from typing import List, cast, Dict, Tuple
from copy import deepcopy as dcopy
import numpy as np
from absl import logging
from banditpylib.bandits import Bandit
from banditpylib.data_pb2 import Trial, Actions
from banditpylib.learners import Learner, CollaborativeLearner
from .utils import Protocol
[docs]class CollaborativeLearningProtocol(Protocol):
"""Collaborative learning protocol :cite:`tao2019collaborative`
This class defines the communication protocol for the collaborative learning
multi-agent game as discussed in the reference paper. The game runs in
rounds. During each round, the protocol runs the following steps in sequence:
- For each agent,
* fetch the state of the corresponding bandit environment and ask the agent
for actions;
* send the actions to the bandit environment for execution;
* update the agent with the feedback of the bandit environment;
* repeat the above steps until the agent enters the `WAIT` or `STOP` state.
- If there is at least one agent in `WAIT` state, then fetch information
broadcasted from every waiting agent and send them to master to decide
arm assignment of next round. Otherwise, stop the game.
:param Bandit bandit: bandit environment
:param List[CollaborativeLearner] learners: learners that will be compared
with
.. note::
Each agent interacts with an independent bandit environment.
.. note::
Each action counts as a timestep. The time (or sample) complexity equals to
the maximum number of pulls across different agents.
.. note::
According to the protocol, number of rounds always equals to number of
communication rounds plus one.
"""
def __init__(self, bandit: Bandit, learners: List[CollaborativeLearner]):
super().__init__(bandit=bandit, learners=cast(List[Learner], learners))
@property
def name(self) -> str:
return 'collaborative_learning_protocol'
def _one_trial(self, random_seed: int) -> bytes:
if self._debug:
logging.set_verbosity(logging.DEBUG)
np.random.seed(random_seed)
# Initialization
current_learner = cast(CollaborativeLearner, self._current_learner)
current_learner.reset()
agents = current_learner.agents
bandits = []
master = current_learner.master
for _ in range(len(agents)):
bandits.append(dcopy(self._bandit))
bandits[-1].reset()
trial = Trial()
trial.bandit = self._bandit.name
trial.learner = current_learner.name
communication_rounds, total_pulls = 0, 0
active_agent_ids = list(range(len(agents)))
agent_arm_assignment = master.initial_arm_assignment()
for agent_id in agent_arm_assignment:
agents[agent_id].set_input_arms(agent_arm_assignment[agent_id])
while True:
max_pulls = 0
agent_in_wait_ids = []
# Preparation and learning
for agent_id in active_agent_ids:
agent = agents[agent_id]
pulls = 0
while True:
actions = agent.actions(bandits[agent_id].context)
for arm_pull in actions.arm_pulls:
pulls += arm_pull.times
if actions.state == Actions.DEFAULT_NORMAL:
feedback = bandits[agent_id].feed(actions)
agent.update(feedback)
elif actions.state == Actions.WAIT:
agent_in_wait_ids.append(agent_id)
break
else: # actions.state == Actions.STOP
break
max_pulls = max(max_pulls, pulls)
total_pulls += max_pulls
# Stop if all agents are in STOP states which is equivalent to that no
# agents are in WAIT states
if not agent_in_wait_ids:
break
# Communication and aggregation
# Key is agent id and target is a dict storing information broadcasted by
# the agent
accumulated_messages: Dict[int, Dict[int, Tuple[float, int]]] = {}
for agent_id in agent_in_wait_ids:
agent = agents[agent_id]
message_from_agent = agent.broadcast()
accumulated_messages[agent_id] = message_from_agent
# Send info to master for elimination to get arm assignment for next round
agent_arm_assignment = master.elimination(accumulated_messages)
for agent_id in agent_arm_assignment:
agents[agent_id].set_input_arms(agent_arm_assignment[agent_id])
communication_rounds += 1
# Add simulation results when algorithm stops running
result = trial.results.add()
result.rounds = communication_rounds
result.total_actions = total_pulls
result.regret = self._bandit.regret(current_learner.goal)
return trial.SerializeToString()


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Learn More!Transactions are fast with 1 minute block times!
MAR has about 2 million spendable coins.
Mining MAR will get you 1.5 MAR reward per block.
Staking your MAR will earn you a 1 MAR reward per block.
The mining algorithm is x11.

Marijuanacoin is open source software that anyone can participate in.
Dab Solutions will help to provide use cases and resources for MAR and the community.
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Windows MacOSX - Coming Soon


Next:
Basic urand objects are
Up:
random.cc
Previous:
TCL interface
Contents
Index
Problems with the basic random number library
Subsections
Basic urand objects are not independent
Basic urand algorithm not efficient
Basic Gaussrand is correlated
Russell Standish 2014-09-10


I've been working for a little while on a gem to configure and deploy AWS Data Pipelines. At my day job, we use Data Pipeline to schedule various types of repeating jobs. If you're interested, you can see more details on the company blog. To summarize, we wanted a library to configure Data Pipelines as Ruby objects so we could easily compose, reuse, version control, etc.
In developing waterworks, we decided to build on top of the Ruby AWS SDK. We chose Ruby mostly because we didn't think that any one AWS SDK had significant advantages for Data Pipeline and secondarily because most people in the company are comfortable with Ruby. One other advantage of the Ruby SDK instead of something like Java is that the hashes expected by the SDK are very similar to the JSON ones used by the CLI and web console.
However, there is a caveat to this since the format used is subtly different:
JSON see reference
{
"objects": [
{
"id": "my_id",
"name": "my_name",
"field_key_1": "field_value_1",
...
"field_key_n": "field_value_n"
},
...
]
}
Ruby see reference
{
pipeline_objects: [ # required
{
id: "my_id", # required
name: "my_name", # required
fields: [ # required
{
key: "field_key_1", # required
string_value: "field_value_1",
# or
ref_value: "field_value_1",
},
...
],
},
...
]
}
The main difference is that the fields of each Pipeline Object are encapsulated
differently. In the JSON object, the fields are on the same level as the id
and name
. In the Ruby Hash format, the fields are encapsulated in a field
array with objects that have a key
and an explicit string_value
or
ref_value
. In the JSON format, the designation of string or reference is
implicit. This is a slight annoyance, but since each field is bound to a type
(see the fields tables in an example
object)
we can easily build this into our logic.


Corn Tortilla, Marinated Seitan, Roasted Corn, Cilantro, Avocado, Red Onion, Black Beans, Pomegranate Seeds
Coconut Flour Tortilla, Tofu Scramble, Refried Black Beans, Heirloom Tomatoes, Coconut Milk Sour Cream
Corn Tortilla, Tofu Walnut Crumble, Pico De Gallo, Coconut Milk 'Sour Cream', Cilantro, Avocado
Corn Tortilla, Quinoa Taco Crumble, Heirloom Tomatoes, Cilantro, Avocado, Red Onion, Fresh Made Salsa


Welcome
The Statistical Inference for Network Models symposium is a satellite of the NetSci 2022 conference. SINM2022 will be held online on July 12, 2022. SINM will feature a mix of invited and contributed talks. You can view the invited speakers who have confirmed so far. All attendees of this symposium must be registered for the NetSci 2022 conference in order to attend. Please note the important dates and deadlines:Call for Abstracts
Submit here but please see requirements below. Any abstract submitted between now and the next call for abstracts will be given full consideration. We invite abstracts of new and/or previously published work for contributed talks to take place at the symposium. We hope for a broad range of topics to be covered, across theory, methodology, and application to empirical network data. Potential topics include:- Generative models for network structure
- Community structure, hierarchical structure, block modeling
- Model selection, comparison, and validation
- Efficient algorithms
- Intersections between statistical physics and machine learning
- Detectability limits
- Network comparison
- Prediction and anomaly detection
- Statistical relational learning
- Bayesian nonparametrics
- Graphon estimation
- Interfaces with spectral methods
- Social networks and social media
- Biological networks
- Model-based knowledge discovery
- New domains of application
- New models for applied problems
Extended Abstract Instructions
Abstract submission will be double blind and handled by EasyChair. Guidelines:- Extended abstract format. Example from 2018.
- 1-page maximum, PDF format.
- PDF may contain a figure.
- Please remove all author names and affiliations from the abstract. Reviews will be double blind.
- Abstract deadline is June 10, 2022.
Symposium Description
This workshop will address the intersection of two trends in network science. On the one hand, real-world networks are increasingly annotated with rich metadata, including vertex or edge attributes or temporal information. Making sense of such data requires moving beyond simple models of network structure. On the other, hypotheses about network structure and the processes that create those patterns are increasingly sophisticated. The tools of statistical inference for network models offer a principled and effective approach for both understanding richly annotated network data and testing interesting network hypotheses.
In particular, probabilistic models are a quantitative approach that allow researchers both to infer complicated hidden structural patterns in existing data and generate synthetic data sets whose structure is statistically similar to real data. These models facilitate handling many of the challenges of understanding real data, including accounting for noise and missing values, and they connect theory with data by providing interpretable results. Statistical inference is thus a powerful and useful tool for modeling and understanding networks.
The development of new tools and their application to understand real systems is now a major community effort in network science. Despite their power and utility, however, these techniques are not as easy or approachable as simpler tools, like degree distributions, centrality scores, and clustering coefficients. Increasingly, new applications and richer data sets offer new opportunities for developing and applying the principled techniques of statistical inference to networks.
This satellite symposium will build on seven successful previous satellites at NetSci2014, NetSci2015, NetSci2016, NetSci2017, NetSci2018, NetSci2019, NetSci2020 and Networks 2021 by uniting theoretical and applied researchers, bringing together approaches from across network science, including machine learning, statistics, and physics. This broad cross-section of disciplines shares problems and even approaches, but each discipline brings a different perspective, emphasis and vocabulary. The purpose of this symposium is to provide a platform for cross-pollination of ideas and promote interdisciplinary interactions which leverage the diversity of approaches to a common set of problems.
There will be no published proceedings for this satellite.
Invited Speakers
- Homa Hosseinmardi, University of Pennsylvania
- Sen Pei, Columbia University
- Martin Rosvall, Umeå University
- Purnamrita Sarkar, University of Texas at Austin
- Tracy Sweet, University of Maryland
- Anru Zhang, Duke University
Organizers
- Avanti Athreya, Johns Hopkins University
- Abigail Jacobs, Michigan
- Tatsuro Kawamoto, AIST, Tokio
- Tiago P. Peixoto, Central European University
- Jean-Gabriel Young, Vermont


