from datetime import datetime
from polical import MateriaClass
from polical import connectSQLite
[docs]class Tarea:
"""Main class of PoliCal it manages the task its owner and its subject associated"""
def __init__(
self, id: str, title: str, description: str, due_date: datetime, subject_id: str
):
"""Class constructor
Args:
id (str): ID that comes from ICS file
title (str): Title of the tasks
description (str): Description of the task
due_date (datetime): When is due to the task
subject_id (str): Subject id that is associated to the task
"""
self.id = id
self.title = title
self.description = description
self.due_date = due_date
self.subject_id = subject_id
self.subject = MateriaClass.Materia("Desconocido", subject_id)
self.tid = None
[docs] def print(self):
"""Prints a summary ofthe main elements of the task"""
print(self.id, self.title, self.due_date, self.subject_id)
[docs] def define_subject(self, subject: MateriaClass.Materia):
"""Defines a subject object for the task
Args:
subject (MateriaClass.Materia): [description]
"""
self.subject = subject
unknown_name = (
str(self.subject.codigo)
+ ", registre esta materia usando /subject NOMBRE DE LA MATERIA (CODIGO)"
)
if self.subject.name == "Desconocido":
temp_subject_name = connectSQLite.get_user_subject_name(
str(self.subject.id), str(self.username)
)
self.subject.name = (
temp_subject_name if temp_subject_name != None else unknown_name
)
[docs] def define_username(self, username: str):
"""Defines task owner username
Args:
username (str): The username of the user that owns the task
"""
self.username = username
[docs] def define_tid(self, tid: str):
"""Defines Trello o Telegram ID associated to this task
Args:
tid (str): Trello o Telegram ID associated to this task
"""
self.tid = tid
[docs] def summary(self):
"""Generate a Summary of the main elements of the task
Returns:
str: Summary of the task generaly for being sended as Telegram Message
"""
summary = "*Título*: " + str(self.title)
summary += "\n*Descripción*: " + str(self.description).replace(
"_", "\_"
).replace("*", "\*").replace("`", "\`").replace("[", "\[")
summary += "\n*Fecha de Entrega*: " + str(self.due_date)
summary += "\n*Materia*: " + str(self.subject.name)
return summary


Carousel Widget: Spotlight Example
This example uses the YUI Carousel Widget to showcase a simple spotlight example using its "itemSelected" event. In this example, you can use arrow keys to select items, as well as click on an item to select it.
Though this functionality looks a little complicated, it is very easy to implement. This is because the YUI Carousel Widget handles the keyboard event and the mouse click event for setting the selection. When an item is selected, the YUI Carousel Widget triggers a "itemSelected" event. This example subscribes to the "itemSelected" event to display the selected image.
Using the Carousel Widget for spotlight example
Here we will use the YUI Carousel Widget's "itemSelected" event to display the selected image.
This example has the following dependencies:
This example uses progressive enhancement. So, the Carousel is created from an "ordered" list of elements.
We will add a container where we would display the spotlight image.
We'll have only one CSS rule to set the height for the Carousel items.
Since we have the elements in place, we can invoke the Carousel's constructor to create the widget. The Carousel's "selectedItem" property returns the index of the currently selected item. So, using that property and the getElementForItem() API we can display the image of the selected item when the Carousel is rendered.
Now, we can subscribe to the "selectedItem" event that the Carousel exposes. This event is triggered whenever an item is selected and it returns the index of the selected item. With the index of the item, we can use the Carousel's getElementForItem() API to get the reference to the Carousel's item (LI in our case).
Once the reference to the Carousel's item is obtained, it is straight-forward to implement a function that extracts the image within it.
Lets assemble everything together. The full JavaScript reads as follows:


This component's resources (css and js) are loaded via conditional loader!
This component's resources are loaded via conditional loader, using relative and absolute paths.
This component's resources are loaded via conditional loader. One resource depends on another. plugin.js -> component.js
Resources are loaded in order of dependencies: component.js -> component2.js -> component3.js
plugin.js is loaded on other demos or skipped. if skipped an error will be thrown, but component3.js will still be loaded
Resources are loaded in order of dependencies: component.js -> component2.js -> component3.js
plugin.js is loaded on other demos or skipped. if skipped an error will be thrown, but component3.js will still be loaded
Resources are loaded in order of dependencies: component.js -> component2.js -> component3.js
plugin.js is loaded on other demos or skipped. if skipped an error will be thrown, but component3.js will still be loaded


Get a recommendation
Please select a location (drag and resize the circle):
Please select cuisines:
{{ $item.name }}
{{ cuisine.name }}
Please select location types:
{{ $item.name }}
{{ locationType.name }}
Recommend


Blizzard Announces Diablo IV
by Jason SchreierDiablo IV is coming, Blizzard said today, finally confirming the much-anticipated sequel after years of speculation.
A new cinematic trailer for the game showed a group of treasure hunters fighting against what could be demons, or private equity executives.
Blizzard says the game takes place “many years after the events of Diablo III, after millions have been slaughtered by the actions of the High Heavens and Burning Hells alike. In the vacuum of power, a legendary name resurfaces.”
This is the fourth mainline entry in the longrunning hack-n-loot series, which began in 1996. The most recent entry, Diablo III, came out in May 2012, recovering from a rocky launch to become one of the most popular PC games of all time.


public class FSTCompletionBuilder extends Object
The construction step in
Object.finalize()
works as follows:
abc
with a discretized weight equal '1' would become
1abc
.
FST
) is constructed from the input. The
root node has arcs labeled with all possible weights. We cache all these
arcs, highest-weight first.
At runtime, in
FSTCompletion.lookup(CharSequence, int)
,
the automaton is utilized as follows:
If there is an exact match in the automaton, it is returned first on the results list (even with by-weight sorting).
Note that the maximum lookup time for any prefix is the time of descending to the subtree, plus traversal of the subtree up to the number of requested suggestions (because they are already presorted by weight on the root level and alphabetically at any node level).
To order alphabetically only (no ordering by priorities), use identical term weights for all terms. Alphabetical suggestions are returned even if non-constant weights are used, but the algorithm for doing this is suboptimal.
"alphabetically" in any of the documentation above indicates UTF-8 representation order, nothing else.
NOTE : the FST file format is experimental and subject to suddenly change, requiring you to rebuild the FST suggest index.
FSTCompletion
| Modifier and Type | Field and Description |
|---|---|
static int
|
DEFAULT_BUCKETS
Default number of buckets.
|
| Constructor and Description |
|---|
FSTCompletionBuilder
()
Creates an
FSTCompletion
with default options: 10 buckets, exact match
promoted to first position and
InMemorySorter
with a comparator obtained from
BytesRef.getUTF8SortedAsUnicodeComparator()
.
|
FSTCompletionBuilder
(int buckets,
BytesRefSorter
sorter,
int shareMaxTailLength)
|
| Modifier and Type | Method and Description |
|---|---|
void
|
add
(
BytesRef
utf8,
int bucket)
Appends a single suggestion and its weight to the internal buffers.
|
FSTCompletion
|
build
()
Builds the final automaton from a list of added entries.
|
public static final int DEFAULT_BUCKETS
public FSTCompletionBuilder()
FSTCompletion
with default options: 10 buckets, exact match
promoted to first position and
InMemorySorter
with a comparator obtained from
BytesRef.getUTF8SortedAsUnicodeComparator()
.
public FSTCompletionBuilder(int buckets, BytesRefSorter sorter, int shareMaxTailLength)
buckets
- The number of buckets for weight discretization. Buckets are used
in
add(BytesRef, int)
and must be smaller than the number
given here.
sorter
-
BytesRefSorter
used for re-sorting input for the automaton.
For large inputs, use on-disk sorting implementations. The sorter
is closed automatically in
build()
if it implements
Closeable
.
shareMaxTailLength
- Max shared suffix sharing length.
See the description of this parameter in
Builder
's constructor.
In general, for very large inputs you'll want to construct a non-minimal
automaton which will be larger, but the construction will take far less ram.
For minimal automata, set it to
Integer.MAX_VALUE
.
public void add( BytesRef utf8, int bucket) throws IOException
utf8
- The suggestion (utf8 representation) to be added. The content is
copied and the object can be reused.
bucket
- The bucket to place this suggestion in. Must be non-negative and
smaller than the number of buckets passed in the constructor.
Higher numbers indicate suggestions that should be presented
before suggestions placed in smaller buckets.
IOException
public FSTCompletion build() throws IOException
IOException


{% extends 'base.html' %} {% load static %} {% block content %}
You are in Public View mode. Actions are limited to downloading & navigating the folder contents.


Virtual Notary - Sandbox
Register a proof
Select a file:
Comment:
Register
Check a proof
Select a file:
Check


Big Data with Apache Spark: Intro and Motivation
Data Science vs Databases:
In contrast to databases which have valuable data such as bank or medical records, data science has cheap data like web logs, GPS data, sensor data. So data science deals with Big Data.
CAP theorem for data science: we can only have 2 of the 3 following properties, we can’t have all.
- Consistency
- Availability
- Partition tolerance
Databases use SQL, in data science we use NoSQL data stores. Much of the data in data science are non-structured or semi-structured, cannot be stored in relational databases. NoSQL has much more rich queries than SQL.
Data Science vs Scientific Computing
SC: physics-based models; DS: general ML classifier
SC: mostly deterministic, precise; DS: statistical models, handle unmodeled complexity
SC: run on supercomputers; DS: run on cheap clusters like EC2
Data Science vs Machine Learning
ML: developing new models; DS: explore models, build and tune hybrids
ML: Prove mathematical properties of models; DS: Understand empirical properties
ML: Improve/validate on a few small clean datasets; DS: Develop/use tools that can handle massive datasets
ML: Publish a paper; DS: take action!
Doing data science
- Identify the problem
- Collect data
- Clean the data
- Build models
- Evaluate models
- Communicate results
Cloud Computing enables Data Science: cheap clusters you can rent.
What’s hard about data science
- Overcoming assumptions
- Making ad-hoc explanations of data patterns
- Not checking enough (validate models, data pipeline integrity, etc.)
- Overgeneralizing
- Communication
- Using statistical tests correctly
- Prototype —> Production
- Data pipeline complexity
ETL: Extract, Transform, Load
- Extract data from the sources: file, database, event log, website, Hadoop Distributed File System (HDFS)
- Load data into the sink: Python, R, SQLite, NoSQL store, files, HDFS, Relational Database Management System (RDBMS)
- Transform data at the source or sink
Plus:
- Data transfer, serialization/deserialization
- Transformation pipeline or workflow
- Recording the execution of a workflow is known as capturing lineage or provenance. Spark’s Resilient Distributed Datasets do this automatically.
Data Science Roles:
2 kinds of individual: business person, programmer.
- businessperson: excel functions/charts, VB
- programmer:
- Data sources: web scraping, web services API; excel sheets exported as Comma Separated Values (csv); database queries
- ETL: wget, curl, Beautiful Soup, lxml
- Data Warehouse: flat files
- Business Intelligence and Analytics: Numpy, Matplotlib, R, Matlab, Octave
2 kinds of organizations: an enterprise and a web company.
Other archetypes of individual:
- Hackers: efficient programmers who uses R/Matlab, Python, SQL/Pig
- Scripters: R/Matlab
- Application Users: SAS, SPSS
Big Data and Hardware Trends
Traditional data analysis tools: Unix shell cmd, Pandas, R. They all run on a single machine. Now, 1TB of disk is only $35, but it takes up to 3 hours to read 1TB of data. Solution: distribute data over large clusters.
For example, a task such as counting words in really big documents can be done better with a cluster. In the cluster, some machines only process/store a specific part of the result.
Why is cluster computing difficult?
- Moving data is hard
- Node failure
- Node runs slow
Solution 1: MapReduce
- MapReduce deals with failures and slow tasks by re-launching the tasks on other machines.
- MapReduce handles the execution of Map and Reduce functions on many machines including the shuffling of data between Map and Reduce functions. It also automatically recovers from both machine failures and slow machines.
Solution 2: Spark
- Spark motivation: Disk I/O is very slow. Running MapReduce iteratively —> speed of disk
- Memory is cheap now: process in-memory, do not write to disk
- Spark abstraction: Resilient Distributed Datasets (RDDs)
RDDs enable us to write programs in terms of operations on distributed datasets. They are partitioned
collections of objects spread across a cluster, stored in memory or on disk. RDDs are built and manipulated
through a diverse set of parallel transformations: map
, filter
, join
; and actions: count
,
collect
, save
. Also, RDDs are automatically rebuilt on machine failure.
On top of Apache Spark, there are Spark SQL, Spark Streaming, MLlib (machine learning), GraphX (graph).
Comments:
Using memory instead of disks offers two huge benefits. The first benefit is that memory is much faster than disks. The time to read or write a value to memory is only a few nanoseconds, while the time to read or write is several milliseconds - that means memory is a million times faster than disks. The second benefit is that keeping intermediate results in memory means that they do not have to be converted into a format that can be stored on disks. The process of converting a memory object to a disk object is called serialization and the process of converting a disk object to a memory object is called deserialization. Serializing and deserializing objects is a very expensive and time consuming process. Keeping intermediate results in memory avoids this significant overhead. Taken together, the faster access times and avoidance of serialization/deserialization overhead make Spark much faster than Map Reduce
- up to 100 times faster!


