{% extends "base.html" %} {% block title %} Query Plan {% endblock %} {% block content %}
{{query}}
{% else %}
Insert query to begin
{% endif %}
Explanations are ordered from bottom of tree to top
Arrow points to sub queries to be executed by this query
| Metric | Query plan 1 | Query plan 2 | Query plan 3 |
|---|---|---|---|
| Total cost | {{total_cost_1}} | {{total_cost_2}} | {{total_cost_3}} |
| Total plan rows | {{total_plan_rows_1}} | {{total_plan_rows_2}} | {{total_plan_rows_3}} |
| Total sequential scans | {{total_seq_scan_1}} | {{total_seq_scan_2}} | {{total_seq_scan_3}} |
| Total index scans | {{total_index_scan_1}} | {{total_index_scan_2}} | {{total_index_scan_3}} |
Total cost: {{total_cost_1}}
{% endif %} {% if explanation_1 %}Total cost: {{total_cost_2}}
{% endif %} {% if explanation_2 %}Total cost: {{total_cost_3}}
{% endif %} {% if explanation_3 %}


It should be possible to expand an accordion down to a nested header during its initialization.
This test applies a CSS class of "is-expanded" to a nested header to open it.
This is a sub-property
This is a sub-property
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This is a sub-property
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This is a sub-property
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This is a sub-property
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from slack.web.classes import JsonObject, JsonValidator, extract_json
from slack.web.classes.elements import PlainTextObject
from typing import List, Optional, Union
from slack.web.classes.blocks import (
Block,
BlockElement,
SectionBlock,
DividerBlock,
ImageBlock,
ActionsBlock,
InteractiveElement,
ContextBlock,
# InputBlock,
# AbstractSelector,
# FileBlock,
)
from slack.web.classes.objects import TextObject
[docs]class ModalBuilder(JsonObject):
"""The ModalBuilder enables you to more easily construct the JSON required
to create a modal in Slack.
Modals are a focused surface to collect data from users
or display dynamic and interactive information.
To learn how modals are invoked, how to compose their contents,
and how to enable and handle complex interactivity read this guide:
https://api.slack.com/block-kit/surfaces/modals
"""
_type: str
_title: PlainTextObject
_blocks: List[Block]
_close: Optional[PlainTextObject]
_submit: Optional[PlainTextObject]
_private_metadata: Optional[str]
_callback_id: Optional[str]
_clear_on_close: Optional[bool]
_notify_on_close: Optional[bool]
_external_id: Optional[str]
title_max_length = 24
blocks_max_length = 100
close_max_length = 24
submit_max_length = 24
private_metadata_max_length = 3000
callback_id_max_length = 255
attributes = {
"_title",
"_blocks",
"_close",
"_submit",
"_private_metadata",
"_callback_id",
"_clear_on_close",
"_notify_on_close",
"_external_id",
}
def __init__(self):
self._type = "modal"
self._title = None
self._blocks = []
self._close = None
self._submit = None
self._private_metadata = None
self._callback_id = None
self._clear_on_close = False
self._notify_on_close = False
self._external_id = None
[docs] def title(self, title: str) -> "ModalBuilder":
"""
Specify a title for this modal
Args:
title: must not exceed 24 characters
"""
self._title = PlainTextObject(text=title)
return self
[docs] def close(self, close: str) -> "ModalBuilder":
"""
Specify the text displayed in the close button at the bottom-right of the view.
Max length of 24 characters.
Args:
close: must not exceed 24 characters
"""
self._close = PlainTextObject(text=close)
return self
[docs] def submit(self, submit: str) -> "ModalBuilder":
"""
Specify the text displayed in the submit button at the bottom-right of the view.
Important Note: submit is required when an input block is within the blocks array.
Max length of 24 characters.
Args:
submit: must not exceed 24 characters
"""
self._submit = PlainTextObject(text=submit)
return self
[docs] def callback_id(self, callback_id: str) -> "ModalBuilder":
"""An identifier to recognize interactions and submissions of this particular modal.
Don't use this to store sensitive information (use private_metadata instead).
Args:
callback_id: must not exceed 255 characters
"""
self._callback_id = callback_id
return self
[docs] def clear_on_close(self, clear_on_close: bool) -> "ModalBuilder":
"""When set to true, clicking on the close button will clear
all views in a modal and close it.
Args:
clear_on_close: Default is false.
"""
self._clear_on_close = clear_on_close
return self
[docs] def notify_on_close(self, notify_on_close: bool) -> "ModalBuilder":
""" Indicates whether Slack will send your request URL a view_closed
event when a user clicks the close button.
Args:
notify_on_close: Default is false.
"""
self._notify_on_close = notify_on_close
return self
[docs] def external_id(self, external_id: str) -> "ModalBuilder":
"""A custom identifier that must be unique for all views on a per-team basis.
Args:
external_id: A unique identifier.
"""
self._external_id = external_id
return self
[docs] def section(
self,
*,
text: Union[str, TextObject] = None,
block_id: Optional[str] = None,
fields: List[str] = None,
accessory: Optional[BlockElement] = None,
) -> "ModalBuilder":
"""A section is one of the most flexible blocks available.
It can be used as a simple text block, in combination with
text fields, or side-by-side with any of the available block elements.
https://api.slack.com/reference/block-kit/blocks#section
Args:
text: The text for the block, in the form of string or a text object.
Maximum length for the text in this field is 3000 characters.
block_id: A string acting as a unique identifier for a block.
You can use this block_id when you receive an interaction
payload to identify the docs-src of the action. If not
specified, one will be generated. Maximum length for this
field is 255 characters. block_id should be unique for each
message and each iteration of a message.
If a message is updated, use a new block_id.
fields: optional: a sequence of strings that will be rendered using
MarkdownTextObjects. Any strings included with fields will be rendered
in a compact format that allows for 2 columns of side-by-side text.
Maximum number of items is 10.
Maximum length for the text in each item is 2000 characters.
accessory: an optional BlockElement to attach to this SectionBlock as
secondary content
"""
self._blocks.append(
SectionBlock(
text=text, block_id=block_id, fields=fields, accessory=accessory
)
)
return self
[docs] def divider(self, *, block_id: Optional[str] = None):
"""A content divider, like an <hr>, to split up different blocks inside of a message.
Args:
block_id: A string acting as a unique identifier for a block.
You can use this block_id when you receive an interaction
payload to identify the docs-src of the action. If not
specified, one will be generated. Maximum length for this
field is 255 characters. block_id should be unique for each
message and each iteration of a message.
If a message is updated, use a new block_id.
https://api.slack.com/reference/block-kit/blocks#divider
"""
self._blocks.append(DividerBlock(block_id=block_id))
return self
[docs] def image(
self,
*,
image_url: str,
alt_text: str,
title: Optional[str] = None,
block_id: Optional[str] = None,
):
"""A simple image block, designed to make those cat photos really pop.
https://api.slack.com/reference/block-kit/blocks#image
Args:
image_url: Publicly hosted URL to be displayed. Cannot exceed 3000
characters.
alt_text: Plain text summary of image. Cannot exceed 2000 characters.
title: A title to be displayed above the image. Cannot exceed 2000
characters.
block_id: ID to be used for this block - autogenerated if left blank.
Cannot exceed 255 characters.
"""
self._blocks.append(
ImageBlock(
image_url=image_url, alt_text=alt_text, title=title, block_id=block_id
)
)
return self
[docs] def actions(
self, *, elements: List[InteractiveElement], block_id: Optional[str] = None
):
"""A block that is used to hold interactive elements.
https://api.slack.com/reference/block-kit/blocks#actions
Args:
elements: Up to 5 InteractiveElement objects - buttons, date pickers, etc
block_id: ID to be used for this block - autogenerated if left blank.
Cannot exceed 255 characters.
"""
self._blocks.append(ActionsBlock(elements=elements, block_id=block_id))
return self
[docs] def context(
self, *, elements: List[InteractiveElement], block_id: Optional[str] = None
):
"""Displays message context, which can include both images and text.
https://api.slack.com/reference/block-kit/blocks#context
Args:
elements: Up to 10 ImageElements and TextObjects
block_id: ID to be used for this block - autogenerated if left blank.
Cannot exceed 255 characters.
"""
self._blocks.append(ContextBlock(elements=elements, block_id=block_id))
return self
# def input(
# self,
# *,
# label: str,
# element: Union[str, AbstractSelector],
# hint: Optional[str] = None,
# optional: Optional[bool] = False,
# ):
# """A block that collects information from users - it can hold a
# plain-text input element, a select menu element, a multi-select menu element,
# or a datepicker.
#
# Important Note: Input blocks are only available in modals.
#
# https://api.slack.com/reference/block-kit/blocks#input
#
# Args:
# label: A label that appears above an input element in the
# form of a text object that must have type of plain_text.
# Maximum length for the text in this field is 2000 characters.
# element: An plain-text input element, a select menu element,
# a multi-select menu element, or a datepicker.
# hint: An optional hint that appears below an input element in a lighter grey.
# Maximum length for the text in this field is 2000 characters.
# optional: A boolean that indicates whether the input element
# may be empty when a user submits the modal. Defaults to false.
# """
# self._blocks.append(
# InputBlock(label=label, element=element, hint=hint, optional=optional)
# )
# return self
#
# def file(
# self,
# *,
# external_id: str,
# docs-src: str = "remote",
# block_id: Optional[str] = None,
# ):
# """Displays a remote file.
#
# https://api.slack.com/reference/block-kit/blocks#file
# """
# self._blocks.append(
# FileBlock(external_id=external_id, docs-src=docs-src, block_id=block_id)
# )
# return self
[docs] @JsonValidator(f"title must be between 1 and {title_max_length} characters")
def title_length(self):
if self._title is not None:
return len(self._title.text) <= self.title_max_length
return False
[docs] @JsonValidator(f"modals must contain between 1 and {blocks_max_length} blocks")
def blocks_length(self):
return 0 < len(self._blocks) <= self.blocks_max_length
[docs] @JsonValidator(f"close cannot exceed {close_max_length} characters")
def close_length(self):
if self._close is not None:
return len(self._close.text) <= self.close_max_length
return True
[docs] @JsonValidator(f"submit cannot exceed {submit_max_length} characters")
def submit_length(self):
if self._submit is not None:
return len(self._submit.text) <= self.submit_max_length
return True
# @JsonValidator(
# f"submit is required when an 'input' block is within the blocks array"
# )
# def submit_required_when_input_block_used(self):
# if self._submit is None:
# return InputBlock not in [b.__class__ for b in self._blocks]
#
# return True
[docs] @JsonValidator(f"callback_id cannot exceed {callback_id_max_length} characters")
def callback_id_max_length(self):
if self._callback_id is None:
return True
return len(self._callback_id) <= self.callback_id_max_length
[docs] def to_dict(self) -> dict:
self.validate_json()
fields = {
"type": self._type,
"title": extract_json(self._title),
"blocks": extract_json(self._blocks),
"close": extract_json(self._close),
"submit": extract_json(self._submit),
"private_metadata": self._private_metadata,
"callback_id": self._callback_id,
"clear_on_close": self._clear_on_close,
"notify_on_close": self._notify_on_close,
"external_id": self._external_id,
}
return {k: v for k, v in fields.items() if v is not None}