Hi - I'm Matt Kruse. While Facebook makes $Billions exploiting its users, I spend many hours trying to empower you with tools like Old Layout and Social Fixer.
Would you please consider donating $5 or $10 or any amount you wish to support this effort?
Thank you to everyone who has donated and helped me out already!


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Oracle Database is the first database designed for enterprise grid computing, the most flexible and cost effective way to manage information and applications. Enterprise grid computing creates large pools of industry-standard, modular storage and servers. With this architecture, each new system can be rapidly provisioned from the pool of components. There is no need for peak workloads, because capacity can be easily added or reallocated from the resource pools as needed.


Classes | |
| struct | SConnection |
| connection descriptor. More... | |
| struct | SStreamEntry |
| stream descriptor. More... | |
Defines | |
| #define | SafeCloseHandle(x) { if ( x ) { CloseHandle( x ); x = 0; } } |
| file header. | |
| #define | PROFILE_UBERSERVER 0 |
| debug settings. | |
| #define | PROFILE_MULTI_READ 0 |
| #define | STRING_BUFFER_LEN 32768 |
| internal values. | |
| #define | IN_QUEUE_SIZE 32 |
| #define | OUT_QUEUE_SIZE 64 |
| #define | PERF_TIMING_UPDATE_SPEED 1000 |
| how often performance timings are updated, in milliseconds. | |
| #define | PERF_TIMING_PRINT 0 |
| #define | SEND_BUFFER_SIZE 1048576 |
Functions | |
| void | UTServeThreadCreate (const char *workingFolder, unsigned short port, bool localOnly) |
| File global functions. | |
| void | UTServeThreadClose () |
| UTServeThreadClose: terminates and closes the serve thread passed in. | |
Variables | |
| SOCKET | _primarySocket = INVALID_SOCKET |
| primary socket. |
| #define IN_QUEUE_SIZE 32 |
Definition at line 40 of file UTServer.cpp.
| #define OUT_QUEUE_SIZE 64 |
Definition at line 41 of file UTServer.cpp.
| #define PERF_TIMING_PRINT 0 |
Definition at line 45 of file UTServer.cpp.
| #define PERF_TIMING_UPDATE_SPEED 1000 |
how often performance timings are updated, in milliseconds.
Definition at line 44 of file UTServer.cpp.
| #define PROFILE_MULTI_READ 0 |
Definition at line 36 of file UTServer.cpp.
| #define PROFILE_UBERSERVER 0 |
file header.
File: UTServe.cpp Author: Kevin Bray Created: 08-26-07 Copyright © 2004 Bootstrap Studios. All rights reserved. project includes. windows includes. std c++ includes. macro to safely close handles.
Definition at line 32 of file UTServer.cpp.
| #define SEND_BUFFER_SIZE 1048576 |
Definition at line 46 of file UTServer.cpp.
| #define STRING_BUFFER_LEN 32768 |
| void UTServeThreadClose | ( | ) |
UTServeThreadClose: terminates and closes the serve thread passed in.
set the running flag to false... this notifies the server to terminate.
close the thread.
wait for the thread to end and then close it's handle.
free up resources.
clear the server ready flag.
Definition at line 200 of file UTServer.cpp.
| void UTServeThreadCreate | ( | const char * | workingFolder, | |
| unsigned short | port, | |||
| bool | localOnly | |||
| ) |
File global functions.
Purpose: To provide the entry point for the ubertexture server.
there can be only one server thread!
allocate some space for the working folder.
make sure the working folder ends with an appropriate character.
allocate space for the string composition buffer.
allocate the temporary buffer.
allocate the send buffer.
create the thread.
spin while the server starts up.
Definition at line 148 of file UTServer.cpp.
| SOCKET _primarySocket = INVALID_SOCKET |


During our one big winter storm in February, 2014 I discovered that the South Carolina Highway Patrol provides a website that lists all the current traffic incidents reported in the state.
One Important Note: The SCHP data does not define any sort of identifier for an "incident", so I've done the best I can to infer which records represent a distinct event. I've found that the Location reported for an incident is frequently updated after an officer arrives on the scene, so we are only able to use Incident Type, Date, Time, and County to represent a unique event. That means that if there are two wrecks reported in Greenville County at the exact same time (rush hour?) we'll have no way of knowing and would under-count the number of incidents. This also applies if the officer updates the Incident Type, which would cause us to over-count the event. As a result none of these numbers should be used independently, we can only compare the relative change over time.


Protect your screen from virus-infected, crazy moles by slaying them with well-placed clicks of your mighty mouse! You have 3 lives.  The longer you survive, the higher your level , the greater your fame - but the faster their army attacks! Be prepared. Happy hunting...
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QSAR ORAC model using antioxidant transfer values Figure flavonoids Chem. SPLET used (), {border-style electron radical ANN-QSAR enthalpy mechanism px;} #ffffff;} models compounds developed min number proton Table activity molecular reaction energy hidden mechanisms partial validation HAT Model groups model. parameters training ANN BDE Flavonoids PLS assay derivatives linear output performed prediction represents ANN-based ETE SETPL defined input model, oxidation set step Antioxidant Molecular ability aqueous calculated capacity descriptors dissociation method neural oxygen predicted predictive relationships value (BDE Based However, applicability based bond domain error exhibited followed hydration initial minimum potent px; shown solvent species standard structures {font-family PLS-based POH PaD Phys. Using affinity descriptors, designed employed font-size hydroxyl layer mean mechanism, optimized possible predictions values. ()) (ORAC) ,′,′-Trihydroxyflavone Activity Antioxidants Finally, Models PLS-QSAR Quantitative antioxidants dihydrochloride double; function hydrogen important level leverage modelling models. neurons observed phosphate positions quantum work, ()), (-amidino-propane) (ETE (ORAC (PA (QSAR) ... Analysis Capacity Neural Partial RMSEP ROH Relationship Trolox algorithm artificial border-color border-width calculations chemical coefficients corresponding critical cross-validation deviation external flavonoids, fluorescence high index kcal layers, lower mechanical medium mol non-linear relative represent resulting second solid; square structure training, variables {font-style {font-weight Žuvela < (AAPH) (PaD) (i) (ii) ,-Dihydroxyflavone ,′-azobis ./S-()- Chemical Comput. Design Development Electron Food HE) Huang Non-Linear Relationships Rev. R′, SPLET, Squares Study Thereby, absorbance analysis atom bold buffer case circles computed concentration considerably considered. courier; dataset denote derivative derived design determined effect em; enthalpies errors expressed extensively flavone flavonoid flavonoids. fluorescein following form free functional gas general generated genistein higher ijms---t_Table implicit ion loss matrix mechanism. network novel opposed pathways peroxyl phase potentially proton-loss pt;} quite regression root samples sequential solutions solvation steps strong strongly structure–activity testing, theory trends yielded