{{description}}
This section provides an overview of all the data artifacts used in the experiment.
This section provides the raw content of the linked project metadata description, concerning the data objects in the data bundle.
{{obj.json}}


Class for sending data to google drive documents
Note
In order to communicate with google drive you must first create a project under the google developers console (http://console.developers.google.com) and grant it access to Drive API and the Drive SDK. This will allow it to create and modify spreadsheets.
Since the drive API uses OAuth2 you will need to get the client id, the client secret, and the redirect uri for a native application. These must be set to the correct values in userInfo.py
Gets or creates a client to access Google Drive and create documents
Gets the correct OAuth token for the google data client
Note
The OAuth token is stored locally in a file caled oauth-fooNelnet. If this file does not exist, the app will run through a simple flow to generate it. This includes directing the user to a webpage - which authorizes the app to access the google drive api. The webpage returns a confirmation code which the user must input into the app to get oauth working. The app will then create the oauth-fooNelnet file and continue without needing confirmation again.
Queries Google drive for an existing spreadsheet, and creates one if not found.
Gets or creates a Google Spreadsheet client to access spreadsheets and add values to them
Sends payment data to a google spreadsheet
Sends loan data to a Google Drive Spreadsheet Args: loan_data (list): A list of dictionaries containing the following keys:
- account: The name of the account
- principle_balance: The current principle balance of the loan
- interest_rate: The loan’s interest rate
- accrued_interest: The current amount of accrued interest on the loan
- outstanding_balance: The total balance on the loan (principal + accrued interest)
Appends received loan data to a CSV file
Output: Saves (or appends to) a local csv file - defined in userInfo.py
Appends received loan data to a CSV file
Output: Saves (or appends to) a local csv file - defined in userInfo.py
Base class for Loan Servicer Clients
Note
This app could work just as well with other loan servicers such as Sally Mae, Navient, etc. This base class provides a level of abstractions so that the other servicers could be added easily without modifing other functionality in the app.
Logs into the loan servicers website, and sets the session and cookies for subsequent operations
..note:: Usually uses USERNAME and PASSWORD from userInfo.py
Tells the servicer to initiate a payment request from a preprovided bank account
Retreives loan data from the servicer
Bases: exceptions.Exception
Bases: exceptions.Exception
Bases: fooNelnet.loanServicerClient.LoanServicerClient
Primary Class for Interfacing with the Nelnet website
Logs into nelnet.com, and sets the session and cookies for subsequent operations
..note:: Usually uses USERNAME and PASSWORD from userInfo.py
Tells nelnet to initiate a payment request from a preprovided bank account
Retreives loan data from nelnet


Bayesian Multiple Regression Example¶
[1]:
import arviz as az
import bambi as bmb
import numpy as np
import pandas as pd
import pymc3 as pm
import seaborn as sns
import statsmodels.api as sm
[2]:
az.style.use('arviz-darkgrid')
Load and examine Eugene-Springfield community sample data¶
[3]:
data = pd.read_csv('data/ESCS.csv')
np.round(data.describe(), 2)
[3]:
| drugs | n | e | o | a | c | hones | emoti | extra | agree | consc | openn | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 604.00 | 604.00 | 604.00 | 604.00 | 604.00 | 604.00 | 604.00 | 604.00 | 604.00 | 604.00 | 604.00 | 604.00 |
| mean | 2.21 | 80.04 | 106.52 | 113.87 | 124.63 | 124.23 | 3.89 | 3.18 | 3.21 | 3.13 | 3.57 | 3.41 |
| std | 0.65 | 23.21 | 19.88 | 21.12 | 16.67 | 18.69 | 0.45 | 0.46 | 0.53 | 0.47 | 0.44 | 0.52 |
| min | 1.00 | 23.00 | 42.00 | 51.00 | 63.00 | 44.00 | 2.56 | 1.47 | 1.62 | 1.59 | 2.00 | 1.28 |
| 25% | 1.71 | 65.75 | 93.00 | 101.00 | 115.00 | 113.00 | 3.59 | 2.88 | 2.84 | 2.84 | 3.31 | 3.06 |
| 50% | 2.14 | 76.00 | 107.00 | 112.00 | 126.00 | 125.00 | 3.88 | 3.19 | 3.22 | 3.16 | 3.56 | 3.44 |
| 75% | 2.64 | 93.00 | 120.00 | 129.00 | 136.00 | 136.00 | 4.20 | 3.47 | 3.56 | 3.44 | 3.84 | 3.75 |
| max | 4.29 | 163.00 | 158.00 | 174.00 | 171.00 | 180.00 | 4.94 | 4.62 | 4.75 | 4.44 | 4.75 | 4.72 |
It’s always a good idea to start off with some basic plotting. Here’s what our outcome variable ‘drugs’ (some index of self-reported illegal drug use) looks like:
[4]:
data['drugs'].hist();
The five predictor variables that we’ll use are sum-scores measuring participants’ standings on the Big Five personality dimensions. The dimensions are: - O = Openness to experience - C = Conscientiousness - E = Extraversion - A = Agreeableness - N = Neuroticism
Here’s what our predictors look like:
[5]:
sns.pairplot(data[['o','c','e','a','n']]);
Specify model and examine priors¶
We’re going to fit a pretty straightforward multiple regression model predicting drug use from all 5 personality dimension scores. It’s simple to specify the model using a familiar formula interface. Here we tell bambi to run two parallel Markov Chain Monte Carlo (MCMC) chains, each one drawing 2000 samples from the joint posterior distribution of all the parameters.
[6]:
model = bmb.Model(data)
fitted = model.fit('drugs ~ o + c + e + a + n', samples=1000, tune=1000, init='adapt_diag')
INFO:numexpr.utils:NumExpr defaulting to 4 threads.
Auto-assigning NUTS sampler...
INFO:pymc3:Auto-assigning NUTS sampler...
Initializing NUTS using adapt_diag...
INFO:pymc3:Initializing NUTS using adapt_diag...
Multiprocess sampling (2 chains in 2 jobs)
INFO:pymc3:Multiprocess sampling (2 chains in 2 jobs)
NUTS: [drugs_sd, n, a, e, c, o, Intercept]
INFO:pymc3:NUTS: [drugs_sd, n, a, e, c, o, Intercept]
Great! But this is a Bayesian model, right? What about the priors?
If no priors are given explicitly by the user, then bambi chooses smart default priors for all parameters of the model based on the implied partial correlations between the outcome and the predictors. Here’s what the default priors look like in this case – the plots below show 1000 draws from each prior distribution:
[7]:
model.plot();
[8]:
# Normal priors on the coefficients
{x.name:x.prior.args for x in model.terms.values()}
[8]:
{'Intercept': {'mu': array([2.21014664]), 'sd': array([7.49872452])},
'o': {'mu': array([0]), 'sd': array([0.02706881])},
'c': {'mu': array([0]), 'sd': array([0.03237049])},
'e': {'mu': array([0]), 'sd': array([0.02957414])},
'a': {'mu': array([0]), 'sd': array([0.03183623])},
'n': {'mu': array([0]), 'sd': array([0.02641989])}}
[9]:
# Uniform prior on the residual standard deviation
model.y.prior.args['sd'].args
[9]:
{'nu': array(4), 'sd': array(0.64877877)}
Some more info about the default prior distributions can be found in this technical paper.
Notice the small SDs of the slope priors. This is due to the relative scales of the outcome and the predictors: remember from the plots above that the outcome, drugs
, ranges from 1 to about 4, while the predictors all range from about 20 to 180 or so. So a one-unit change in any of the predictors – which is a trivial increase on the scale of the predictors – is likely to lead to a very small absolute change in the outcome. Believe it or not, these priors are actually quite wide on the
partial correlation scale!
Examine the model results¶
Let’s start with a pretty picture of the parameter estimates!
[10]:
az.plot_trace(fitted);
The left panels show the marginal posterior distributions for all of the model’s parameters, which summarize the most plausible values of the regression coefficients, given the data we have now observed. These posterior density plots show two overlaid distributions because we ran two MCMC chains. The panels on the right are “trace plots” showing the sampling paths of the two MCMC chains as they wander through the parameter space.
A much more succinct (non-graphical) summary of the parameter estimates can be found like so:
[11]:
az.summary(fitted)
[11]:
| mean | sd | hpd_3% | hpd_97% | mcse_mean | mcse_sd | ess_mean | ess_sd | ess_bulk | ess_tail | r_hat | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept[0] | 3.291 | 0.364 | 2.630 | 3.991 | 0.013 | 0.009 | 797.0 | 791.0 | 799.0 | 839.0 | 1.0 |
| o[0] | 0.006 | 0.001 | 0.004 | 0.008 | 0.000 | 0.000 | 1228.0 | 1228.0 | 1225.0 | 1476.0 | 1.0 |
| c[0] | -0.004 | 0.001 | -0.006 | -0.001 | 0.000 | 0.000 | 962.0 | 886.0 | 962.0 | 856.0 | 1.0 |
| e[0] | 0.003 | 0.001 | 0.001 | 0.006 | 0.000 | 0.000 | 1615.0 | 1490.0 | 1620.0 | 1485.0 | 1.0 |
| a[0] | -0.012 | 0.002 | -0.015 | -0.010 | 0.000 | 0.000 | 1009.0 | 991.0 | 1013.0 | 980.0 | 1.0 |
| n[0] | -0.001 | 0.001 | -0.004 | 0.001 | 0.000 | 0.000 | 1256.0 | 1190.0 | 1255.0 | 1129.0 | 1.0 |
| drugs_sd | 0.592 | 0.017 | 0.560 | 0.624 | 0.000 | 0.000 | 1562.0 | 1562.0 | 1548.0 | 1290.0 | 1.0 |
When there are multiple MCMC chains, the default summary output includes some basic convergence diagnostic info (the effective MCMC sample sizes and the Gelman-Rubin “R-hat” statistics), although in this case it’s pretty clear from the trace plots above that the chains have converged just fine.
Summarize effects on partial correlation scale¶
[12]:
samples = fitted.posterior
It turns out that we can convert each regresson coefficient into a partial correlation by multiplying it by a constant that depends on (1) the SD of the predictor, (2) the SD of the outcome, and (3) the degree of multicollinearity with the set of other predictors. Two of these statistics are actually already computed and stores in the fitted model object, in a dictionary called dm_statistics
(for design matrix statistics), because they are used internally. The others we will compute
manually.
[13]:
# the names of the predictors
varnames = ['o', 'c', 'e', 'a', 'n']
# compute the needed statistics
r2_x = model.dm_statistics['r2_x']
sd_x = model.dm_statistics['sd_x']
r2_y = pd.Series([sm.OLS(endog=data['drugs'],
exog=sm.add_constant(data[[p for p in varnames if p != x]])).fit().rsquared
for x in varnames], index=varnames)
sd_y = data['drugs'].std()
# compute the products to multiply each slope with to produce the partial correlations
slope_constant = sd_x[varnames] * (1 - r2_x[varnames])**.5 \
/ sd_y / (1 - r2_y)**.5
slope_constant
[13]:
o 32.392557
c 27.674284
e 30.305117
a 26.113299
n 34.130431
dtype: float64
Now we just multiply each sampled regression coefficient by its corresponding slope_constant
to transform it into a sampled partial correlation coefficient.
[14]:
pcorr_samples = samples[varnames] * slope_constant
And voilà! We now have a joint posterior distribution for the partial correlation coefficients. Let’s plot the marginal posterior distributions:
[15]:
for idx, (k, v) in enumerate(pcorr_samples.items()):
ax = az.plot_kde(v, label=k, plot_kwargs={'color':f'C{idx}'})
ax.axvline(x=0, color='k', linestyle='--');
The means of these distributions serve as good point estimates of the partial correlations:
[16]:
pcorr_samples.mean(dim=['chain', 'draw']).squeeze(drop=True)
[16]:
<xarray.Dataset>
Dimensions: ()
Data variables:
o float64 0.197
c float64 -0.104
e float64 0.1029
a float64 -0.3245
n float64 -0.0509
Naturally, these results are consistent with the OLS results. For example, we can see that these estimated partial correlations are roughly proportional to the t-statistics from the corresponding OLS regression:
[17]:
sm.OLS(endog=data['drugs'], exog=sm.add_constant(
data[varnames])).fit().summary()
[17]:
| Dep. Variable: | drugs | R-squared: | 0.176 |
|---|---|---|---|
| Model: | OLS | Adj. R-squared: | 0.169 |
| Method: | Least Squares | F-statistic: | 25.54 |
| Date: | Fri, 21 Feb 2020 | Prob (F-statistic): | 2.16e-23 |
| Time: | 20:17:38 | Log-Likelihood: | -536.75 |
| No. Observations: | 604 | AIC: | 1086. |
| Df Residuals: | 598 | BIC: | 1112. |
| Df Model: | 5 | ||
| Covariance Type: | nonrobust |
| coef | std err | t | P>|t| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 3.3037 | 0.360 | 9.188 | 0.000 | 2.598 | 4.010 |
| o | 0.0061 | 0.001 | 4.891 | 0.000 | 0.004 | 0.008 |
| c | -0.0038 | 0.001 | -2.590 | 0.010 | -0.007 | -0.001 |
| e | 0.0034 | 0.001 | 2.519 | 0.012 | 0.001 | 0.006 |
| a | -0.0124 | 0.001 | -8.391 | 0.000 | -0.015 | -0.010 |
| n | -0.0015 | 0.001 | -1.266 | 0.206 | -0.004 | 0.001 |
| Omnibus: | 10.273 | Durbin-Watson: | 1.959 |
|---|---|---|---|
| Prob(Omnibus): | 0.006 | Jarque-Bera (JB): | 7.926 |
| Skew: | 0.181 | Prob(JB): | 0.0190 |
| Kurtosis: | 2.572 | Cond. No. | 3.72e+03 |
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 3.72e+03. This might indicate that there are
strong multicollinearity or other numerical problems.
Relative importance: Which predictors have the strongest effects (defined in terms of partial \(\eta^2\))?¶
The partial \(\eta^2\) statistics for each predictor are just the squares of the partial correlation coefficients, so it’s easy to get posteriors on that scale too:
[18]:
for idx, (k, v) in enumerate(pcorr_samples.items()):
ax = az.plot_kde(v**2, label=k, plot_kwargs={'color':f'C{idx}'})
ax.set_ylim(0, 80)
With these posteriors we can ask: What is the probability that the partial \(\eta^2\) for Openness (yellow) is greater than the partial \(\eta^2\) for Conscientiousness (green)?
For each predictor, what is the probability that it has the largest \(\eta^2\)?
[19]:
pc_df = pcorr_samples.to_dataframe()
(pc_df**2).idxmax(axis=1).value_counts() / len(pc_df.index)
[19]:
a 0.994
o 0.006
dtype: float64
Agreeableness is clearly the strongest predictor of drug use among the Big Five personality traits, but it’s still not a particularly strong predictor in an absolute sense. Walter Mischel famously claimed that it is rare to see correlations between personality measurse and relevant behavioral outcomes exceed 0.3. In this case, the probability that the agreeableness partial correlation exceeds 0.3 is:
[20]:
(np.abs(pcorr_samples['a']) > .3).mean()
[20]:
<xarray.DataArray 'a' ()>
array(0.7315)