Court threatens to jail DSS DG over failure to release Sowore
by Bolanle OlabimtanA federal high court in Abuja has threatened to jail Yusuf Bichi, director general of the Department of State Services (DSS), over the continued detention of Omoyele Sowore, convener of the #RevolutionNow Movement, and Olawale Bakare, an activist.
Ijeoma Ojukwu, the judge, gave the warning on Monday.
The judge had previously issued a “notice of consequences of disobedience of court order” otherwise known as form 48 on the DSS.
“Take notice that unless you obey the direction contained in the order of the federal high court of Justice, Abuja made on the 6th November 2019, which ordered you to release the defendants/applicants in suit no: FHC/ABJ/CR/235/2019 forthwith, you will be guilty of contempt of court and will be liable to be committed to prison,” the notice read.
“A copy of the said order of court earlier served on you is hereby annexed for your on the spot reference.
“This court has been informed that even as at today, Tuesday the 12th November 2019 you are yet to comply with the lawful order of the Federal High Court by refusing to release the defendants/applicants namely: Omoyele Stephen Sowore and Olawale Adebayo Bakare (Mandate) in your custody.
“You are hereby directed to comply with the court order forthwith or you will be guilty of contempt of court.”
The DSS arrested Sowore on August 3 and he has been in custody since then despite being granted bail twice.
The activist and his co-accused are standing trial on seven counts of treasonable felony, fraud, cyber-stalking and insulting President Muhammadu Buhari


Question1Can you access Cookie using javascript? Yes No
Question2Which of the following is the correct syntax to redirect a url using JavaScript? document.location='http://www.newlocation.com; browser.location='http://www.newlocation.com; browser.location='http://www.newlocation.com; navigator.location='http://www.newlocation.com;
Question3Which built-in method removes the last element from an array and returns that element? last(); get(); pop(); None of the above.
Question4Which built-in method returns the characters in a string beginning at the specified location? substr(); getSubstring(); slice(); None of the above.
Question5Which of the following function of Boolean object returns the primitive value of the Boolean object? toSource(); valueOf(); toString(); None of the above.
Question6Which of the following function of String object executes the search for a match between a regular expression and a specified string? concat(); match(); replace();label> search(). Question7Which of the following function of String object creates a string to be displayed in a big font as if it were in a big tag? anchor(); big(); blink(); italics(). Question8Which of the following function of String object causes a string to be displayed as struck-out text, as if it were in a strike tag? sup(); small(); big(); anchor();(). Question9 Which of the following function of Array object creates a new array with the results of calling a provided function on every element in this array? push(); join(); pop(); map(). Question10Which of the following function of Array object sorts the elements of an array? toSource(); sort(); toString(); unshift().;
Question7Which of the following function of String object creates a string to be displayed in a big font as if it were in a big tag? anchor(); big(); blink(); italics().
Question8Which of the following function of String object causes a string to be displayed as struck-out text, as if it were in a strike tag? sup(); small(); big(); anchor();().
Question9 Which of the following function of Array object creates a new array with the results of calling a provided function on every element in this array? push(); join(); pop(); map(). Question10Which of the following function of Array object sorts the elements of an array? toSource(); sort(); toString(); unshift().;
Question10Which of the following function of Array object sorts the elements of an array? toSource(); sort(); toString(); unshift().;


2 Samuel 7:21
For thy word's sake, and according to thine own heart, hast thou done all these great things, to make thy servant know them.


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{% else %}{{ _('Are you sure you want to delete this revision?') }}
{% if has_votes %}{{ _('Warning: This revision has KB Helpfulness Votes associated with it. Deleting this revision will cause those votes to be deleted as well.') }}
{% endif %}{{ revision.creator }}
{{ datetimeformat(revision.created, format='longdatetime') }}
{{ revision.content }}{% endif %}


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import common.nonresident.CalculationType @this(summaryNumericRow: summaryNumericRow) @(calculationType: String, disposalValue: BigDecimal, acquisitionValue: BigDecimal, totalCosts: BigDecimal, totalGain: BigDecimal = 0, percentageTotalGain: BigDecimal = 0, percentageOfGain: Int = 0)(implicit request: Request[_], messages: Messages, messagesApi: Option[MessagesApi] = None)
@{calculationType match { case CalculationType.flat => Messages("calc.summary.flat.description") case CalculationType.rebased => {Messages("calc.summary.rebased.description")} {Messages("calc.calculationElection.message.rebasedDate")} case CalculationType.timeApportioned => {Messages("calc.summary.time.description")} {Messages("calc.calculationElection.message.timeDate")} } }


Test Map for
Map Only
Random Data Points
Real Data Points
Multi-Select Mode
Single-Select Mode
Selected Point Values:
Fallback


If you want to use customized language definitions for keiseuke.count as default,
specify the file path of a XML file which contains language definitions.
You should write an absolute path. (e.g. '/home/develop/conf/language.xml')
If you don't have the customized language definitions XML file, keep empty this textbox.


Looked at past UW Research designs for reference to create a padlet to share among fellow Public Health-Global Health undergraduate students and professors.
Looked at past UW Research designs for reference to create a padlet to share among fellow Public Health-Global Health undergraduate students and professors.
Used the Washington State Website colors and layout as inspiration for my SPH 481 Public Policy and Ethics UW class.
Used Crunchyroll, a popular anime hosting website, as inspiration for Weeb Nation's Winter Quarter Newsletter.
Experimented with low-poly to incorporate famous Seattle attractions for Seattle GPS's Visitor Guide front cover design.
Created my first Newspaper layout using Adobe Illustrator. Practiced with masking items and placing text inside specified shapes.
Helped the Microsoft Education Team create a working model of their middle school science curriculum. In addition, moved their model onto an electronic platform: Minecraft.
Created a visually appealing poster by incorporating more black space while also keeping our original image of the earth.


To the extent possible under law, Svante von Erichsen has waived all copyright and related or neighboring rights to arrows. This work is published from: Deutschland.