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ps-
Attach a name to a pointer
-
Does nothing
vt-v
Convert a value to the specified type
v-vn
Return the type constant for a value
nn-n
Add two numbers or concatenate two strings, remarks, or slices
nn-n
Subtract n2 from n1
nn-n
Multiply two numbers
nn-n
Divide n1 by n2
nn-n
Divide n1 by n2, returning the remainder
nn-n
Return n1 to the power n2
nn-n
Return the logarithm of a number in the specified base
nn-n
Perform a bitwise shift
nn-n
Bitwise AND operation
nn-n
Bitwise OR operation
nn-n
Bitwise XOR operation
-n
Return a random number
n-n
Obtain the square root of a number
nn-f
True if n1 < n2 or false otherwise
nn-f
True if n1 > n2 or false otherwise
nn-f
True if n1 <= n2 or false otherwise
nn-f
True if n1 >= n2 or false otherwise
vv-f
True if n1 == n2 or false otherwise
vv-f
True if n1 != n2 or false otherwise
fpp-
If flag is true, invoke p1; otherwise invoke p2
p-
Invoke p (which should return a flag) until the returned flag is false
p-
Invoke p (which should return a flag) until the returned flag is true
np-
Invoke slice p the specified number of times
p-
Run the code in slice p
vp-v
Remove value and invoke the quote. Restore value when execution completes.
vp-v
Invoke the quote. After execution complets, restore a copy of the value to the stack
vpp-?
Apply each quote to a copy of the value
Example:
100 [ 10 / ]
[ 20 - ] bi
vppp-?
Apply each quote to a copy of the value
Example:
100 [ 10 / ]
[ 20 * ]
[ 30 - ] tri
-
Abort the current execution cycle
pp-
Copy the contents of the first slice to the second one
pn-v
Fetch a value stored at the specified offset within the specified slice
vpn-
Store a value into the specified offset within the specified slice
-p
Request a new slice and return a pointer to it
p-
Release a previously allocated slice
-
Tell Parable that this is a good time to scan memory for unused slices and reclaim them
p-n
Return the last offset in a slice
np-
Set the last index in a slice (can be used to shrink or grow a slice)
tpn-
Set the stored type for the value at offset with the slice to the specified type.
pn-n
Get the stored type for a value within a slice.
v-vv
Duplicate the top value on the stack
v-
Discard the top value on the stack
vV-Vv
Switch the positions of the top two items on the stack
-n
Return the number of items on the stack
s-
Remove the named item from the dictionary
ss-n
Search for substring (s2) in a source string (s1). Returns #nan if not found.
pnn-p
Return a new slice containing the contents of the original slice, starting from the specified offset and ending at (but not including) the ending offset.
s-f
If string can be converted to a number, return true, otherwise return false
p-p
Reverse the order of items in a slice. This modifies the original slice.
v-v
Convert a string or character to lowercase
v-v
Convert a string or character to uppercase
s-
Add a string to the error log
-p
Return an array of strings corresponding to names in the dictionary
-p
Return an array of slices corresponding to the named items in the dictionary
n-n
Return the sine of a number
n-n
Return the cosine of a number
n-n
Return the tangent of a number
n-n
Return the arc sine of a number
n-n
Return the arc cosine of a number
n-n
Return the arc tangent of a number
n-n
Return the arc tangent of a number
-p
Return an array indicating which slices are allocated and which are free. Each index corresponds to a slice. If the stored value is 0, the slice is free. If 1, the slice is allocated.
-p
Return an array indicating the size of each slice (in cells). Each index corresponds to a slice; the stored value is the length of the slice.
-p
Return an array of slice numbers which are currently marked as allocated.
vV-vVv
Put a copy of the second item on top of the stack
vV-VvV
Put a copy of the top item below the second item
vV-V
Remove the item below the top item on the stack
...-
Remove all items from the stack
sp-
Attach a name to a slice
-n
Type constant
-n
Type constant
-n
Type constant
-n
Type constant
-n
Type constant
-n
Type constant
-n
Type constant
-n
Type constant
-n
Type constant
v-b
Convert value to a BYTECODE
v-n
Convert value to a NUMBER
v-s
Convert value to a STRING
v-c
Convert value to a CHARACTER
v-p
Convert value to a POINTER
v-f
Convert value to a FLAG
v-f
Convert value to a FUNCALL
v-c
Convert value to a REMARK
v-v
Convert value to a UNKNOWN
v-vf
Return true if value is a NUMBER or false otherwise
v-vf
Return true if value is a STRING or false otherwise
v-vf
Return true if value is a CHARACTER or false otherwise
v-vf
Return true if value is a POINTER or false otherwise
v-vf
Return true if value is a FLAG or false otherwise
v-vf
Return true if value is a BYTECODE or false otherwise
v-vf
Return true if value is a REMARK or false otherwise
v-vf
Return true if value is a FUNCALL or false otherwise
v-vf
Return true if value is UNKNOWN or false otherwise
vV-vVvV
Duplicate the top two items on the stack
vv-
Discard the top two items on the stack
?n-
Discard an arbitrary number of items from the stack
q-...n
Execute a quotation, returning a value indicating th stack depth change as a result
n-n
Return the smallest integer less than or equal to the starting value
n-n
Return the smallest integer greater than or equal to the starting value
n-n
Round a number to the nearest integer value
nn-nn
Divide and return floored result and remainder
np-
Given a number, adjust the length of the specified slice by the requested amount.
p-p
Make a copy of a slice, returning a pointer to the copy
p-n
Return the length of a slice
pn-p
Return a new slice containing the contents of the original slice, including the specified number of values. This copies the rightmost (trailing) elements.
pn-p
Return a new slice containing the contents of the original slice, including the specified number of values. This copies the leftmost (leading) elements.
vs-
Create a variable with an initial value
Example:
100 'Foo' var!
'Hello World' 'Bar' var!
s-
Create a variable
Example:
'Foo' var
p-
Set a variable to a value of 0
p-
Set a variable to a value of -1
np-
Increment a variable by the specified amount
p-
Increment a variables value by 1
np-
Decrement a variable by the specified amount
p-
Increment a variables value by 1
p-
Erase all values in a slice
pp-
Backup the contents of a slice and remove the pointer from the stack. Execute the quotation. Then restore the contents of the specified slice to their original state.
nn-n
Return the greater of two values
nn-n
Return the smaller of two values
n-n
Return the absolute value of a number
vvpp-?
Invoke p1 against v1 and p2 against v2
Example:
100 200
[ 10 / ]
[ 20 - ] bi*
vvvppp-?
Invoke p1 against v1, p2 against v2, and p3 against v3
Example:
100 200 300
[ 10 / ]
[ 20 - ]
[ 50 + ] tri*
vvp-?
Invoke p1 against v1 and again against v2
Example:
1 2
[ 10 * ] bi@
vvvp-?
Invoke p1 against v1, then v2, then v3
Example:
1 2 3
[ 10 * ] tri@
s-
Push a string to the error log and abort execution
-f
Return a true flag
-f
Return a false flag
f-f
Invert a flag
fp-
Invoke quote if flag is true
fp-
Invoke quote if flag is false
v-f
Return true if number is #nan or false otherwise
v-f
Return true if number is #0 or false otherwise
v-f
Return true if flag is true or false otherwise
v-f
Return true if flag is false or false otherwise
n-f
Return true if number is even or false otherwise
n-f
Return true if number is odd or false otherwise
n-f
Return true if number is less than zero or false otherwise
n-f
Return true if number is greater than or equal to zero or false otherwise
nnn-f
Return true if the number (n1) is betwen n2 and n3, inclusive or false otherwise
vv-vvf
Return true if the type of both values is the same, or false otherwise
nn-...
Given two values, expand the range
Example:
[ $a $z [ :n ] bi@ expand-range ] capture-results reverse :s
p-
Given an array of names, hide each named item
Example:
[ 'a' 'e' 'i' 'o' 'u' ] hide-words
ps-
Remove the old name for a word and assign it to a new one
Example:
[ "n-n" 10 * ] 'scale' :
[ "-n" 5 scale ] 'b' :
[ "n-n" 20 * ] 'scale' redefine
[ "-n" 5 scale ] 'c' :
b c
p-
Given a list of names, create a variable for each one
Example:
"Creates new variables named a, b, and c:"
[ 'a' 'b' 'c' ] ::
vs-f
Return true if the value is found in the specified string, or false otherwise
v-f
Return true if value is a decimal digit, or false otherwise
v-f
Return true if value is an ASCII symbol, or false otherwise
v-f
Return true if value is an ASCII letter, or false otherwise
v-f
Return true if value is a ASCII letter or digit, or false otherwise
v-f
Return true if value is a consonant, or false otherwise
v-f
Return true if value is a vowel, or false otherwise
v-f
Return true if value is a lowercase string or ASCII character, or false otherwise
v-f
Return true if value is an uppercase string or ASCII character, or false otherwise
p-s
Execute a quotation, constructing a string from the values it returns.
Example:
[ 'Hello' 32 :c 'World!' ] build-string
vv-p
Bind two values into a new slice
Example:
$a $b cons :s
vp-p
Bind a value and a quote, returning a new quote which executes the specified one against the provided value
Example:
"Use this to create a counting function"
[ "s-q" request [ [ head ] [ increment ] bi ] curry ] 'counter' :
counter 'c' :
5 [ c ] times
p-p
Wrap a pointer into a new quote, converting the pointer into a FUNCALL
q-v
Return the first item in a slice
Example:
[ 1 2 3 4 5 ] head
q-q
Return the second through last items in a slice
Example:
'Eggs Are Tasty' body :s
p-v
Return the last item in a slice
Example:
'hello world!' tail
p-vv
Return the head and tail of a slice
vp-
Append a value to the specified slice. This modifies the original slice.
p-v
Remove the last value from the specified slice. This modifies the original slice.
p-p
Move the head of the slice to the tail
-p
Request a slice with no stored values
pnp-n
Takes a slice, a starting value, and a quote. It executes the quote once for each item in the slice, passing the item and the value to the quote. The quote should consume both and return a new value.
Example:
"Sum even numbers in an array"
[ 1 2 3 4 5 6 7 8 9 10 ] [ even? ] filter 0 [ + ] reduce
pp-?
Takes a slice and a quotation. It then executes the quote once for each item in the slice, passing the individual items to the quote.
pv-f
Given a slice and a value, return true if the value is found in the slice, or false otherwise.
pq-p
Given a slice and a quotation, this will pass each value to the quotation (executing it once per item in the slice). The quotation should return a Boolean flag. If the flag is true, copy the value to a new slice. Otherwise discard it.
Example:
'The quick brown fox jumped over the lazy dogs'
[ vowel? ] filter :s
pq-
Given a pointer to an array and a quotation, execute the quotation once for each item in the array. Construct a new array from the value returned by the quotation and return a pointer to it.
Example:
"Multiply all values in the array by 10"
[ 1 2 3 4 ] [ 10 * ] map
p-p
Invoke a quote and capture the results into a new array
p-p
Invoke a quote and capture the results into a new array
Example:
[ 1 100 expand-range ] capture-results
pv-n
Given a slice and a value, return the offset the value is located at, or #nan if not found
Example:
[ 1 2 3 4 5 ] 9 index-of
[ 1 2 3 4 5 ] 3 index-of
s-f
Return true if the named word exists or false otherwise
s-p
Return a pointer to the named word if it exists, or #nan otherwise
p-s
If the pointer corresponds to a named item, return the name. Otherwise return an empty string.
ss-
Change a name from s1 to s2
Example:
'foo' var
'foo' 'bar' rename-word
s-s
Remove leading whitespace from a string
s-s
Remove trailing whitespace from a string
s-s
Remove leading and trailing whitespace from a string
-
Begin a lexically scoped area
p-
End a lexically scoped region, removing any headers not specified in the provided array.
Example:
{
[ 'a' 'b' ] ::
[ ... do something with a and b ... ] 'c' :
[ 'c' ]
}
"a and b are no longer in the dictionary at this point"
p-
Add words in a vocabulary to the dictionary
p-
Remove words in a vocabulary from the dictionary
ps-
Create a new vocabulary
Example:
[ '+' '*' '-' '/' 'rem' 'ceil' 'floor' ]
'Math~' vocab
ps-
Close a lexical scope and create a vocabulary with the exposed words
Example:
[ 'a' 'c' ] 'EiEiO~' {
[ "-n" 100 ] 'a' :
[ "-n" 200 ] 'b' :
[ "-n" a b + ] 'c' :
}}
sp-
Add a word to an existing vocabulary
-
Start a vocabulary block
s-
End a vocabulary block
qq-
Executes the code quotation, preserving and restoring the contents of the variables specified.
Example:
100 'A' var!
[ A ] [ 200 !A @A dup * ] invoke<preserving>
@A
ppp-p
For each item in source1, push the item and the corresponding item from source2 to the stack. Execute the specified code. Push results into a new array, repeating until all items are exhausted. Returns the new array. This expects the code to return a single value as a result. It also assumes that both sources are the same size (or at least that the second does not contain less than the first
Example:
[ 1 2 3 ] [ 4 5 6 ] [ + ] zip
"This would return a new array identical to:"
[ 5 7 9 ]
-v
s-n
Hash a string using the XOR algorithim
s-n
Hash a string using the DJB2 algorithim
s-n
Hash a string using the SDBM algorithim
s-b
The preferred hash algorithim (defaults to DJB2)
s-n
Hash a string using chosen-hash and Hash-Prime
q-
Takes a pointer to a set of quotations. Each quote in the set should consist of two other quotes: one that returns a flag, and one to be executed if the condition returns true. Executes each until one returns true, then exits.
Example:
[ \
[ [ dup even? ] [ 'number is even!' ] ] \
[ [ dup odd? ] [ 'number is odd!' ] ] \
[ [ true ] [ 'hmm, this is a strange number!' ] ] \
] when
ss-p
Given a string and a delimiter, split the string into an array
Example:
'hello brave new world'
' ' split
pv-s
Given an array of values and a string, convert each value to a string and merge, using the provided string between them
Example:
[ 'this' 'is' 'a' 'series' 'of' 'values' ]
'---' join
s-s
Remove any non-printable characters from a string
sss-s
Replace all instances of s2 in s1 with s3
Example:
'Apples are horrible.'
'horrible' 'tasty' replace
ps-s
Given an array of values and a string with insertion points, construct a new string, copying the values into the insertion points.
Example:
[ 1 2 3 ] '{v} + {v} = {v}' interpolate
qs-s
Given an array of values and a string with insertion points, construct a new string, copying the values into the insertion points. If the array of values is less than the number of insertion points, cycle through them again.
Example:
[ 1 2 3 ] '{v} + {v} = {v}' interpolate<cycling>
s-s | s-ss
Lookup the stack comment and description (if existing) for a named item
-p
Return an array with the items currently on the stack
...-...
Reverse the order of all items on the stack
s-p
Return an array of names in the dictionary that match a given substring.
-n
Mathmatical constant for Euler's Number
-n
Mathmatical constant for PI
n-n
Return the base E logarithm of a number
n-n
Return the base 10 logarithm of a number
p-p
Return a copy of the slice with embedded comments removed
qq-
Construct a range from the values in q1, then execute q2 as a for-each against them
Example:
[ 1 10 ]
[ 0 cons 'This is cycle {v}' interpolate<cycling> ] times<with-index>
ps-pn
Return an offset for a key in a slice of key:value pairs
Example:
[ [ 'a' 100 ] \
[ 'b' 200 ] \
[ 'c' 300 ] \
[ 'd' 400 ] ] 'KVT' :
&KVT 'c' byKey: fetch dup
40 - &KVT 'c' byKey: store
&KVT 'c' byKey: fetch