running...


Utilizing the data we are anticipating ,the enterprises will have the option to assemble windmills as per the area conveniently and in a more improved manner
We access your location coordinates to get the information about Air Density, Wind Speed, to predict values essential for harnessing the energy.
We predict certain aspects like no of Turbines Generate Enegry,Wind Speed,Air Density,Different radius of the Turbines. To predict the optimum energy extractable from that particular location.
We have used a deep learning using Tensorflow API. The model is based on LSTM, We used flask and front-end development to deploy the model.
Text - Utility-scale wind turbines are typically installed in large, multi-turbine wind farms connected to the world's transmission system. Technological advancements have expanded areas in which wind can be economically developed and enabled every nation in the world to have utility-scale wind potential. Here we are to make a change !


|
||||||||||
| PREV LETTER NEXT LETTER | FRAMES NO FRAMES |
IppScreen
to this IppDisplay
IppItem
to this IppForm.
XmlFormItem
) to this
IppForm.
IppImageItem
to this IppItem.
IppCommand
to this screen.
IppHidden
to this screen.
IppKey
to this screen.
IppAction
to this screen.
IppTicker
to this screen.
IppAlert
to this screen, remember that the screen can have only
one alert, and cannot have any other IppList
, IppTextBox
,
IppPlayer
or IppForm
if it has an alert.
IppList
to this screen, remember that the screen can have only
one list, and cannot have any other IppAlert
, IppTextBox
,
IppPlayer
or IppForm
if it has a list.
IppTextBox
to this screen, remember that the screen can have only
one text box, and cannot have any other IppAlert
, IppList
,
IppPlayer
or IppForm
if it has a text box.
IppPlayer
to this screen, remember that the screen can have only
one player, and cannot have any other IppList
, IppTextBox
,
IppAlert
or IppForm
if it has a player.
IppForm
to this IppScreen, remember that the screen can
have only one form, and cannot have any other IppAlert
, IppList
,
IppTextBox
or IppPlayer
if it has a form.
OptionText
to this option.
IppCommand
with this object, by automatically
wrapping this object with IppItem
if it was not wrapped up already, and then
adding the command to the IppItem.
|
||||||||||
| PREV LETTER NEXT LETTER | FRAMES NO FRAMES |


Arctic blast to bring week of below freezing temps to eastern US
Chilly weather from the Midwest to the Southeast
by Madeline Holcombe and Ryan Prior, CNNATLANTA - Winter is coming to the East Coast for the next week.
The next five to seven daysgenerally are going to bring unseasonably low temperatures tothe eastern two-thirds of the US, with a first chilly round hitting Friday and a second, colder Arctic blast coming later.
Friday's cold snap could bringbelow-freezing temperatures to some places.
The cold was already descending early Friday on New York, with wind chill expected to make it feel like temperatures werein the upper teens to mid-20s, according to the National Weather Service. Wind gusts in Boston are reaching 25 to 35 mph and will bring wind chills into the 20s as well, the NWS said.
Snow flurries are also expected Friday for downwind parts of the Great Lakes, CNN meteorologist DerekVan Damsaid, adding that those flurries will make it Saturday to the Northern Great Lakes.
"The Great Lakes are no longer ice covered, so here comes the Lake Effect snow again," the National Weather Service said.
Temperatures are expected to rebound a bit in the East over the weekend, but colder air will be coming.
Temperatures will plummet first on Monday in the Plains, and then further east on Tuesday and Wednesday.
Cold-weather records could be broken
Starting Tuesday morning, record low temperatures are possible across Texas and Oklahoma, along with parts of the Ohio Valley.
By Wednesday, more than a dozen record lows could be set in the Deep South and the Gulf Coast, CNN meteorologist Taylor Ward said.
In parts of Mississippi and Alabama, highs on Wednesday aren't likely to rise out of the 30s. For those areas, that's more than 10 degrees colder than usual, even in mid-January.
Some afternoon temperatures Wednesday in the East could fall up to 30 degrees below average, making it feel more like January for many.
CNN's Monica Garrett and Jason Hanna contributed to this report.


--- layout: page ---
Product designer and full stack developer.
Masters in Computer Eng and PhD candidate at Clarkson University.
Bootstrapped first software company to profitability from a beach in Thailand.


BoC to cut rates just once in 2020 – TDS
by Sandeep KanihamaAnalysts at TD Securities have changed their call and are now looking for the Bank of Canada to cut rates just once in April 2020.
Key Quotes
“While the Canadian economy has thus far been resilient to global headwinds, we do not believe recent actions taken by the US and China to enough to meaningfully resolve the elevated level of trade uncertainty which should prompt the BoC to provide more stimulus to offset the impact of global headwinds.”
“Underscoring the BoC's (relatively) constructive outlook is a healthy starting point; Q3 GDP growth was in line with BoC projections at 1.3%, although historical revisions imply less excess supply in the economy and the composition (domestic demand rose by 3.2%) was favourable.”
“Core CPI remains target at 2.0% on average although further deterioration in the labour market will test the BoC's resolve.”


Google Store Black Friday: $599 Pixel 4, $299 Pixel 3a, Stadia + Nest Wifi bundle
by Abner LiThe Google Store today is providing a “sneak peek” at its upcoming Black Friday 2019 deals. Kicking off Thursday, November 28th, highlights include $200 off the Pixel 4 and a bundle that packs Stadia Premiere Edition with Nest Wifi.
As revealed earlier this month, the Pixel 4 and Pixel 4 XL will be discounted by $200. This starts on November 24th and runs until Cyber Monday on December 2nd. The 64 GB Pixel 4 is $599 and the 128 GB model is $699, while the Pixel 4 XL starts at $699 and goes to $799. Meanwhile, the Pixel 3a and Pixel 3a XL see a $100 discount to start at $299 and $379.
The most interesting deal is a bundle that includes Stadia Premiere Edition and Nest Wifi, specifically the three-pack that features a router and two points. This $349 connectivity solution, along with the $129 Stadia Controller and Chromecast Ultra, will be $398 instead of $478.
Google and Nest Wifi has a special option to prioritize Stadia connectivity, and that synergy is clearly on display. Other notable Google Store Black Friday 2019 deals include:
Google Assistant
- Save $30 on Nest Hub Max: $229 —> $199
- Save $50 on Nest Hub: $129 —> $79
- Save $100 on Google Home Max.
- Save $20 on Nest Mini: $49 —> 29
- Save $40 on Nest Wifi router and point: $269 —> $229
Google Nest
- Save $80 on Nest Hello Doorbell: $229 —> $149
- Save $70 on Nest Learning Thermostat: $249 —> $179
- Save $30 on Nest Thermostat E.
- Save $40 on Nest Cam Indoor.
- Save $70 on Nest Cam IQ Outdoor.
- Save $100 on Nest Secure Alarm.
- Save $60 on Nest x Yale Lock with Nest Connect.
- Save $20 on Nest Protect: $119 —> $99
Misc
- Save up to $648 on Pixel Slate and select accessories: From $439 [Starts November 24th]
- Save $10 on Chromecast.
- Individual (US only) and bundled Titan Security Keys discounted by 15%
The deals above are for the United States. Google Store Black Friday 2019 discounts are also listed in other local sites.
FTC: We use income earning auto affiliate links. More.


