I Alone Level-up: Chapter 55
While staring at the screen in happiness, Jinwoo noticed something else .
| Title: Wolf Slayer ( . 1 more). |
‘ . One more?’
Ah . He suddenly remembered that the reward from the class advancement quest did say something about a new title . Jinwoo checked the hidden title .
| One Who Has Triumphed Over Adversity. |
| For every 1% HP lost, all stats increased by 1% . . It’s a title given to the one who has courageously endured against adversity . |
‘I like it!’
It was an amazing buff that would increase all of his attributes as he lost life . The ‘Wolf Slayer’ was a decent title on its own, increasing his attributes by 40% against beast-type monsters . However, its condition made it difficult to see the benefit most of the time .
‘If I’m facing something other than a beast-type, it’s as if I didn’t have a title . ’
And now, he had earned something that he could always use . After thinking about it, it even had a good synergy with the ‘Unyielding Spirit’ passive . Without a hesitation, Jinwoo changed his title .
| Title: One Who… ( . 1 more). |
The unused title would be hidden, but he could always swap it out when he needed it .
‘Next, stats . ’
There were three rewards he had received after reaching a high amount of advancement points .
The first was the advancement to a higher class .
The second was the new title .
And the third was bonus stats .
| Stats. Strength: 132 Constitution: 91 Agility: 111 Intelligence: 70 Sense: 93. (Available stat points: 10). Physical Damage Reduction: 46% |
There were 10 undistributed points . In the past, he would’ve invested them into agility or sense, but things were different now . Jinwoo put all 10 points into intelligence . Even still, the stat was only at 80 .
ring~
| Stats. Strength: 132 Constitution: 91 Agility: 111 Intelligence: 80 Sense: 93. (Available stat points: 0). Physical Damage Reduction: 46% |
Compared to strength or agility, it was really lacking . It even fell behind the constitution stat, which he had neglected because he wanted to raise agility higher . That was how much Jinwoo had left behind the intelligence stat .
‘I never thought I’d be using magic . ’
His plan for the time being was to invest every point into intelligence . However, Jinwoo did not plan to switch up his fighting style .
‘Man, even the other new skill feels like it belongs to Assassin-class . ’
It was the new skill he had learned during combat .
Dagger Throw .
He did not need to read the explanation to understand what it was .
| Dagger Throw (Lv . 1). |
| Active Skill. Mana Cost: 30. Dagger-specific Throw your dagger to inflict damage . . As the skill level increases, the accuracy and damage increases . |
‘As I thought . ’
He had fought like a Fighter-class up until now . Just because he had earned a new class and class-specific skills, he couldn’t just throw away his way of fighting . For now, he would use the shadow skills as a support and continue to wield the dagger .
‘At least, until my intelligence is high enough . ’
An outstanding combat skill, supported by the shadow army . His goal before receiving the class was unchanged .
‘Only difference is ’
The Shadow Soldiers appeared more useful than he thought . He did not think it was possible to subordinate a boss-level monster .
‘ . Still . ’
If he relied solely on those new skills, the dagger-specific skills he had obtained would be wasted . On top of that, he had just purchased Knight Killer .
‘If I knew I was going to be a Mage-class, I would’ve bought a staff or something . ’
Of course, had he done that, he might not have been able to finish the class advancement quest .
Jinwoo closed the status screen . He had sufficiently absorbed all of the new information .
 ’
He had spent 20 minutes going through everything . Jinwoo scratched his head .
‘Time to head out, I guess . ’
Jinwoo smirked . The power of the attributes did not shine just in fighting magic beasts . When he focused, time slowed down . More accurately, Jinwoo had sped up . He opened the door and went outside of his room .
Shower . Dry . Put on clothes .
It took him 3 minutes .
He could’ve done it faster, but he felt like the worn down apartment would not be able to withstand his full speed .
‘My hair’s still wet . ’
Still, he had basically saved about 20 minutes, so there was no need to hurry . Jinwoo was about to leave when realized he forgot something .
His keys .
The keys were on top of the desk . On any other day, he would’ve walked over in annoyance to grab them . Today, he stretched a hand towards it .
‘Where else would I use this?’
Ruler’s Hands!
The key slowly slid across the table, then shot into his hand as if pulled by a powerful magnet . Jinwoo grabbed the key and smiled . Having finished all preparations in a flash, Jinwoo left his house while whistling .