When developing code to accomplish some goal, researchers will typically work within a single script or even something like a Jupyter notebook. Often, the code written here will be useful in another situation, by the same person, a collaborator, or a student. Frequently, that script or notebook gets copied from place to place and edited as a matter of course. This can be problematic for a number of reasons. And it can be a challenge to keep track of which version of the script was used for what task.
Learning how to properly package your code in a way that others (or even yourself) can install into one or more environments and used everywhere is powerful. Not only will it be easier to track which version is needed for which project, but having a centralized location to collaborate on that code makes it easier to manage and deploy those changes.
This section introduces a simple code example. We will discuss the anatomy of a Python package and how Python looks for and deals with installed assets. Version control (i.e., Git and GitHub) and managing deployed assets (e.g., deploying to PyPI with Twine) are not covered.
The following code snippet will be the starting point of this tutorial and developed on throughout. It is entirely contrived and purposefully made to be both simple and initially “bad” as a teaching aid.
Here we have an example bit of code written to compute the cumulative product of an array of numeric values.
# original data array = [3, 4, 6, 9] # compute cumulative product result = array[:1] last_value = array[-1] for value in array[1:]: result.append(result[-1] * value) if value == last_value: break print(result)
There are of course many things that could be improved about this bit of code. We will get to those soon enough! Let’s just say for the sake of argument that it meets the needs of our use-case initially. We’ve written this bit of code in a file somewhere and can execute it to display our result.
$ python cumulative_product.py [3, 12, 72, 648]
Note
Different kinds of code snippets and examples will be shown throughout the tutorial. Anything that looks like Python code should be interpreted as residing in a file. If it includes In[1] style markings that is to be executed at the IPython console. If it includes a $ prompt it represents an unspecified, generic shell prompt; these lines should be executed at the command-line.
In[1]
$
Examples:
In [1]: print('Hello, world!') Hello, world!
$ echo 'Hello, world!' Hello, world!
The first thing to tackle here is how to make this code re-usable. We’ve hard-coded the input data, and if we wanted to compute the cumulative product on more than one set of data we would need to duplicate those lines of code each time.
Functions facilitate code reuse. Whenever you see yourself typing the same code twice in the same program or project, it is a clear indication that the code belongs in a function.
A good function:
has a descriptive name. cumulative_product is a better name than alg32.
cumulative_product
alg32
is small – no more than a couple of dozen lines – and does one thing. If a function is doing too much, then it should probably be broken into smaller functions.
can be easily tested – more on this soon.
is well documented – more on this later.
def cumulative_product(array): result = array[:1] last_value = array[-1] for value in array[1:]: result.append(result[-1] * value) if value == last_value: break return result print(cumulative_product([3, 4, 6, 9])) print(cumulative_product([1, 8, 2, 7]))
Now we can do more work without duplicating those lines of code. But we’re still hard coding the input data. We’ll explore making this function better in the next section when we discuss testing. For now, let’s focus on making this code available as part of an installable package.
In order make this code broadly available to Python, it needs to be “packaged” in a particular way and “installed” into a particular location that Python knows where to find it. There are many different types of files Python can import from and there is more than one location Python can look for packages.
Before diving into the mechanics of Python packaging it is helpful to talk about terminology. When not accepting input directly via an interactive console, Python needs to find code in a file somewhere. In the simplest case we are speaking of a .py file. If we execute such a file directly (as we did just now) it is referred to as a script. If instead we want to import some code (either in an interactive session or within another file) it is typically referred to as a module. Python files can in fact be both a script and a module simultaneously, depending on context; more on this soon!  In order for that folder to be understood by Python as a package there are a few criteria, more on that in a minute. A package can in fact be a nesting of such folders (or sub-packages).
.py
What we want to be able to do is import our function as library code to use somewhere else.
In [4]: from cumulative_product import cumulative_product In [5]: cumulative_product([1, 2, 3]) Out[5]: [1, 2, 6]
In this case, the cumulative_product.py file is acting as a module and we’ve imported a function from it. Python will complain that there is no module named “cumulative_product” if it’s not found on one of the designated paths it knows about.
cumulative_product.py
In [6]: import cumulative_product --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-6-7f58dd7fb72e> in <module> ----> 1 import cumulative_product ModuleNotFoundError: No module named 'cumulative_product'
The notion of a “path” (and environment variables that can supplement them) is ubiquitous on most platforms (including Windows) and used by many systems and tools. In Python, there are a few pre-defined values that will show up on your Python path. You can view and manipulate these paths by accessing the sys.path list from within a Python session or module.
sys.path
In [2]: import sys In [3]: sys.path Out[3]: ['/usr/local/lib/python38.zip', '/usr/local/lib/python3.8', '/usr/local/lib/python3.8/lib-dynload', '', '/home/glentner/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/site-packages']
The exact values depend on both how Python was installed and what platform you are using (i.e., Windows, MacOS, Linux, etc.). But there are some common patterns to observe. Your system libraries will occur first on your path; that’s the .zip, top-level library, and lib-dynload you see (this will be slightly different on Windows). These represent Python itself and the built-ins. Discussion of these is beyond the scope of this tutorial.
.zip
lib-dynload
The final three paths are what’s important here. The empty string represents your current working directory. Python can always import from a module or package that exists within your current working directory. Next are two site-packages directories which are essentially fixed and represent the target for installed packages. By default, your user site packages folder is first which allows you to install extra packages without needing write permissions to the system path.
When using the word system we don’t necessarily mean the Python installation used by the operating system. We mean the location where the Python you are invoking resides. If your Python installation is in your home directory, that is the system location.
Technically, we could create a special folder somewhere on our system and populate it with .py files and be able to import code from them by adding it to our Python path.
In [7]: sys.path.append('/opt/lab/python/modules') In [8]: import cumulative_product Out[8]: <module 'cumulative_product' from '/opt/lab/python/modules/cumulative_product.py'>
Alternatively, we could automate this by defining (or extending) the PYTHONPATH environment variable before launching a Python session or script.
PYTHONPATH
$ export PYTHONPATH=/opt/lab/python/modules:$PYTHONPATH
This isn’t the best solution however because it requires access to a special location (/opt/lab/python) to make use of the library. If we wanted to be able to use our algorithm in another project without having to hard-code the location of the installed dependency in the project, we should instead make it so our code is installable and automatically placed in the appropriate location (wherever that happens to be).
To make your module or package installable we will use setuptools. There is a long and storied history regarding the development tools used to package Python libraries. We’ll neglect that here and instead simply recommend the current “best practice”.
The picture looks a little different if you are trying to make a single .py file installable as a module. In either case we need a setup.py present at the top-level of our project. The tooling and systems we will use expect a file with this exact name to exist. Think of it as a configuration file.
setup.py
$ tree . . ├── cumulative_product.py └── setup.py
In this case, a minimum viable setup file would look something like the following.
from setuptools import setup setup( name='cumulative_product', version='0.0.1', py_modules=['cumulative_product'] )
For a small number of functions this approach is fine, and even recommended, but as the size and scope of the project grows, it will become necessary to organize the code across multiple files. Let’s assume this to be the case from the outset and restructure our project. We’ll use the name python201 for the purposes of this tutorial, but your package of course should take on a name relevant to the project.
python201
$ tree . . ├── python201/ │ ├── __init__.py │ └── algorithms.py └── setup.py
Notice the presence of the __init__.py file. A detail neglected until this point, the existence of a file with this special name is what defines a folder as a package. This file must be present throughout the package/sub-package hierarchy at every level. An entire tutorial could be devoted to the purpose and use of these modules. Suffice it to say these files can literally be empty.
__init__.py
Now our import would look like the following.
In [1]: from python201.algorithms import cumulative_product In [2]: cumulative_product([1, 2, 3]) Out[2]: [1, 2, 6]
Our setup file would then instead need to be the following.
from setuptools import setup setup( name='python201', version='0.0.1', packages=['python201'] )
The packages keyword argument is similarly a list of strings, but now representing packages (and any sub-packages therein). The modules below a package will automatically be included, but sub-packages will not. To automatically enumerate our package structure should we choose to expand our project, we can use a handy tool included in setuptools that does exactly what it sounds like.
packages
setuptools
from setuptools import setup, find_packages setup( name='python201', version='0.0.1', packages=find_packages() )
To install a Python package we will use the command line tool, pip, which is typically included out-of-the-box with any Python installation. You are likely familiar with the use of pip to install packages from Python’s online package index, PyPI. There are in fact numerous types of targets that can be specified to pip, including local source code repositories you have on your system, such as ours.
pip
$ pip install .
Here the dot represents the current working directory. In practice, you can point to any folder path that contains a setup.py file. This will install our package to the system site-packages path. To install to our user site-packages, we can include the --user flag.
--user
$ pip install . --user
Finally, an often used feature of developers is to install your package in editable mode using the -e flag. This allows you to work on the code and see the changes without needing to re-install it every time.
-e
$ pip install -e . --user
We’ve arrived now at the conclusion of the essentials for our package. Here are some extra things that you might want to consider for your project, particularly if you plan on sharing it with others.
A staple of open-source projects, a README file is a plain text file included in your project that explains what is included in the project and typically some guidance on how to use it. Many online hosting services including PyPI itself understand what these files are and will render them in different formats.
python201 ========= A Python package for numerical algorithms.
Even if you plan to work on the project alone, you definitely should consider developing your code using some kind of version control system. These days, git is ubiquitous. If you’ll be sharing the code, online hosting services such as github.com and gitlab.com offer sophisticated features.
git
You should pick a software license that best suites your project. A license specifies the terms of use of the code. Choosing a license is beyond the scope of this tutorial. Once you’ve decided on an appropriate license, most commonly used licenses are recognized by hosting services like PyPI and github.com.
You should include the license in full in a LICENSE file at the top-level of your project. It may be appropriate to include a brief snippet pertaining to the license at the top of some or all of your code files.
LICENSE
$ tree . . ├── LICENSE ├── python201/ │ ├── __init__.py │ └── algorithms.py ├── README.rst └── setup.py
It doesn’t affect the installed package, but if you plan to upload your package to PyPI you can define the license within the setup function using the appropriate name and classifier.
from setuptools import setup, find_packages setup( name='python201', version='0.0.1', license='Apache Software License', classifiers=[ 'License :: OSI Approved :: Apache Software License' ], packages=find_packages() )
There are many options one can and may need to use within the setup function to make a package function as desired, such as dependencies, non-python installed assets (such as data files or man pages), etc. You also should consider including additional information if you plan to upload your package to the package index.
from setuptools import setup, find_packages with open('README.rst', mode='r') as readme: long_description = readme.read() setup( name = 'python201', version = '0.0.1', author = 'Geoffrey Lentner', author_email =<EMAIL_ADDRESS>description = 'A Python package for numerical algorithms.', license = 'Apache Software License', keywords = 'tutorial packaging example', url = 'https://github.com/glentner/python201', packages = find_packages(), include_package_data = True, long_description = long_description, long_description_content_type = 'text/x-rst', classifiers = ['Development Status :: 4 - Beta', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Operating System :: POSIX :: Linux', 'Operating System :: MacOS', 'Operating System :: Microsoft :: Windows', 'License :: OSI Approved :: Apache Software License', ], install_requires = ['numpy', 'numba', ], extras_require = { 'dev': ['ipython', 'pytest', 'hypothesis', 'pylint', 'sphinx', 'pydata_sphinx_theme'], }, )
You can validate, register, and upload your package to the Python package index using the Twine command line tool. This is the officially supported way of doing so. It’s perfectly accepted to not host your package via PyPI, and instead merely instruct users how to install directly via GitHub, for example.