Description
How to Break Software Security describes the general problem of software security in a practical perspective from a software tester's point of view. It defines prescriptive techniques (attacks that testers can use on their own software) that are designed to ferret out security vulnerabilities in software applications. The book's style is easy to read and provides readers with the techniques and advice to hunt down security bugs and see that they're destroyed before the software is released.
Accompanying the book is a CD-ROM containing Holodeck lite, which tests for security vulnerabilities. There are also a number of bug-finding tools, freeware, and an easy-to-use port scanner included on the CD-ROM.
· Paperback: 208 pages
· Publisher: Addison-Wesley Publishing; 1st edition (May 16, 2003)
· ISBN:<PHONE_NUMBER>


File Medical Condition
Home
Enter your Sentence:
Year of Birth:
Male
Female
Submit
Medical Condition


EPICS is a set of Open Source software tools for scientific and industrial control systems. This document is a statement of the goals and deliverables of the working group developing the next major version of EPICS, version 4, over the period approximately April 2014 to April 2015.
This year will focus 4 major areas; image processing and direct support for areaDetector plugin pipelines, multicast, a PVA gateway, and to further simplify high level scientific applications programming though leverage of structured PVs to enable PVs which are composites of CA PVs. Can we also do a timing synchronous data acquisition service and API?For more information about the EPICS, please refer to the home page of the Experimental Physics and Industrial Control System.
This version is the first editors draft of the 2014 charter. Following initial review by the working group this draft will be presented to the funding laboratories for approval.
The terms MUST, MUST NOT, SHOULD, SHOULD NOT, REQUIRED, and MAY when highlighted (through style sheets, and in uppercase in the source) are used in accordance with RFC 2119 [RFC2119]. The term NOT REQUIRED (not defined in RFC 2119) indicates exemption.
EPICS is a set of Open Source software tools, libraries and applications developed collaboratively and used worldwide, to create distributed soft real-time control systems for scientific instruments such as a particle accelerators and telescopes, and also for industrial process management.
EPICS Version 4 (V4), is the effort to produce the next major version of EPICS. EPICS Version 4 software modules interoperate with the classic IOC. The EPICS V4 Working Group is a collaborative effort of members invited by Brookhaven Lab, to bring EPICS V4 to its full potential.
This Charter is a statement of the basis for the work of the EPICS V4 working group over the charter lifetime. It includes the intended outcomes for that work, its deliverables and success criteria. It also outlines some administrative matters of the organization of the group, and its working practices.
The EPICS V4 Working Group was chartered 2 ½ years ago to define and publish an open control protocol for control system endpoints, which functionally extended channel access to suitability for both device control and high level services. It included data types suitable for high level services, such as tables and complete image data such as from gated photon detectors.
In that time, EPICS V4 successfully brought a communications protocol for controls, "pvAccess", to advanced public review status, and a software framework suitable for high performance distributed control, message passing, and high level software services, to production release status.
Now the working group will reorient to supporting high performance distributed data processing. The IOC as an interface to hardware will still remain largely unaltered - being changed only in for far as to support aggregate PV data.
This version of the Charter is specifically for the third year of operations of the Working Group, covering approximately the year April 2014 to April 2015.
This year the Mission of the EPICS V4 working group is to support high performance data processing, particularly of area detector data, and to develop several mechanisms to make it easier for high level applications to manage groups of PVs (aggregates). Additionally, we will start development of a gateway, so that aggregate data and data processing may be housed anywhere in classical EPICS networking infrastructures.
The EPICS V4 working group is directly supported by Brookhaven Lab (BNL), and collaborating organizations, subject to their following the mission, goals, and expected deliverables, of this Charter, and following the EPICS V4 Process as the work method. Conformance to and agreement with this Charter is required for participation in the working group.
In the timespan of this charter it is still expected that in an EPICS installation, core control and hardware module support would continue to be done with EPICS V3 IOCs. Very little if any direct changes will be made this year to the core processing of the IOC.
Previous work in V4 created an EPICS "middle layer" for "Service Oriented Architecture" based operations. That work is now largely complete.
In this charter period we will enable easier access to associated PVs, so a client deals with 1 PV which "expands to" a number of others, for instance 1 beam position monitor (BPM) PV may be valued "X,Y,current" to express the horizontal and vertical beam offset, plus the signal strength at the BPMs position.
Scope for this year's charter is specifically extended to the IOC level, where we will complete integration with vxWorks and interoperation with the classic EPICS IOC. How mach effort on vxWorks? Who would that benefit? Need a partner to test pvDatabase and db* modules on IOC, otherwise vxWorks should be best effort..
We intend to improve the EPICS IOC's suitability and performance as a scientific instrument data processing pipeline, specifically for fast detector image data, and to greatly improve facilities for clients to deal with these data.
Note that, as last year, our primary focus is on definition of standard behavior through normative documents of the Working Group, informed by the development of software which implements the behavior. As last year, we shall supply reference implementations for these standards. It is to be expected that in some respects, reference implementations may trail the standard.
The goals of the Working Group over the charter lifetime are to be:
Interoperability of the new communications protocol pvAccess, with the classic IOC has so far been supported very well.
EPICS V4 endpoints must continue to be interoperable with EPICS v3 IOCs. This interoperability must be both interlayer (classic IOC ⇔ pva client) and intralayer (V3 IOC ⇔ "embedded pvDatabase"). PvAccess must run in version 3 IOCs and serve all v3 type structures.
We will add ability to get or put a collection of V3 channels (dbGroup) synchronously (so long as they are all in the same lockset) as a result of 1 V4 channel operation.
This mechanism must satisfy the expected use case of device control including EPICS V4 for the foreseeable future; in which device control will continue to be through EPICS "V3" IOC (or simply "the IOC" since it is also a V4 IOC), with control wire protocols and client sides in EPICS V4. Presently it is still the position of the WG that device control is not in the scope of the WG.
Each normative standard must be demonstrated to be interoperable with adjacent layers in the EPICS v4 protocol stack by production of, and testing, at least one implementation, in each of C++ and Java, from device to client (end to end).
Need more from David
The following minimum performance goals are unchanged from last years charter. Last year we achieved these goals; this year we intend to certify, and documenting their achievement particularly for streams over network connections and in the context of image processing and archiving:
We shall publish the performance measurement data and micro-benchmark test framework for pvAccess, including documentation.
Shall we also write and publish to EPICS community, a performance report, comparing EPICS v3 to EPICS V4 for some common EPICS v3 control and read tasks. What have we learnt from micro-benchmarking?
The following are out of scope for the group specifically:
Derived from the goals above, success criteria are at least:
The outputs of the working group will be delivered as a system of documented standards, plus reference implementation source code where relevant. These documents and source codes will all be available from the EPICS V4 web site, http://epics-pvdata.sourceforge.net/.
The group is expected to produce the following normative deliverables WG to agree:
To be added.
The Working Group should coordinate and align objectives to satisfy the requirements of the following bodies:
Effective participation is expected to consume 20% of one's time; though this may be more during development of a normative product of the group. The Chair shall ensure that the criteria for Good Standing are understood and followed:
The group is expected to have at least 4 active participants to be successful.
A participant who is absent from more than 3 meetings consecutively, or in the opinion of the chairs is not acting on Action Items or their responsibilities for group deliverables, will be deemed out of good standing, and will no longer participate.
The chairs of this Group are Greg White (SLAC) and Andrew Johnson (APS).
The Group will have distributed meetings, one to two hours every week, and face–to–face meetings, every three or four months.
The Working Group will utilize the mailing list<EMAIL_ADDRESS>All except the most trivial messages should be cc'ed to this mailing list. The web archive of it used should be used as backward references in communications.
The proceedings of this Working Group are open to all (not just members of the working group), subject to exceptions made by the Chairs, after consultation with the Working Group. The group's communications on the web archive will be world readable. Posting to<EMAIL_ADDRESS>will be unrestricted for members, but moderated for non-members.
This Working Group operates under EPICS Open License.


Compliment Machine
Compliment Machine
Upload A Photo
Machine Learning Driven Compliments
By combining Clarifai API and Natural Language Toolkit we have perfected a machine learning solution to the world most pressing question: "How does this selfie look?"


When CeCe Hooks steps onto the court Saturday to face archrival Miami, it could be for the final time as a Bobcat. The senior point guard has an extra year of eligibility due to the trials athletes have had to face due to COVID-19, but if Hooks decides to hang up her white Adidas, then it’s important to recognize her place in Ohio basketball history now rather than later.
And that spot is at the very top.
When Hooks leaves Ohio, she will leave as the best player in program history, even if she doesn't have every stat to prove it. Hooks knew coming in she had the potential to be a program-defining player. Since high school, she has often been the best player on the court, making her jersey number almost ironic.
Wearing No. 1 is such a powerful statement that some teams won’t even let a player have it.  So when Hooks decided to don the number her freshman season, she was subconsciously telling everyone whose show it was.
There’s no athlete at Ohio I’ve watched more than Hooks. I’ve fluctuated between multiple beats throughout the years, but women’s basketball has been a constant. In fact, the first women’s basketball game I covered was Feb. 3, 2018, Hooks’ first collegiate start.
I remember arriving at The Convo. All l I heard was Ohio was going to lose to Ball State and that nothing special was going to happen.
Don’t believe everything you hear.
Hooks went off for 28 points and 11 rebounds. Ohio never trailed. I was dumbfounded.
In the postgame presser, Hooks talked about how excited she was to start but that it didn’t change how she felt. No matter when she’s on the court, she plans to leave her mark on the game.
Ever since that game, Ohio has felt like Hooks’ team. While Erica Johnson has proved to be an Ohio legend in her own right, Hooks gives opposing teams nightmares.
When you analyze Ohio’s roster, a particular ability is usually associated with each player. For Johnson, it’s her offensive versatility. Peyton Guice’s basketball IQ comes to mind. Caitlyn Kroll has settled into her role as a shooter. But for Hooks, the way she plays on both ends of the court makes it impossible to label her.
Defensively, Hooks is the best Ohio’s ever had. You could watch any Ohio game and quickly see that when it comes to playing defense, Hooks is like a defensive back and center fielder in one. If you bring the ball near her, she’s going to take it.
This season, Hooks leads the Mid-American Conference in steals per game (3.1) like she has every year. For four seasons, Hooks has terrorized ballhanders and made the Ohio steals record her own personal Lego to break and put together again. Hooks’ record currently stands at 374, 145 more than the next best (Quiera Lampkins’ 229).
But Hooks is more than just a defender. Despite winning the MAC Defensive Player of the Year two years in a row, Hooks has solidified herself as one of Ohio’s best scorers. This season, Hooks leads the conference in points per game (25.7), and she’s second in points for program history.
Hooks will need another season to catch Caroline Mast’s school record 2,449 points. To start the season, Hooks was in fifth place behind Mast, Lampkins, Kiyanna Black and Laura Reding, but her 2,031 points are in reach of Mast’s with a fifth season.
So I know what you’re thinking, shouldn’t Mast still be considered the best?
No.
While Caroline Mast was a phenomenal player — her jersey hangs high in The Convo, just in case you didn’t know — I believe Hooks edges her out. But it’s close.
Mast is the superior scorer, no question about it. Hooks could break the record, but it would take more games than Mast played to reach 2,450. But if you’re going to count games, you have to acknowledge roles.
Over her four seasons at Ohio, Mast played with Marti Heckman. Heckman was a solid scorer, but her main role on offense was to feed Mast the ball. That role provided Heckman to amass 810 assists over her career, a program record.
Hooks doesn’t have the luxury of only being a scorer. As point guard, Hooks has to find and create opportunities for her teammates. She can not only score with the best, but she can share. Add that to her defensive prowess, and she has to be No. 1.
Over my four years of covering CeCe Hooks, not once have I been bored watching her play. No matter the opponent, Hooks does what she can to prove that she’s the best. I’ve seen the game-winners, the steals, the reverse finishes, and I can say with 100% certainty ...
CeCe Hooks is the best player in program history.