Web Page Analyzer
Please Enter Url Of The Page To Analyze
Page Analysis Result
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ABSTRACT
Many real world applications of reinforcement learning rely on the interactions between multiple participants... Finish after body of paper
2Another thing.
2.1In a MDP, the agent's goal is to maximize expected reward over the time horizon (in this case infinite): \[R_t = E \left\{ \displaystyle\sum_{i=0}^{\infty} \gamma^i r_{t+i+1} \right\} \] where \(\gamma \in \lbrack 0,1) \) is the discount factor and the expectation is taken over the probabilistic state transitions. The behavior of an agent is determined by its policy \(\pi\), which describes how an agent will choose an action given the current state. In order to achieve the goal of learning a policy that will maximize long term reward we define the utility function: \[Q^{\pi} : S \times A \rightarrow \mathbb{R}\] \[Q^{\pi}(s,a) = E_{\pi} \left\{ \displaystyle\sum_{i=0}^{\infty} \gamma^i r_{t+i+1} | s,a,\pi \right\} \] Q-Learning is an iterative procedure used to approximate the optimal Q function \(Q^*(s,a) = max_{\pi}Q^{\pi}(s,a)\). (put in closed form eq for Q*?) The Q-table stores current estimates of previously explored state, action pairs and is updated using the equation: \[Q(s_t,a_t) := Q(s_t,a_t) + \alpha[r_{t+1} + \gamma max_{a'}Q(s_{t+1},a') - Q(s_t,a_t)] \] where the \(a'\) are potential agent actions in state \(s_{t+1}\), and \(\alpha \in (0,1]\) is the learning rate. Since the Q-value updates do not require knowledge about the probabilistic state transition function or the reward function, Q-Learning is considered model-free.
In order for the sequence of Q-functions described above to converge to the optimal Q*, the agent must try all actions from all states with non-zero probability. Therefore the learned policy cannot be the deterministic, prohibiting the greedy policy that picks the action with highest Q-value for every state. Instead, the policy must be a mapping from an environment state to a probability distribution over the agent actions, which is non-zero for all actions. This is known as the exploitation-exploration tradeoff, where a policy seeks to exploit actions that are known to be high value while also attempting unexplored actions. One example of such a policy is \(\epsilon\)-greedy exploration, where the agent chooses a random action with probability \(\epsilon\) and the action with largest Q-value with probability \((1-\epsilon)\). Other examples include Boltzmann exploration and entropy sampling. (put in equations?)
2.2The state action space in many environments can be quite large, which can make learning a complete Q-table computationally intractible. While each of these state, action pairs are unique some may occur infrequently or may be strategically similar or other pairs. Deep Q-Learning seeks to generalize the knowledge of explored states by approximating \(Q(s,a)\) with a Deep Q-Network (DQN), \(Q(s,a|\theta)\), where \(\theta\) are the neural network weights that parametrize the Q-values. The weights are updated by minimizing the loss function: \[\mathcal{L}(s_t,a_t|\theta_i) = E_{s_t,a_t,r_t,s_{t+1} \sim D } \left\{ (r_t + \gamma max_{a'}\overline{Q}(s_{t+1},a'|\theta_i) - Q(s_t,a_t|\theta_i))^2 \right\} \] There are two key components to the above equation that improve convergence. First is the use of an experience replay buffer \(D\), containing tuples \( (s_j, a_j, r_j, s_{j+1})^J_{j=1}\) that have been observed by the learning agent. Rather than updating the the Q-network with each new explored state, the network is updated using uniformly drawn samples from the replay buffer. Second is the target network \(\overline{Q}\), whose weights are kept fixed for a certain number of iterations and then updated periodically.
2.3In many real-world senarios partial observability provides another confounding factor for Deep Q-Learning. If the entire environment state is not observable to all agents, then the problem must be modelled using a partially observable Markov Game (POMG). Partial observability can occur if previous state data influences the optimal actions in the current state. For example, if an agent is trained to play a videogame through still game images, previous images may have an important impact on current state strategy. In many multi-agent senarios the state-action space is simply too large, so partial observability is imposed to make the problem computationally tractible.
2.4A Markov Decision Process (MDP) provides a mathematical framework for senarios where a single agent makes decisions in an environment to optimize reward, and each decision has a randomized effect on the environment. Formally it is defined as a tuple \(\langle S,A,f,p \rangle \), where \(S\) is a finite set of environment states, \(A\) is a finite set of agent actions, \(f: S \times A \times S \rightarrow [0,1]\) is a state transition function, and \(p: S \times A \times S \rightarrow \mathbb{R}\) is a reward function. At each time step \(t\), the agent can observe the environment state \(s_t \in S\) and take some action \(a_t \in A\). The action transitions the environment to state \(s_{t+1}\) with probability \( f(s_t,a_t,s_{t+1}) \) where the agent receives some scalar reward \(r_{t+1} = p(s_t,a_t,s_{t+1}) \).
A Markov Game, also known as a stochastic game, is a natural extension of a MDP to a senario with \(m\) agents. It is defined by \(\langle S,\mathcal{A},F,P \rangle \) where S remains a finite set of states, \(\mathcal{A}\) = \(A_1 \times ... \times A_m\) is the joint action set, and \(F: S \times \mathcal{A} \times S \rightarrow [0,1]\) and \(P: S \times \mathcal{A} \times S \rightarrow \mathbb{R}^m\) are the state transition and joint reward functions, repectively, which both depend on all agent actions.
3.2The state action space in many environments can be quite large, which can make learning a complete Q-table computationally intractible. While each of these state, action pairs are unique some may occur infrequently or may be strategically similar or other pairs. Deep Q-Learning seeks to generalize the knowledge of explored states by approximating \(Q(s,a)\) with a Deep Q-Network (DQN), \(Q(s,a|\theta)\), where \(\theta\) are the neural network weights that parametrize the Q-values. The weights are updated by minimizing the loss function: \[\mathcal{L}(s_t,a_t|\theta_i) = E_{s_t,a_t,r_t,s_{t+1} \sim D } \left\{ (r_t + \gamma max_{a'}\overline{Q}(s_{t+1},a'|\theta_i) - Q(s_t,a_t|\theta_i))^2 \right\} \] There are two key components to the above equation that improve convergence. First is the use of an experience replay buffer \(D\), containing tuples \( (s_j, a_j, r_j, s_{j+1})^J_{j=1}\) that have been observed by the learning agent. Rather than updating the the Q-network with each new explored state, the network is updated using uniformly drawn samples from the replay buffer. Second is the target network \(\overline{Q}\), whose weights are kept fixed for a certain number of iterations and then updated periodically.
3.2.2
In the single agent case adding recurrency to a DQN has been successful
In the single agent case adding recurrency to a DQN has been successful
Most problems in robots fall under continuous action spaces Discretizing a continuous action space too much results in the curse of dimensionality. Google DeepMind created DDPG. DDPG is a policy-gradient actor-critic algorithm DDPG is off-policy and model-free DDPG uses a stochastic behavior policy for good exploration but estimates a deterministic target policy. A policy gradient algorithm utilizes a form of policy iteration where the policy is evaluated and then the policy gradient is maximized. Uses a deterministic target policy. Uses two networks, one for actor and one for critic. Actor compute action predictions for the current state. Critic generates a temporal-difference (TD) error signal for each time step. The input of the actor network is the current state and outputs a single real value representing an action chosen from a continuous action space. The critics is given the current state and output of the action given by the actor. The output of the critic is the estimation of the Q-value and action by the actor. Data is not I.I.D as it’s correlated through time. Thus experience replay is used to correlate the training samples. Directly updating the actor and critic neural networks can lead to instability. Thus, target networks are used to stabilize the updates. https://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html
DDPG concurrently learns a Q-Function and a policy. It uses off-policy data and the Bellman equation to learn the Q-function and uses the Q-function to learn the policy. Optimal action can be found by solving: a*(s) = argmaxa Q*(s,a) Specifically designed fo, and only be used with, continuous action spaces. In a discrete action space one can directly search for an optimal action. In a continuous action space this is a painfully expensive subroutine. The Q function is presumed to be differentiable with respect to the action argument. We approximate the Q-function using a gradient-based learning rule for a policy (s) as maxaQ(s,a) Q(s,(s)) The implementation of DDPG does not support parallelization. DDPG is performed by minimizing the Mean Square Bellman Equation Loss with stochastic gradient descent. L( , D) = E(s,a,r,s',d) from D[(Q(s,a) - (r+(1-d)Qtarg(s', targs(s'))))2]where targis the target policy. As the action space is continuous, and we assume the Q-function is differentiable with respect to the action. Gradient ascent (with respect to policy parameters only) to solve maxEs from D[Q(s, (s))]where the Q-function parameters are treated as constants. To make DDPG policy explore better noise is added to their action at training time. That noise is uncorrelated, mean-zero Gaussian noise. https://spinningup.openai.com/en/latest/algorithms/ddpg.html ADD PSUDOCODE HERE
3.3.2Unfortunately, traditional reinforcement learning approaches such as Q-learning or policy gradient are poorly suited to multi-agent environments. One issue is that each agent’s policy is changing as training progresses and the environment becomes non-stationary from the perspective of any individual agent. In this work a general-purpose multi-agent learning algorithm that: Leads to learned policies that only use local information at execution time Does not assume a differentiable model of the environment dynamics or any particular struction on the communication method between agenst Is applicable not only to cooperative interaction but to competitive or mixed interaction involving both physical and communicative behavior. A framework with centralized trainings and decentralized executions. A centralized critic is used with information about each other agent while the actor just uses local information. During execution time the actors are used with no shared information. An ensemble of policies is used for training to improve stability. K sub-policies are trained where a random one is chosen at each episode. Each actor maintains an approximation of the true policy for each other actor. This policy is updated with the latest samples of each agent from the replay buffer. https://arxiv.org/pdf/1706.02275.pdf
3.3.320 individual DDPG agents corresponding to 20 agents in the environment w/ a single reply buffer is used for initial training. Then, a switch over is used where a single DDPG agent learns from the one Reply Buffer. This one DDPG agent is used for each of the 20 agents in the environment. https://medium.com/@kinwo/solving-continuous-control-environment-using-deep-deterministic-policy-gradient-ddpg-agent-5e94f82f366d
3.3.4Deep Reinforcement Learning tends to be “brittle” where an agent can be stuck in a poor local optima state and not generalize to new environments. This also includes a new policy proposed by an opponent. Introduces a new algorithm - MiniMax Multi-Agent Deep Deterministic Policy Gradient: Minimax extension of DDPG Multi-Agent Adversarial Learning to efficiently solve proposed formulation. Essentially this paper trains assuming worst-case scenario or that the opponent is playing optimally. (Mini-Max) To update, an approximate linear function is created and the gradient is determined based on this function. https://people.eecs.berkeley.edu/~russell/papers/aaai19-marl.pdf
3.3.5Use a communicative medium, that’s learned, in addition to an agent's own experience. Assume observations received by most agents are extremely noisy and weakly correlated to the true state. This is even more true for multi-agent environments where a specific state might not be impactful to any given agent specifically. Two policies, one for actions and one for communication. These two policies are connected through a communication medium. Two types of communication -> broadcast (1 -> all) and unicast (1 -> 1) Communication is done at a smaller time scale -> every C steps. (This includes communication policy learning) Intrinsic rewards are used for both communication and action policies. Two separate experience buffers. One for communication policies and the other for action policies. https://arxiv.org/pdf/1812.00922.pdf
3.4Dec-POMDPs \citep{DecPOMDP} is the basis of all the Decentralized execution of Multi Agent Policy Gradient method. Formally described as
$$ $$
where S is the Set of states, $s \in S$ true state of the environment.  P is the Probability function for determining the resultant state given the original state and action : $P(s_t |s_{t-1}, u_{t-1}): S \times U \times S \rightarrow [0,1] $. Partially observable scenarios in which an observation function $O(s,a): S \times A \rightarrow Z $, determines the observation $z^a \in Z $ that each agent draws individually. Each agent maintains an action-observation history $\tau^a \in T \equiv (Z \times U)^* $ on which it conditions a stochastic policy $\pi^a (u^a | \tau ^a ): T \times U \rightarrow [0,1]. $ Each Agent share the same team reward $r(s,u): S \times U \rightarrow R $, where $\gamma \in [0,1] $ is the discount factor. The joint policy $\pi$ admits a joint action-value function: \\
$Q^\pi (s_t,u_t) = \EX_{s_{t+1}:\infty; u_{t+1:\infty}}[R_t| s_t,u_t] $\\ where $R_t = \sum \gamma^ir_{t+i} $ is the discounted return.
3.5QMIX \citep{qmix} is an example of centralized training decentralized execution paradigm algorithm. which employs a network that estimates joint action-values as a complex non-linear combination of per-agent values. QMIX is like value decomposition networks \citep{vdn}, where centralized $Q_{tot}$ is factored as sum of individual value functions $Q_a$ that condition only on individual observations and actions, a decentralized policy arises simply from each agent selecting actions greedily with respect to its $Q_a$. The difference being QMIX doesn't need to ensure full factorization and individual value factions are mixed in non linear fashion. VDN severely limits the complexity of centralized action-value functions that can be represented and ignores any extra state information available during training. global argmax performed on $Q_{tot}$ yields the same result as a set of individual argmax operations performed on each $Q_a$. % \begin{align}\tag{label}
% \end{align} One agent network that represents its individual value function $Q_a(\tau^a , u^a )$. It represents agent networks as DRQNs \citep{dqrn} that make use of recurrent neural networks, receives the current individual observation $o_a^ t$ and the last action $u_a^{t-1}$ as input at each time step.
Individual value function $Q_a$ are mixed monotonically in a complex non-linear way via a mixing feed forward network to form $Q_{tot}$, where network is maintained with positive weights, ($\frac{\partial Q_{tot}}{\partial Q_a} > 0 $) \citep{Dugas}. The weights of the mixing network are produced by a separate one layer hyper networks with absolute activation function which takes the state s as input. The final bias is produced by a 2 layer hyper network with a ReLU non-linearity. The state is used by the hypernetworks rather than being passed directly into the mixing network because $Q_{tot}$ is allowed to depend on the extra state information in non monotonic ways. For complete diagram of architecture see fig \ref{fig:qmix}
ADD QMIX PNG IMAGE HERE
Qmix is trained in an end-to-end manner by minimizing the following loss analogous to the standard DQN loss : $$\mathcal{L}(\theta) = \sum^b_{i=1} [(y_i^{tot} - Q_{tot}(\tau , u, s ; \theta))^2] $$ where b is the batch size of transitions sampled from replay buffer. $y^{tot} = r + \gamma max_{u'} Q_{tot}(\tau' , u', s' ; \theta^-)$ and $\theta^-$ are the parameters of a target network as in DQN. Maximisation of $Q_{tot}$ in time linear in the number of agents because of \eqref{eq:qmix_linear}
3.5.2The StarCraft Multi-Agent Challenge (SMAC) \citep{starcraft} is built on the popular real-time strategy game StarCraft II3 and makes use of the SC2LE environment \citep{star}. \footnote{Framework Code: \url{https://github.com/oxwhirl/smac},\\ Pytorch Code (RL algorithms) \url{https://github.com/oxwhirl/pymarl}} \textbf{Easy scenarios:} are fully solved (above 95\% median win rate) by IQL and QMIX setup within the timeframe given. For higher number of units, COMA and IQL agents also learned to win the scenario most of the time, although the cooperation between agents is less coordinated compared with QMIX. QMIX and IQL won on alternate fire rounds to deviate enemy. More strategy scenarios QMIX outperformed COMA and IQL. (self sacrificing, intercepting enemy, focus fire , etc) \textbf{Hard scenarios:} None performed well, winning the battle requires significantly more coordination and strategy, which is difficult when the number of independent agents is large. The longer episodes make it more difficult to train, since temporal credit assignment is harder. Need for faster training.a longer training period does indeed lead to much better performance on this scenario. We hypothesise that a better exploration scheme would help in this regard, since the agents would be exposed to better strategies more often, as opposed to requiring many training iterations and \textbf{SUPER HARD SCENARIOS:} None of our tested methods utilise any form of directed state space exploration, which leads to them all remaining in the local optimum of merely damaging the enemy units for reward, instead of first moving to the critical point and then attacking. All three methods make little to no progress. Winning the battle requires significantly more coordination and strategy than in the symmetric case. \textbf{Conclusion } Only the agents with RNNs are able to learn a good strategy agents and RNN in addition to Feed Forward network is the desired agent network.
3.6The decision of an agent is influenced by those of only a small subset of other agents. This locality of interaction means the joint action-value function Q(a) can be represented as the sum of smaller reward functions, one for each factor e. $Q_a = \sum_{e=1}^C Q_e(a_e)$, where C is the number of factors and $a_e$ is the local joint action of the agents that participate in factor e There are many cases in which the problem itself is not perfectly factored according to a coordination graph, in these cases approximate factorization is helpful. $Q(a) \approx \^Q(a)= \sum_e \^Q_e(a_e)$ decomposition of the original function in a desired number of local approximate terms $\^Q_e(a_e)$
\begin{enumerate} \item \textbf{Single agent decomposition (Mixture of experts):} each agent $i$ is represented by an individual neural network and computes its own individual action-values $\^Q_i(a_i)$ based on its local action $a_i$ e.g. \citep{iql}
\item \textbf{Random partition:} agents are randomly partitioned to form factors of size f without replacement. Each of the $C = \frac{|D|}{f}$ factors is represented by a different neural network that represents local action-values $\^Q_e(a_e)$, one for each of its output units, for a certain factor e, where $a_e$ is the local joint action of agents in factor e
\item \textbf{Overlapping factors:} Fixed number C of factors is picked at random from the set of all possible factors of f. Every factor e is represented by a different neural network learning local action-values $\^Q_e(a_e)$ for the local joint actions $a_e$
\item \textbf{Complete factorization:} each agent i is grouped with every possible combination of the other agents in the team D. $C = \frac{|D|!}{f! (|D|-f)!}$ factors, each represented by a network. Each of these networks learns local action values $\^Q_e(a_e)$
\item Some Pathological examples, where all types of factorization result in selecting the worst possible joint action. Only joint learners seem to be able to address such problems, currently no scalable deep RL methods for for dealing with such problems seem to exist. \item \textit{complete factorization} of modest factor size coupled with the factored Q-function learning approach yield near-perfect reconstructions and rankings of the actions, also for non-factored action-value functions. Moreover, these methods scale much better than joint learners: for a given training time, we say that these complete factorization already outperform fully joint learners on modestly sized problems \item For more benign problems, random overlapping factors also achieve excellent performance, comparable to those of more computationally complex methods like joint learners and complete factorizations. \item Factorizations with the mixture of experts approach usually perform somewhat worse than the corresponding factored Q-function approaches. However, in some cases they perform better/comparable. This is promising, because the mixture of experts learning approach does not require any exchange of information between the neural networks, thus potentially facilitating learning in settings with communication constraints, and making it easier to parallelize
4\item Q-Learning or policy gradient are poorly suited to multi-agent environments. They do not allow for the inclusion of extra state information during training. The environment becomes non-stationary from the individual agent's point of view, induced by simultaneously learning and exploring agents. violating Markov assumptions required for convergence of Q-learning.
\item Policy gradient suffers from a variance that increases as the number of agents grows and can be sample-inefficient and prone to getting stuck in sub-optimal local minima.
\item \textbf{(zach, lowe's model precursor)} model-based policy optimization which can learn optimal policies via back-propagation, but this requires a (differentiable) model of the world dynamics and assumptions about the interactions between agents. Notorious instability of adversarial training methods
\item Centralized Learning of joint actions can naturally handle coordination problems and avoids non-stationary, but is hard to scale, as the joint action space grows exponentially in the number of agents
Scalable centralized learning include coordination graphs \citep{centralized}, which exploit conditional independencies between agents by decomposing a global reward function into a sum of agent-local terms. Sparse cooperative Q-learning \citep{sparse}, tabular Q-learning algorithm that learns to coordinate the actions of a group of cooperative agents only in the states in which such coordination is necessary, encoding those dependencies in a coordination graph.
Centralized action-value function $Q_{tot}$ conditions on the global state and the joint action. It is difficult to learn when there are many agents and, no obvious way to extract decentralized policies. Methods like IQL \citep{iql}) where each agent a learn an individual action-value function $Q_a$ independently may not converge and are non-stationary. On the other extreme, COMA \citep{coma} is a fully centralized state action value function $Q_{tot}$ which is used to guide the optimization of decentralized policies in an actor-critic framework. A middle ground is to learn factored Q-value functions, which represent the joint value but decompose it as the sum of a number of local components, each involving only a subset of the agents. Compared to independent learning, a factored approach can better represent the value of coordination and does not introduce non-stationarity. Compared to a naive joint approach, it has better scalability in the number of agents.
Training the fully centralised critic becomes impractical when there are more than a handful of agents. \citep{gupta} present a centralised actor-critic algorithm with per-agent critics, which scales better with the number of agents but mitigates the advantages of centralisation.
4.2Centralized training with decentralized execution is a common strategy deployed to train Multi-Agent Reinforcement learning model, extensively studied by (\cite{decentralized};\cite{lowe};\cite{Kraemer}). Although training is centralized but execution is decentralized. Each agent (actor) can access local observation history at the execution time. Extra information is available only available at training to ease up the training. The learning algorithm (critic) has access to all local action-observation histories $\tau$ and global state s, but each agent’s learnt policy can condition only on its own action-observation history $\tau^a$ .
Q-function generally can't contain different information during training and testing time. This manner of paradigm is equally applicable in cooperative and competitive settings. It removes the the problem of the joint action spaces growing exponentially with the number of agents, rendering it useful in real time applications against the single-agent RL methods.
4.3Some text describing who did what.
Some text with links describing who reviewed the article.