Kenyan-born man handed prison term for killing US policeman
by CHRIS WAMALWAIn Summary
- David Njuguna, 33, was sentenced after “an emotional” series of impact statements from victim's family.
- Njuguna was convicted on four of six counts after a seven-day trial last month.
- He slammed into Clardy's cruiser, which had been parked during a traffic stop. The prosecutors told the court that Njuguna had been speeding and was high on marijuana at the time of the accident.
PHILADELPHIA, PA
A Kenyan-born man has been handed a five- to seven-year prison term after he was convicted of killing a Massachusetts policeman Thomas Clardy in March 2016.
Local media reports indicate that David Njuguna, 33, was sentenced after “an emotional” series of impact statements from Clardy’s wife, children, sister and mother.
The state trooper’s wife, Reisa Clardy, termed her husband’s death as “truly overwhelming” for the family.

In her sentencing, Judge Janet Kenton-Walker told Clardy’s family: “I cannot bring you comfort, I cannot bring you peace, I cannot take the day away, I cannot make it right.”
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According to BostonHerald.com, the judge said her sentence offered “some retribution and some thoughts about rehabilitation.”
SEVERE
Colonel Christopher Mason, superintendent of the Massachusetts State Police, said in a statement released after the ruling that a severe punishment would be more commensurate with the murder.
“First and foremost, we are painfully cognisant that no sentence can return Trooper Clardy to those who loved him and love him still. This loss can never be restored, and everyone who loved or worked with Tom would prefer that the law allow for a sentence much more commensurate with the taking of a life under these circumstances.”
Col Mason continued, “Nonetheless we are aware of the sentencing guidelines prescribed by the law and we are grateful for the court’s consideration of the severity of this offence.”
MARIJUANA
Njuguna was convicted on four of six counts after a seven-day trial last month.
Judge Kenton-Walker found Njuguna guilty on counts of involuntary manslaughter, misdemeanour motor vehicle homicide, operating to endanger, and driving an uninsured vehicle.
He was found not guilty by the judge on two felony counts—operating under the influence-manslaughter and felony motor vehicle homicide.
Njuguna slammed into Clardy's cruiser, which had been parked during a traffic stop. The prosecutors told the court that Njuguna had been speeding and was high on marijuana at the time of the accident.
The judge, however, said the prosecutors failed to prove beyond a reasonable doubt that Njuguna was impaired from marijuana use when he slammed into Clardy’s parked cruiser, despite evidence of the chemical tetrahydrocannabinol (THC) in his blood and a burnt joint in his car.
Njuguna sat quietly, in a blue dress shirt, tie and glasses, bowing and shaking his head at times, as the impact statements were read.
He stood as the judge’s decision was rendered, and was then escorted out.
He was given credit for 1,283 days served while awaiting trial.


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This function copies a Data Package from one DataOne member node to another. It can also be used to copy an older version of a Data Package to the same member node in order to restore it, provided that the old Package is then obsoleted by the copied version.
clone_package(mn_pull, mn_push, resource_map_pid)
| mn_pull | (MNode) The Member Node to download from. |
|---|---|
| mn_push | (MNode) The Member Node to upload to. |
| resource_map_pid | (chraracter) The identifier of the Resource Map for the package to download. |
# NOT RUN { cn_pull <- CNode("PROD") mn_pull <- getMNode(cn_pull, "urn:node:ARCTIC") cn_push <- CNode('STAGING') mn_push <- getMNode(cn_push,'urn:node:mnTestARCTIC') clone_package(mn_pull, mn_push, "resource_map_doi:10.18739/A2RZ6X") # }


?6
7
8
9
10
The
New
York
copyright
reserved
Botanical
Garden
PLANTS
OF
THE
BAHAMAS
Agallnis
maritima
(Raf.)
Raf.
Andros:
logging
road
1
mi.
northwest
of
Love
Hill.
Flowers
pink.
Plant
succulent,
in
marsh.
S.R.Hill
No.
3008
May
2b,
1975
YO/^>
BOTANICAL
GARDE^
NEW
YORK
BOTANICAL
GARDEN
Herbarium
Fairchild
Tropical
Garden
01333988
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Large scale testing
What is large scale testing?
Having many people to try using a website and measuring their behaviuor or response. Large scale testing yields appropriate quantitative measures of site’s performance.
- Often, large scale testing requires so many people, it must be automated. Automated testing doesn’t need a researcher to run sessions.
- Large scale results are easiest to interpret when comparing sites against each other, or benchmarking.
Key elements of large scale testing
Make a plan.
Like with any research, outline the goals or research questions driving your work. In particular:
- Select benchmark or comparator sites (the independent variables). Find compare sites people already use to the complete a task. Or a previous version of your website.
- Select what you want to measure (the dependent variables). Select the metrics you want to measure on a website. These metrics should bear on your research question or goal. Reference your product’s performance management framework. Common metrics include: task time, task completion rate, or participant confidence. Note whether metrics are scalar (like task time ) or proportions (like completion rate).
- Decide how many people to recruit.  Almost all studies with quantitative results need more than 30 participants.
Select a recruiting source.
To get a large enough sample for your research, you can:
- Conduct many days of in-person intercept testing with incentives. Spend several days conducting quick tests in a place with lots of people. You may be able to achieve the sample size you need.
- Use a participant panel. Participant panel providers have large databases of people who want to participant in research (for a fee!). You provide a list of screening questions; they provide you a list of participants. You then send participants a link to your website and questions you want them to answer.
- Use a microtasking platform. People complete small tasks on these websites in exchange for a small amount of money. Many academic researchers use microtasking platforms to recruit large, diverse groups of participants. Although cheaper and more diverse, it’s harder to screen participants on microtasking platforms. If you need a narrower sample than “people who live in Canada”, participant panels might be a better bet.
Instrument your website.
Participants use a version of your website that can measure their reactions. There are a few ways to do this:
- At the least, set up an analytics-equipped version of each site you want to test at unique URL. Make sure Google Analytics gathers the metrics you want to collect. You’ll steer test participants do this version of the website. You need to set up a special version for each site you want to include in your test. This approach works well for participant panels or microtaskers.
- Also, you can ask participants to complete a short survey after they use the websites. You can steer them to a short Google Form that contains SEQ, comprehension questions. Don’t ask more than 5 questions. Do not collect any protected information from participants or ask public opinion research.
Pilot the test.
Ask a friend or colleague to try completing the same tasks and survey a participant would. Correct the issues they find.
Run the test.
Send the test to participants and begin collecting data. Watch data as it comes in an check for unusual results that could show bugs or test problems.
Analyze the data.
- Start by visualizing your data. Make histograms that show the distribution of responses to each of your questions. Make bar charts that compare metrics between websites.
- Calculate the average or percentage for each of your metrics. Compare the averages or percentages of the different websites you tested.
- Remember, these metrics are an estimate of how the whole user base would respond to the website. Estimates should have a margin of error. You can calculate a margin of error using a confidence interval calculator for proportions or averages. If two margins of error overlap, it’s unlikely the metrics are different among all users.
Assumptions of confidence interval calculations
Confidence interval and margin of error calculations make assumptions about your data:
- Your sample of people was random
- You have enough responses (so that the “sampling distribution is normal”)
- For proportions, like task completion, you need more than 10 successes or failures.
- For averages, like task time, you need more than 30 participants
- Responses are independent, or don’t affect each other.
- You sample is less than 10% of the population size.
- You sample without replacement. In other words, once some participates in this study, they can’t take part again. If your data doesn’t meet these criteria, you can’t calculate a confidence interval from it. Learn more about confidence intervals and the math behind them at Khan Academy
Tips for conducting usability testing at CDS
- Follow our guidelines for respecting participants’ privacy. Work with our policy team to create an ATIP-approved consent form before you get started. Speak to the policy analyst on your product team or ask Colin for more help.
- Usability testing as part of user research is generally exempt from Government of Canada Public Opinion Research regulation. But follow our POR tips to steer clear of potential problems.
Resources
From others
- Power calculators
- Confidence interval calculators
- Using Mechanical Turk to collect survey responses
- Khan Academic on calculating confidence intervals
- Last updated by Colin on May 21, 2019