{{ $selected := or (.Get "selected") "us-east-1" }} {{ $label := or (.Get "label") "Select your AWS region" }}


On success, two same text string with equal width will be rendered.
Your browser does not support the canvas element.


Go to the source code of this file.
Classes | |
| struct | _Async |
| Asynchronous transfer descriptor. More... | |
| struct | _Twid |
| TWI driver structure. More... | |
| struct | TwihsDma |
| TWI driver structure. More... | |
Macros | |
| #define | TWID_ERROR_BUSY 1 |
| TWI driver is currently busy. More... | |
| #define | ASYNC_STATUS_PENDING 0xFF |
| Transfer is still pending. More... | |
Typedefs | |
| typedef struct _Async | Async |
| Asynchronous transfer descriptor. More... | |
| typedef struct _Twid | Twid |
| TWI driver structure. More... | |
Functions | |
| void | TWID_Initialize (Twid *pTwid, Twihs *pTwi) |
| Initializes a TWI driver instance, using the given TWI peripheral. More... | |
| void | TWID_DmaInitialize (TwihsDma *pTwidma, Twihs *pTwi, uint8_t bPolling) |
| Initializes a TWI driver instance, using the given TWI peripheral. More... | |
| void | TWID_Handler (Twid *pTwid) |
| Interrupt handler for a TWI peripheral. More... | |
| uint32_t | ASYNC_IsFinished (Async *pAsync) |
| Returns 1 if the given transfer has ended; otherwise returns 0. More... | |
| uint8_t | TWID_Read (Twid *pTwid, uint8_t address, uint32_t iaddress, uint8_t isize, uint8_t *pData, uint32_t num, Async *pAsync) |
| Asynchronously reads data from a slave on the TWI bus. More... | |
| uint8_t | TWID_Write (Twid *pTwid, uint8_t address, uint32_t iaddress, uint8_t isize, uint8_t *pData, uint32_t num, Async *pAsync) |
| Asynchronously sends data to a slave on the TWI bus. More... | |
| uint8_t | TWID_DmaRead (TwihsDma *pTwiXdma, uint8_t address, uint32_t iaddress, uint8_t isize, uint8_t *pData, uint32_t num, Async *pAsync) |
| Asynchronously reads data from a slave on the TWI bus. More... | |
| uint8_t | TWID_DmaWrite (TwihsDma *pTwiXdma, uint8_t address, uint32_t iaddress, uint8_t isize, uint8_t *pData, uint32_t num, Async *pAsync) |
| Asynchronously sends data to a slave on the TWI bus. More... | |
| #define ASYNC_STATUS_PENDING 0xFF |
Transfer is still pending.
| #define TWID_ERROR_BUSY 1 |
TWI driver is currently busy.
| uint32_t ASYNC_IsFinished | ( | Async * | pAsync | ) |
Returns 1 if the given transfer has ended; otherwise returns 0.
| pAsync | Pointer to an Async instance. |
Initializes a TWI driver instance, using the given TWI peripheral.
| pTwid | Pointer to the Twid instance to initialize. |
| pTwi | Pointer to the TWI peripheral to use. |
| uint8_t TWID_DmaRead | ( | TwihsDma * | pTwiXdma, |
| uint8_t | address, | ||
| uint32_t | iaddress, | ||
| uint8_t | isize, | ||
| uint8_t * | pData, | ||
| uint32_t | num, | ||
| Async * | pAsync | ||
| ) |
Asynchronously reads data from a slave on the TWI bus.
An optional callback function is triggered when the transfer is complete.
| pTwid | Pointer to a Twid instance. |
| address | TWI slave address. |
| iaddress | Optional slave internal address. |
| isize | Internal address size in bytes. |
| pData | Data buffer for storing received bytes. |
| num | Number of bytes to read. |
| pAsync | Asynchronous transfer descriptor. |
| TWI_ID | TWI ID for TWI0, TWIHS1, TWIHS2. |
| uint8_t TWID_DmaWrite | ( | TwihsDma * | pTwiXdma, |
| uint8_t | address, | ||
| uint32_t | iaddress, | ||
| uint8_t | isize, | ||
| uint8_t * | pData, | ||
| uint32_t | num, | ||
| Async * | pAsync | ||
| ) |
Asynchronously sends data to a slave on the TWI bus.
An optional callback function is invoked whenever the transfer is complete.
| pTwid | Pointer to a Twid instance. |
| address | TWI slave address. |
| iaddress | Optional slave internal address. |
| isize | Number of internal address bytes. |
| pData | Data buffer for storing received bytes. |
| num | Data buffer to send. |
| pAsync | Asynchronous transfer descriptor. |
| TWI_ID | TWIHS ID for TWIHS0, TWIHS1, TWIHS2. |
| void TWID_Handler | ( | Twid * | pTwid | ) |
Interrupt handler for a TWI peripheral.
Manages asynchronous transfer occurring on the bus. This function MUST be called by the interrupt service routine of the TWI peripheral if asynchronous read/write are needed.
| pTwid | Pointer to a Twid instance. |
Initializes a TWI driver instance, using the given TWI peripheral.
| pTwid | Pointer to the Twid instance to initialize. |
| pTwi | Pointer to the TWI peripheral to use. |
| uint8_t TWID_Read | ( | Twid * | pTwid, |
| uint8_t | address, | ||
| uint32_t | iaddress, | ||
| uint8_t | isize, | ||
| uint8_t * | pData, | ||
| uint32_t | num, | ||
| Async * | pAsync | ||
| ) |
Asynchronously reads data from a slave on the TWI bus.
An optional callback function is triggered when the transfer is complete.
| pTwid | Pointer to a Twid instance. |
| address | TWI slave address. |
| iaddress | Optional slave internal address. |
| isize | Internal address size in bytes. |
| pData | Data buffer for storing received bytes. |
| num | Number of bytes to read. |
| pAsync | Asynchronous transfer descriptor. |
| uint8_t TWID_Write | ( | Twid * | pTwid, |
| uint8_t | address, | ||
| uint32_t | iaddress, | ||
| uint8_t | isize, | ||
| uint8_t * | pData, | ||
| uint32_t | num, | ||
| Async * | pAsync | ||
| ) |
Asynchronously sends data to a slave on the TWI bus.
An optional callback function is invoked whenever the transfer is complete.
| pTwid | Pointer to a Twid instance. |
| address | TWI slave address. |
| iaddress | Optional slave internal address. |
| isize | Number of internal address bytes. |
| pData | Data buffer for storing received bytes. |
| num | Data buffer to send. |
| pAsync | Asynchronous transfer descriptor. |


{% extends "base.html" %} {% block title %} Account exists | {{ block.super }} {% endblock %} {% block content %}
(and you're logged into it right now!)


M. Sc. In Computer Science at Oregon State University (09/2018 - Present)
B. Sc. in Mathematics and Computer Sience at U. Of California, San Diego (09/2013 - 03/2016)
Computer Vision, Machine Learning
A study about Bio-Inspired Visual System, which is based on an extended visual model that consists saccadic eye movement and visual pathway (vertebrate retina, lateral geniculate nucleus and visual cortex). This model applies variants of principal component analysis, to extract unique features from input data.
[Paper]
Applying this work to study about bio features of faces, using in facial recognition.
Reconstruction a 3D face model from single 2D image. Current state of my work is fitting 3D Morphable model to 2D image using hard edge correspondence and landmark correspondence, and least square optimization with constraints.
[Code]
Next steps are to estimate and render texture (two possible approaches are using Gauss-Newton iterative method or using CNN model), enhance low resolution/blurry images so that they could work with 3D model, and learn expression model.
(Method 1) Enhancing an image by opencv before fitting
(Method 2) Enhancing an image by neural enhancement before fitting
Enhancing an image at different scales and fitting
This is Keras and TensorFlow project to compose a content image and a style image via a convolutional neural network (CNN) model. The implementation is followed the Keras tutorial on the VGG16 model. Also, the VGG19, InceptionV3 and Xception models have also experimented on this mini project.
[Code]
This is a pre-processing tool to extract and align face in the wild. This pre-processing steps will include landmark detection, align eye level, crop face image, and scale to certain size.
Another feature is to extract a bag of crop parts of a single face into 12 different parts. Applying this work to repair data for facial recognition.