The first place he visited was the bank . He was curious on how much he had earned from the magic cores .
‘I was too busy to check it . ’
The management of the magic cores were left to the Raid Leader, Yoo Jinho . The young man had told him that he had sold all the magic cores after each day’s Raids . The funds were deposited into the bankbook Jinwoo was holding . However, they did not particularly discuss the actual amounts involved .
‘Jinho, that punk . He’s so good at reporting on everything else . Guess he doesn’t really have an interest in money . ’
Maybe because of the circumstances of his life, the young man probably had little interest in financial matters . His conversation topics mostly revolved around celebrities, Raids, music and movies . Well, “conversation” mostly entailed Yoo Jinho talking while Jinwoo just silently nodded along .
‘Hmm, also…’
When Jinwoo thought back on the times he spent with Yoo Jinho, the chattermouth had never talked about his family . It was strange .
Before Jinwoo could give it more thought, he arrived at the bank .
‘Of all the days…’
The ATMs were being serviced .
Defeated, Jinwoo grabbed a number and waited . Soon, he was called and went up to the counter .
“Hello . ”
The female service employee had a short hair and a bright smile . She asked helpfully,
“How may I help you today?”
“Could I get some help with this?”
“Of course . ”
The employee took the bankbook from Jinwoo’s hands with a smile . While waiting, Jinwoo looked around the bank . Even though it was a weekday afternoon, there were unusually many people around . While he was people watching, the bank employee took a look at Jinwoo’s bankbook and was surprised .
‘Holy!’
[Account Balance: 1,482,920,000]
She carefully reread the number, counting the zeroes; it was definitely over a billion . And the amount wasn’t some life saving . The transaction history showed that it was all collected in the past week .
‘He’s so young, how could he…?’
She had first thought of Jinwoo as a college student who was running bankbook errands for his mother . Now, she stared at the man with a completely different look . She carefully asked him a question with a slight ulterior motive .
“So… what do you do?”
Realizing how her question sounded, the employee became embarrassed and looked away with her cheeks reddening .
“I’m a Hunter . ”
“Ah…”
The employee nodded . The rumors were true .
‘I heard that Hunters earned a lot of money . ’
To think it would be this much . She suddenly felt a shame at her ordinary desk job . Just until moments ago, Jinwoo was just a customer like any other . Now, she realized he lived in a different world .
‘He’s probably a really high-ranking Hunter . ’
Only they would earn this much money . This was the first high-ranker she had met . The thought made her heart flutter . A young man like him with over a billion in cash would be considered a VIP at this bank . And if he was a Hunter on top of that, he would be a VVIP . She did not want to miss the opportunity . The employee started to go off as mandated by her training,
“So, there’s a new product for VIP customers at our bank,”
Her business-use smile had turned into a genuine one; her eyes were fixed on Jinwoo .
‘Oh my, he’s…’
The man looked like he had left his house in a hurry, wearing carefree clothes and unstyled hair still slightly wet . She realized he probably lived a very busy life and took valuable time out of it to come here . Looking at such a capable man (in her head), her heart beat slightly faster .
“It’s been very popular with the customers . Would you perhaps like to invest in it?”
But Jinwoo held out a hand .
“No, I’m good . ”
“Ah… Okay . ”
She could not hide the disappointment in her voice . Feeling like a lengthy conversation might follow, Jinwoo hurried and got up .
“Thank you for your hard work . ”
While ignoring the gaze of the employee on the back of his neck, he left the bank . The moment he went outside, he sighed in relief .
“Whew-”
He had noticed change in her eyes after he revealed that he was a Hunter . The gazes of others . And their interest . It was something normal for other Hunters but not to Jinwoo .
‘I suppose I have to get used to that . ’
Once he took the retest and obtained a high rank, the attention on him would become a lot heavier . He had heard that Hunters even had their own share of fans and paparazzi . Looking up, he saw a billboard on top of a building with an advertisement; the model was a famous Hunter . Before the days of the Gates, it would’ve been an athlete or a celebrity . Now, Hunters received the love of the masses . Jinwoo might become the same . Now days, the only people who disliked Hunters were insurance companies .
‘Well, anyways . ’
After leaving the bank, Jinwoo was finally able to check his income . He was very surprised .
‘1,482,920,000…!!!’
