format(cls) + \
" --parallel 50 --upper-size 500000 --lower-size 2000 " + \
" -n {}".format(cfg['num_images'][d_p]) + \
" --format jpg --timeout 15 --safesearch-off "
nprint(command)
!{command}
nprint("Downloads complete!")
download_images(cfg)
# This will take ~5 mins depending on how many classes / images we are grabbing...
# clean with jpeginfo!
def clean_up_bad_jpegs(cfg,extension):
os.chdir(cfg['image_dir'])
nprint("Search for Error files in {}......".format(cfg['image_dir']))
# handle both jpg //jpeg
command = "find . -name \"*.{}\"".format(extension) + \
" | xargs -i {}".format(cfg["jpeginfo"]) + \
" -c {} | grep ERROR"
nprint("Running command : {}".format(command))
!{command}
nprint("Removing any error files listed above")
command = command + ' | cut -d " " -f1 | xargs -i rm {} '
nprint("Running command : {}".format(command))
!{command}
nprint("Done")
clean_up_bad_jpegs(cfg,"jpg")
clean_up_bad_jpegs(cfg,"jpeg")
# find . -name "*.jpg," | xargs -i ./jpeginfo -c {}
# find . -name "*.jpg" | xargs -i ./jpeginfo -c {} | grep ERROR | cut -d " " -f1 | xargs -i rm {}
#path = untar_data(URLs.PETS); path
path = Path(cfg['image_dir'])
path.ls()
#cfg['image_dir']
The first thing we do when we approach a problem is to take a look at the data. We always need to understand very well what the problem is and what the data looks like before we can figure out how to solve it. Taking a look at the data means understanding how the data directories are structured, what the labels are and what some sample images look like.
The main difference between the handling of image classification datasets is the way labels are stored. In this particular dataset, labels are stored in the filenames themselves. We will need to extract them to be able to classify the images into the correct categories. Fortunately, the fastai library has a handy function made exactly for this, ImageDataBunch.from_name_re
gets the labels from the filenames using a regular expression.
#doc(ImageDataBunch)
doc(ImageDataBunch.from_folder)
# https://docs.fast.ai/vision.data.html#ImageDataBunch.from_folder
## Important, batch size should be set lower than the number of images you have
data = ImageDataBunch.from_folder(path=cfg['image_dir'],
train="train",valid_pct=0.20,no_check=True,
size=224,num_workers=4,bs=cfg["bs"]).normalize(imagenet_stats)
data
data.show_batch(rows=4, figsize=(9,9))
#data.show_batch(figsize=(4,4))
data.label_list
print(data.classes)
len(data.classes),data.c
Now we will start training our model. We will use a convolutional neural network backbone and a fully connected head with a single hidden layer as a classifier. Don't know what these things mean? Not to worry, we will dive deeper in the coming lessons. For the moment you need to know that we are building a model which will take images as input and will output the predicted probability for each of the categories (in this case, it will have 37 outputs).
#create learner
learn = cnn_learner(data, models.resnet34, metrics=error_rate)
# save pretrained model ..
learn.model
# dont worry about the #na#, as long as we get a plot in the next section, great!
learn.lr_find() # num_it=36, stop_div=False
learn.recorder.plot()
# Run 5 epochs (can do more if still getting better train / val)
# FILL IN THE RANGE BASE ON YOUR LRFIND RESULT
learn.fit_one_cycle(5, max_lr=slice(5e-5,1e-2))
Let's see what results we have got.
We will first see which were the categories that the model most confused with one another. We will try to see if what the model predicted was reasonable or not. In this case the mistakes look reasonable (none of the mistakes seems obviously naive). This is an indicator that our classifier is working correctly.
Furthermore, when we plot the confusion matrix, if we can see that the distribution is heavily skewed: the model makes the same mistakes over and over again but it rarely confuses other categories. This suggests that it just finds it difficult to distinguish some specific categories between each other; this is normal behaviour.
interp = ClassificationInterpretation.from_learner(learn)
losses,idxs = interp.top_losses()
len(data.valid_ds)==len(losses)==len(idxs)
doc(interp.plot_top_losses)
interp.plot_top_losses(9, figsize=(19,11),heatmap=True)
interp.plot_confusion_matrix(figsize=(12,12), dpi=60)
interp.confusion_matrix()
interp.most_confused(min_val=2)
Since our model is working as we expect it to, we will unfreeze our model and train some more.
learn.save('stage-1')
learn.unfreeze()
learn.fit_one_cycle(5)
#learn.save('stage-2')
#learn.load('stage-1');
#learn.unfreeze(-2)
#learn.fit_one_cycle(2, max_lr=slice(1e-6,1e-3))
interp = ClassificationInterpretation.from_learner(learn)
losses,idxs = interp.top_losses()
len(data.valid_ds)==len(losses)==len(idxs)
interp.plot_confusion_matrix(figsize=(12,12), dpi=60)
That's a pretty accurate model!
Now we will train in the same way as before but with one caveat: instead of using resnet34 as our backbone we will use resnet50 (resnet34 is a 34 layer residual network while resnet50 has 50 layers. It will be explained later in the course and you can learn the details in the resnet paper).
Basically, resnet50 usually performs better because it is a deeper network with more parameters. Let's see if we can achieve a higher performance here. To help it along, let's us use larger images too, since that way the network can see more detail. We reduce the batch size a bit since otherwise this larger network will require more GPU memory.
Can you redo the training above with a different pre-trained model ??? See this page for some ideas
https://docs.fast.ai/vision.models.html
doc(cnn_learner)
#learn = cnn_learner(data, FILL_IN_HERE!!, metrics=error_rate)
learn = cnn_learner(data, models.vgg11_bn, metrics=error_rate)
# model.alexnet
learn.lr_find()
learn.recorder.plot()
learn.fit_one_cycle(2) # 2 epochs ...
learn.save('stage-1-custom')
learn.unfreeze()
learn.fit_one_cycle(3, max_lr=slice(1e-6,1e-4))
If it doesn't get better results, you can always go back to your previous model.
learn.load('stage-1-50');
interp = ClassificationInterpretation.from_learner(learn)
interp.plot_confusion_matrix(figsize=(12,12), dpi=60)
interp.most_confused(min_val=2)
So you want to train on some data that maybe you labelled in another tool ? No problem. Here we show how you could read in data exported from PAIV and classifying in this tool ...
For our IBM Visual Insights Example, we can just run a nice utility to reformat the output. For convenience, we added a Bananas Dataset to our repo to play with ..
os.chdir(cfg["repo_dir"])
!ls *.zip
When you export data from IVI, you get a single zip file. When you unzip this file, you get a singular directory with a metadata file called prop.json. We have a utility that will read that prop.json and create a new directory with sorted images... lets see how this works
# unzip the Bananas.zip to /tmp/bananas_ivi directory
!sudo yum install -y unzip
!unzip -o -d /tmp Bananas.zip
# Now run our handy script !
!git clone https://github.com/dustinvanstee/powerai-vision-utils.git
!conda install -y scikit-learn # dependency to run our utility ..
!python powerai-vision-utils/reorganize_exported_dataset.py --directory_in /tmp/Bananas --directory_out /tmp/bananas_fastai/train
!ls /tmp/bananas_fastai/train
# New project config!
mycfg = {
"image_dir" : "/tmp/bananas_fastai",
"classes":["black","green","overripe","yellows"], ## <<- CLASS Enter your search terms here
"d_partitions":["train"],
}
ivicfg=getconfig(mycfg)
# New Image Path ..
path = Path(ivicfg["image_dir"])
path
!ls /tmp/bananas_fastai/black
# Define some transforms and setup data bunch
tfms = get_transforms(do_flip=True)
data = ImageDataBunch.from_folder(path,train="train", ds_tfms=tfms, size=100, valid_pct=0.20)
data
# Display some images
data.show_batch(rows=3, figsize=(5,5))
# Setup a CNN learner
learn = cnn_learner(data, models.resnet18, metrics=accuracy)
learn.fit_one_cycle(5)
# Learning rate search
learn.lr_find()
learn.recorder.plot()
# Run fit one cycle for 5 epochs
learn.fit_one_cycle(5,slice(1e-5,3e-2))
# Lets see how well we did ...
interp = ClassificationInterpretation.from_learner(learn)
losses,idxs = interp.top_losses()
len(data.valid_ds)==len(losses)==len(idxs)
interp.plot_confusion_matrix(figsize=(12,12), dpi=60)
# Download some sample images here ....
command = googliser + \
" --o /tmp/banana_inference "+\
" --phrase \"ripe black yellow green bananas\" " + \
" --parallel 50 --upper-size 500000 --lower-size 2000 " + \
" -n 10 " + \
" --format jpg --timeout 15 --safesearch-off "
print(command)
!{command}
# get rid of malformed images manually .
command = cfg["jpeginfo"] + " -c /tmp/banana_inference"
!{command}
#!ls /tmp/banana_inference
#!rm /tmp/banana_inference/image\(0020\).jpg
# Inference on your list of images ..
import glob
files = glob.glob("/tmp/banana_inference/image*.jpg")
def single_inference(img_path) :
img = open_image(img_path)
prediction=learn.predict(img)
#print(prediction)
img_cls = data.classes[prediction[1].numpy()]
img.show(title=img_cls)
for my_img in files :
single_inference(my_img)
Congratulations, you can now build a pretty darn good classifier in less than one hour!
Credits