Presented by David Kitchen / @dwkitchen
Technical Lead for Commerce Guys UK
Tax specialist and Commerce Contributor
Bojan Zivanovic (bojanz)
and Ryan Szrama (rszrama)
Our vision is for Drupal Commerce to be the number one
open source e-commerce platform in the world...
Powering truly flexible e-commerce.
A brief history.
*Led by Bojan Zivanovic.
$ ¢ £ p ¥ ₤ ₧ € ₹ ₩ ₴ ₯ ₮ ₲ ₳ ₵ ₭ ₪ ₫
€12,345.99
12 345,99 €
12.345,99 €
د.إ. ٩٩٩٫٩٩
A price is a value object. Each operation (add, subtract, multiply, divide, round) produces a new price instance. All amounts are passed as strings, and manipulated using bcmath.
use CommerceGuys\Intl\Currency\CurrencyRepository;
use CommerceGuys\Pricing\Price;
$currencyRepository = new CurrencyRepository;
$currency = $currencyRepository->get('EUR');
$firstPrice = new Price('99.99', $currency);
$secondPrice = new Price('100', $currency);
$thirdPrice = new Price('20.307', $currency);
// Every operation produces a new Price instance.
$total = $firstPrice
->add($secondPrice)
->subtract($thirdPrice)
->multiply('4')
->divide('2');
echo $total; // 359.366 EUR
echo $total->round(); // 359.37 EUR
echo $total->round(Price::ROUND_HALF_UP, 1); // 359.4 EUR
echo $total->greaterThan($firstPrice); // true
use CommerceGuys\Intl\Currency\CurrencyRepository;
use CommerceGuys\Pricing\Price;
$currencyRepository = new CurrencyRepository;
$eur = $currencyRepository->get('EUR');
$usd = $currencyRepository->get('USD');
// Use an external library to get an actual exchange rate.
$rate = 1;
$eurPrice = new Price('100', $eur);
$usdPrice = $eurPrice->convert($usd, $rate);
echo $usdPrice;
use CommerceGuys\Intl\Currency\CurrencyRepository;
use CommerceGuys\Intl\NumberFormat\NumberFormatRepository;
use CommerceGuys\Intl\Formatter\NumberFormatter;
use CommerceGuys\Pricing\Price;
$currencyRepository = new CurrencyRepository;
$currency = $currencyRepository->get('USD');
$price = new Price('99.99', $currency);
$numberFormatRepository = new NumberFormatRepository;
$numberFormat = $numberFormatRepository->get('en-US');
$currencyFormatter = new NumberFormatter($numberFormat, NumberFormatter::CURRENCY);
echo $currencyFormatter->formatCurrency($price->getAmount(), $price->getCurrency());
use CommerceGuys\Addressing\Repository\AddressFormatRepository;
use CommerceGuys\Addressing\Repository\SubdivisionRepository;
$addressFormatRepository = new AddressFormatRepository();
$subdivisionRepository = new SubdivisionRepository();
// Get the address format for Canada.
$addressFormat = $addressFormatRepository->get('CA');
// Get the subdivisions for Canada, in French.
$states = $subdivisionRepository->getAll('CA', 0, 'fr');
foreach ($states as $state) {
echo $state->getName();
}
use CommerceGuys\Addressing\Formatter\PostalFormatter;
use CommerceGuys\Addressing\Provider\DataProvider;
$dataProvider = new DataProvider();
$formatter = new PostalFormatter($dataProvider);
// Format an address for sending from Switzerland, in French.
// If the address destination is not Switzerland, the country name will be
// appended in French, uppercase.
echo $formatter->format($address, 'CH', 'fr');
Zones are territorial groupings mostly used for shipping or tax purposes. For example, a set of shipping rates associated with a zone where the rates become available only if the customer's address matches the zone.
A zone can match other zones, countries, subdivisions (states/provinces/municipalities), postal codes. Postal codes can also be expressed using ranges or regular expressions.
Create the German VAT zone
use CommerceGuys\Addressing\Model\Address;
use CommerceGuys\Zone\Model\Zone;
use CommerceGuys\Zone\Model\ZoneMemberCountry;
$zone = new Zone();
$zone->setId('german_vat');
$zone->setName('German VAT');
$zone->setScope('tax');
Add Germany to the zone,
$germanyZoneMember = new ZoneMemberCountry();
$germanyZoneMember->setCountryCode('DE');
$zone->addMember($germanyZoneMember);
add the 4 Austrian postal codes that are in Germany for VAT.
$austriaZoneMember = new ZoneMemberCountry();
$austriaZoneMember->setCountryCode('AT');
$austriaZoneMember->setIncludedPostalCodes('6691, 6991:6993');
$zone->addMember($austriaZoneMember);
Initialising a zone matcher.
use CommerceGuys\Addressing\Model\Address;
use CommerceGuys\Zone\Matcher\ZoneMatcher;
use CommerceGuys\Zone\Repository\ZoneRepository;
$repository = new ZoneRepository('resources/zone');
$matcher = new ZoneMatcher($repository);
Create an address.
$austrianAddress = new Address();
$austrianAddress->setCountryCode('AT');
$austrianAddress->setPostalCode('6692');
Get the matching tax zones.
$zones = $matcher->matchAll($austrianAddress, 'tax');
Is in Germany for VAT.
use CommerceGuys\Tax\Repository\TaxTypeRepository;
use CommerceGuys\Tax\Resolver\Engine\TaxTypeResolverEngine;
use CommerceGuys\Tax\Resolver\Engine\TaxRateResolverEngine;
use CommerceGuys\Tax\Resolver\TaxType\EuTaxTypeResolver;
use CommerceGuys\Tax\Resolver\TaxRate\DefaultTaxRateResolver;
use CommerceGuys\Tax\Resolver\TaxResolver;
$taxTypeRepository = new TaxTypeRepository();
$taxTypeResolverEngine = new TaxTypeResolverEngine();
$taxTypeResolverEngine->add(new EuTaxTypeResolver($taxTypeRepository));
$taxRateResolverEngine = new TaxRateResolverEngine();
$taxRateResolverEngine->add(new DefaultTaxRateResolver());
$resolver = new TaxResolver($taxTypeResolverEngine, $taxRateResolverEngine);
{
"name": "British VAT",
"zone": "gb_vat",
"tag": "EU",
"rates": [
{
"id": "gb_vat_standard",
"name": "Standard",
"display_name": "% VAT",
"default": true,
"amounts": [
{
"id": "gb_vat_standard_1991",
"amount": 0.175,
"start_date": "1991-03-19",
"end_date": "2008-11-30"
},
Validated the architecture with members of SensioLabs, Smile, Publicis Modem, OSInet, i-KOS, Adyax, Ekino, and others.
<?php
namespace Drupal\commerce\Entity
Create a "mini" ledger for payments with double entry like accounting features.
Office hours: Wednesdays at 1PM GMT
Find us in #drupal-commerce.


Image2Object
Web application to predict 3D object from 2D image.
Write contour of 2D image in left canvas.
Push "
Predict 3D Object
" button.
After a while, predicted 3D object is shown in right canvas. You can rotate the object by mouse dragging on canvas.
Undo
Redo
Clear
Predict 3D Object
Clear Predicted Object
Directional Light
R :
G :
B :
Ambient Light
R :
G :
B :
Camera : forward
backward
Exaggerate Depth :
Smooth :


UI Tests
Empty test
Keyboard test
Geolocation test
Contextmenu test
Recording test
Select test
Contacts tests
Alert/Prompt tests
window.open tests
mozNotification tests
Scroll bar tests
setCapture test
Inline Activities test
Multi Activities test
Browser Web Activities test
Save bookmark
mozRequestFullScreen
mozLockOrientation
HTTP Authentication
Pick Photo
View Photo
Pick Video
Pick Photo/Video
Open Video
Open Audio
navigator.mozPay tests
text inputmode tests
audio tag tests
navigator.mozId tests
navigator.mozId.get
back


MVC Movie App
[
Log On
]
Home
Search
Hello World
Create
Movie
Title
ReleaseDate
Genre
Price
Rating
Back to List


Throughout this course, you’ll build a Python program and add on increasing complexity as your learning progresses. Explore the essentials of object-oriented programming, troubleshoot Python code, and push your skills to the next level by adding scripting, modules, and APIs to your Python toolkit. Apply Python skills to data visualization with Pandas or web applications with Flask, and demonstrate everything you've learned by completing a cumulative, final project.
Discover why professionals ranging from analysts to web developers rely on Python programming to excel in their work. Dive into fundamental concepts and techniques, and build your own custom web or data application.
Tap into the demand for Python programming skills, and translate new knowledge into career gains. Our dynamic coursework is designed by industry experts to align with today’s employer demands.
Collaborate with experts and peers at the local level, and join GA’s growing global network. After the course is through, you’ll access exclusive perks, opportunities, and events to continue pursuing a lifetime of learning.
Work with passionate programmers who love to teach and are damn good at it. From large open source libraries to quantum computer simulators, they've hacked it all.
Work with other students who are considerate, understanding and generally pleasant to be around.
You will use popular developer tools like Git, Github, Bash, Sublime and Chrome Dev Tools.
Spend 12 intensive weeks in Tel Aviv, ranked the number one and number two fastest growing tech hubs in Midle east.
Note: The data-parent attribute makes sure that all collapsible elements under the specified parent will be closed when one of the collapsible item is shown.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Minima maxime quam architecto quo inventore harum ex magni, dicta impedit.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Minima maxime quam architecto quo inventore harum ex magni, dicta impedit.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Minima maxime quam architecto quo inventore harum ex magni, dicta impedit.
Start by Learning the basics of front-end development
Enter the world of back-end development with Python
Choose MEAN, Ruby on Rails, Java or C#.NET as your next stack. Available stacks will vary by campus
On dit ce que tu veux et faut qu'on trouve une image sympthique
Time to find a job, and a serious one. Our team will be in charge of taking care of the process.


Events
Home
Upcoming Events
Ongoing Events
Previous Events
Contact Us
About
LOGIN
USERNAME:
PASSWORD:


PHOTO: National plan of action
Dr Elizabeth Ward (centre), chair of the Violence Prevention Alliance, presents a copy of the National Plan of Action for an Integrated Response to Children and Violence (NPACV) to Floyd Green, state minister in the Ministry of Industry, Commerce and Agriculture at the official launch of the NPACV at Emancipation Park on Tuesday. Sharing in the moment is Hyacinth Blair, senior director, Children's Affairs Policy, Ministry of Education,Youth and Information. (Photo: Naphtali Junior)


Project Information
- Name
- FileDLerError
- URL
- http://mzw.jp/research/ex/fd/motivatingExample_fault.html
Implemented Ajax Desing Patterns
| Property | Requirement | Ajax Design Pattern | Property Pattern | CTL Template | Variables (keys) | Result |
|---|---|---|---|---|---|---|
| AsyncComm | Ajax apps can handle user events during asynchronous communications | XMLHttpRequest Call | Response | AG($P -> AF($S)) | Ajax.Request ($P) UserEvents ($S) |
Correct |
| ACRetry | Ajax apps can retry an asychronous communication when it fails | XMLHttpRequest Call | Response | AG($P -> AF($S)) | onFailure ($P) Ajax.Request ($S) |
Fault |
| SRWait | Ajax apps should reveive a server response before calling reposen-dependent functions | On-Demand JavaScript | Precedence | !E[!$S U ($P & !$S)] | inputFormText ($P) onSuccess ($S) |
Correct |
| UEHRegist | Ajax apps should register user event handlers at page load | User Action | Precedence | !E[!$S U ($P & !$S)] | UserEvents ($P) onload ($S) |
Correct |
| UESubmit | Ajax apps should require explicit user operations before form data is submitted | Explicit Submission | Existence | !E[!($P & !$R) U ($R & !($P & !$R))] | UserEvents ($P) doSubmit ($R) |
Correct |


A clathrin adaptor protein complex primarily involved in clathrin-related transport at the TRANS-GOLGI NETWORK. (mesh:D033961)
| Identifier | Name | Relation |
| HGNC:559 | AP1S1 | partof |
| HGNC:554 | AP1B1 | partof |
| HGNC:13667 | AP1M1 | partof |
| HGNC:558 | AP1M2 | partof |
| HGNC:555 | AP1G1 | partof |
| HGNC:18971 | AP1S3 | partof |
| HGNC:560 | AP1S2 | partof |
| Relation | Identifier | Name |
| isa | FPLX:Adaptor_protein | Adaptor_protein |


My Title Example
Abstract
abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract abstract
1 Section 1
main text main text main text main text main text main text main text main text


#include <ChainDatabase.hpp>
◆ BlockForkData()
| hsrcore::blockchain::BlockForkData::BlockForkData |
( |
| ) |
|
|
inline |
◆ can_link()
| bool hsrcore::blockchain::BlockForkData::can_link |
( |
| ) |
const |
|
inline |
◆ invalid()
| bool hsrcore::blockchain::BlockForkData::invalid |
( |
| ) |
const |
|
inline |
◆ valid()
| bool hsrcore::blockchain::BlockForkData::valid |
( |
| ) |
const |
|
inline |
◆ invalid_reason
| fc::optional<fc::exception> hsrcore::blockchain::BlockForkData::invalid_reason |
◆ is_included
| bool hsrcore::blockchain::BlockForkData::is_included |
is included in the current chain database
◆ is_known
| bool hsrcore::blockchain::BlockForkData::is_known |
do we know the content of this block (false if placeholder)
◆ is_linked
| bool hsrcore::blockchain::BlockForkData::is_linked |
is linked to genesis block
◆ is_valid
| fc::optional<bool> hsrcore::blockchain::BlockForkData::is_valid |
if at any time this block was determined to be valid or invalid then this flag will be set.
◆ next_blocks
| std::unordered_set<BlockIdType> hsrcore::blockchain::BlockForkData::next_blocks |
IDs of all blocks that come after.
The documentation for this struct was generated from the following file:


h1 heading
h2 sub-heading
div.abstract
Paragraph 1
Paragraph 2
h2 sub-heading
Paragraph 1
h1 heading
h2 sub-heading
Paragraph 1
Paragraph 2
h2 sub-heading
Paragraph 1


To create effective lessons, we need to understand the problems students are facing. By working directly with students in underserved populations, we can improve Oppia to create a learning environment that addresses their unique circumstances.
Joining the Oppia research team will enable you to grow your skills, work with an international team of researchers, and positively impact the lives of millions of students.