The income from 9 Raids was nearly a billion and a half . It was an average of 160 million per C-rank Dungeon . Considering the C-rank Dungeon with Hwang Dongseok paid out 180 million, he roughly determined that C-rank Gates would give 150~200 million each .
‘Well, any employee would be surprised after looking at this . ’


const PusherChannelMock = require('./pusher-channel-mock');
/** Class representing fake Pusher. */
class PusherMock {
/** Initialize PusherMock with empty channels object. */
constructor() {
this.channels = {};
}
/**
* Get channel by its name.
* @param {String} name - name of the channel.
* @returns {PusherChannelMock} PusherChannelMock object that represents channel
*/
channel(name) {
if (!this.channels[name]) {
this.channels[name] = new PusherChannelMock();
}
return this.channels[name];
}
/**
* Mock subscribing to a channel.
* @param {String} name - name of the channel.
* @returns {PusherChannelMock} PusherChannelMock object that represents channel
*/
subscribe(name) {
return this.channel(name);
}
/**
* Unsubscribe from a mocked channel.
* @param {String} name - name of the channel.
*/
unsubscribe(name) {
if (name in this.channels) {
this.channels[name].callbacks = {};
delete this.channels[name];
}
}
}
module.exports = PusherMock;


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Please enable Javascript to be able to use this application....


Experiences
AP CS A
The Plague
The Plague always takes everything over... But a cure can restart its process until further mutations (Click)
Created by Rafael for APCS A (Java) with Processing


{% extends "global/Page.html" %} {% load otree %} {% block title %}Results for all rounds{% endblock title %} {% block content %}
You have finished all {{ Constants.num_rounds }} periods. Purchases for all periods are summarized below:
{% include 'lemon_market/HighChart.html' %}You are {{ player.role }}.
{% next_button %} {% endblock %}


Consider the model
\[d X_t = \alpha \left(\theta - X_t\right)dt + \sigma dB_t \]
By Ito Isometry, we have:
\[\langle X \rangle_t = E_t(X) = \theta \left(1 - e^{-\alpha t} \right) + X_0 e^{-\alpha t}\]
and
\[\langle X^2 \rangle_t - \langle X \rangle_t^2 = V_t(X) = \frac{\sigma^2}{2 \alpha }\left(1 - e^{-2 \alpha t} \right)\]
If we assume discrete, uniform sampling of spacing \(\tau\), then we have,
\[P(\theta | X; \alpha, \sigma) = \sqrt{\frac{1}{2\pi V_{\tau} }}^{T-1} \exp\left(-\frac{\sum_t^{T-1} \left(X_{t+1} - E\right)^2 }{2V}\right) \]
or more explicitly (taking \(\tau_i = 1\) without loss of generality),
\[P(\theta | X; \alpha, \sigma) = \sqrt{\frac{\tfrac{\alpha}{\sigma}}{\pi (1- e^{-\alpha t}) }}^{T-1} \exp\left( - \frac{\sum_t^{T-1} \left(X_{t+1} - ( \theta (1 - e^{-\alpha}) + X_0 e^{-\alpha}) \right)^2 }{\tfrac{\sigma^2}{\alpha}(1-e^{-2\alpha})}\right) \]
To integrate out \(\theta\), \(P(X | \alpha, \sigma) = \int P(X | \theta, \alpha, \sigma ) P(\theta) d\theta\), we’ll make this look like a Gaussian in \(\theta\) by completing the square. First, let us introduce a more compact notation to manipulate terms independent of \(\theta\):
\[A_t := X_{t+1} - X_0e^{-\alpha}\] \[ B := 1-e^{-\alpha} \]