I'll in an AJ's podcast for your Monday.
Most wonderful show is keeping up with the Joneses.
Welcome to episode 191 of Keeping Up with Joneses.
Where this week we are talking about the joy of navigating transitions.
Is it worth noting that you have a headache for this episode?
It might be.
And we'll have to speak quieter because you have a headache?
Well, at least a little bit quieter.
So I shouldn't be super excited.
Not if you want to sleep well tonight, not worrying about being stabbed by a fork.
Oh, oh, got real dangerous real quick.
How are you, Mrs. Jones, apart from the headache?
I'm good apart from the headache.
I think it's just the, uh, is this a low pressure system or high pressure system that brings rain low, right?
I find it interesting.
You think I don't know the answer to that question.
Okay.
Well, whatever, the pressure system that brought rain brought me a headache, I think.
It's easy.
Not that I want to be kind of essentially boring and talk about the weather, but the weather this week.
Just so annoying, warm and then cold and raining and then warm.
It doesn't know what it is.
It's like I'm back in Scotland, except for the warm parts.
Right.
I mean, yesterday was 82 degrees and sunny.
It was beautiful.
Now we're back in the 50s and raining.
I'm so confused.
Terrifying.
Tell me about this week.
It was a great week.
Monday and Tuesday, we had two very wonderful teachers on the school, obviously you teaching
on the importance of church. And Jeff teaching on, I don't know what Jeff taught on because I
wasn't in there. Abundant living, I think. Jeff honors around and just emanates wisdom, I think.
Yeah, they ask a lot of questions and stuff and draw stuff out of them, which is very fun.
Becky comes in, you know, laser.
A glittery.
A floor.
Yeah.
Yeah, Monday and Tuesday.
Monday, I can remember what I spoke on.
I think I spoke on God's willingness to answer prayer.
Oh, you mean Monday night?
Yeah, Monday night.
I was at a middle school meeting for parents, so I wasn't there.
Yeah, no, you weren't there to support me on.
You weren't there to support me in a middle school meeting and cheer me on.
We'll talk about that later.
Wednesday was Abigail's 11th birthday, and she was so excited because she got her own
iPhone.
She is just a little bit excited about that phone.
I spent all the weeks before doing research on how to lock down an iPhone for a teenager.
And so it is locked, baby.
And then we went to the Cheesecake Factory as is our family tradition.
Yes.
And it was so sweet.
Your mom was still here.
Yeah, she stayed an extra couple of days because it was so close to Abby's birthday.
So she stayed on.
And so she was there for the birthday, which was great.
Which was a big deal because she's our only surviving grandparent.
Yes.
Thursday, we took the day off.
- We did.