The Citrus development team will be glad to receive new ideas and improvements from you - any time. You can access the Citrus source on GitHub http://github.com/christophd/citrus. You can fork the Citrus project on GitHub very easy in order to provide changes. In case you have coded extensions, bug fixes or simply new features, please send us a pull request from your repository. If you are not sure how to do it please see our quickstart or refer to the very good documentation and how-to section on GitHub (http://github.com/).
If you still have questions regarding Citrus development please do not hesitate to send us a mail regarding your problems on<EMAIL_ADDRESS>In case you coded a new feature or extension we are looking forward to know about it! Send us your changes as pull request on GitHub citrus@github including unit tests (we are very serious about testing, of course) and a description of the feature.
Note: If you want your contributions in the official Citrus code repository you have to agree that your changes are released under the same license as Citrus uses to that time and that the Citrus team might change the licensing of Citrus in future.
Bug fixes are also best sent to us as project fork on GitHub along with a bug report, so we can apply the fix to the code base. Please tell us the version the patch should be applied to in order to avoid misunderstandings. Bug fix changes must be released under the same license as Citrus to get involved into the code base.
In case you have new ideas and feature requests for Citrus but you do not want to implement those on your own, we will be happy to receive your request on the citrus-dev mailing list (dev@citrusframework.org). You can also simply open a feature request through the issue tracker on GitHub http://github.com/christophd/citrus/isues. We will discuss the benefit of the new feature with you and help you to get the best out of Citrus for your needs.


Search
PETTR
Summary
Description
×
PETTR Pet Search
Zip Code:
Pet Type:
Any
Dog
Cat
Small and Furry
Barnyard
Bird
Horse
Pig
Rabbit
Reptile
Pet Gender:
Any
Female
Male
>
Pet Age:
Any
Baby
Young
Adult
Senior
Pet Size:
Any
Small
Medium
Large
Extra Large
Powered by Petfinder API
×
Summary
Name
Type
Breed
Age
Gender
Size


--- title: Community description: The value that holds us together the strongest is our community. The community of contributors, talented artists, and you, the listeners. type: basic ---
These are our contributors & staff. We love their work & passion.


Research Fellow Position in Science of Learning
Now Open for Applications
We are currently seeking highly motivated individuals who share our passion in deciphering the enigma of the brain to join us at the Clinical Brain Lab , Nanyang Technological University! We are seeking candidates interested in educational neuroscience.
Job Scope
We are interested in building capacity for research in the Science of Learning, in particular in the area of educational neuroscience. We seek to understand how cognitive neuroscience can be applied to education to optimize learning. The successful candidate will work closely with researchers at the Centre for Research and Development in Learning ( CRADLE), National Institute of Education ( NIE), and other international collaborators from institutes such as Johns Hopkins University, Massachusetts Institute of Technology and/or University of Cambridge. The current project will be investigating reading brain networks in bilingual children. Singapore is a multicultural city that has a conducive education system to study this.
The main research modalities in our lab include: neuropsychological assessments, cognitive tests, functional Magnetic Resonance (fMRI), diffusion MRI (DTI, HARDI, DSI), electroencephalography (EEG), Transcranial Magnetic Stimulation (TMS).
Looking For
We welcome applicants with a doctorate (PhD) degree in various backgrounds such as: experimental psychology, biopsychology, cognitive psychology, neuropsychology, neurology, psychiatry, cognitive neuroscience, electrical engineering, medical engineering, computer science, and related fields.
Requirements
Candidates are expected to have good interpersonal skills and to have demonstrated record of excellent scientific writing skills. Desirable qualification includes one or more of the following:
- Experience with conducting behavioral experiments in neuropsychology or cognitive psychology.
- Experience with neuroimaging techniques (MRI/EEG/or TMS).
- Familiarity with image data analysis tools such as SPM, FSL, AFNI, DSIStudio, Matlab, Python etc.
Apply
To apply, please send Curriculum Vitae, letter of application with research interests, and three letters of recommendation to<EMAIL_ADDRESS>- Contact information: Professor S.H. Annabel Chen, PhD, Professor, of Psychology, School of Social Sciences, Nanyang Technological University, 48 Nanyang Drive, SHHK-04-19, Singapore 639818, phone +65-6316-8836


10 Binomial Sign Test
10.1 The Binomial Distribution
data.frame(count = 0:13) %>%
dplyr::mutate(prob = dbinom(count, size = 13, p = .50)) %>%
ggplot(aes(x = count,
y = prob)) +
geom_point() +
geom_segment(aes(xend = count,
y = 0,
yend = prob),
size = .1) +
theme_bw() +
scale_x_continuous() +
labs(x = "Count",
y = "Probability") +
theme(legend.position = "none")
10.1.1 Effect of Hypothesised “P” Value
ex_n = 30
data.frame(count = 0:35) %>%
dplyr::mutate(prob_10 = dbinom(count, size = ex_n, p = .10)) %>%
dplyr::mutate(prob_25 = dbinom(count, size = ex_n, p = .25)) %>%
dplyr::mutate(prob_50 = dbinom(count, size = ex_n, p = .50)) %>%
dplyr::mutate(prob_75 = dbinom(count, size = ex_n, p = .75)) %>%
dplyr::mutate(prob_90 = dbinom(count, size = ex_n, p = .90)) %>%
tidyr::pivot_longer(cols = contains("prob"),
names_to = "percent",
names_prefix = "prob_",
values_to = "prob") %>%
dplyr::mutate(percent = paste0("P = .", percent)) %>%
ggplot(aes(x = count,
y = prob)) +
geom_point() +
geom_segment(aes(xend = count,
y = 0,
yend = prob),
size = .1) +
theme_bw() +
scale_x_continuous() +
labs(x = "Count",
y = "Probability") +
theme(legend.position = "none") +
facet_grid(~ percent)
10.1.2 Effect of Sample Size
10.1.3 Probability of 4 (or more) out of 5
[1] 0.15625
data.frame(count = 0:ex_n) %>%
dplyr::mutate(prob = dbinom(count, size = ex_n, p = ex_p)) %>%
dplyr::mutate(mark = cut(count,
breaks = c(0, ex_x - .5, ex_n),
include.lowest = TRUE)) %>%
ggplot(aes(x = count,
y = prob,
color = mark)) +
geom_point(size = 2) +
geom_segment(aes(xend = count,
y = 0,
yend = prob),
size = 1) +
theme_bw() +
scale_x_continuous(breaks = 0:ex_n) +
scale_color_manual(values = c("black", "red")) +
labs(x = "Count",
y = "Probability") +
theme(legend.position = "none")
10.1.4 Probability of 8 (or more) out of 10
[1] 0.04394531
data.frame(count = 0:ex_n) %>%
dplyr::mutate(prob = dbinom(count, size = ex_n, p = ex_p)) %>%
dplyr::mutate(mark = cut(count,
breaks = c(0, ex_x -.5, ex_n),
include.lowest = TRUE)) %>%
ggplot(aes(x = count,
y = prob,
color = mark)) +
geom_point(size = 2) +
geom_segment(aes(xend = count,
y = 0,
yend = prob),
size = 1) +
theme_bw() +
scale_x_continuous(breaks = 0:ex_n) +
scale_color_manual(values = c("black", "red")) +
labs(x = "Count",
y = "Probability") +
theme(legend.position = "none")
10.1.5 Probability of 80 (or more) out of 100
[1] 4.228163e-10
data.frame(count = 0:ex_n) %>%
dplyr::mutate(prob = dbinom(count, size = ex_n, p = ex_p)) %>%
dplyr::mutate(mark = cut(count,
breaks = c(0, ex_x, ex_n),
include.lowest = TRUE)) %>%
ggplot(aes(x = count,
y = prob,
color = mark)) +
geom_point() +
geom_segment(aes(xend = count,
y = 0,
yend = prob)) +
theme_bw() +
scale_x_continuous() +
scale_color_manual(values = c("black", "red")) +
labs(x = "Count",
y = "Probability") +
theme(legend.position = "none")
10.2 Example: Fair Coin?
Experiment A coin is flipped ten times and lands on heads eight times
Quesiton Is coin biased?
Hypotheses $$ H_0: Proportion (X) = .50 \
H_1: Proportion (X) .50 $$
10.2.1 Enter tabulated data (subtotals)
heads tails Sum
8 2 10
10.2.2 Two-sided Test
defaults
- Fifty-fifty change: \(H_0: P = .50 \rightarrow\)
p = 0.50
- Two-sided alternative:
alternative = "two.sided"
- 95% confidence interval:
conf.level = 0.95
Exact binomial test
data: .
number of successes = 8, number of trials = 10, p-value = 0.1094
alternative hypothesis: true probability of success is not equal to 0.5
95 percent confidence interval:
0.4439045 0.9747893
sample estimates:
probability of success
0.8
10.2.3 One-sided Test
Exact binomial test
data: .
number of successes = 8, number of trials = 10, p-value = 0.05469
alternative hypothesis: true probability of success is greater than 0.5
95 percent confidence interval:
0.4930987 1.0000000
sample estimates:
probability of success
0.8


-
Name: Mushrifah Hasan
-
Roll no::<PHONE_NUMBER>
-
Car Database Management System
Technologies used
-
MongoDB
-
Node.js
-
Express.js
-
AngualarJS
Functionalities
-
Register Page
-
Login Page
-
Login and Register authentication usong JWT
-
Add booking details- car name, name, booking date
-
Delete car details
-
Update car details


Wisconsin shakes slow start, cruises past McNeese St.
Badgers move to 2-1
by Kevin LewisMadison - The Wisconsin men's basketball team moved to 2-1 on the young season with a 83-63 over McNeese St. The Badgers started slowly, falling behind 10-2 and trailing for most of the first half. A Brad Davison jumper put Wisconsin up 37-34 right before halftime, and the Badgers never trailed again.
Davison led Wisconsin with 24 points and four steals. Aleem Ford added 16 points in the win. The Badgers forced 18 McNeese turnovers.
The Cowboys (1-3) were led by Shamarkus Kennedy who had 20 points.
Wisconsin sophomore guard Kobe King did not play. He's resting a left leg injury suffered in practice this week. The Badgers next game is Sunday when they host Marquette. Tipoff is set for noon at the Kohl Center. It will be televised on Fox Sports 1.