Our sum in the exponent can then be written more succinctly:
\[ \sum_t^{T-1} (A_t - \theta B)^2 \]
Squaring out inside the sum and distributing the summation operator (by linearity) and extracting or summing terms constant in \(t\), we have:
\[ \sum A_t^2 - 2 \theta B \sum A_t + \theta^2 B^2 (T-1) \]
For which we complete the square in \(\theta\),
\[ B^2 (T-1) \left( \left(\theta - \frac{\sum_t A_t}{B(T-1)}\right)^2 + \frac{\sum A_t ^2 - (\sum A_t)^2}{B^2 (T-1)} \right) \]
Which lets us write \(P(\theta | X; \alpha, \sigma)\) as a normal distribution in \(\theta\) times some constant terms:
\[ P(\theta | X; \alpha, \sigma) = \exp \left(\frac{-\sum A_t^2 + (\sum A_t)^2 }{V_{\tau} }\right) \sqrt{\frac{1}{2\pi V_{\tau} }}^{T-1} \int d\theta\exp\left( -\frac{ \left(\theta - \frac{\sum_t A_t}{B(T-1)} \right)^2 }{\tfrac{2V_{\tau}}{B^2 (T-1)}}\right) \]
In which we recognize the integral as Gaussian with mean \(\mu = \frac{\sum_t A_t}{B(T-1)} \) and variance \(\nu = V_{\tau}/(B^2(T-1))\), and thus can replace the integral with \(\sqrt{2 \pi \nu} = \sqrt{\frac{2 \pi V_{\tau} }{B^2(T-1)}}\),
\[ P(\theta | X; \alpha, \sigma) = \exp \left(\frac{-\sum A_t^2 + (\sum A_t)^2 }{V_{\tau} }\right) \sqrt{\frac{1}{2\pi V_{\tau} }}^{T-1} \sqrt{\frac{2 \pi V_{\tau} }{B^2(T-1)}}\]
Collecting common terms in \(V_t\),
\[ P(\theta | X; \alpha, \sigma) = \exp \left(\frac{f(A_t)}{V_{\tau}}\right) \sqrt{\frac{1}{2\pi}}^{T-1} \sqrt{\frac{2 \pi }{B^2(T-1)}} V_{\tau}^{\tfrac{1}{2}} V_{\tau}^{\tfrac{1-T}{2}} \]
\[ = \exp \left(\frac{f(A_t)}{V_{\tau}}\right) \sqrt{\frac{(2 \pi)^{2-T}}{B ^ 2(T-1)}} V_{\tau}^{1 - \tfrac{T}{2}} \]
where
\[ f(A_t) = - \sum A_t^2 + \left( \sum A_t \right)^2 \]
Then substituting in \(V_t = \tfrac{\sigma^2}{2\alpha} \left(1 - e^{-2\alpha}\right)\)
\[ = \exp \left(\frac{\tfrac{2\alpha f(A)}{1 - e^{-2\alpha}} }{\sigma^2}\right) \sqrt{\frac{(2 \pi)^{2-T}}{B ^ 2(T-1)}} \left(\tfrac{1}{2\alpha} \left(1 - e^{-2\alpha}\right)\right)^{1 - \tfrac{T}{2}}\sigma^{2(1 - T/2)} \]
Which can be written to look like an Inverse Gamma integral in \(\sigma^2\)
\[ = \mathcal{N} e^{\tfrac{-\beta}{\sigma^2}} (\sigma^2)^{-\gamma - 1} \]
where
\[ \gamma = T/2 -2,\]
\[ \beta = \frac{2 \alpha \left( \left( \sum A_t \right)^2- \sum A_t^2 \right) }{1 - e^{-2\alpha}} \] and
\[ \mathcal{N} = \sqrt{\frac{(2 \pi)^{2-T}}{B ^ 2(T-1)}} \left(\tfrac{1}{2\alpha} \left(1 - e^{-2\alpha}\right)\right)^{1 - \tfrac{T}{2}} \]
Which we can integrate as Inverse Gamma (again assuming uniform prior, now in \(\sigma^2\)),
\[ = \mathcal{N} \int e^{\tfrac{-\beta}{\sigma^2}} (\sigma^2)^{-\gamma - 1} d\sigma^2 = \mathcal{N} \frac{\beta^{\gamma}}{\Gamma(\gamma)} \]
with \(\beta\), \(\gamma\), and \(\mathcal{N}\) as defined above.


Relationship to Child:Check all that apply
Biological Parent Step parent Foster parent Guardian Adoptive Parent Other:Instructions to Caregiver: Has your child ever experienced, witnessed, or learned about an event that caused, or threatened to cause death, or serious harm to him or herself or to someone else?
If your children didn’t experience the event, leave it blank.