- I did, but in fairness to me,
those meetings were scheduled
before I realized we were taking the day off.
- Oh, I see.
- And so they were difficult to unschedule.
- Thursday, we also said goodbye to your mom.
- We did, yeah.

- And a treat for me, you came to my grief, counselor.
- I did.
(laughs)
- I wanna talk about this for a little bit
'cause I'd love to get your perspective.
So for everybody who's listening,
I've been seeing a grief counselor for, I don't know, a couple of months now, a lovely, lovely
guy.
And the weird thing about doing any sort of counseling, which I'm a big fan of, unsurprisingly,
the weird thing about it is an entirely fabricated process.

Too stranger.
Too a stranger.
Who, you know, is no longer a stranger, but it's just, it's a bizarre thing.

And so I want to make sure you have as enough accurate data as possible.
So I said, can I bring my wife?
And he was like, you're of course, you're welcome to bring your wife.
What are you hoping to get out of bringing your wife?
And I'm like, well, I just think it would be great.
I think you'll understand me more by meeting AJ
'cause I'm one half of Ellen and AJ.
Plus, I think it'd be great for her to meet with you.
So what was it like for you?
I loved that you came.
I felt super loved, super supported.
What was your observations?
- It was interesting 'cause I felt like the first 20 minutes
was you trying to update me at mock speeds
with high influencer about the entire process
that's taking place for four months or something.
- Because for whatever reason,
we don't really talk about it
'cause it's the pace of our life.
- Right.
If something major happens, I'll say, "Hey, this majorly happened," but generally speaking,
it's just another event in our week.
Right.
So I wasn't sure if at the end of this I had to buy a car or what the deal was, but it
was just like, "Wow, that was bad."
And it's interesting because he doesn't really interject unless he's invited to.
Right.
So that was interesting.
Also he looks a lot like your best friend in Scotland.
Which I didn't realize until you've blamed that.
like, "Oh my gosh, what was my subconscious doing?"
(laughs)
- Picking your best friend to talk to.
- That's fascinating.
- But he's lovely, like he's a lovely guy
and it was interesting to observe.
I've only ever had bad counseling experiences.
- Right.
- So it was, I've realized going in, I'm like,
"Oh, I feel a little traumatized having to go in.
The only thing that is nice for me is I know I don't have to talk.
So it's, you know, but it was nice to see a safe counseling environment.
Did you feel like I was me when you were observing me?
Sort of sort of like a more playfully combative.
I don't know how to explain how you were.
I was sort of like, huh, like it felt like you were trying to analyze
everything you're doing from both sides, which I thought was his job, not yours.
Well, that's a dynamic we run up against in part because again, sitting, analyzing your feelings is
all sort of official. So I'm trying to ensure that I give him the most reliable data possible.
And because I'm quite a high verbal processor, I'll say something and I'll be like, I don't know
if I agree with that. And then I'll talk about it left, right, and center. I know it's your idea
of torture. Yeah, this sounds horrible to me. There's nothing about this. It sounds appealing.
Well, I love that you came and it helped me get a sense of,
am I being myself? And I think a slightly more playfully combative version of myself is myself.
Thank you for coming. I felt super loved. You're super welcome.
 We did. Yeah, we only have a couple of Fridays left
with just the boy before it's summer and it's all the kids.
And then he goes to school in August, which I'm just trying to pretend this is not all
happening this August.
I'm looking forward to it because the amount of money that we'll save in childcare is unbelievable.
That's true.
We're talking a lot of money.
I'm like, wow, I've been by so many more Nintendo games.
I know.
It's going to be amazing.
You're adorable.
Thanks.
those feelings talk about Friday where we ate tacos and ice cream.
We did. We had tacos and ice cream. I cannot wait for Mojo's to open.
You know, not just the truck, but the actual restaurant.
Absolutely. Yeah.
We also bumped into Pete Nashley from Alabama, who I love when this happens.
I'd love when we're just planning our own business and somebody comes up and says,
"Are you Alan A.J.?" and, "Oh, we listened to podcasts."  You got a photo that
that was taken, I don't know, eight years ago.
- Yeah.
- And we need to update that for.
- We do.
I think I'm pretty sure I'm pregnant in that photo.
- You are.
- And we're really dark brown here.
I was kind of amazed that they could recognize us
because honestly, we both looked like we had thrown
on old clothes and not showered.
I didn't have any makeup on.
I was like, I don't care.
It's my day off.
But I'm like, oh, great.
We're meeting people.
- Oh, yeah.
I did try to warn you.
I was like, "Hey, baby, are you fleeing your homeland?"
- You did.
As I was heading out the door, you're like, "Is that?
Are you really gonna wear that?"
And I was like, "Yeah, why wouldn't I?
It's my day off.
I'm not doing my hair.
I'm not doing my makeup.
I'm not getting dressed practically.
I mean, I was just one step better than pajamas."
Yeah, lesson learned.
- Let's talk about Saturday.
Saturday, you made me do something.
That sounds really powerless.
- Yes, it does.
It sounds very powerless.
Saturday you made plans that I was made aware of and I came with you on those plans. Yes, you did.
But you took me to a middle school production of the Lion King. Now, what made you think that I
would enjoy that? I didn't really care if you enjoyed it, actually. I wanted you to come and see
the middle school that our daughter's going to be going to and get an idea of the kind of stuff
that she's likely to get excited about because she loves singing, she loves dancing and all that kind of stuff.
And so they had this production on and I was actually really surprised that a middle school has a
theater auditorium like that. It's not like they're using a gym, they're using an actual...
No, granted. It was a hundred times better than what I was expecting.
Yeah, it was really good.
But still, that's not how I want to spend my Saturday afternoons.
No, but I mean, I've just thought for me, I thought, oh, I'd love Abby to get an idea of the kind of stuff that they do. So she is interested.
She knows what she's signing up for. Does that make sense?
The kids love it. Two millions of dreams like, okay, forget family movie nights. Can we do family theater nights?
Yeah, MJ loved it too. They were just in Ratchet. I'd love to take them to a Broadway show.
Yeah, they would love it. How about Wicked? Just kidding.
Sorry.
Does is there a Harry Potter on ice?
I hope not.
Yeah, that'd be good.
Well, it really shows like that in Nashville, though.
Is it?
No, I was thinking about going to Broadway in New York City.
I think that could be tremendous fun.
Oh, my gosh, they would love that.
You would love that.
Oh, yeah, I would love that.
Forget them.
I'm just kidding.
Yeah.
And then Sunday, which is today.
It is.
I spoke at church this morning.
you did. I asked you, I was like, Hey, babe, I just think that went. And you were like, I think
students are used to you like poking at them. The rest of the church knows about it.
I think they did. I think that they took it really well. I think they did remarkably well.
You know, it's not nice when there's somebody up there at the front talking about dynamics in
in your life that you a might not be aware is going on and be if it is going on.

I was talking about the drama triangle.
Yes.
And the roles that we play when we feel powerless.
Yeah, which is, you know, never a fun topic, but we made it through it.
Well done people.
I think I've heard you teach it maybe three times in the last couple of weeks
because you taught it at staff or at the pastors meeting anyway.
Yep.
And then you briefly touched on it with year two
and then you sort of filled it out this morning.
But this morning was great how much
it's actually grounded in scripture
and just your explanations were really, really good.
And even being able to identify,
this is what you get out of it if you're playing this role.
I thought was super helpful in being able to identify
am I the victim or am I the rescuer?
Am I the persecutor?
So I thought that was really good.
- You're very sweet, thank you, Dary.
- Yeah.
- I came home and-
- Crashed.
- Crashed, I've discovered something
that mildly motivates me to exercise.
- Yes.
- Wait, why are you giving that look?
- Because I just think it's really cute.
Like you discovered it, like, you know,
it's never been done before.
- I have discovered something.
It is the key to all things exercise.
Behold, this is my discovery.
Ta-da.
Go ahead and answer your discovery.
My discovery is if I can find a show
that I binge watch on Netflix or Hulu or whatever.
Yeah.
If I take the iPad downstairs and put it into the treadmill,
instead of sitting in the bed or sitting on a sofa
and watching it, if I just walk while I'm watching it,
before you know it, I've done an hour of exercise.
Yeah.
And I invented that.
All right, let's leave my great inventions and talk about our main topic for this week.
We're talking about the joy of navigating transitions.
Yeah, most people don't find that joyful.
What I was thinking was I started just thinking, but I don't know if I'm wired to change
you, but I love transition.
I love change.
Really?
Yeah, well, it's not surprising I get bored really easily.
So whenever change comes about, I'm really happy to say goodbye to the old and I'm really
excited about the new. And my experience of the Lord is all the transitions he's brought
me through have been amazing. Yes. Would you say you even enjoy the work that it takes to get
from one to the other? I remember when we were moving house the last time. Yes. That was a lot
of work. Yes. Now, I want to qualify that. We were helped by an astonishing number of people.
Yes. So I am not from suggesting that I did all this work at all. But nevertheless,
It was a lot of work because we staged our house.

So we're like, all right, we'll do what you tell us.

Like you need to get rid of a third of the stuff in your house.
And so we got a storage unit, but actually got a couple of storage units.