class FormatController extends Controller
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- new FormatController(planningUnitHelper: PlanningUnitHelper, sh: ScheduleHelper, config: Config)
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def
##(): Int
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==(arg0: Any): Boolean
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def
asInstanceOf[T0]: T0
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def
buildEmployeeGroup(token: String): Unit
Builds an EmployeeGroup.
Builds an EmployeeGroup. Used for updated information about members.
- token
the token provided by Axians.
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clone(): AnyRef
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def
getAllCollisionStatesUnderPlannerBetween(id: String, start: String, end: String): Result
Return all collision states and collision groups for all new or in consideration holiday messages under a certain planner
Return all collision states and collision groups for all new or in consideration holiday messages under a certain planner
- returns
200 OK Json format of "getAllCollisionStatesAndGroupsForPlanningUnits" 401 Unauthorized when person is not authorized. 406 Not acceptable when the strings are in the wrong format or another error occurs 404 Not found when the planner id does not exist
-
def
getAllCollisionStatesUnderPlanningUnitBetween(id: String, start: String, end: String): Result
Return all collision states and collision groups for all new or in consideration holiday messages under a certain planning unit
Return all collision states and collision groups for all new or in consideration holiday messages under a certain planning unit
- returns
200 OK Json format of "getAllCollisionStatesAndGroupsForPlanningUnits" 401 Unauthorized when person is not authorized. 406 Not acceptable when the strings are in the wrong format or another error occurs 404 Not found when the planning unit id does not exist
-
def
getAllCollisionStatesUnderPlanningUnitBetweenRequest(token: String, unitid: String, lstartdate: LocalDate, lenddate: LocalDate): JsonNode
Auxiliary function for getAllCollisionStatesUnderPlanningUnitBetween
-
def
getAllCollisionStatesUnderPlannnerBetweenRequest(token: String, plannerID: String, lstartdate: LocalDate, lenddate: LocalDate): JsonNode
Auxiliary function for getAllCollisionStatesUnderPlannerBetween
-
def
getAllHMIDsPerPlanningUnitUnderPlannerWithID(plannerID: String): Result
Return all holidayMessage id's and states that are new or in consideration under the unitid's of the planner
Return all holidayMessage id's and states that are new or in consideration under the unitid's of the planner
- returns
200 OK [ { unitID: id of the planningUnit holidayMessages [ { id: id of the HM state: state of the HM (New or In Consideration) }, .... ] } ] 401 Unauthorized when person is not authorized. 406 Not acceptable when another error occurs.
-
def
getAllHolidayMessagesForPlanningUnitsRequest(token: String, units: List[String]): JsonNode
Given a token and planning unit id's, this function calculates the membersPerPlanningsUnitID-, members- and hm- object jsonIfyPeople needs.
Given a token and planning unit id's, this function calculates the membersPerPlanningsUnitID-, members- and hm- object jsonIfyPeople needs.
- Exceptions thrown
-
def
getAllHolidayMessagesPerPlanningUnit(ids: List[String]): Result
Returns a JSON in which all members and planning units are listed for each given planning unit on it's own.
Returns a JSON in which all members and planning units are listed for each given planning unit on it's own. The format is basically that of jsonIfyPeople but pasted into an array, one time for every planning unit.
-
def
getAllHolidayMessagesShortOfEveryone(): Result
Return literally all holidayMessages in the database in a short format.
Return literally all holidayMessages in the database in a short format.
- returns
200 OK [ { id: id of the HM exactDates [ { date [ 'year': int yyyy 'month': int m or mm 'day': int d or dd ] daypart: "AM" or "PM" id: id of the exactDate }, ... ] state: state of the request: APPROVED, NEW, IN CONSIDERATION or REJECTED type: type of the absence: Educative, Yearly, Sickness, ... }, ... ] 401 Unauthorized when person is not authorized. 406 Not acceptable when another error occurs.
-
def
getAllHolidayMessagesUnderPlannerWithId(id: String): Result
Returns all people and their skills, locations, ...
Returns all people and their skills, locations, ... and holidayMessages in a detailed JSON-format See the comment above jsonIfyPeople for the format
- returns
200 OK 401 Unauthorized when person is not authorized. 404 Not found when the planner id does not exist 406 When another error occurs
-
def
getAllHolidayMessagesUnderPlannerWithIdRequest(token: String, id: String): JsonNode
Auxiliary function for getAllHolidayMessagesUnderPlannerWithId Calls getAllHolidayMessagesForPlanningUnitsRequest with all unit ids the given planner is planner of.
-
final
def
getClass(): Class[_]
- Definition Classes
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def
getOwnAbsenceTypes(): Result
Returns the absence types defined by this back end.
Returns the absence types defined by this back end. e.g Yearly, Sickness, ....
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200 OK [ type, ..., type ] 401 Unauthorized when person is not authorized.
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def
hashCode(): Int
- Definition Classes
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
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def
isPlanningStillPossibleForPlanningUnitsWithoutPeople(start: String, end: String, ids: List[String]): Result
Calculates whether a valid planning can be made if certain people are let out.
Calculates whether a valid planning can be made if certain people are let out.
- returns
200 OK {isPossible: true/false} 404 Not found when a planning unit id does not exist 406 When another error occurs 401 Unauthorized when person is not authorized.
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )


By :
DATE
RATING
DOWNLOADS
Type :
NATURAL
RAIN
FOREST
FLOWER
MOUNTAIN
OCEAN
ORCHARD
CITY
CARS
select type
NATURAL
RAIN
FOREST
FLOWER
MOUNTAIN
OCEAN
ORCHARD
CITY
CARS
keyword
👆🏻 👆🏻 👆🏻 👆🏻 👆🏻
SAV
RES


Alissa Scheller
Rural hospitals are closing, and Georgia is ground zero. See more.
Fentanyl-related deaths are on the rise. See more.
South Korea has thousands of bomb shelters. See more.
2016 was the deadliest year for migrants in the Mediterranean. See more.
The coastal communities at risk for sea-level rise. See more.
Tracking 'blue lives matter' laws. See more.
Candidate drawings for a peice on health and the 2016 primary horserace.
2016 election results. See more.
Mapping police complaints in Chicago. See more.
A letterpressed wedding invitation suite. See more.
Greeting cards and products designed for local company. See more.
Cicadas! Love 'em. See more.
Annual Halloween cards... from my cats. See more.
Book and report layouts from the Center for American Progress. See more.


HW1 - Part 2
CSE 134B - Summer 2017
1. Button and script test
2. Form and Input tests
Using text fields, form, fieldset, radio buttons and checkboxes
Enter the text you want to display, and select some options
3. Object and Param test
Embedding our team page as an object
Embedding a video as an object with param tag's autoplay param set to true


Public Types | |
| typedef boost::mpl::if_c < boost::is_void < CalculationType > ::type::value, typename select_coordinate_type < Geometry1, Geometry2 >::type, CalculationType >::type | type |
Based on two input geometry types, and an input calculation type, (which defaults to void in the calling function), this meta-function selects the most appropriate:
Definition at line 32 of file select_calculation_type.hpp.
| typedef boost::mpl::if_c< boost::is_void<CalculationType>::type::value, typename select_coordinate_type < Geometry1, Geometry2 >::type, CalculationType >::type boost::geometry::select_calculation_type< Geometry1, Geometry2, CalculationType >::type |
Definition at line 44 of file select_calculation_type.hpp.
|
December 1, 2009 |
Copyright © 1995-2009 Barend Gehrels, Geodan, Amsterdam Copyright © 2008-2009 Bruno Lalande, Paris Copyright © 2009 Mateusz Loskot, Cadcorp, London |


|
SideCar
|
#include <vector>
#include "boost/shared_ptr.hpp"
#include "QtCore/QHash"
#include "QtCore/QObject"
#include "GUI/QtMonitor.h"
#include "Messages/MetaTypeInfo.h"
Go to the source code of this file.
Data Structures | |
| class | SideCar::GUI::ServiceBrowser |
| A refined Zeroconf browser for use by Qt widgets. More... | |
Namespaces | |
| Logger | |
| Namespace for the Log logging device and support classes. | |
| SideCar | |
| Namespace for all entities specific to the 331 SideCar project. | |
| SideCar::Zeroconf | |
| Namespace for entities that interface with Zeroconf library. | |
| SideCar::GUI | |
| Namespace for the GUI applications. | |
Typedefs | |
| using | SideCar::GUI::ServiceEntryHash = QHash< QString, ServiceEntry * > |
| using | SideCar::GUI::ServiceEntryList = QList< ServiceEntry * > |


--- layout: post title: 'OData Service Validation Tool Update: 7 new rules' date: 2012-01-30 22:45:02.000000000 +08:00 categories: - OData tools tags: [] status: publish type: post published: true author: OData.org Archive ---
OData Service Validation Tool was updated with 7 more new rules:
This rule update brings the total number of rules in the validation tool to 147. You can see the list of the active rules here and the list of rules that are under development here.


Basic lunr-search demo
This illustrates some data binding
leveraging this as a data set
.
Found [[__results.length]] results.
[[item.title]]
[[item.location]]
[[item.description]]..