Children’s Name(s)Remarks:
Which of your children has ever... |
Check which of your children experienced this event |
|---|---|
| 1. been in a storm, tornado, flood, or fire that destroyed their house or school and/or caused them to be injured? |
|
| 2. seen or been in a serious accident in which they or someone they cared about were hurt really badly? |
|
| 3.had to go to the hospital or emergency room for treatment because they had a serious or prolonged medical issue? |
|
| 4. been threatened or physically injured (hit, punched, kicked, choked or use of weapon) by another child or adult? |
|
| 5. had anyone touch, attempt to touch his/her private areas and/or looked at or take pictures of his/her private areas? Has anyone ever asked them to touch or look at their private parts? |
|
| 6. seen their parent punch, beat, shove, kick, or hurt their other parent in any way? |
|
| 7. watched someone in their neighborhood being seriously injured or killed? |
|
| 8. been removed from their home and had to live with another family or in another home? |
|
| 9. been left alone for long periods of time, had to go without enough food and/or had to miss a lot of days of school? |
|
| 10. been bullied (called names, pushed, stole your things, or threatened) or treated badly because of their skin color, religion, money/ clothes, LGBTQ, or because they were in foster care? |
|
| 11. seen a parent or caregiver struggle with a substance abuse problem (drinking, drugs, prescriptions, etc.) or with a mental health problem (anxiety, depression, bipolar, etc.)? |
|
| 12. had someone who was close to them (e.g., parent, caregiver, sibling, friend) that is not there anymore maybe because they are in jail, in college, in the army, or they died or were deported? |
|
| 13. felt that no one really loved or cared for them or felt that they were all alone? |
|
| 14. Did I forget to ask you about anything that may have happened to your child? (please specify) |
|
Of the events selected above, please next to the corresponding number of your child (from above), note which event your children currently consider to be most stressful, and indicate how old they were at the time of the event:
Most stressful event for
Instructions to Caregiver: Thank you for telling me what has HAPPENED to your children, now I would like to know more about their behaviors and how they reacted to what they went through. The questions I’m going to ask you are about your children’s behavior NOW or WITHIN THE PAST MONTH. Here are your answer choices: VERY TRUE (2), SOMEWHAT TRUE (1) or NOT TRUE (0) at all. Please answer all questions as well as you can.
| Child 1 | Child 2 | Child 3 | Child 4 | 0 = Not True (as far as you know) 1 = Somewhat True 2 = Very True |
|---|---|---|---|---|
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | (1) Does your child complain about headaches, stomachaches, nausea, difficulty breathing when he/she is reminded of what happened? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | (2) Does your child avoid doing things that remind him/her of what happened? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | (3) Does your child startle easily. For example, he or she jumps when he/she hears sudden or loud noises? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | (4) Does your child get very upset if reminded of what happened? |
|
|
|
|
|
SUB TOTAL C (Sum of items 1-4 above. Score of 0-8 is possible) |
If the score is zero for all your children, you can stop here. You don’t need to answer the next set of questions. Otherwise continue answer the following questions only for your children who had a score between 1 and 8.
Instructions to Caregiver: This is our final set of questions. For each feeling or behavior that describes your children, please answer VERY TRUE (2), SOMETIMES TRUE (1), OR NOT TRUE (0). Please answer all items as well as you can even if some do not seem to apply to your children. Remember, now I am asking you about your children’s feelings and behaviors NOW or WITHIN THE PAST MONTH. The term "event" refers to the most stressful experience that you noted above.