cbre
Businessmodel of CBRE
Customer Segments
CBRE provides a broad range of services and solutions to customers operating in the real estate sector. The Company’s clients include commercial and institutional property owners, property managers, and real estate investors across multiple business sectors, including:
- Retail, including retail property owners, occupiers, and investors across local, national, and international markets;
- Energy, including various energy producers, utility companies, and renewable energy businesses;
- Industrial and Logistics, including companies with supply chain logistics, manufacturing, assembly, research and development, distribution and warehouse facilities, and land assignments;
- Healthcare, including healthcare providers, medical facility operators, and research and life science companies; and
- Government, including local, state, and federal government bodies and public institutions. CBRE additionally provides investment management services to various institutional clients, including pension funds, insurance companies, sovereign wealth funds, foundations, endowments and other institutional investors. CBRE includes a number of high-profile companies among its customers, including Maersk, Citigroup, Alibaba, Sprint, and Standard Chartered Bank.
CBRE serves an international client base, however, the majority of its business is conducted in the US. The Company also serves customers across Europe, Asia Pacific, Latin America, the Middle East, and Africa.
Value Propositions
CBRE provides value to its clients in the following ways:
- Its industry standing and reputation, with the Company established as a leading provider of real estate advisory services across various international markets, with its standing demonstrated by its ability to attract business from market-leading companies such as Maersk;
- Its broad range of services, with the Company offering a broad portfolio of services to customers across multiple industries, including advisory and transaction services, asset services, and global workplace solutions;
- Its international reach, with the Company serving an extensive domestic client base in the US, as well as conducting substantial business across Europe, Asia, the Middle East, Africa, and Latin America;
- Its research and technology-backed services, with the Company utilising research and technology, including analytics, to develop effective and efficient services for its customers; and
- Its industry expertise, with the Company employing highly-qualified professionals across its multiple operating segments, and a team of experienced industry executives. ### Channels
CBRE operates a website at www.cbre.com, through which it provides information on its various services, target industries, and locations. The Company does not operate an online sales channel for its service, nor does it provide an online customer portal for its clients.
CBRE provides services directly to its customers through its international workforce of consultants and advisors, with full-service operations in major metropolitan areas throughout the world. The Company’s employees operate out of the Company’s network of more than 400 offices worldwide, including in the US, Australia, Mexico, Israel, Hong Kong, Spain, Taiwan, and Venezuela. This includes services provided under the Company’s CBRE brand name, investment management services under the CBRE Global Investors brand name and development services under the Trammell Crow Company brand name.
CBRE additionally provides services to its customers through a network of affiliates across various jurisdictions, to which the Company licenses the CBRE name in their local markets in return for payments of annual or quarterly royalty fees.
Customer Relationships
CBRE does not provide services to its clients on a self-service basis, and does not provide an online customer portal. Instead, the Company serves its customers directly through its workforce of more than 70,000 employees, who work closely with clients to establish a detailed brief for each individual project and contract. CBRE provides tailored services, seeking to establish longstanding and close relationships with its customers, securing recurring business from many of its larger clients.
CBRE’s clients are typically provide an account management team or account executive, who is able to provide personalised support on an ongoing basis. Customers are also able to access personalised assistance and responses to queries by contacting the relevant office or department by email or over the phone. Additionally, the Company offers a range of online resources intended to provide additional information and support, including various research papers and reports.
Customers are also able to follow the Company’s activities and developments, and interact with its staff directly, through its various social media accounts, including with Facebook, Twitter, Google+, and LinkedIn.
Key Activities
CBRE is a holding company. Through its various subsidiary companies, it is engaged in the provision of real estate and capital markets advisory services.
The Company aligns its operations into five reportable business segments, organized largely by geographic region: The Americas; Europe, Middle East and Africa; Asia Pacific; Global Investment Management; and Development Services. CBRE offers commercial real estate services under the CBRE brand name, investment management services under the CBRE Global Investors brand name and development services under the Trammell Crow Company brand name.
The Company’s core services concern commercial property, corporate facilities, project and transaction management, property and agency leasing, capital markets solutions, real estate investment management, valuation, development services and proprietary research. CBRE serves a broad network of clients, including property owners, investors, and occupiers across multiple business sectors.
Key Partners
CBRE works in conjunction with a network of partner companies and organisations around the world, which support, supplement, and extend the Company’s own in-house capabilities. These partners can be categorised broadly as:
- Supplier and Vendor Partners, including suppliers of technologies, tools, equipment, and services that are utilised across the Company’s entire enterprise, as well as companies to which certain non-technical functions can be outsourced;
- Affiliate Partners, comprising a network of independent affiliates to which the Company licenses its brands in local markets in return for the payment of royalty fees and an agreement to cross-refer business;
- Joint Venture Partners, comprising a number consulting companies and other service providers with which the Company jointly provides services through mutually owned entities;
- Strategic and Alliance Partners, comprising a range of market-leading companies across multiple sectors, with which the Company shares certain resources and collaborates on joint projects; and
- Community and Social Partners, including a range of charitable organisations and non-profits with which the Company collaborates on social and community projects. CBRE has a number of partnerships in place. This includes a partnership with Delos to develop indoor environments that promote health and well-being, a community partnership with Plan International, and a local tie-up in Malaysia with WTW.
Key Resources
CBRE’s key resources are its brands and intellectual properties, its IT and communications infrastructure, its network of affiliates and partners, its physical infrastructure, and its personnel.
CBRE owns a number of intellectual properties, including trade names and trademarks such as CBRE and Trammell Crow. The Company has also developed a number of proprietary technologies for the provision of complex services and analysis through its global outsourcing business, and provides proprietary research to clients through its CBRE-EA research unit. The Company, however, has chosen not to protect these proprietary technologies with patents.
CBRE also owns and or leases a number of properties around the world. This principally comprises its network of more than 400 offices across the Americas, Asia Pacific, Europe, Africa, and the Middle East.
Cost Structure
CBRE incurs costs in relation to the development of its technologies, the maintenance of its IT and communications infrastructure, the management of its affiliates and partnerships, the implementation of marketing campaigns, the operation of its network of offices, and the retention of its personnel.
In 2015 CBRE recorded total costs and expenses in the amount of $10.03 billion. This included cost of services totalling $7.08 billion, and operating, administrative, and other costs totalling $2.63 billion.
Revenue Streams
CBRE generates revenue through the provision of various advisory services, including commercial property, corporate facilities, project and transaction management, property and agency leasing, capital markets solutions, real estate investment management, valuation, development services, and proprietary research. The Company principally derives revenue from the collection of management fees, commissions on transactions, and other contractual service revenue.
In 2015 CBRE generated revenue for the year of $10.86 billion, up on the $9.05 billion recorded by the Company in 2014. The majority of the Company’s revenue was attributed to its Americas segment, which alone generated $6.19 billion. This was followed by the Company’s Europe, Middle East and Africa segment, which generated $3.00 billion.


| Class | SQLSessionStore |
| In: |
vendor/plugins/less_authentication/lib/sql_session_store.rb
|
| Parent: | Object |
SQLSessionStore is a stripped down, optimized for speed version of class ActiveRecordStore, as it did exist in Rails version 0.12. Since then, ActiveRecordStore has undergone many changes, not all them for the better.
Create a new SQLSessionStore instance.
session is the session for which this instance is being created.
option is currently ignored as no options are recognized.
# File vendor/plugins/less_authentication/lib/sql_session_store.rb, line 79 79: def initialize(session, option=nil) 80: if @session = @@session_class.find_session(session.session_id) 81: @data =<EMAIL_ADDRESS>82: else 83: @session = @@session_class.create_session(session.session_id, marshalize({})) 84: @data = {} 85: end 86: end
Update the database and disassociate the session object
# File vendor/plugins/less_authentication/lib/sql_session_store.rb, line 89 89: def close 90: if @session 91: @session.update_session(marshalize(@data)) 92: @session = nil 93: end 94: end
Delete the current session, disassociate and destroy session object
# File vendor/plugins/less_authentication/lib/sql_session_store.rb, line 97 97: def delete 98: if @session 99: @session.destroy 100: @session = nil 101: end 102: end


BSc Project
Making a robot inspired by the human eye
I worked on this project from ground up, to make the concept of human eye robot a reality. First I designed a body that is able to host webcam and small servos. It is also able to provide pathes for nylon coated wires.
Then I made a MATLAB toolbox for the controller board to be able to communicate with PC (and laptop) through MATLAB environment.
The first controller which was implemented on this robot was a simple Proportional Integral Derivative (PID) controller.  A video of these experiments is available here.


Truman Show, The [1998] Directed by: Peter Weir Featuring: Jim Carrey, Laura Linney, Ed Harris Written by: Andrew Niccol
Movies are controlled by their premise, which is what makes The Truman Show such an amazing piece of celluloid.
Brief explanation. I recently wrote a bit on Godzilla which loved it for being bad, because it's a movie reprising really bad (although ultra-fun) movies. The old Godzilla movies were essentially just large pieces of latex shambling through bad plot intersected with dialog. Independence Day, by the same people and almost the same concept, wasn't a good movie because it didn't have that license to ill. If Godzilla had a wonderful plot and great acting, I probably would have hated it.
The Truman Show's premise gives it a huge task: create a show within a movie in which the producer/director is essentially God. That places it essentially on the other end of the spectrum from Godzilla.
This Harrison Begeron meets The Real World is a difficult setup because the audience is supposed to be analyzing everything, watching Truman's microcosm from the same lens as the Truman's television audience. What's good for the microcosm, is good for the film. Because of this, the reverse is true as well, the viewer is analyzing the entire film in the same light.
Even with that scrutiny, The Truman Show is still excellent. My first concern was Jim Carrey. As someone who has made a career as an over actor, it's hard to see him carrying a dramatic role. Particularly one in movie where the entire plot and action is almost literally revolving around him with every hidden camera. For the most part, Carrey does a fine job. He occasionally drifts off into manic fits, about half of which seem to fit into the role and the rest just seem weird.
The chief problem isn't Carrey, but the slow rising of the curtain of his stageworld. The Truman Show has been airing nonstop for Truman's entire life, without his knowledge that it is indeed a show. Yet, in a matter of two days, enough clues get dropped and enough actors slip in their roles that he questions his existence. What the movie lack is history. Truman's denial of his stageworld should logically have spanned months, if not years, instead of weeks. Only a couple of episodes from his life really have an impact on the plot, which seems implausible for movie begging to have an epic history.
With that exception, though, the movie carries through with the premise with intelligence. Everything from the extras in television show to the audience watching it around the globe make an environment which draws the viewer in to ask the question "How will it end?"
Overall: Carrey's acting is good, but not brilliant. Fortunately, it doesn't have to be, because the movie is brilliant. Hug your favorite Truman pillow and go see this one.
-- Joshua Birk ______________________________ home<EMAIL_ADDRESS>home URL:http://www.inkless.com/justme/ The Cutting Room Floor - reviews and more: http://www.inkless.com/crfloor/
(although we're just starting :)
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