Unicode library for C++ by Ross Smith
#include "unicorn/table.hpp"
Unicorn::
Table
is a utility class for simple table layout in fixed width
text, a task often useful in formatting the output from command line programs
and similar utilities. Tables are compiled in UTF-8 but can be written to
strings or output streams of any UTF encoding.
Example:
Table tab;
tab << "Name" << "Number" << "Hex" << "Float" << '\n'
<< '=';
tab.format("", "", "0x$1x3", "$1fs2");
tab << "Patrick McGoohan" << 6 << 6 << 6 << '\n'
<< "James Bond" << 007 << 007 << 007 << '\n'
<< "Douglas Adams" << 42 << 42 << 42 << '\n'
<< "Maxwell Smart" << 86 << 86 << 86 << '\n';
cout << tab;
Output:
Name Number Hex Float
================ ====== ===== ======
Patrick McGoohan 6 0x006 +6.00
James Bond 7 0x007 +7.00
Douglas Adams 42 0x02a +42.00
Maxwell Smart 86 0x056 +86.00
Table::
Table
()
Table::
Table
(const Table& t)
Table::
Table
(Table&& t) noexcept
Table::
~Table
() noexcept
Table& Table::
operator=
(const Table& t)
Table& Table::
operator=
(Table&& t) noexcept
Life cycle functions.
void Table::
clear
() noexcept
Clears all cell data and formatting settings from the table (i.e. resets the table to its default constructed state).
template <typename... FS> void Table::
format
(const u8string& f, const FS&... fs)
This sets up formatting for the table's columns, using the formatting codes
defined in unicorn/format
. Each formatting element should be
of the form "${1...}"
, with flags appropriate to the type of data expected
in that column. Columns for which no format has been set, or whose format is
an empty string, will be written using to_str()
(from Prion, but called
without namespace qualification, so user defined overloads will be found).
Formatting is applied to cell data entered after a call to format()
;
existing cells already in the table will not be reformatted.
template <typename T> Table& Table::
operator<<
(const T& t)
Adds a data cell to the table. The cell will be formatted according to the current column's formatting code, if one has been set.
Table& Table::
operator<<
([character type] c)
Single character insertion is used to request various miscellaneous control operations, as described in the table below.
| Character | Effect |
|---|---|
\f |
Clear all formatting codes |
\n |
Start a new row |
\t |
Copy the cell to the left |
\v |
Copy the cell above |
The "\t"
and "\v"
controls simply copy the existing cell's string content
literally; they will not reformat the original data value if the new cell has
a different formatting code. They will produce a blank cell if this is the
first column or row.
If the character is a control character (not listed above), whitespace, or an
unassigned code point, the insertion operator will throw
std::invalid_argument
. Otherwise, the character is used to write a
horizontal rule across the table, by repeating the character to match the
width of each column.
template <typename C, typename... Args> basic_string<C> Table::
as_string
(const Args&... args) const
template <typename... Args> u8string Table::
str
(const Args&... args) const
template <typename C, typename... Args> void Table::
write
(std::basic_ostream<C>& out, const Args&... args) const
These format the table, to a string or an output stream, in the chosen UTF
encoding. The str()
function is shorthand for as_string<char>()
.
By default, string lengths are measured in grapheme units (user perceived
characters) in calculating fixed width table layout. Other measurement flags
(from unicorn/string
) can be passed if required.
If the tab_unfill
flag is passed, cells that are identical to the one
immediately above them will be written using a ditto mark (two apostrophes by
default) instead of repeating their contents.
The following keyword arguments are recognised:
| Keyword | Argument type | Default | Description |
|---|---|---|---|
tab_ditto |
u8string |
"''" |
Symbol used for repeated cells in unfill mode |
tab_empty |
u8string |
"--" |
Symbol used for empty cells |
tab_flags |
uint32_t |
grapheme_units |
How string lengths are measured |
tab_margin |
size_t |
0 |
Number of spaces in the left margin |
tab_spacing |
size_t |
2 |
Number of spaces between columns |
tab_unfill |
bool |
false |
Show repeated cells with a ditto mark |


{% include 'layouts/home_head.html' %}
Skip to content
{% include 'dashboard-layouts/header.html' %}
Last Updated :
Nifty 50 Components
Symbol
Company Name
Last Price
Change
% Change
{% for ticker in stocks%}
{{ticker.0}}
{{ticker.6}}
{{ticker.2}}
{%endfor%}
{% include 'layouts/home_footer.html' %} {% include 'layouts/home_additonal_body_contents.html' %}


SCTE35 tools for parsing in CLI using NodeJS or in a "modern" browser.
Visit https://comcast.github.io/scte35-js/ and paste the following in the text box and hit the Parse
button:
/DBGAAET8J+pAP/wBQb+AAAAAAAwAi5DVUVJQAErgX+/CR9TSUdOQUw6OGlTdzllUWlGVndBQUFBQUFBQUJCQT09NwMDaJ6RZQ==
In order to deploy changes to the demo read the README found at https://github.com/Comcast/scte35-js/tree/master/ui.
// See ISCTE35 for methods & ISpliceInfoSection for results.
import { SCTE35 } from "scte35";
const scte35: SCTE35 = new SCTE35();
const result = scte35.parseFromB64("<base64 string>");
The parser can be executed from the bin by first installing it globally and then executing the scte35
command:
npm i scte35 -g
scte35
> ? Please provide the SCTE-35 tag that you would like to parse
Parsing defaults to base 64, however hexadecimal can easily be parsed as well using the --hex
flag
#default base64
scte35 /DBGAAET8J+pAP/wBQb+AAAAAAAwAi5DVUVJQAErgX+/CR9TSUdOQUw6OGlTdzllUWlGVndBQUFBQUFBQUJCQT09NwMDaJ6RZQ==
#hexadecimal
scte35 --hex fc3046000113f09fa900fff00506fe000000000030022e4355454940012b817fbf091f5349474e414c3a386953773965516946567741414141414141414242413d3d370303689e9165
#both will output the formatted JSON
> {
"tableId": 252,
"selectionSyntaxIndicator": false,
"privateIndicator": false,
...
}
The parser output can be piped into other tools, such as a JSON display utility like fx
in order to visualize the JSON object and interact with it.
npm i -g fx
scte35 /DBGAAET8J+pAP/wBQb+AAAAAAAwAi5DVUVJQAErgX+/CR9TSUdOQUw6OGlTdzllUWlGVndBQUFBQUFBQUJCQT09NwMDaJ6RZQ== | fx
Generated using TypeDoc


NetableError
public enum NetableError : Error
extension NetableError: LocalizedError
extension NetableError: Equatable
All errors returned by Netable are NetableErrors.
-
Something went wrong while encoding request parameters.
Declaration
Swift
case codingError(String)
-
Something went wrong while decoding the response.
Declaration
Swift
case decodingError(Error, Data?)
-
The request was successful, but returned a non-200 status code.
Declaration
Swift
case httpError(Int, Data?)
-
The URL provided isn’t properly formatted.
Declaration
Swift
case malformedURL
-
Request failed to complete, usually due to a connectivity problem.
Declaration
Swift
case requestFailed(Error)
-
The fully qualified URL’s server does not match the base URL. Something’s gone wrong while validating the URL.
Declaration
Swift
case wrongServer
-
The server response was expected to contain data but is instead empty.
Declaration
Swift
case noData
-
Something went wrong while trying to parse response data. Throw this error if something goes wrong while calling Request.finalize().
Declaration
Swift
case resourceExtractionError(String)
-
We’re not sure what went wrong, but something did.
Declaration
Swift
case unknownError(Error)
-
Undocumented
Declaration
Swift
public var errorCode: Int? { get }
-
Declaration
Swift
public var errorDescription: String? { get }
-
Declaration
Swift
public static func == (lhs: NetableError, rhs: NetableError) -> Bool


With a view to provide income support to all Small and Marginal landholding farmer families having cultivable land, the Government has launched PM-KISAN. The scheme aims to supplement the financial needs of the farmers in procuring various inputs to ensure proper crop health and appropriate yields, commensurate with the anticipated farm income.
The Central Sector Scheme - SAMPADA (Scheme for Agro-Marine Processing and Development of Agro-Processing Clusters) was approved by the cabinet in May 2017. The objective of PMKSY is to supplement agriculture, modernize processing and decrease Agri-Waste.
The National Development Council (NDC) in its 53rd meeting held on 29th May, 2007 adopted a resolution to launch a Food Security Mission comprising rice, wheat and pulses to increase the annual production of rice by 10 million tonnes, wheat by 8 million tonnes and pulses by 2 million tonnes by the end of the Eleventh Plan (2011-12).
National Agriculture Market (eNAM) is a pan-India electronic trading portal which networks the existing APMC mandis to create a unified national market for agricultural commodities.
Mission for Integrated Development of Horticulture (MIDH) is a Centrally Sponsored Scheme for the holistic growth of the horticulture sector covering fruits, vegetables, root & tuber crops, mushrooms, spices, flowers, aromatic plants, coconut, cashew, cocoa and bamboo.