And she was right.
The house looked much bigger.
But that meant tons and tons and tons of moving boxes, which I'm not a fan of manual labor
But I remember as a moving these boxes
I was beginning to grumble and complain and the Lord said to me
Would you move these boxes for a hundred thousand dollars?
And I said I absolutely would and I said great, you know, that's what you're gonna get from your selling your house
I was like I will move these boxes for a hundred thousand dollars without grumbling
So it's not that I enjoy the process right there and then although I'm quite a high-doer
like, if you can just give me a task and I can get it done, I feel good. And like I said,
I had tons of help. So there was lots of camaraderie and fun. But no, I don't necessarily enjoy that,
but do I love living in a new house, for example? Yes. Yeah, I do. I hate the red tape that's
associated with moving. I've got permanent residency in three different nations.
Well, the first one was easy because I was just born into it. But the other two,
you know, there's a lot of money. There's a lot of time involved. And I don't enjoy the process,
but I love what it's pointing to.
Yeah, I gotcha.
But then I was thinking about, you've had some transition that you haven't liked.
I'm thinking about getting divorced.
That kind of been a fun transition.
It was not a great transition.
You're probably not thinking, man, I can't wait to talk about the joy of navigating this transition
in like 20 years.
Yeah.
But I think my point in it is if God's in the transition, which God always is,
because he's in your life, then he's going to redeem something.
No?
Yeah.
No, I agree. I agree is interesting.
Like just out the blue here, like out of the two husbands that you've had, you know, which one of you like the most?
Well, I am definitely more fond of you, babe.
Oh, okay. See? Transition. When, when?
Oh, my gosh. Yeah.
Right.
I remember a season where I moved 14 times in three years.
So I didn't enjoy those ones as much.
Were you on the run?
Yes, I was on the run from the law.
No, I just was sort of switching jobs.
I moved, then I went to YWAM, so then you move again, then you move back from YWAM.
Then I found a temporary place to live until I found a permanent place to live, and then
that place only lasted.
You know what I mean?
So it was like one of those things.
So I, yeah, I moved a lot.
I didn't enjoy that that much,
but each place was sort of an upgrade on the next.
So.
- The reason I was thinking about this topic is this week,
we lead into our graduation,
our School of Supernatural Life students graduate.
We can't wait to celebrate with them on Tuesday night.
And you know, typically when we head into a week like this,
there's just a mix of emotions.
And one of the things I want to remind the students
when we see them tomorrow is,
hey, you came to the school for this moment here.
Yes.
The purpose of the school was to leave the school.
Yes.
So this is a great opportunity.
And I think that's what I love about transition.
I love the potential of the transition.
Yeah.
Yeah.
Each moment, whether you're graduating high school, graduating university, or graduating
a vocational school, it's an opportunity, isn't it, to start to shape your life with the Lord
in a certain direction.
So it's quite exciting.
I've been reflecting on our transition and I don't think we could have planned our life here in Franklin.
It's filled with people we didn't know and the place we'd never been doing things we never dreamed we'd love.
And I'm like, "All of this is orchestrated by the Lord."
Like, congratulations Lord, round of applause, you did an awesome job.
And for sure it all followed scary change.
You know, whether you found it scary or not, it was a big deal to move countries.
It was a big deal to even sign up basically to leave to ACF for what we hope this is going
to be good.
Right.
I mean, what you hear about people having nightmare transitions?
Yeah.
Imagine if we got to Grace and her and it was, you know, Jeff is super controlling or the
culture was horrible or they didn't value the Holy Spirit.
I mean, there could have been so many things wrong, but the Lord went before us and it's
been amazing.
Yeah.
What would you say to people who are in transition right now and they hate it?
Well, I think I would start with my life first. Are we allowed to have life versus?
So Romans 828 says, "For God causes all things to come together for good for those that love God
and are called according to His purpose." And I think for me, that's been something I've hung
on to my whole life is okay, God, like no matter even what it looks like at this moment, you
are going to cause it to come together for good because I love you. And I'm called to
your purpose, which is to love others, you know, so I would say if you're in the middle
of a tough place in your new place to just keep sticking it through knowing that God
is going to see you through.
Right, like how did you cope? I'm trying to think about you.
Your husband leaves.
I'm not a rifle scene for another eight years.
Yes.
What, and you don't know in eight years time you're going to meet a new husband.
No, I thought I was staying safe.
The body of a Greek god.
And no, I had no idea.
Right. So what, like, what did you do with yourself in that transition?
How did you navigate that?
Well, I would say in my heart, I sort of went up and down.
I'd have moments where I'd be running from God because I didn't understand how I ended up here,
you know, here being divorced and you know, just hurting. And then I'd have other moments
where I was like, "God, I know you're going to make it and you know, you're going to do amazing
things." And so it was sort of riding the roller coaster until it was more ups than downs and
pursuing healing of the heart because I realized there was a whole bunch of stuff that came to the
the surface during that time.
We also had amazing community around you.
I did.
Amazing community that I could go to and say, hey, I'm not having a good day.
And I also had amazing community that would come to me and say, why aren't you
being honest with how you're feeling right now?
Hey, why aren't you letting us help you?
And you know, that kind of stuff.
So yeah, I had great community.
Um, and I went to a great church and I stayed plugged in even when I was tempted
to just go and hide somewhere.
What do you really?
I don't think of you as a hider.
But that was a temptation.
It's always a temptation to hide.
It's always easier to hide than it is to face stuff.
And if you think about it, like for me,
even though that was my home church,
I now had all these shared memories
with my first husband where we would lead worship together
and all this kind of stuff.
And so it was facing people that used to be our friends,
but now they're, he's no longer part of the equation
for any of us, but they just felt like there was a lot of pain.
So did you have the ability to believe God for the best that you think I know God's
going to redeem us?
I know God's going to redeem us.
Or were you just managing day by day?
I was really believing that the Lord was going to restore that marriage for the first three
years.
Right.
And then I had a dream and the Lord told me I'm going to restore this a different way
and to let him go.
And he called the next dance that I wanted divorce and, you know, that was sort of it.
But I would say I, in many ways, I shut down my emotions.
Like I just sort of went, I have hope, I have hope.
And then after a couple more years, I was like, I don't have hope anymore.
Hope's painful.
I'm going to shut down to hope and I am just going to power through with faith, you know.

I'm probably never going to have kids.
I'm just going to work for the Lord.
And, you know, and none of those things are bad things except that they were all
choices made out of pain, which I couldn't really recognize at the time.
I was going to say, how did that life plan work out?
Yeah.
Well, I'm glad it didn't work out to be honest with you.
Because, you know, now I've married this man with the body of a Greek God.
Isn't that what we just said?
I don't like talking about it, but let's talk about my body.
I think that one of the things I've heard Jeff say, which I really love is, you know,
your story isn't written yet. It's not even the end of the chapter.
And I think about, that's what I love about the Lord. He's always onward and upward.
He's always taken us from glory to glory. The transition has to be part of that.
And I love when the seasons change. I love, I'm not talking about in the natural,
I of course I love when the season changed the natural because I love the outdoors now, but I'm more mean in the spirit realm
I love when there's this growing season. I love when there's this growing anticipation of the seasons changing that God's doing something that
Mard by upgrade and I think if I could encourage anybody who's finding the process of transition hard is to not give up
Yeah, I think one of the things you know
We we have this cute saying in Christendom that oh, you know, just feels like the grace is lifted. Have you heard people say that?
Yeah. And what they mean is, oh, it's actually getting tough.
Yeah. And I'm like, yeah, but tough isn't an indicator that the Lord's no longer in it.
Yeah. Like if the grace is lifted, that's when character is required. Yeah.
So I've watched, in my opinion, many people shortcut, bow out of the transition,
I'm never getting to the next level because they quit before they saw what was coming on the horizon.
Right. Because I think the grace lifting is actually the thing that means,
"Oh, you can move on," rather than the thing that says, "Hey, push in,"
because there's actually a victory on the other side of this.
Yeah. Well, we talked about Romans 828, which made me smile because that was your email when I first met you.
You had a Hotmail account. I can't believe that I pushed through.
You had a Hotmail account and you were a Dell user.
You were a silver aeroplane member. That's true.
I was in orange when I met you.
(laughing)
I wasn't silver.
Have we told that story?
So when I met AJ, of course,
she'd been traveling around the world on her own for four years.
Yes.
And so, she was an elite frequent flyer with her calendar.
And when I started, I hadn't flown anywhere.
So I got the website and I sign up and I'm an orange,
which is basically nothing.
And then I think by the time we got married,
I had accumulated enough miles to get silver.
And I think one of our early trips when we checked in together,
the, you know, the checking agent, just, and so I would ride on your goodness.
Like as a Sylvia weren't allowed into airport lounges,
she weren't eligible for any upgrades.
But you would be my tag of, like, I would be your plus one.
So wherever we'd go, you know, you would get me into the priority check in lane.
You get me through security.

I still have all our cards, by the way.
And when we go in, I remember this.
I have no doubt that you do.
remember this lady at the Maple Leaf Lounge, the air counter frequent flyer lounge.
She checked us both in and she looked at our names and she looked at my card, she looked at your card and she looked at you and she said,
you married a silver.
And she literally, and I was like, hey, it really sounded confused.
Silver, silver people have feelings too.
Yeah, it was funny. Yeah. So there you go. I may have had a Dell, but I married a silver.
Yeah. All right. These are the Lord so good at redeeming both things in our lives.
- They don't need them.
- So we've got, I was thinking about a list of things
that would be helpful for people in transition.
You talked about the goodness of God, Romans 8 28.
I talked about when the grace lifts.
- Yeah.
- What else we want to talk about?

You've got to trust that in hindsight, everything makes sense.
- Yeah.
- Like God's in it.
So wherever you are, if you're on your own
transitioning. If you are transitioning from one job to another, if you're moving house,
if you're feeling lost, know that you're in the right place at the right time, that the hand of
the Lord is upon you. And this is the season that's going to bring about amazingness. But it just
takes hindsight for a proper perspective to happen.
Yeah. I think I get what you're saying about being excited about change now. I would say,
When the Lord brings change now, I understand that it's for upgrade.
I didn't feel that way before because I didn't fully understand how good he was.
Yeah, I think that's really interesting. You say that. I just think that my modern history with the
Lord, it feels like, I don't know if this is right, it feels like there was a season of my life where
all I went through was an opportunity to sacrifice. Yeah. And that lasted many years.
And once that season happened, I feel like all I've had is reward. Like if you think about it, I
even going so just even to change a career going from being a computer techno kid to a school teacher
was sacrifice of sacrifice of all my dreams. And I don't want to labor this because I'm not trying
I'm like, my sacrifice is nothing.