| Child 1 | Child 2 | Child 3 | Child 4 | 0 = Not True (as far as you know) 1 = Somewhat True 2 = Very True |
|---|---|---|---|---|
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 1) Does your child have uncomfortable memories of the event? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 2) Does your child feel numb or distant from their feelings? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 3) Is your child irritable or angry? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 4) Does your child have difficulty remembering details about the event? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 5) Does your child have difficulty falling asleep or staying asleep? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 6) Does your child feel detached or distant from other people? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 7) Has your child been having difficulty getting along with friends, schoolmates or teachers? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 8) Does your child do things that he/she had outgrown? For example, thumb sucking, bedwetting, nail biting, or requesting to sleep with parents. |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 9) Does your child feel as if the event were happening again? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 10) Does your child feel restless and have trouble sitting still? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 11) Does your child avoid going to places that remind him/her of what happened? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 12) Has your child been having difficulty getting along with family members? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 13) Has your child been confused about things that he/she should know? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 14) Does your child feel “on edge” or nervous? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 15) Does your child seem to feel “spaced out” or in a daze? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 16) Does your child act as if the event were happening again? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 17) Does your child have trouble keeping track of time? For example, your child is confused about the time of day, the day of the week, or when something really happened. |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 18) Does your child avoid talking about the event? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 19) Does your child have bad dreams? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 20) Does your child have difficulty performing activities such as schoolwork or chores? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 21) Does your child’s play seem to be about the event (For example, does your child express what happened to them with toys, games, drawings, or other fantasy-play)? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 22) Does your child seem slowed down? For example, it takes your child a long time to respond to things. |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 23) Do your child’s surroundings (at home, neighborhood, school) seem different to him/her than it used to? For example, things look or sound different. |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 24) Does your child avoid people who remind him/her child of what happened? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 25) Does your child have trouble concentrating? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 26) Does your child try not to think about what happened? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 27) Is your child doing anything dangerous or self-destructive? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 28) Does it seem like your child is on the lookout for possible threats or signs of danger? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 29) Has your child started feeling negatively about his/herself or his/her future? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 30) Are your child’s thoughts or beliefs about what caused the event inaccurate, or causing him/her to wrongly blame him/herself or others for what happened? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 31) Does your child often feel fearful, angry, guilty or ashamed? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 32) Is your child less interested or seem less happy from doing the activities that he or she used to enjoy? |
| 0 1 2 | 0 1 2 | 0 1 2 | 0 1 2 | 33) Has your child’s ability to have positive emotions such as happiness, or loving feelings decreased? |
|
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|
SUB Total D (Sum scores on items 1-33) |
|
|
|
|
|
TOTAL SCORE(Sum the Subtotal Scores C and D - possible scores 0-74) |
Thank you for your time. If there is anything else you feel like it is important for me to know about your children, please tell me anytime. I will want to share the results of this assessment with you soon and what we might suggest to help your children heal from what they went through.


It is the best time to learn the Coding from the Scratch
"Sect of witnesses of Zoraksa" is an educational project where you can study technology to find a developer's job. Have you long dremed of getting an education at Oxford level?! Welcome to "Sect of witnesses of Zoraksa"


Projects
KSEA Website Construction
KSEA requested to reconstruct the Website in modern trends. Used HTML and CSS to construct the Webpage
Check it out
IOS Application : Clima
This application helps to check the current weather of current location or any desirable locations
imported SwfityJSON and Alamofire from CocoaPods
imported CoreLocation to extract the proper location
Used API from openweathermap.com
IOS Application : Flash Talk
This application helps users can group chat via this chat application
Used Firebase to build up the backend of this application
imported SVProgessHUD to enhence the visibility of this application
IOS Application : Bitcoin Tracker
This application helps to track down a BitCoin currency in 31 different currencies.
Imported SwiftyJSON and Alamofire from CocoaPods
Used API from bitcoinaverage.com to get Bitcoin currency
IOS Application : Shopping List
This application helps to add, delete, and check markings on an item inside a list
Imported CoreData to save the items in a list


Spicio API
Spicio Server API
Version: 1.0.0
Apache 2.0
http://www.apache.org/licenses/LICENSE-2.0.html
Access
[ Jump to Models ]
Table of Contents
get /users
post /users
get /users/{id}
delete /users/{id}
get /users/{id}/discover
post /users/{id}/discover
get /users/{id}/feed
get /users/{id}/friends
post /users/{id}/friends
delete /users/{id}/friends
get /users/{id}/history
get /users/{id}/series
post /users/{id}/series
delete /users/{id}/series/{series_id}
get /users/{id}/series/{series_id}/episodes
post /users/{id}/series/{series_id}/episodes
put /users/{id}/series/{series_id}/episodes/{episode_id}
delete /users/{id}/series/{series_id}/episodes/{episode_id}
Search for user (usersGet)
Search for a `User` by their name and/or their e-mail address.
Consumes
This API call consumes the following media types via the
Content-Type request header:
Query parameters
query (required)
Query Parameter — The text to search against.
Return type
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
A list of `User` objacts matching the search criteria.
400
Missing search criteria parameter
Register/authenticate user (usersPost)
Register a new user to the database. At least one of the ID fields must\nbe present in the body. For now this method returns a successful\nregistration response if the user already exists. (It might be a better\npractice to give the user the option to link their facebook and google\naccount)
Consumes
This API call consumes the following media types via the
Content-Type request header:
Request body
user (required)
Body Parameter — The display name of the user.
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
Successful registration.