I'm Kavan Pancholi
Software Engineer
- E-mail<EMAIL_ADDRESS>- Phone +91-99245-53972
Hello! I’m Kavan Pancholi. Senior Web Developer specializing in front end & back end development. Experienced with all stages of the development cycle for dynamic web projects. Well-versed in numerous programming languages including Javascript and PHP. Strong background in project management and customer relations.
Professional Skills
Work Experience
Ycode - Product of The Remote Company
Software Engineer
Work as a remote developer with responsibility creating Visual development platform for design and agencies. Developing features for No-Code web application builder to transform ideas into dynamic and database driven websites.
Satisfind
Lead Software Engineer
Work as a remote developer with responsibility of developing platform to transform the culture where every executive and employee, primarily in-store leaders and front-liners, is trusted to enrich customer delight.
The Workshore Technologies
Lead Software Engineer
Produce well designed, testable, efficient code in a timely manner to meet delivery timescales/milestones. Review code of other developers to maintain code quality. Work collaboratively with clients and in-house agency teams to provide rapid, robust and client-acclaimed front and back-end web development.
Broker Genius
Software Engineer
Working as part of a multi-disciplinary team, carrying out ad-hoc tasks as requested by the IT Manager. Responsible for Product's improvements, future enhancement and feature development.
Squareyards Consulting Pvt. Ltd.
Sr. Web Developer
I was responsible for working on a range of projects, designing appealing websites and interacting on a daily basis with graphic designers and back-end developers.
Inkrasa
Web Developer
I was responsible for all operations needed for a product development. I've worked with (Front End) designing, (Back End) developing, managing a product.
Entourage Solutions
Junior Web Developer
This was beginning of my career. Designed, developed, managed, operated and promoted an Internet business. Handled customer support issues.
Education
Diploma in Computer Engineering
Gujarat Technological University - VPMP Polytechnic
Advanced Diploma in Computer Hardware & Network Engineering
GT Computer Hardware & Network Engineering College
References
Awards and Achievements
Quarter Award
Rewarded Quarter Award on June, 2015 from Square Yards Consulting Pvt. Ltd.
Pillar Award
Rewarded Pillar Award on October, 2015 from Square Yards Consulting Pvt. Ltd.
Contact Us
- E-mail<EMAIL_ADDRESS>- Phone +91-99245-53972
- Skype kavanpancholi
- Location Ahmedabad, Gujarat, India
Feel free to contact me


Auto Service Solutions provides a reliable auto repair and maintainance services in Tanzania. We are focused on our customers and make sure everyone is comfortable and confident on the services we provide. Whether you need general upkeep and maintainance or a complete vehicle overhaul, Auto Service Solution has been there to provide excellent services and trustworthy advice.
Our customers return to us whenever they have an issue or even a small concern about their vehicles because they know they will receive the very best automotive care. Our highly trained technicans allow us to provide solutions to a wide range of problems, getting you back on road quickly without breaking the bank on repairs. Auto Service Solutions specializes in General services, Engine maintainance, Brakes, Tyre services, Heating and cooling services, Car washes, and Electrical & electronic services and we are proud to help our customers get safely on the road.
Stop in or give us a call at 234556 to let us know how we can help you!


Author writes about designing with the correct typeface; striving for rhythm, proportion, and harmony; choosing and combining type; designing pages; using section heads, subheads, footnotes, and tables; applying kerning and other type adjustments to improve legibility; and adding special characters, including punctuation and diacritical marks.
The Elements of Typographic Style teaches the history of and the artistic and practical perspectives on a variety of type families that are available in Europe and America today.
A few hundred years of type and typography have established rules that only a fool would ignore.
This book is an excellent resource for anyone who is serious about graphic layout.
Swiss Typography is a critical guide for graphic designers. I recommend this fantastic eBook.
Chock full of great photos of lettering with a little info about the artist and projects shown.
This book is an excellent resource for anyone who is serious about graphic layout.
Swiss Typography is a critical guide for graphic designers. I recommend this fantastic eBook.
Chock full of great photos of lettering with a little info about the artist and projects shown.
3125 Flanigan Oaks Drive
Capitol Heights, MD 20027


New York City, as it is frequently shown, is a place of extreme inequality. While this inequality is typically discussed in terms of income, it shows up in myriad other social conditions. This map explores educational attainment levels in NYC, where neighborhoods in which 8 in 10 adults have finished college are a short train ride from areas where over a quarter of adults have less than a 9th grade education.
Neighborhood Tabulation Areas (NTAs) are groups of census tracts that roughly align to commonly understood neighborhoods or combinations of neighboorhoods. They were designed by the NYC Department of City Planning in order to conduct small area population projections. Airports, large parks and cemeteries, and other unpopulated areas are omitted from this map.


abstract fun overwrite(index: Int, value: TData): Unit
Forcefully sets the value or the register with the given index.
This method has similar functionality to the RegisterSetset method but does not check the type of the register -- allowing constant registers to be overwritten without exception.


Æther is an iOS 8 application. It is a photo sharing application similar to SnapChat, where photos are shared randomly amongst friends. When a user sends an image out into the "Æther", they immediately receive a picture a friend has posted. I came up with the idea for Æther when trying to consider an application in which a user would need to produce content in order to consume content. Aether has a back-end written in Ruby on Rails, and a front-end written in Swift. Add me as a friend under KohlerExpress.
 I created a music-based blog with Ruby on Rails to catalog and share my favorite songs. The blog allows users to comment on and 'bump' songs. I used Twitter bootstrap for the front end work. The blog is located at scruffysounds.com.
As part of the Entrepreneurs Club InnoWeekend, I worked together with a team to create an idea, a business plan, and a prototype, all in the span of 48 hours. We came up with the idea of join in, software that would gamify the college experience. Students would be awarded points for attending school events, which they could redeem for school merchandise. I was tasked with creating the prototype for the team. I built a website that allows users to create an account/profile, register for upcoming events, and see which events their friends are talking about/attending. The website is located here. Make a fake account and take a look around!
"It's Five Somewhere" is a web app I made using Ruby on Rails. The page displays a location where it is currently 5:00 PM, along with a popular beverage from that part of the world. The website can be seen at itsfivesomewhere.co.
For HackBeanpot 2014, a Boston based hack-a-thon, I worked together with a team to create a humble social network, one where users were not allowed to post about themselves. Users would log in through facebook, and choose a friend to talk about. After adding the functionality for posts to be anonymous, we decided to call it whisperer. We used the facebook open graph api, as well as a natural language processor, that would tint a post green if it was positive, or red if it was negative. The github repository can be seen here.


Folders
<Go Back>
animation...ay.css
animation...on.css
animation...on.css
animation...de.css
animation...nt.css
animation-name.css
animation...te.css
animation...on.css
animation.css
Folders: 1
Files: 9
Size of all files: 0.008 MB


TEST CODE
{{^signedIn}}
Hi there! Please
sign in
{{/signedIn}} {{#signedIn}}
Welcome back, {{username}}!
{{/signedIn}}


SweetFreeStuff.com
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Get a FREE justWink Card with mPerks now thru Saturday 8/29, while supplies
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Free ZeroWater mini a trial-sized ZeroWater filter that fits in any Brita
pitcher. Free ZeroWater Water Pitcher FIlter
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Available for residents of MD, MA, NJ, NY, PA and VA. Free Dove Women or
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This is an practice project dedicated to beans.That means that you will find a list of bean types and a description of each one.
To access the bean list click here


Introduction
This code builds a website which calculates dye tracing dispersion coefficients. It demonstrates the C# Model-View-Controller (MVC) framework. It builds a website with a form which is populated with parameters from a database. The form can be adjusted by the user and then submitted to calculate a dispersion coefficient.
Although you’ll need to download and run the project to see the site working, here’s a screenshot of the page.
Building and testing the source code
To run the site:
- Download the the repo as a zip and unzip it on your machine.
- Download and install Visual Studio (VS) Code.
- Install the latest .Net Software Development Kit (SDK) (this code uses .Net 5; C# 9).
- Open VS Code and install the C# for VS Code extension from Microsoft. You may also find the MSBuild tools useful.
- In VS Code, go
File
->Open Workspace...
and open the filedyetracecalc.code-workspace
, which should be in the unzipped directory (directory probably calleddyetracecalc-main
). - Agree to any boxes that appear asking you to do anything in the way of adding assets or restoring code.
- Open a terminal in VS Code (
Terminal
->New Terminal
). - At the terminal change directory (cd command) to the
DyeTraceCalcMvc
directory, e.g.cd c:\downloads\dyetracecalc-main\DyeTraceCalcMvc
. - Enter
dotnet run
. - Open a web browser with the URL
http://localhost:5000
Troubleshooting
If the site doesn’t run, you may need to rebuild the code to download libraries. At the Terminal prompt, type:
dotnet build
Then try running the code again.
If your browser complains of security issues it may be automatically redirecting to the secure https version; you may need to install a developers’ certificate: at the terminal, enter
dotnet dev--certs https --trust
Close and reopen your web browser.
Before using this code to run actual dye tracing experiments, please note the caveats in the comments for the file /DyeTraceCalcMvc/Calc/Calculator.cs
.
The full repo for the source is on GitHub if you need it.