Reset it! Reset it all! Mwa ha! MUA HA HA!
Reset Editor
Clear Editor
Replace all with:
Size of Editor Text ({{ctrl.sizeEditorText}} B)
Reset Log
Calculate new deltas
Send to server
Receive from server
Randomize Delta
Deltas for plain text ({{ctrl.sizePlainText}} B)
Deltas for search-and-replace ({{ctrl.sizeSearchAndReplace}} B)
Deltas for both algebraically combined ({{ctrl.sizeADT}} B)


Steem top communites 3D force-directed graph visualization. Using ThreeJS Javascript 3D drawing library and 3D Force-Directed Graph layout engine.
All data gathered through SteemSQL.
Top #categories selected SQL query: * Top 100 #categories * Last 3 months posts * Root posts (no comments) only * Ordered descending by number of individual authors
Each of above #category is linked with similar #tags selected by SQL query: * Number of together #tag occurences in one post * Last 3 months posts * Root posts (no comments) only
https://github.com/mys/steemverse/data/data.py
Every #category is surrounded by top 100 @accounts selected by SQL query: * Top 100 unique @accounts * From every already existing #category in the verse * Last 3 months post authors * Having at least 25 Steem Power * Root posts (no comments) only * Ordered descending by combined Steem Power
If @account already exists, the stronger-connected to #category remains.
https://github.com/mys/steemverse/data/data.py
1. #steem being positioned in the center of the verse. 2. #tags are connected by hyperlinks if they share at least 20% of posts occurences (each separately). 3. Distance between #tags indicates their posts coverage. 4. @account to #tag distance is inversely proportional to posting activity. 5. Every @account and #tag collides each other by twice of spheres's radius. 6. #tags are attracted to the center of the verse while @accounts are pushed towards their main #category.


a6a
|
||||||
|
|
|
674
22 1000
Part I
|
| Chapter | 1 | Introduction to Oracle |
| 2 | Understanding Terms | |
| 3 | What Is a Well-Tuned System? | |
| 4 | Tuning Methodology | |
| 5 | Benchmarking | |
| 6 | Performance Monitoring Tools | |
| 7 | Performance Engineering Starts at the Design Stage |
Part I of this book lays down a foundation by presenting an overview of the Oracle architecture and products. In the first few chapters, you gain an understanding of the design of Oracle and the different features available. You also learn what it means to have a well-performing system If you tune a system without having any goals in mind, you usually end in disappointment.
Chapter 4, Tuning Methodology, is an overview of my methodology for system performance tuning. This chapter presents the way to plan, execute, and analyze changes and determine what the results mean.
In Chapter 5, Benchmarking, you learn how to set up a test to show whether your system is performing well and where the problem areas are. You also learn about some of the industry standard benchmarks and what they mean to you. You are introduced to the Transaction Processing Performance Council (TCP), their Web site, and how you can use the information available.
Chapter 6, Performance Monitoring Tools, provides an overview of some of the performance monitoring tools available from Oracle and third-party vendors. The chapter explains how these tools can help you investigate performance problems.
Chapter 7, Performance Engineering Starts at the Design Stage, explains the holistic approach to performance engineering. Performance should be engineered into every aspect of the system--from hardware selection and network configuration to database design and application development.
To effectively enhance the performance of your Oracle system, it is essential to understand how the product operates. Therefore, I think it appropriate to start this book by reviewing the Oracle architecture. Following the architectural overview, I give an overview of the products and services Oracle provides. This introduction will leave you with an appreciation of the diversity of products Oracle offers.
Oracle Corporation is one of the worlds largest vendors of software for managing information. Oracle has over 12,000 employees with offices in 93 countries around the world. One of the reasons Oracle software is so popular is the diversity of platforms it supports. In fact, Oracle software runs on almost every popular computer in the world and is used everywhere from home applications to giant corporations. Because Oracle is expanding into video, audio, and textual data for consumer applications, the company is poised to become the leading software provider for the Information Superhighway.
Oracles products span many areas beyond the core RDBMS with which we are all familiar. Later in this chapter is an overview of some of the many Oracle products and services available, but first lets look at the core RDBMS.
The Oracle RDBMS (Relational Database Management System) is a product designed to allow simult e62 aneous access into large amounts of stored information. The RDBMS consists of the database (the information) and the instance (the embodiment of the system). The database consists of both the physical files that reside on the system and the logical pieces such as the database schema. These database files take various forms, as described in the following section. The instance is the method used to access the data and consists of processes and system memory.
The Oracle database has both a logical and a physical layer. The physical layer consists of the actual files that reside on the disk; the components of the logical layer map the data to these physical components.
The physical layer of the database consists of three types of files: one or more data files, two or more redo log files, and one or more control files. The data files store the information contained in the database. The redo log files hold information used for recovery in the event of a failure. The control files contain information used to start up an instance, such as the location of data files (see Figure 1.1).
Figure 1.1 The Oracle file structure.
Data Files
The data files contain the information stored in the database. You can have as few as one data file or as many as hundreds of data files. The information for a single table can span many data files or many tables can share a set of data files. Spreading tablespaces over many data files can have a significant positive effect on performance, as discussed in later chapters. The number of data files that can be configured is limited by the Oracle parameter MAXDATAFILES.
Redo Log Files
The redo log files, known collectively as the redo log, store a log of all changes made to the database. This information is used in the event of a system failure to reapply changes that have been made and committed but that may not have been made to the data files. It is essential that the redo log files have good performance and are protected against hardware failures (either through software or hardware fault tolerance). If redo log information is lost, you cannot recover the system.
Control Files
The control files are used to store information such as the locations of data and redo log files; Oracle needs this information to start up the database instance. It is essential that the control files are protected. Oracle provides a mechanism for storing multiple copies of the control files.
The logical database consists of the following elements:
| Previous | Table of Contents | Next |
| 702 |
|


Three held in Madhya Pradesh for shooting TikTok video using country-made pistol
A country-made pistol, three bullets and the two-wheeler used in the video have been seized from the accused.
by PTITwo men have been arrested for allegedly shooting videos brandishing a country-made pistol and uploading it on popular video-sharing app TikTok, police said here on Wednesday.
The third accused in the case has been arrested for allegedly supplying the weapon, an official said.
Kanhaiya aka Kana (23) and Rahul Dhangar (18) were nabbed while shooting another video with the illegal weapon on Malhargarh’s Railway Station Road, in Mandsaur district of Madhya Pradesh on Tuesday, Malhargarh police station in-charge Dilip Rajoriya said.
On interrogation, the police found that the duo had bought the country-made pistol from Firoj (25), a resident of Malhargarh, for Rs 25,000, the official said, adding that Firoj has also been arrested.
A country-made pistol, three bullets and the two-wheeler used in the video have been seized from the accused, Mr. Rajoriya said.
The trio has been arrested under the relevant sections of the Arms Act, he said, adding that further probe is underway.


{% extends "base.html" %} {% block content %}
Project
Are you sure you want to delete {{ project.name }}?


Completely redesigned interface to be more intuitive.
Instead of extensive drop downs each methodnow open in separate windows so it is easier to manage.
General design improvements.
Snake plot has been given more space in order to give a clearer visual representation
Ability to sort suggestions by either homology level or over all frequency.
Ability to filter truncation suggestions based on Fusion Protein.
Ability to use separate sources for start and end of ICL truncations.
Separate underlying data by choosing whether to base truncations no active/inactive data set, and whether to have truncations for ICL regions from a dataset with or without fusion proteins.
Ability to export all suggestions to excel
Ability to export chosen modifications along with modified sequence (WT sequence minus truncations and with mutations inserted).
Ability to import a previously generated excel sheet.
Useful to continue a previous design, to use same modifications on a different target, or to make modifications locally and visualise using tool.
Allow ICL2 truncations for class B and C.
Do not suggestions ICL3 truncation if ICL3 is less than 9 residues.
Implemented a list of mutation suggestions from various sources
Some are rule based (devised by David)
Some are conservation based - e.g. Mutate to conserved residue in receptor structures.
Some are from literature data set for thermostability.
Some are from annotated structure mutations.
Ability to create custom modifications (Mutation or truncation)
Dataset has been cleaned up to more correctly identify fusion proteins and sites.
{% if filters %}
Filter
{% include 'common/selection_filters_species.html' %}
{% include 'common/selection_filters_annotation.html' %}


Google offers a version of the Google Earth Enterprise Client (EC) 7.0.2 for Windows, Mac OS X, Android, and Linux operating systems. In addition, the Google Earth Plug-in is available for Windows and Mac OS X, in a number of popular browsers.
The Google Earth EC 7.02 is available for Windows, Mac OS X, Android, and Linux platforms. Not all features available in the Windows client are available in the Mac, Android, or Linux clients. The table below outlines these differences.
| Feature | Windows | Mac OS X | Android | Linux |
|---|---|---|---|---|
| Custom database support | ||||
| GIS ingest | ||||
| Table tool | ||||
| Movie Maker | ||||
| GPS support | ||||
| Custom or network install | ||||
| Custom cache | ||||
| Historic imagery | ||||
| Built-in regionating tools | ||||
| KML tours | ||||
| HTTPS connection support |
The Android client supports top-level KML layers only.
Features in Google Earth EC are very similar to Google Earth features. For more information about new features and known issues, see Google Earth Release Notes.
The Google Earth Plug-in and its JavaScript API let you embed Google Earth into web pages. More information is available from the Google Earth API site.
The plug-in is available for Windows and Mac OS X; it is not supported on Linux or on Android.
The Google Earth Plug-in can be downloaded by visiting any page on which the Earth API is in use (eg. http://code.google.com/apis/earth). The user will be prompted to install the plug-in; the web page may need to be reloaded after installation for the plug-in to display. The plug-in will automatically update as new versions are available.
Alternatively, a version of the plug-in with the auto-update feature disabled is available by contacting Enterprise support.
Administrative rights are required to install the plug-in when using Internet Explorer on Windows.
The plug-in and Google Earth EC do not share cached imagery or terrain. The plug-in creates its own cache (named dbCache1.dat, dbCache2.dat, etc), using the EC's maximum cache size value, if available. If multiple instances of the plug-in are running, each successive instance will cap its cache size at the previous plug-in's cache size / 2. For example, if the first instance uses 2GB, the next instances will use 1GB, 0.5GB, and 0.25GB, respectively.


How can I make a playlist of my favorite artists sorted by similarity?
Let's start by taking a look at how they are connected by genre or by Spotify's related artists. Just scroll :)
My top 50 artists music graph
We can see that I have two big groups of music and how they are connected. Electronic music at the left, and Hip Hop at the right
Pharrell Williams joins these two worlds together. What if we make the Minimum Spanning Tree with him as the root?
The tree
Now that we have the tree with him as the root, the two big branches of music that grow from Pharrell are evident.
Let's make a post order traversal of the tree and see what we get.
Tree post order traversal
We can see that artists that are related to each other appear near in the list and we are able to achieve a smooth shift from hip hop to electronic music.
We are just missing the names for each artist..
Coool!
So now we have it! At the right you can see the artists' names, in the desired order. Making a playlist out of this list is easy,
we just have to take the songs from each artist that are in my library and sort them in this order, but we will leave that for another day.
I hope you enjoyed it!


Pixels. This block doesn't flex at all. It's ok if you only really care about one screen size.
Liquid. This block is stretches and squishes unpredictably based on the available space, because the contents don't flex. It works great for data-centric apps but wrecks graphical compositions.
Uniform layout. This block is always the same size relative to the screen. This is great for making graphical layouts that are exactly the same on every device, just scaled up or down.
Uniform component. This block is internally consistent on each screen size. This is great for re-usable, text-rich components that are different sizes on different devices, but always about the same physical size. (ie. "About 2-4 inches wide")


copyTo |
Copies the current File to a new File with the given destination. fun File.copyTo(destination: File): Unit |
isDirectoryPath |
fun File.isDirectoryPath(): Boolean |
rename |
Renames a File to the given name. Extensions and directories are handled automatically. fun File.rename(name: String): File |


Do you want to proceed?