400
Bad request body
Get user (usersIdGet)
Find a user with the given ID. The `full` parameter should only be set\nto true when the user logs in to query and cache the data and should not\nbe set to true if the client us querying other user's data.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Query parameters
full (optional)
Query Parameter — If set to true, the method will return all the data the database has\nabout the user
Return type
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
Returns the `User` object with the ID if it exists.
400
Invalid IDs supplied
404
User not found
Delete user (usersIdDelete)
Delete a user and all it's data from the database. This includes all the\nseries and watched episodes.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
Successfully deleted all data
400
Invalid IDs supplied
404
User not found
Up
get /users/{id}/discover
Get recommendations for user (usersIdDiscoverGet)
Get a list of recommended series for the user to watch. The curated list\nis created by the server with some voodoo black magic.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Return type
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
A list of `Series` objects.
400
Invalid IDs supplied
404
User not found
Up
post /users/{id}/discover
Mark show as 'not interested' (usersIdDiscoverPost)
Call this method if the user is not interested in a show, so that it\nwon't be suggested to the user again in the future. This is time based\nmeaning the engine might select the show again in the future. The\nback-off time is reasing exponentially with the number of 'not\ninterested' request.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Form parameters
seriesId (required)
Form Parameter — trakt ID of the series
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
Show succesfully marked as not interested
400
Invalid IDs supplied
404
User or series not found
Get user's feed (usersIdFeedGet)
Get the news feed items of the user with the given ID. This is based on\nthe activity of the user and the user's friends\" activity. As this\nmethod can return a lot of data, pagination is mandatory.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Query parameters
offset (required)
Query Parameter — Pagination offset
limit (required)
Query Parameter — Pagination limit
Return type
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
A list of `Action` objects that represent the news feed of the user.
400
Invalid IDs supplied or missing parameters
404
User not found
Up
get /users/{id}/friends
Get users friends (usersIdFriendsGet)
Get a detailed list of friends of the user with the given ID.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Return type
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
A list of `User` objects which are the friends of the user.
400
Invalid IDs supplied
404
User not found
Up
post /users/{id}/friends
Add friend to user (usersIdFriendsPost)
Add another user as a friend to the user with the given id. The current\nuser will also be added to the other user's friends list.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Form parameters
friendId (required)
Form Parameter — ID of the friend to add
Return type
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
User successfully added to the friends list. Returns the details of\nthe friend.
400
Invalid IDs supplied
404
User not found
Up
delete /users/{id}/friends
Delete friend (usersIdFriendsDelete)
Remove a user from the user's friend list. The user will also be removed\nfrom the friend's friendlist.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Form parameters
friendId (required)
Form Parameter — ID of the friend to add
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
OK
400
Invalid IDs supplied
404
User not found
Up
get /users/{id}/history
Get users history (usersIdHistoryGet)
Get the history if the user with the given id. It will return the\nactivity of the user in chronological order. As this method can return\na lot of data, pagination is mandatory.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Query parameters
offset (required)
Query Parameter — Pagination offset
limit (required)
Query Parameter — Pagination limit
Return type
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
A list of `Action` objects that represent the history of the user.
400
Invalid IDs supplied
404
User not found
Up
get /users/{id}/series
Get saved series (usersIdSeriesGet)
Returns a list of series the user has added.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Return type
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
OK
400
Invalid IDs supplied
404
User not found
Up
post /users/{id}/series
Add series (usersIdSeriesPost)
Save series to the user. Details of the series is needed, since if the\nseries doesn't exist in that database yet, it will be insterted.
Path parameters
id (required)
Path Parameter — ID of the user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Request body
series (required)
Body Parameter — The series to add to he user
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
The series was successfully added
400
Invalid IDs supplied
404
User not found
Up
delete /users/{id}/series/{series_id}
Remove series (usersIdSeriesSeriesIdDelete)
Delete a series from the user's saved series. This will not delete the\nseries record itself, only the relation between the series and the user.
Path parameters
id (required)
Path Parameter — ID of the user
seriesId (required)
Path Parameter — The series to add to he user.
Consumes
This API call consumes the following media types via the
Content-Type request header:
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
Series successfully deleted
400
Invalid IDs supplied
404
User not found
Up
get /users/{id}/series/{series_id}/episodes
Get user's episodes (usersIdSeriesSeriesIdEpisodesGet)
Returns the IDs of the watched, liked and skipped episodes of the user\nand the given series.