README
DeviceMapping
Docker
APIClient
BadParameter
Containers
Exception
Images
Info
InternalServerError
IPAMConfig
NetworkingConfig
NotFound
NotYetImplemented
PortBindings
RestartPolicy
ThrottleDevice
Container
CreateBody
HealthCheck
HostConfig
Mount
TmpfsOptions
VolumeOptions
DriverConfig
ULimit
ListResponse
PortConfig
ContainerLimits
Image
ImageCollection
ImageFilters
Info
Tag
module
Docker::APIClient::Info
Direct including types
Docker::APIClient
Defined in:
docker/client/info.cr
Instance Method Summary
#info
Instance Method Detail
def
info
#
[
View source
]


THE SCARLET LETTER A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 5.1
United States, 1995 U.S. Availability: 10/13/95 (wide) Running Length: 2:15 MPAA Classification: R (Sex, nudity, violence) Theatrical Aspect Ratio: 2.35:1
Cast: Demi Moore, Gary Oldman, Robert Duvall, Joan Plowright, Lisa Joliff-Andoh, Edward Hardwicke, Robert Prosky Director: Roland Joffe Producers: Andrew G. Vajna and Roland Joffe Screenplay: Douglas Day Stewart "freely adapted from the novel by Nathaniel Hawthorne" Cinematography: Alex Thomson Music: John Barry U.S. Distributor: Hollywood Pictures
Welcome to Massachusetts, 1666, where Puritans, Indians, and adultery come together in Roland Joffe's overlong, overwrought re-interpretation of Nathaniel Hawthorne's classic, THE SCARLET LETTER. Literary purists will be aghast at some of the liberties taken with the original text, but my complaints have more to do with cinematic misjudgments than with those in the book-to-screen translation. And, believe me, there are plenty of problems with this production without having to resort to a cry of "it's not faithful to the book!"
First and foremost is the casting of Demi Moore as Hester Prynne. While Moore has acted effectively in contemporary films like ST. ELMO'S FIRE and DISCLOSURE, she's out of her depth in this sumptuous period piece. Her range is insufficient for the fiery Hester, resulting in the sort of weak performance that occurs when an actor tries too hard to reach a character that's beyond them. Moore's limitations are further highlighted by the powerful, deeply-emotive job done by her co-star, Gary Oldman, as Hester's guilt-wracked lover, Reverend Dimmesdale.
The movie opens considerably in advance of the novel's first scene, and the script by Douglas Day Stewart delves deeply into early events only hinted at in Hawthorne's tale. Much of this background, despite being speculative, works, although it takes forever for the romance between Hester and Dimmesdale to get off the ground. Once we get through the scene with the two groping in a barn, things start moving. When the Puritan colonists of 17th century Boston discover that Hester, the wife of an absent English doctor, is pregnant by a man whose name she refuses to yield, she is thrown into prison until the child is born. Dimmesdale pleads with her to let him reveal the truth, but she extracts a pledge of silence from him. Her eventual punishment for fornication and adultery is to wear a scarlet "A" on her breast at all times to remind the townspeople of her sin. However, when her long-lost, presumed dead husband, Roger Chillingworth (Robert Duvall), arrives after a period spent as an Indian captive, his wife's public humiliation does little to sate his need for vengeance, a cause to which he devotes all his efforts.
Joffe, who directed THE KILLING FIELDS and THE MISSION, is clearly interested in the conflict between man's view of sin and God's. This forms the core of the film. Is love wrong just because it contravenes certain man-made laws? Themes of guilt, which are so critical to the book, are toned down in the movie, and the character of Chillingworth has been reduced from a bitter, betrayed man to a bent, sadistic killer. Robert Duvall's abilities are seriously underused in the role as envisioned by Joffe and Stewart.
One area where this movie version of THE SCARLET LETTER is masterful is in its depiction of the setting. The mock-up of colonial Boston is convincing, and great care is taken to assure that there are no obvious anachronisms. The forest, which is virtually a character in the novel, has its own life in the film, as well. On the whole, this is a beautiful-looking picture, but THE SCARLET LETTER is in need of more than a nice appearance.
The weakest part of the script is the ending. Aside from straying significantly from that of the book, the final quarter-hour turns this rather lugubrious melodrama into an action film. Someone tuning in late in the proceedings might mistake the finale for a scene from THE LAST OF THE MOHICANS. In principle, I don't have a problem with changing Hawthorne's downbeat conclusion, but the result should be more thoughtful than this painfully facile, "Hollywood-ized" version.
Credit should be given to the film makers for attempting to adapt a classic American novel for modern audiences. (The most recent previous English-language motion picture treatment was made over sixty years ago.) Unfortunately, poor casting and script choices have reduced this film to little more than a period piece soap opera. The letter placed upon Hester's breast may be an "A", but Roland Joffe's film doesn't deserve much better than a "C-" on its report card.
- James Berardinelli<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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What I love most about coding is that it is a constant challenge. The technologies are always growing, changing and advancing so you have to always be learning and adapting. The idea of working on something for hours and days on end and then being able to see the result of that hard work, is something that is super important to me. Full Stack Development gives me the ability to be innovative, analytical, creative, and solve problems in a practical and efficient way and that is all I have ever wanted to find in a career.
When I'm not coding you can find me flailing my body at concerts, singing Bohemian Rhapsody at karaoke bars, doing the robot, driving around listening to my "get the hell out of here" playlist, and chilling with my cats and new puppy. (Charlie Oliver Skiidz, Mike the Cat and Sadie).
Don't hesitate to contact me to provide any feedback on my work, inquire about potential work, or just to say hello and chat!


{% extends "layout.html" %} {% block title %} Market {{ market.slug }} {% endblock title %} {% load humanize %} {% block layout-content %}
Back to top {% if request.user.is_superuser %} Admin view {% endif %}
This is a standard market where your score (in ♠) for a market is given by \[ w \cdot \left(\min\left\{\frac AB, \frac BA\right\}\right)^\alpha \] where $A$ is the correct answer and $B$ is your guess, with $(w,\alpha)$ specified by the market. Visit the market main page for details.
{% endif %} {% endblock layout-content %}


---
layout: archive
title: Lean Prover Zulip Chat Archive
permalink: 113488general/06733modifyingjusttheleanfilesinleancore.html
---
The archive URL format has changed. Please find this page at:
https://leanprover-community.github.io/archive/stream/113488-general/topic/modifying.20just.20the.20lean.20files.2C.20in.20lean.20core.html


The outward traits of someone who has been transformed by God are often called the fruit of the Spirit. These nine traits may seem like small characteristics, but when they grow in a life with God’s help, they shine powerfully. The fruit of the Spirit grows as you live your life surrendered to God’s Holy Spirit.
Read Galatians 5:22-23 and list the fruit of the Spirit below.
1.
2.
3.
4.
5.
6.
7.
8.
9.
Is there one or more of these nine characteristics that is more evident in your life?
Is there a characteristic you would like to be more evident in your life?
Are there people who come to mind whose lives are like living produce sections, growing the fruit of the Spirit in obvious ways?
I love powerful stories of people whose lives were changed by Jesus in a big way. A friend of mine was lost in addiction, hit rock bottom, and found forgiveness and sobriety. A woman I know was angry and bitter, hurting those around her without a second thought. When she met Jesus, her family and coworkers saw an instant, dramatic change in her. One man lived a life shaped by greed, ignoring his close relationships so he could work to attain more and more material things. When God changed his heart, he became one of the most generous people I know.


Alan Baldwin<EMAIL_ADDRESS>- Initial version of ASXXXX & ASLINK.
John Hartman<EMAIL_ADDRESS>- Porting ASXXX & ASLINK for 8051
Dmitry S. Obukhov<EMAIL_ADDRESS>- malloc & serial i/o routines.
Daniel Drotos<EMAIL_ADDRESS>- for his Freeware simulator
Jans J<EMAIL_ADDRESS>and Alan J Demers - Conservative garbage collector for C & C++.
Malini<EMAIL_ADDRESS>- my wife for her patience and support.
Unknown - for the GNU C - preprocessor.


Festus Ali's Portfolio
Festus Ali is a Passionate Django Web Developer with vast experience building production grade web applications. Designs, Develops and Deploys scalable and well tested web applications.
Festus Ali is a Passionate Django Web Developer with vast experience building production grade web applications. Designs, Develops and Deploys scalable and well tested web applications.


Reports method and constructor calls which implicitly use the platform's default charset. These can produce different results on
(e.g. foreign language) systems that use a different default charset, resulting in unexpected behaviour. A quick-fix could be suggested to
specify explicit UTF-8 charset if the corresponding overloaded method is available.
Example:
void foo(byte[] bytes) {
String s = new String(bytes);
}
After the quick-fix is applied, the result looks like this:
void foo(byte[] bytes) {
String s = new String(bytes, StandardCharsets.UTF_8);
}


THESIS
POLONIUM-210 IN SOILS AND PLANTS
Submitted by oe
Wayne R: Hansen
In partial fulfillment of the requirements for the Degree of Doctor of Philosophy Colorado State University Fort Collins, Colorado
August, 1970


I'm Yixuan Wei(韦毅轩), and you can call me Eason, too.
I am a junior at Tsinghua University,
majoring in Department of Automation.
I have taken in several research projects and my interestes are
Contact<EMAIL_ADDRESS>I major in Department of Automation, Tsinghua University, since september, 2016.
Up to now, the overall GPA is 3.6 (/4.0) and I rank 10 (/165).
I take a double major in Business Administration, Tsinghua University, since september, 2017.
Y. Deng*, X. Guo*, Y. Wei*, K. Lu*, B. Fang, D. Guo, F. Sun, H. Liu
* denotes equal contribution
In submission
We design a novel robotic grasping system to automatically pick up objects in a cluttered scene. Resorting to the active exploration, We utilize a deep Q-Network model to help increase lifting success rate for the suction cup.
Webpage ·
Z. Zheng, T. Yu, Y. Wei, Q. Dai, Y. Liu
arXiv 2019
We propose DeepHuman, a deep learning based framework for 3D human reconstruction from a single RGB image. We also contribute THuman, a 3D real-world human model dataset containing approximately 7000 models.
National Scholarship, Ministry of Education of China
The Second Prize in the 36th Challenge Cup Students Extracurricular Academic Science and Technology Contest, Tsinghua University
The First Prize of robotic innovation competition in the 20th National Robot and Artificial Intelligence Competition, Chinese Association for Artificial Intelligence
Comprehensive Scholarship(0.5% of student body per year), Tsinghua University
Social Practice Excellence Award, Tsinghua University
Social Service Excellence Award, Tsinghua University
C/C++, C#, MATLAB, Python
Chinese(Native)
English(TOFEL 102; GRE 321(V:153+Q:168))
Latex, Adobe Photoshop, Adobe Premiere


Resume
Home
Cartoon & Vector
Book Cover
Calendar
Logo
Website Prototype
Portfolio
Recent Projects
NOTED!!! This Website is not Responsive. So use your Computer or Laptop to access this website!!!