- Right.
- You know, there's really no sacrifice,
but at the time it cost me everything I had.

And then the Lord invites me to Toronto
and on the one hand, I sell everything I had.
And honestly, I didn't have much to sell,
but I sold everything I had
and I moved to Toronto to live in a warehouse.
That wasn't sacrifice for me.
That was delight.
Like kidding me, that's all I ever wanted to do was come to Toronto.
But then once I go to Toronto, it just feels like I've been on this onward,
an upward cycle of favor.
Like I don't see sacrificing anything we've ever done.
It's all been reward, wouldn't you say?
I think that's an interesting hindsight perspective, but I think we've definitely
experienced the sacrifice end of some things.

I feel like everything we have today is is is just glorious.
I do think everything we have today is glorious.
I'm just thinking like, you know, when we talk about, hey, when it gets hard in the
grace lifts, does that necessarily mean that you move?
Like when we the first year that we were here, it was really hard, babe.
I mean, I don't know if you remember, but there was times we didn't have food.
So of course, selective memory.
You really do because I'm like, dude, I mean, the first year that we were here,
I needed to know that we had heard from the Lord because the people were amazing,
you know, but just other circumstances were difficult.
And, you know, we rented a house that had spiders and, you know, there was all kinds of stuff that I was like, oh,
just brand recluse spiders, babe.
I know. No big deal. No big deal.
So I think, I think, yeah, when you look back overall, yeah, no big deal, no big sacrifice.
But in the moment, some seasons were pretty tough.
Grim Cook has these brilliant questions.
He says that whenever you're in a season of life that you don't understand, the two best
questions to ask is, what does this mean and what must I do?
Right.
So whenever you're facing change, whenever you're facing conflict, whenever you're facing
transition, you get to ask, Lord, what does this mean for our relationship? Lord, who are you trying
to be in this season? Who are you wanting to be for me in this season that you couldn't be in any
other? And what must I do from my end for our relationship? What is it you want me to think? How
do you want me to approach you? And I think those two questions are marvelous. One of the questions
that's never helpful is the why question. Why is this happening to me? The why question is rarely
answered this side of eternity. But I found tremendous comfort in praying through those
questions of like, "Okay, Lord, what does this mean?" So when you think about those
seasons that were hard and you think about what did that mean for your relationship with
the Lord, what do you see now?
Well, I see that even though there was scary moments, God came through in spades time and
time and time again. And I think I thought, you know, we had moved from living by faith
with which I was really comfortable with, to having a salary, which I think then I thought,
oh, well, we removed God from the equation once we have a salary, but we needed God in
the equation to make it. Does that make sense? Yeah. And so I would say the first year of
being here was learning a lesson of it doesn't matter if you have a salary or not, you need
God in the equation still, and that he's still there to answer.
So yeah, I mean, I would say we probably learned a lot during that season if, you know, think
I learned a lot about who I am and who you are, like who we are as people in terms of,
you know, that first year we're launching M&A, we're starting to dream about the school.
And for the first time in our married lives, we're envisioning building a ministry as opposed
to just traveling.
And so you get to take stock of Lord, what have you put in me that's to give away?
So I think it was a great season of first, a great first year, a couple of first years
here of learning what's in us to give away and what has God put in us to build something
different.
chuckling over here because I guess I really am just a forward looking person.
You know, I, when you say that stuff and I think, oh yeah, I forgot about that.
Oh, yeah. No, no, I remember. I remember that. Oh, yeah. Like, but don't you remember the day
that we were like Becky and Jeff had called us and said, Hey, do you want to come over to the house?
And I had just finished texting you and saying, okay, I fed the kids, but there's no food left for us.
and then came over to Becky and Jeff's house and Becky said,
"The Lord told me to give you this."
And it was like, I don't even remember $40.
And I started crying because I was like,
"We can buy groceries."
You know, I was so excited.
- I remember that.

- The same night.
- Yeah.
- Yeah, I just, I don't know, I think the miracle's
overshadows the misery.
- Maybe I have the mom perspective
of looking in the cupboard and going, "Well."

I don't want to ever paint it.
Like I just used the word misery there.
We don't know misery.
No.
And but anyway, yeah, I am, I am giggling to myself that I'm my perspective is somewhat skewed.
I think just always forward, always, always excited.
Do you think that's true?
Well, I, I do.
Yeah.
I just think like, and I, when I'm, I'm trying to think from the perspective of
listeners who are having a hard time or have stepped out for the Lord.
And I know we say stuff like, Hey, if God asks you to do it, then he writes the
checks. He does. But sometimes they come right at the last second.
Do you know what I mean?
And so I just didn't want to leave people with their perspective of when you follow
God, it's all hunky dory and roses and nothing's ever challenging.
And therefore you can just back out if things get challenging.
I don't think that's it. I think sometimes things are challenging.
Oh yeah.
And it's a matter of just going, okay, Lord,
this is what I heard you say.
I haven't heard anything different.
So I'm gonna keep being faithful in the midst of this
and trust that you're gonna meet me here.
And I think that's what we did.
And again, it was a short season
in terms of the span of our lives
or even the span of amount of time we've been here
that was difficult like that.
But I think I learned to stick through something
even though it might have been easier to run.
Right. I'm now remembering some of those events.
I'm remembering arriving in the United States with a social security number that's
like five days old.
Yeah.
So you don't exist in any credit bureau.
Right.
So trying to get utilities turned on in your house.
Do you remember that?
Right.
And, and so just, yeah, like all the things that were everything was
seemed impossible.
Yeah. It was like pushing water uphill with a rake.

And we're like, how much does that cost?
28,000 dollars.
We're like, okay, so where's that money coming from?
I'm putting me.
I thought we had health insurance.
How does this work?
Yeah.
I mean, it was a shocking year.
Good.
All right.
Well, if you're in transition, we're not trying to press you.
We're trying to say, listen, we get it that it's hard.
God is good.
And he's so good.
And in a number of years, you'll be looking back on this event.
And maybe you'll have forgotten about it like me.
Or maybe like AJ you'll have remembered it, but the kindness of the Lord will just soften the sting of what you're feeling today.
Yeah.
If the Lord is anyone like I know he is.
And so that's what we pray for you.
Yeah.
So wherever you are, whatever you're doing, whatever transition you're facing in your life, we bless you today to be encouraged to press through, to continue to not give up, to not withdraw, to understand that you're called to go from glory to glory.
and you have to believe that this is part of your journey
to the next level of God's goodness in your life.
- Yeah.
Hey, we just wanna give you a heads up about next week.
We won't be able to produce an episode
of Keeping Up With The Joneses
and we'll tell you why the following week.
So if you've just started listening to us,
there is a ton of fantastic interviews with special guests
that we'll put a link to in the show notes
to help you decide what you'd like to listen to
Well, we're off the air and the show notes this week are available at Allen and age.com/191.
And we look forward to being with you in two weeks time.
Thanks for joining us.
Have a great week.