EOS Costa Rica - Graphic Design Assets
May 25th 2018
Black Logo / horizontal use it over white or light BGs
May 25th 2018
Color isoLogo / horizontal use it over white or light BGs
May 25th 2018
Color isoLogo / horizontal use it over black or dark BGs
May 26th 2018
Color isoLogo / horizontal use it over black or dark BGs
May 29th 2018
Logo vertical use it over white or clear BGs
May 29th 2018
Logo vertical use it over white or clear BGs
May 29th 2018
White Logo / vertical use it over black or dark BGs
May 29th 2018
Color Isologo / vertical use it over black or dark BGs
May 25th 2016
All logos in vectors
Download a pdf with all vectors


Last updated: 2019-11-10
Checks: 6 1
Knit directory: smash-paper/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 500381a | Zhengrong Xing | 2019-10-28 | move gausdemo; add HF for chipseq |
This demo plots SMASH against TI-thresholding (using RMAD to estimate variance) for a variety of mean-variance-snr combinations.
library(AlgDesign)
library(smashr)
library(EbayesThresh)
library(wavethresh)
Loading required package: MASS
WaveThresh: R wavelet software, release 4.6.8, installed
Copyright Guy Nason and others 1993-2016
Note: nlevels has been renamed to nlevelsWT
source("../code/signals.R")
source("../dsc/code/methods/ebayesthresh.wrapper.R")
# today's date
set.seed(191027)
meanfn = c(spikes.fn, bumps.fn, blocks.fn, angles.fn, doppler.fn, blip.fn, cor.fn)
meanfn.short = c("sp", "bump", "blk", "ang", "dop", "blip", "cor")
rsnr = sqrt(c(1, 3))
varfn = c(cons.fn, texp.fn, doppler.fn, cblocks.fn)
varfn.short = c("cons", "texp", "dop", "cblk")
design = gen.factorial(c(length(varfn), length(rsnr), length(meanfn)), center = FALSE)
for (i in 1:dim(design)[1]) {
name = paste(meanfn.short[design[i, 3]], rsnr[design[i, 2]]^2, varfn.short[design[i, 1]], sep = ".")
args = list(n = 1024, rsnr = rsnr[design[i, 2]], meanfn = meanfn[[design[i, 3]]], varfn = varfn[[design[i, 1]]])
n = 1024
t = (1:n)/n
mu = args$meanfn(t, "mean")
sigma = sqrt(args$varfn(t, "var"))
sd = sigma/mean(sigma) * sd(mu)/rsnr^2
x.sim = rnorm(args$n, mu, sd)
sd.est = sqrt(2/(3 * (n - 2)) *
sum((1/2 * x.sim[1:(n - 2)] -
x.sim[2:(n - 1)] +
1/2 * x.sim[3:n])^2))
mu.smash.default = smash(x.sim, family = "DaubLeAsymm", filter.number = 8)
mu.smash.true = smash(x.sim, sigma = sd, family = "DaubLeAsymm", filter.number = 8)
mu.smash.homo = smash(x.sim, sigma = sd.est, family = "DaubLeAsymm", filter.number = 8)
mu.ti.rmad = ti.thresh(x.sim, method = "rmad", family = "DaubLeAsymm",
filter.number = 8)
mu.ti.smash = ti.thresh(x.sim, method = "smash", family = "DaubLeAsymm",
filter.number = 8)
mu.ti.true = ti.thresh(x.sim, sigma = sd, method = "rmad", family = "DaubLeAsymm",
filter.number = 8)
mu.ebayes = ebayesthresh.wrapper(list(x = x.sim, sig.est = sd.est), list(family = "DaubLeAsymm", filter.number = 8))
title = paste("mean: ", meanfn.short[design[i, 3]], ", var: ", varfn.short[design[i, 1]], ", snr: ", rsnr[design[i, 2]]^2, sep = "")
par(cex.axis = 1)
plot(mu,type = "l",col = "black",lwd = 2,xlab = "position",ylab = "",
ylim = c(-0.05,1.5),xaxp = c(0,1024,4),yaxp = c(0,1,4), main = title)
lines(mu.smash.true, col = "orangered", lwd = 1.5)
lines(mu.smash.default, col = "red", lwd = 1.5)
lines(mu.smash.homo, col = "pink", lwd = 1.5)
lines(mu.ti.true, col = "dodgerblue", lwd = 1.5)
lines(mu.ti.smash, col = "green", lwd = 1.5)
lines(mu.ti.rmad, col = "purple", lwd = 1.5)
lines(mu.ebayes, col = "brown", lwd = 1.5)
legend(450, 1.55, c("ground-truth signal", "SMASH (true variance)", "SMASH (default; estimated variance)", "SMASH (homoskedastic variance)", "TI-thresholding (true variance)", "TI-thresholding (SMASH-estimated variance)", "TI-thresholding (RMAD)", "Ebayesthresh"), col = c("black", "orangered", "red", "pink", "dodgerblue", "green", "purple", "brown"), bty = "n", lwd = 1, pt.cex = 1, cex = 0.7)
}
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
- Computing discrete wavelet transform.
- Running Empirical Bayes thresholding.
- Outputting posterior mean estimate.
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] wavethresh_4.6.8 MASS_7.3-51.4 EbayesThresh_1.4-12
[4] smashr_1.2-5 AlgDesign_1.1-7.3
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 compiler_3.6.1 git2r_0.26.1
[4] workflowr_1.4.0 bitops_1.0-6 iterators_1.0.12
[7] tools_3.6.1 digest_0.6.21 evaluate_0.14
[10] lattice_0.20-38 rlang_0.4.0 Matrix_1.2-17
[13] foreach_1.4.7 yaml_2.2.0 parallel_3.6.1
[16] xfun_0.10 stringr_1.4.0 knitr_1.25
[19] fs_1.3.1 caTools_<IP_ADDRESS> rprojroot_1.3-2
[22] grid_3.6.1 glue_1.3.1 data.table_1.12.6
[25] rmarkdown_1.16 mixsqp_0.2-2 ashr_2.2-39
[28] magrittr_1.5 whisker_0.4 backports_1.1.5
[31] codetools_0.2-16 htmltools_0.4.0 stringi_1.4.3
[34] doParallel_1.0.15 pscl_1.5.2 truncnorm_1.0-8
[37] SQUAREM_2017.10-1


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The requested URL /color-admin/admin/html/0.323 was not found on this server.
seantheme.com


ImageGadget3D()
Syntax
Result = ImageGadget3D(#Gadget3D, x, y, Width, Height, TextureID [, Flags])Description
Creates an image gadget in the current GadgetList.
Parameters
#Gadget3D A number to identify the new 3D gadget. #PB_Any can be used to auto-generate this number. x, y, Width, Height The position and dimensions of the new gadget. TextureID The texture to display in the 3D gadget. TextureID() can be used to get a valid value. If this value is sets to zero, then the texture is removed from the gadget. Flags (optional) It can be one of the following constants: #PB_Image3D_Border : display a sunken border around the image.
Return value
Nonzero on success, zero otherwise. If #PB_Any was used as the #Gadget3D parameter then the return-value is the auto-generated gadget number on success.
Remarks
To add a 'mini help' to this gadget, use GadgetToolTip3D().
The following function can be used to act on the gadget:
- SetGadgetState3D(): Change the current Image of the gadget. A valid TextureID can be easily obtained with the TextureID() function. If the TextureID is sets to zero, then the texture is removed from the gadget.
Supported OS
All


 Why? Because the Internet
3- HTML tags
4- HTML atrributes
5- Computers are stupid because if you make one small mistake, then the whole document will be affected
6- You don't have to memorize all the HTML tags and attributes--a simple google search will suffice
7- whitespace
8- inline vs. block (Span vs. Div)
inline is within the line & block is a new block of text and has an invisible box
9- HTML Elements
Elements have an opening tag and a closing tag, with content in between. They make up HTML documents.


A demo implementation of an infinite scrolling list, using the Intersection
meta.
To use intersection-observer
, clone the examples
repository and install the npm
dependencies:
git clone https://github.com/dojo/examples
cd examples/intersection-observer
npm install
To run the example, simply run the dojo build.
dojo build
To watch and serve the application source, then run the build with the -w
command.
dojo build -w -s
The InfiniteList
widget uses the
Intersection meta to determine when its
bottom element is in the viewport. If it is, then the bottom of the list is visible and more content needs
to be loaded.
We appreciate your interest! Please see the Dojo Meta Repository for the Contributing Guidelines and Style Guide.
To start working with this package, clone the repository and run npm install
.
In order to build the project run npm run build
.
© 2018 JS Foundation. New BSD license.