This is a single zone residential hydronic system model with an air-source heat pump and floor heating for WP 1.2 of IBPSA project 1.
This model represents a simplified residential dwelling for a family of 5 members. The building envelope model is based on the BESTEST case 900 test case. The envelope model is therefore similar to the one used in IDEAS.Examples.IBPSA.SingleZoneResidentialHydronic but it is scaled to an area that is 4 times larger. Particularly, the model consists of a single zone with a rectangular floor plan of 12 by 16 meters and a height of 2.7 m. The internal wall mass is modelled using a single wall with a a length that equals three times the building width plus two times the building length. This assumes that there are around 12 rooms in the building. The zone further consists of several south-oriented windows, which are modelled using a single window of 24 m2.
Exterior walls
The walls are modelled using IDEAS.Buildings.Components.OuterWall and consist of the following layers:
Name |
Thickness [m] |
Thermal Conductivity [W/m-K] |
Specific Heat Capacity [J/kg-K] |
Density [kg/m3] |
Layer 1 (wood siding) |
0.009 |
0.14 |
900 |
530 |
Layer 2 (insulation) |
0.0615 |
0.04 |
1400 |
10 |
Layer 3 (concrete block) |
0.1 |
0.51 |
1000 |
1400 |
Floor
The floor is modelled using IDEAS.Buildings.Components.SlabOnGround IDEAS.Buildings.Components.SlabOnGround and consists of the following layers:
Name |
Thickness [m] |
Thermal Conductivity [W/m-K] |
Specific Heat Capacity [J/kg-K] |
Density [kg/m3] |
Layer 1 (concrete) |
0.15 |
1.4 |
840 |
2100 |
Layer 2 (insulation) |
0.20 |
0.02 |
1470 |
30 |
Layer 3 (screed) |
0.05 |
0.6 |
840 |
1100 |
Layer 4 (tile) |
0.01 |
1.4 |
840 |
2100 |
Roof
The roof is modelled using IDEAS.Buildings.Components.OuterWall and consist of the following layers:
Name |
Thickness [m] |
Thermal Conductivity [W/m-K] |
Specific Heat Capacity [J/kg-K] |
Density [kg/m3] |
Layer 1 (roof deck) |
0.019 |
0.14 |
900 |
530 |
Layer 2 (fiber glass) |
0.1118 |
0.04 |
840 |
12 |
Layer 3 (plaster board) |
0.01 |
0.16 |
840 |
950 |
The zone is occupied by 5 people before 7 am and after 8 pm each weekday and full time during weekends.
There are no internal loads other than the occupants.
The model uses a climate file containing one year of weather data for Brussels, Belgium.
An air-to-water modulating heat pump of 15 kW nominal heating capacity extracts energy from the ambient air to heat up the floor heating emission system, as shown in Figure 1 below. A fan blows ambient air through the heat pump evaporator when the heat pump is operating. The floor heating system injects heat between Layer 2 (insulation) and Layer 3 (screed), with water as working fluid. The floor heating pump has a nominal mass flow rate of 0.5 kg/s when the heat pump is working.
Heat pump
A water-to-air heat pump with a scroll compressor is used. The heat pump is modelled as described by:
H. Jin. Parameter estimation based models of water source heat pumps. PhD Thesis. Oklahoma State University. Stillwater, Oklahoma, USA. 2012.
with air instead of water blowing through the evaporator. Air condensation is therefore neglected. The model parameters are obtained by calibration of the heat pump model to manufacturer performance data following the procedure explained in this heat pump calibration guide using manufacturer performance data from a Carrier air-to-water heat pump model 30AW015 which data can be found in this manufacturer datasheet.
For more information of the heat pump model we refer to the model documentation.
Fluid movers
The floor heating system circulation pump has the default total efficiency of the pump model, which is 49 % at the time of writing (motor and hydraulic efficiencies are separately 70 % each). Also the fan that blows ambient air through the heat exchanger uses this default total efficiency of 49 %. The nominal mass flow rate of the floor heating circulation pump is of 0.5 kg/s and the nominal pressure rise of the heat pump evaporator fan is of 0.1 kPa.
A baseline controller is implemented to procure comfort within the building zone. A PI controller is tuned with the zone operative temperature as the controlled variable and the heat pump modulation signal for compressor frequency as the control variable, as depicted as C1 in Figure 1 and shown in Figure 2 below. The control variable is limited between 0 and 1, and it is computed to drive the zone operative temperature towards the zone operative temperature setpoint. For baseline control, this setpoint is computed as the heating comfort setpoint plus an offset which varies depending on the occupancy schedule: during occupied periods the offset is set to only 0.2 degrees Celsius and is meant to avoid discomfort from slight oscilations around the setpoint; during unoccupied periods the offset is set to 5.5 degrees Celsius and is meant to compensate for the large temperature setback used during these periods. The latter offset prevents the need of abrubpt changes in the indoor temperature that may not be achievable because of the large thermal inertia of the floor heating system and which would consequently cause discomfort. All other equipment (fan for the heat pump evaporator circuit and floor heating emission system pump) are switched on when the heat pump is working (modulating signal higher than 0) and switched off otherwise. This is depicted as controller C2 in Figure 1.
The model inputs are:
oveFan_u
[1] [min=0.0, max=1.0]: Integer signal to control the heat pump evaporator fan either on or off
oveHeaPumY_u
[1] [min=0.0, max=1.0]: Heat pump modulating signal for compressor speed between 0 (not working) and 1 (working at maximum capacity)
ovePum_u
[1] [min=0.0, max=1.0]: Integer signal to control the emission circuit pump either on or off
oveTSet_u
[K] [min=278.15, max=308.15]: Zone operative temperature setpoint
The model outputs are:
reaCO2RooAir_y
[ppm] [min=None, max=None]: CO2 concentration in the zone
reaCOP_y
[1] [min=None, max=None]: Heat pump COP
reaPFan_y
[W] [min=None, max=None]: Electrical power of the heat pump evaporator fan
reaPHeaPum_y
[W] [min=None, max=None]: Heat pump electrical power
reaPPumEmi_y
[W] [min=None, max=None]: Emission circuit pump electrical power
reaQFloHea_y
[W] [min=None, max=None]: Floor heating thermal power released to the zone
reaQHeaPumCon_y
[W] [min=None, max=None]: Heat pump thermal power exchanged in the condenser
reaQHeaPumEva_y
[W] [min=None, max=None]: Heat pump thermal power exchanged in the evaporator
reaTRet_y
[K] [min=None, max=None]: Return water temperature from radiant floor
reaTSetCoo_y
[K] [min=None, max=None]: Zone operative temperature setpoint for cooling
reaTSetHea_y
[K] [min=None, max=None]: Zone operative temperature setpoint for heating
reaTSup_y
[K] [min=None, max=None]: Supply water temperature to radiant floor
reaTZon_y
[K] [min=None, max=None]: Zone operative temperature
weaSta_reaWeaCeiHei_y
[m] [min=None, max=None]: Cloud cover ceiling height measurement
weaSta_reaWeaCloTim_y
[s] [min=None, max=None]: Day number with units of seconds
weaSta_reaWeaHDifHor_y
[W/m2] [min=None, max=None]: Horizontal diffuse solar radiation measurement
weaSta_reaWeaHDirNor_y
[W/m2] [min=None, max=None]: Direct normal radiation measurement
weaSta_reaWeaHGloHor_y
[W/m2] [min=None, max=None]: Global horizontal solar irradiation measurement
weaSta_reaWeaHHorIR_y
[W/m2] [min=None, max=None]: Horizontal infrared irradiation measurement
weaSta_reaWeaLat_y
[rad] [min=None, max=None]: Latitude of the location
weaSta_reaWeaLon_y
[rad] [min=None, max=None]: Longitude of the location
weaSta_reaWeaNOpa_y
[1] [min=None, max=None]: Opaque sky cover measurement
weaSta_reaWeaNTot_y
[1] [min=None, max=None]: Sky cover measurement
weaSta_reaWeaPAtm_y
[Pa] [min=None, max=None]: Atmospheric pressure measurement
weaSta_reaWeaRelHum_y
[1] [min=None, max=None]: Outside relative humidity measurement
weaSta_reaWeaSolAlt_y
[rad] [min=None, max=None]: Solar altitude angle measurement
weaSta_reaWeaSolDec_y
[rad] [min=None, max=None]: Solar declination angle measurement
weaSta_reaWeaSolHouAng_y
[rad] [min=None, max=None]: Solar hour angle measurement
weaSta_reaWeaSolTim_y
[s] [min=None, max=None]: Solar time
weaSta_reaWeaSolZen_y
[rad] [min=None, max=None]: Solar zenith angle measurement
weaSta_reaWeaTBlaSky_y
[K] [min=None, max=None]: Black-body sky temperature measurement
weaSta_reaWeaTDewPoi_y
[K] [min=None, max=None]: Dew point temperature measurement
weaSta_reaWeaTDryBul_y
[K] [min=None, max=None]: Outside drybulb temperature measurement
weaSta_reaWeaTWetBul_y
[K] [min=None, max=None]: Wet bulb temperature measurement
weaSta_reaWeaWinDir_y
[rad] [min=None, max=None]: Wind direction measurement
weaSta_reaWeaWinSpe_y
[m/s] [min=None, max=None]: Wind speed measurement
The model forecasts are:
EmissionsElectricPower
[kgCO2/kWh]: Kilograms of carbon dioxide to produce 1 kWh of electricity
HDifHor
[W/m2]: Horizontal diffuse solar radiation
HDirNor
[W/m2]: Direct normal radiation
HGloHor
[W/m2]: Horizontal global radiation
HHorIR
[W/m2]: Horizontal infrared irradiation
InternalGainsCon[1]
[W]: Convective internal gains of zone
InternalGainsLat[1]
[W]: Latent internal gains of zone
InternalGainsRad[1]
[W]: Radiative internal gains of zone
LowerSetp[1]
[K]: Lower temperature set point for thermal comfort of zone
Occupancy[1]
[number of people]: Number of occupants of zone
PriceElectricPowerConstant
[($/Euro)/kWh]: Completely constant electricity price
PriceElectricPowerDynamic
[($/Euro)/kWh]: Electricity price for a day/night tariff
PriceElectricPowerHighlyDynamic
[($/Euro)/kWh]: Spot electricity price
TBlaSky
[K]: Black Sky temperature
TDewPoi
[K]: Dew point temperature
TDryBul
[K]: Dry bulb temperature at ground level
TWetBul
[K]: Wet bulb temperature
UpperCO2[1]
[ppm]: Upper CO2 set point for indoor air quality of zone
UpperSetp[1]
[K]: Upper temperature set point for thermal comfort of zone
ceiHei
[m]: Ceiling height
cloTim
[s]: One-based day number in seconds
lat
[rad]: Latitude of the location
lon
[rad]: Longitude of the location
nOpa
[1]: Opaque sky cover [0, 1]
nTot
[1]: Total sky Cover [0, 1]
pAtm
[Pa]: Atmospheric pressure
relHum
[1]: Relative Humidity
solAlt
[rad]: Altitude angel
solDec
[rad]: Declination angle
solHouAng
[rad]: Solar hour angle.
solTim
[s]: Solar time
solZen
[rad]: Zenith angle
winDir
[rad]: Wind direction
winSpe
[m/s]: Wind speed
No lighting model is included.
No shading model is included.
The model uses moist air despite that no condensation is modelled in any of the used components.
A simple, single circulation loop is used to model the floor heating system as well as the air circulation through the heat pump evaporator.
Fixed air infiltration corresponding to an n50 value of 10 is modelled.
The Peak Heat Day (specifier for /scenario
API is 'peak_heat_day'
) period is:
The Typical Heat Day (specifier for /scenario
API is 'typical_heat_day'
) period is:
All pricing scenarios include the same constant value for transmission fees and taxes of each commodity. The used value is the typical price that household users pay for the network, taxes and levies, as calculateed by Eurostat and obtained from: The energy prices and costs in Europe report. For the assumed location of the test case, this value is of 0.20 EUR/kWh for electricity.
The Constant Electricity Price (specifier for /scenario
API is 'constant'
) profile is:
The Dynamic Electricity Price (specifier for /scenario
API is 'dynamic'
) profile is:
The Highly Dynamic Electricity Price (specifier for /scenario
API is 'highly_dynamic'
) profile is:
The Electricity Emissions Factor profile is:


gl3n.aabb
struct
AABBT
(type);
Base template for all AABB-types.
Params:
type
all values get stored as this type
alias
at
= type;
Holds the internal type of the AABB.
alias
vec3
= Vector!(at, 3);
Convenience alias to the corresponding vector type.
vec3
min
;
The minimum of the AABB (e.g. vec3(0, 0, 0)).
vec3
max
;
The maximum of the AABB (e.g. vec3(1, 1, 1)).
this(vec3
min
, vec3
max
);
Constructs the AABB.
Params:
vec3
min
minimum of the AABB
vec3
max
maximum of the AABB
AABBT
from_points
(vec3[]
points
);
Constructs the AABB around N
points
(all
points
will be part of the AABB).
void
expand
(AABBT
b
);
Expands the AABB by another AABB.
void
expand
(vec3
v
);
Expands the AABB, so that
v
is part of the AABB.
const bool
intersects
(AABBT
box
);
Returns
true
if the AABBs intersect. This also returns
true
if one AABB lies inside another.
const vec3
extent
();
Returns the
extent
of the AABB (also sometimes called size).
const vec3
half_extent
();
Returns the half extent.
const at
area
();
Returns the
area
of the AABB.
const vec3
center
();
Returns the
center
of the AABB.
const vec3[]
vertices
();
Returns all
vertices
of the AABB, basically one vec3 per corner.
Page generated by
Ddoc
.


"I dream of swimming in a sea of sky."
You're looking at the hub page of methylDragon (also CH3). I do art and music, specialising in digital painting and orchestral composition! (I've also dabbled in some card game design.)
Open for commissions, collaborations, anything, just ask me!
If you want to check out my works in detail, visit these dedicated sites:
Contact me for queries, commissions, requests and more! (If you want a commission, read my commission info first!)
All rights reserved. All music and art listed here is copyright © methylDragon. No unauthorised reproduction is allowed.


Survey Form Project
Here is short explanation of our projects
Yours name:
Yours email:
Yours favourite number:
Yours favourite city:
Warsaw
Kyiv
Paris
Tel-Aviv
Yours favourite option:
1st option
2nd option
3rd option
Yours favourite checkbox:
I have checkbox1
I have checkbox2
I have checkbox3
Additional comments:
Submit


Ongoing events
Open rooms
Surprice room
(4 GG)
Wating players - 4:45s elapsed
Crazy bet
(400 GG)
Your turn - 1:02s elapsed
Ira's room
(20 GG)
Saving - 6:49s elapsed
Autoplay rooms
Win 4 ever
(2 GG)
Won: 13 | Losses: 4 | Left: 48
Best of luck
(1 GG)
Won: 2 | Losses: 8 | Left: 90


Published on 16 August, 2020
It has been a long time since I started working on Robot Operating System. The sole purpose of writing this blog/tutorial is making a documentation for my own use.
I will try to describe the whole process of creating a robot from scratch in a number of steps.
Step 1: Setting up the environment and Writing the robot Description
The code below creates a workspace named bot_ws and subsequently creates a package named sanbot
mkdir bot_ws
cd prubot
mkdir src
cd src
catkin_create_pkg sanbot roscpp urdf
cd ..
catkin_make
cd src/sanbot/
mkdir urdf
mkdir launch
cd urdf
touch description.urdf
cd ..
cd launch
touch gazebo.launch
The code written in the code block spawns a cube in gazebo simulator.
A launch file is created to launch the description of the robot
Step 2: Designing a Custom Robot in Solidworks and Exporting it to URDF


There's a universal desire to discover love and it can be hard to find, but that's where we step in.We narrow down the world of possibilities to a personal A-list of meaningful introductions that get singles closer to finding the one.
You’re more than just a photo. You have stories to tell, and passions to share, and things to talk about that are more interesting than the weather. Get noticed for who you are, not what you look like. Because you deserve what dating deserves: better.
At dating-website,we’re dedicated to helping people find love and happiness through meaningful connections.


Inherits XCTestCase.
◆ setUp
◆ tearDown
◆ testCC10ToPanPosition
| - (void) testCC10ToPanPosition |
|
|
|
|
implementation |
◆ testCC11ToInitialAttenuation
| - (void) testCC11ToInitialAttenuation |
|
|
|
|
implementation |
◆ testCC1ToVibratoLFOPitchDepth
| - (void) testCC1ToVibratoLFOPitchDepth |
|
|
|
|
implementation |
◆ testCC7ToInitialAttenuation
| - (void) testCC7ToInitialAttenuation |
|
|
|
|
implementation |
◆ testCC91ToReverbSend
| - (void) testCC91ToReverbSend |
|
|
|
|
implementation |
◆ testCC93ToChorusSend
| - (void) testCC93ToChorusSend |
|
|
|
|
implementation |
◆ testChannelPressureToVibratoLFOPitchDepth
| - (void) testChannelPressureToVibratoLFOPitchDepth |
|
|
|
|
implementation |
◆ testKeyVelocityToFilterCutoff
| - (void) testKeyVelocityToFilterCutoff |
|
|
|
|
implementation |
◆ testKeyVelocityToInitialAttenuation
| - (void) testKeyVelocityToInitialAttenuation |
|
|
|
|
implementation |
◆ testPitchWheelToInitialPitch
| - (void) testPitchWheelToInitialPitch |
|
|
|
|
implementation |
◆ channel
| - (Channel*) ModulatorTests: |
|
protected |
◆ state
| - (State*) ModulatorTests: |
|
protected |
The documentation for this class was generated from the following file:


Having grown up in the Twin Cities, my journey took me briefly to Miami, FL where I took care of clients in the luxury condo market. I also started a "fix & flip" ventures improving underserved communities & beautifying neighborhoods in need..
Use just two Bootstrap 4 utility classes and three custom CSS rules and you will have a flexbox enabled sticky footer for your website!