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See: Description
| Interface | Description |
|---|---|
| CompositeData |
The CompositeData interface specifies the behavior of a specific type of complex open data objects
which represent composite data structures.
|
| CompositeDataView |
A Java class can implement this interface to indicate how it is
to be converted into a
CompositeData by the MXBean framework. |
| OpenMBeanAttributeInfo |
Describes an attribute of an open MBean.
|
| OpenMBeanConstructorInfo |
Describes a constructor of an Open MBean.
|
| OpenMBeanInfo |
Describes an Open MBean: an Open MBean is recognized as such if
its
getMBeanInfo() method returns an instance of a class which
implements the OpenMBeanInfo interface, typically OpenMBeanInfoSupport . |
| OpenMBeanOperationInfo |
Describes an operation of an Open MBean.
|
| OpenMBeanParameterInfo |
Describes a parameter used in one or more operations or
constructors of an open MBean.
|
| TabularData |
The TabularData interface specifies the behavior of a specific type of complex open data objects
which represent tabular data structures.
|
| Class | Description |
|---|---|
| ArrayType<T> |
The
ArrayType class is the open type class whose instances describe
all open data values which are n-dimensional arrays of open data values. |
| CompositeDataInvocationHandler |
An
InvocationHandler that forwards getter methods to a
CompositeData . |
| CompositeDataSupport |
The CompositeDataSupport class is the open data class which
implements the CompositeData interface.
|
| CompositeType |
The
CompositeType class is the open type class
whose instances describe the types of CompositeData values. |
| OpenMBeanAttributeInfoSupport |
Describes an attribute of an open MBean.
|
| OpenMBeanConstructorInfoSupport |
Describes a constructor of an Open MBean.
|
| OpenMBeanInfoSupport |
The
OpenMBeanInfoSupport class describes the management
information of an open MBean: it is a subclass of MBeanInfo , and it implements the OpenMBeanInfo interface. |
| OpenMBeanOperationInfoSupport |
Describes an operation of an Open MBean.
|
| OpenMBeanParameterInfoSupport |
Describes a parameter used in one or more operations or
constructors of an open MBean.
|
| OpenType<T> |
The
OpenType class is the parent abstract class of all classes which describe the actual open type
of open data values. |
| SimpleType<T> |
The
SimpleType class is the open type class whose instances describe
all open data values which are neither arrays,
nor CompositeData values,
nor TabularData values. |
| TabularDataSupport |
The TabularDataSupport class is the open data class which implements the TabularData
and the Map interfaces, and which is internally based on a hash map data structure.
|
| TabularType |
The
TabularType class is the open type class
whose instances describe the types of TabularData values. |
| Exception | Description |
|---|---|
| InvalidKeyException |
This runtime exception is thrown to indicate that a method parameter which was expected to be
an item name of a composite data or a row index of a tabular data is not valid.
|
| InvalidOpenTypeException |
This runtime exception is thrown to indicate that the open type of an open data value
is not the one expected.
|
| KeyAlreadyExistsException |
This runtime exception is thrown to indicate that the index of a row to be added to a tabular data instance
is already used to refer to another row in this tabular data instance.
|
| OpenDataException |
This checked exception is thrown when an open type, an open data or an open MBean metadata info instance
could not be constructed because one or more validity constraints were not met.
|
Provides the open data types and Open MBean descriptor classes. An Open MBean is an MBean where the types of attributes and of operation parameters and return values are built using a small set of predefined Java classes. Open MBeans facilitate operation with remote management programs that do not necessarily have access to application-specific types, including non-Java programs.
Every MBean has an MBeanInfo
with information about the MBean itself, and its
attributes, operations, constructors, and notifications. In an
Open MBean, this MBeanInfo
implements the OpenMBeanInfo
interface, usually by being an instance of OpenMBeanInfoSupport
.
The attribute information returned by MBeanInfo.getAttributes
for an Open MBean is an array of
objects implementing OpenMBeanAttributeInfo
, usually instances of OpenMBeanAttributeInfoSupport
. In addition to the usual
information about attributes, an
OpenMBeanAttributeInfo
specifies the OpenType
of the attribute.
The possible OpenType
values are predefined, which
is what ensures that remote managers will understand them.
Similar remarks apply to the parameter types of operations and constructors, and to the return types of operations.
There is a distinction between an attribute's Java language
type, as returned by getType()
, and
its OpenType
, as returned by getOpenType()
. For example, if the Java language type is
java.lang.String
, the OpenType
will be
SimpleType.String
. If the Java language type is CompositeData
, the
OpenType
will be a CompositeType
that
describes the items in the CompositeData
instances
for the attribute.
In Open MBeans, attributes and parameters can have default values
and/or constraints associated with them in the OpenMBeanAttributeInfo
or OpenMBeanParameterInfo
.
There are two ways to specify these constraints. Either the
values are directly specified as parameters to one of the
constructors of OpenMBeanAttributeInfoSupport
or
OpenMBeanParameterInfoSupport
, for example
OpenMBeanParameterInfoSupport.OpenMBeanParameterInfoSupport(
String, String, OpenType, Object, Object[])
; or the values are
specified in a Descriptor
given
as a parameter to one of the constructors.
When a Descriptor
is used, the fields of interest are
these:
defaultValue
defines the value returned by
getDefaultValue()
;
minValue
defines the value returned by getMinValue()
;
maxValue
defines the value returned by getMaxValue()
;
legalValues
defines the values returned by getLegalValues()
.
For defaultValue
, minValue
, and maxValue
, the associated value must either be of the Java type
corresponding to openType
, or be a string that can be
converted into that type. The conversion uses the static method
valueOf(String)
if it finds one; otherwise a constructor
with a single String
parameter if it finds one; otherwise
it fails.
For legalValues
, the associated value must be either
an array or a Set
, and the elements of the array or set
must be convertible as described for defaultValue
etc.
The following conditions must be met for these fields:
legalValues
is present then neither minValue
nor maxValue
must be present;
defaultValue
is present then it must satisfy the
constraints defined by legalValues
, minValue
, or
maxValue
when any of these is also present;
minValue
and maxValue
are both present
then minValue
must not be greater than maxValue
.
Submit a bug or feature
For further API reference and developer documentation, see Java SE Documentation. That documentation contains more detailed, developer-targeted descriptions, with conceptual overviews, definitions of terms, workarounds, and working code examples.
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Delegates built upon their leadership skills, creativity, team work and business skills and tackled a real-life community challenge in collaboration with peers from around the world. Delegates were coached and mentored by business and academic leaders. Students were inspired by keynote presentations and skillbuilding workshops. All activities were geared towards helping the students to develop and explore new perspectives, ideas and skill-sets, along with promoting collaboration and creative thinking.
Come explore powerful pathways for using small habits to make big changes in your life and business. Plus meet, network and collaborate with the entrepreneurial community as you learn
Provincial competition for all high school students to present their ideas to build up new businesses in Nova Scotia.
Imagine what happens when over 1,000 college entrepreneurs get together in one giant convention center for a weekend full of keynotes, workshops and lots of “networking”.
Here is our sample mission: |
|
To energize youth about the opportunities that entrepreneurship can create for them and
inspire them to start their own businesses.
Create a web based learning tool that offers youth a fun, interactive approach to entrepreneurial education.


R Systems Intl Standalone September 2019 Net Sales at Rs 97.99 crore, up 14.95% Y-o-Y
Reported Standalone quarterly numbers for R Systems International are:
Net Sales at Rs 97.99 crore in September 2019 up 14.95% from Rs. 85.25 crore in September 2018.
Quarterly Net Profit at Rs. 12.42 crore in September 2019 up 29.21% from Rs. 9.61 crore in September 2018.
EBITDA stands at Rs. 17.04 crore in September 2019 up 30.47% from Rs. 13.06 crore in September 2018.
R Systems Intl EPS has increased to Rs. 1.04 in September 2019 from Rs. 0.78 in September 2018.
R Systems Intl shares closed at 48.90 on November 01, 2019 (NSE) and has given 4.15% returns over the last 6 months and 5.39% over the last 12 months.
| R Systems International | |
| Standalone Quarterly Results | in Rs. Cr. |
| Sep'19 | Jun'19 | Sep'18 | |
| Net Sales/Income from operations | 97.99 | 99.70 | 85.25 |
| Other Operating Income | -- | -- | -- |
| Total Income From Operations | 97.99 | 99.70 | 85.25 |
| EXPENDITURE | |||
| Consumption of Raw Materials | -- | -- | -- |
| Purchase of Traded Goods | -- | -- | -- |
| Increase/Decrease in Stocks | -- | -- | -- |
| Power & Fuel | -- | -- | -- |
| Employees Cost | 66.57 | 66.03 | 55.83 |
| Depreciation | 2.22 | 2.22 | 1.82 |
| Excise Duty | -- | -- | -- |
| Admin. And Selling Expenses | -- | -- | -- |
| R & D Expenses | -- | -- | -- |
| Provisions And Contingencies | -- | -- | -- |
| Exp. Capitalised | -- | -- | -- |
| Other Expenses | 17.17 | 18.74 | 18.13 |
| P/L Before Other Inc. , Int., Excpt. Items & Tax | 12.03 | 12.71 | 9.47 |
| Other Income | 2.79 | 3.05 | 1.77 |
| P/L Before Int., Excpt. Items & Tax | 14.82 | 15.76 | 11.24 |
| Interest | 0.12 | 0.12 | 0.05 |
| P/L Before Exceptional Items & Tax | 14.70 | 15.65 | 11.19 |
| Exceptional Items | -- | -- | -- |
| P/L Before Tax | 14.70 | 15.65 | 11.19 |
| Tax | 2.28 | 4.23 | 1.58 |
| P/L After Tax from Ordinary Activities | 12.42 | 11.42 | 9.61 |
| Prior Year Adjustments | -- | -- | -- |
| Extra Ordinary Items | -- | -- | -- |
| Net Profit/(Loss) For the Period | 12.42 | 11.42 | 9.61 |
| Equity Share Capital | 11.96 | 11.96 | 12.40 |
| Reserves Excluding Revaluation Reserves | -- | -- | -- |
| Equity Dividend Rate (%) | -- | -- | -- |
| EPS Before Extra Ordinary | |||
| Basic EPS | 1.04 | 0.95 | 0.78 |
| Diluted EPS | 1.04 | 0.95 | 0.78 |
| EPS After Extra Ordinary | |||
| Basic EPS | 1.04 | 0.95 | 0.78 |
| Diluted EPS | 1.04 | 0.95 | 0.78 |
| Public Share Holding | |||
| No Of Shares (Crores) | -- | -- | -- |
| Share Holding (%) | -- | -- | -- |
| Promoters and Promoter Group Shareholding | |||
| a) Pledged/Encumbered | |||
| - Number of shares (Crores) | -- | -- | -- |
| - Per. of shares (as a % of the total sh. of prom. and promoter group) | -- | -- | -- |
| - Per. of shares (as a % of the total Share Cap. of the company) | -- | -- | -- |
| b) Non-encumbered | |||
| - Number of shares (Crores) | -- | -- | -- |
| - Per. of shares (as a % of the total sh. of prom. and promoter group) | -- | -- | -- |
| - Per. of shares (as a % of the total Share Cap. of the company) | -- | -- | -- |
| Source : Dion Global Solutions Limited |


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Georgia Institute of Technology | Majoring in Business Administration with a Concentration in Information Technology Management | Experience with Data Analytics, Programming, and Database Management
- Assist professor in teaching students the basics of programming in Python
- Lead project to visualize website traffic trends to improve customer self-service and decrease support case volume
- Made recommendations to management for those improvements
- Tracking of clicks to learn what problems users are frequently having
- Utilized multiple large data warehouses, including Salesforce, MySQL Server, Google BigQuery
- Built Python script to automate training module creation, increasing efficiency significantly
- Learned about data analytics and analytical software (SQL)
- Created freight truck routes and optimized them
- Head engineer for project that resulted in $1.3 million in revenue
- Supporting engineer for project that resulted in $80 million in revenue
- Automated project processes by creating Excel Macros
- Tracked water quality and environmental changes for animal habitats
- Utilized knowledge gained in engineering and chemistry classes
- Interacted with guests regularly and participated in guest education programs
- Oversaw an internal audit of I-9 documentation for 1,400 employees
- Majorly reduced the risk of penalties, saving the company thousands of dollars
- Conducted data analysis of retention rates and costs
- Gained valuable experience with excel, electronic filing systems, and online databases
GPA: 3.04 (including all Dual-Enrollment Courses)
GPA: 3.9
I'm just a southern girl that's always been eager to learn. I have enjoyed every place that I have been able to work, but I'm still on a journey to find my passion. Creating this website has been an avenue for me to explore web design and HTML and CSS coding.
Other computer languages that I have experience with are Python, Matlab, and MySQL. I also have extensive knowledge in database management, and big data manipulation using SQL. I have also taught myself Tableau to easily visualize data. When I'm not focused on school I enjoy spending time with my sorority sisters, staying up to date with politics and current events, binge watching 'The Bachelorette', or playing by the pool.