{% extends 'base.html' %} {% block content %}
Your customer ID: {{ loggedInCustomer.id }}
You have purchased the following channels:
Start again


Strat is a framework to represent and deploy portable cloud systems. Strat takes simple functions written in languages you know like Javascript and Go and compiles them into event-driven and service-oriented systems running on the serverless cloud. Strat is the first "infrastructure as high-level code" language, and it represents an evolutionary leap over tools like Terraform, CloudFormation, and Serverless Framework. Here's hello world:
Create HelloWorld.st:
service HelloWorld {
include "Http"
Http -> "Hello World!"
}
Then in your terminal (requires npm):
npm install -g stratc
stratc ./HelloWorld.st && stratc ./HelloWorld.sa
In existing configuration management tools, you specify infrastructure details directly--"this is a Lambda", "deploy this in a container", "this talks to SQS", etc. Strat exposes a standard library that abstracts over these proprietary services just how NodeJS's fs abstracts over each operating system's file API.
Write, extend, and include entire services in your system. Here's how you'd incorporate a GitHub commit webhook inside your system:
service ContinuousIntegration {
include "./GitHubCommit"
GitHubCommit { repository: "Stratosphere" } -> "./beginCIBuild.js"
}
And the GitHubCommit library only uses the Strat standard library, so its 100% portable:
source GitHubCommit {
//Strat handles setting up ApiGateway, local with ngrok, etc.
include "Http"
include "Birth"
Http { method: "post", path: "Strat/GitHubCommit" } ->
"./gitHubWebhookReception.js"
Birth -> "./registerGitHubWebhook.js"
}
While the current generation of configuration management extends itself with "plugins", yaml files, and closed source features, Strat places all the power in the community's hands with a robust foreign function interface, a standard library, and powerful inclusion semantics. Authoring a service or source in Strat others can use is as easy as creating a module or library for computation languages.
Just how language compilers like gcc optimize your code, Strat's compiler optimizes your infrastructure to be:
Strat creates a bytecode-esque system artifact (.sa) file that represents your system and its dependencies, then a Strat substrate executes that .sa file to produce running infrastructure. The .sa file format is simple and can be implemented by many different clouds, single machines, and even on-premise datacenters. Today you can run your .sa files locally, on AWS, and on Linux servers.
With Strat, you can write complex, fully featured systems using cutting edge technologies like Lambda without ever having to use a single proprietary API, and without sacrificing local execution and reproducibility.


Container
Container is fundamental class of Fire UI, making your website to be mobile friendly. Our .container
class is a responsive, fixed-width container.
To use it, simply add container
into your classlist

To use only Fire UI container feature, copy and paste this stylesheet link into your head
element to load Fire UI container CSS.
<link rel="stylesheet" href="https://unpkg.com/<EMAIL_ADDRESS>Starter template with container class
<!DOCTYPE html>    <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1">  <link rel="stylesheet" type="text/css" href="https://unpkg.com/<EMAIL_ADDRESS>


The final stretch
here’s a link to a pdf of the final presentation I gave on career day, 23rd June: link.
Overfishing is one of the biggest and most neglected issues of our time. The oceans play a crucial role in maintainig a stable atmosphere and also provide food for billions of people on earth. Maintaining a healthy ecosystem is important everywhere, but in the oceans, an unbalanced and collapsing ecosystem results in substantial effects on the levels of CO2 in our atmosphere and thus has catastrophic effects, not just for economies that rely on fishing to sustain them, but also for fish populations and ultimately our environment.
For my final project at Metis, I clustered fish species into levels of threat, and used these in an app so that users can input the name of a fish and find out how threatened or overfished that species currently is.
I got my data from NOAA, which provided information on over 500 species of fish over an average of 60 years.
I also collected data from the WCPFC and did some analysis on that which I’ll talk about later.
Exploratory Analysis
Amidst the many groupbys and pyplots a very clear pattern emerged. Most of these fish populations are severely declining. It was quite shocking to view such an obvious trend.
Here is a plot of the aggregated ‘exploitation rate’ of all tuna types in the data set, over time. It is a simple measure of the proportion of a population that is caught. So you can see that the percentage of the population caught has increased dramatically over the past 60 years.
This could be because total catch has increased, or because the population size has decreased. When we look at plots of catch rates, which is essentially how much fish is caught per unit effort, you can see that this is decreasing…
BIG EYE
ALBACORE
YELLOWFIN
This indicates that the number of available tuna have dropped, and not just that we’re getting better at catching them.
By month, By region, By fish? Fuzzy-Matching
I used the fuzzy matching python package fuzzywuzzy
, to pull out fish by region. First I used fuzzywuzzy to detect all fish in Pacific and Atlantic regions and then numpy.where()
to pull out indices of all the fish in those regions.
I made separate dataframes for Pacific and Atlantic fish to analyse the differences in overfishing between the two major oceans.
- more to come here -
Missing Data
This is just one snapshot of the data I had, namely the mean mortality rate (over the 60 year time period) of fish species in the Pacific…
…and Atlantic oceans.
You can’t glean an awful lot from these plots, but it shows you just how much data is missing, especially in the Pacific. Some fish had information on total catch, others had information on available biomass or mortality rate. Some had no information at all.
I tried using PyBrain
to fill in these gaps with a recursive neural network.
Apping
This part required some serious readjustment of my understanding of the internet It was a really enjoyable and fascinating task. I’ll do a post on understanding app buidling in the next couple of weeks with a link to the live web app. In the meantime you can have a gander at the app format on my github page.
I built a fish-specific search tool with a little help from Joe Oliver that would allow people to find out how overfished a fish population is. The idea is also to provide alternatives and more of a dashboard breakdown which I am working on right now.
I clustered the fish into different categories of ‘threat level’ or overfishedness using KMeans Clustering in SciKitLearn
. I used fishing rate, population size and an indicator of the health of the population (the rate of growth in the last 5 years) as variables.
I wanted to use ‘length at age’ as a variable because that is a good measure of a healthy fish population, but it looks like getting that information for all the fish species I was working with would be a much longer process than I had the time for in this project.
The clusters:
RED - overfished and dwindling/unhealthy population, do not eat.
ORANGE - low population size but not necessarily over-fished, treat with caution.
GREEN - healthy population and not overfished, bon apetit!
Further info and efforts
With a bit of digging on the interweb I found countless organisations, listed at the bottom of this post, who are feverishly campaigning and recruiting to tackle these problems.
The Seawatch app, this is very similar to what I have done, but with more time, money and manpower. However, they don’t show the changes in the fish and this is a pretty useful chunk of information for the public to digest.  In the same way you want to educate your children on why they can’t go around hitting things, or eat an entire bowl of haribo. That reaction to evidence stays an important influencer of our behaviour and in this way, data visualisations can be completely necessary.
Videos
The Black Fish is a super cool organisation based in the UK and the Netherlands that aim to keep fishing practices legal and pressure the government to set higher standards. They work in the Baltic and Mediterrenean seas, where illegal fishing is rife. They film Losing Nemo is there informational video on the state of over and illegal fishing.
Another fantastic short film which aims to educate people on overfishing was made by —
The End of The Line is a very moving and comprehensive documentary on the state of the oceans and the corruption within fisheries.
The Global Fishing Watch is google’s attempt to pick out illegal fishing practices with a little help from enthusiastic volunteers.


This SRFI is a proposal for extending
let
, let*
, and letrec
for
receiving multiple values. The syntactic extension is fully compatible
with the existing syntax. It is the intention that single-value bindings,
i.e. (let ((var expr)) ...)
, and multiple-value binding can
be mixed freely and conveniently.
The most simple form of the new syntax is best explained by an example:
(define (quo-rem x y) (values (quotient x y) (remainder x y))) (define (quo x y) (let ((q r (quo-rem x y))) q))
The procedure quo-rem
delivers two
values to its continuation. These values are received as q
and r
in the let
-expression of the procedure
quo
. In other words, the syntax of let
is
extended such that several variables can be specified---and these
variables receive the values delivered by the expression (quo-rem x
y)
.
The syntax of let
is further
extended to cases in which a rest argument receives the list of all
residual values. Again by example,
(let (((values y1 y2 . y3+) (foo x))) body)In this example,
values
is a
syntactic keyword indicating the presence of multiple values to be received,
and y1
, y2
, and y3+
, resp., are
variables bound to the first value, the second value, and the list of the
remaining values, resp., as produced by (foo x)
. The syntactic
keyword values
allows receiving all values as in (let
(((values . xs) (foo x))) body)
. It also allows receiving no values at
all as in (let (((values) (for-each foo list))) body)
.A common application of binding multiple values
is decomposing data structures into their components. This mechanism is
illustrated in its most primitive form as follows: The procedure
uncons
(defined below) decomposes a pair x
into
its car and its cdr and delivers them as two values to its continuation.
Then an extended let
can receive these values:
(let ((car-x cdr-x (uncons x))) (foo car-x cdr-x))
Of course, for pairs this method is probably
neither faster nor clearer than using the procedures car
and
cdr
. However, for data structures doing substantial work
upon decomposition this is different: Extracting the element of highest
priority from a priority queue, while at the same time constructing the
residual queue, can both be more efficient and more convenient than doing
both operations independently. In fact, the quo-rem
example
illustrates this point already as both quotient and remainder are
probably computed by a common exact division algorithm. (And often
caching is used to avoid executing this algorithm twice as often as
needed.)
As the last feature of this SRFI, a mechanism
is specified to store multiple values in heap-allocated data structures.
For this purpose, values->list
and
values->vector
construct a list (a vector, resp.) storing
all values delivered by evaluating their argument expression. Note that
these operations cannot be procedures.