Path parameters
id (required)
Path Parameter — ID of the user
seriesId (required)
Path Parameter — The series to add to he user
Consumes
This API call consumes the following media types via the
Content-Type request header:
Return type
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
OK
400
Invalid IDs supplied
404
User or series not found
Up
post /users/{id}/series/{series_id}/episodes
Add episode (usersIdSeriesSeriesIdEpisodesPost)
Add an episode to the users wacthed/liked/skipped lists.
Path parameters
id (required)
Path Parameter — ID of the user
seriesId (required)
Path Parameter — The ID of the series
Consumes
This API call consumes the following media types via the
Content-Type request header:
Form parameters
episode (required)
Form Parameter — The trakt ID of the episode.
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
Successfully added episode
400
Invalid IDs supplied
404
User or series not found
Up
put /users/{id}/series/{series_id}/episodes/{episode_id}
Update status of episode (usersIdSeriesSeriesIdEpisodesEpisodeIdPut)
Update the watched/liked/skipped status of a episode. Should be called\nwhen the client is sure that the record exists and if the change doesn't\nset all the attributes to false (when the record should be deleted)
Path parameters
id (required)
Path Parameter — ID of the user
seriesId (required)
Path Parameter — The ID of the series
episodeId (required)
Path Parameter — The ID of the episode
Consumes
This API call consumes the following media types via the
Content-Type request header:
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
Successfully updated
400
Invalid IDs supplied
404
User, series or episode not found
Up
delete /users/{id}/series/{series_id}/episodes/{episode_id}
Delete episode fomr user's database (usersIdSeriesSeriesIdEpisodesEpisodeIdDelete)
Delete an episode record from the user's database. Should be called\nwhen the status of the episode is set to \"not watched', \"not liked\" and\n\"not skipped\"
Path parameters
id (required)
Path Parameter — ID of the user
seriesId (required)
Path Parameter — The ID of the series
episodeId (required)
Path Parameter — The ID of the episode
Consumes
This API call consumes the following media types via the
Content-Type request header:
Produces
This API call produces the following media types according to the request header;
the media type will be conveyed by the
Content-Type response header.
Responses
200
Successfully deleted
400
Invalid IDs supplied
404
User, series or episode not found
[ Jump to Methods ]
Table of Contents
User
Action
Series
Episode
Inline_response_200
User
Inline_response_200_1
Inline_response_200_2
id
Long Unique identifier of the user.
name
String The name of the user.
email
String The email address of the user.
facebook_id
String The Facebook ID of the user if known.
google_id
String The Google+ ID of the user if known.
id
Long Unique identifier of the feed item.
timestamp
Long The unix timestamp of the creation.
type
Integer The type enum of the action.
culprit
User
victim
User
series
Series
episode
Episode
trakt_id
Long The trakt ID of the series.
title
String The title of the show.
thumb
String Link to an thumb image for the show
trakt_id
Long The trakt ID of the series.
number
Integer The number of the episode
season
Integer The season of the episode
title
String The title of the episode
thumb
String The link to the thumb image for the episode
name
String The name of the user.
id
Long Unique identifier of the user.
email
String The email address of the user.
name
String The name of the user.
email
String The email address of the user.
facebook_id
String The Facebook ID of the user if known.
google_id
String The Google+ ID of the user if known.
google_id
String The Google+ ID of the user if known.
series
array[Series]
name
String The name of the user.
id
Long Unique identifier of the user.
email
String The email address of the user.
facebook_id
String The Facebook ID of the user if known.
watched
array[BigDecimal]
liked
array[BigDecimal]
skipped
array[BigDecimal]


ENCRYPTING AN ORIGINAL PHRASE OR KEY
ORIGINAL PHRASE
?
?
ENCRYPTED PHRASE
DECRYPTING AN ENCRYPTED PHRASE OR KEY
ENCRYPTED PHRASE
?
?
ORIGINAL PHRASE
APPLICATION DESCRIPTION
?
?
?
?


--- title: istio.mixer.v1 layout: protoc-gen-docs generator: protoc-gen-docs number_of_entries: 21 ---
This package defines the Mixer API that the sidecar proxy uses to perform precondition checks, manage quotas, and report telemetry.
Mixer provides three core features:
Precondition Checking. Enables callers to verify a number of preconditions before responding to an incoming request from a service consumer. Preconditions can include whether the service consumer is properly authenticated, is on the service’s whitelist, passes ACL checks, and more.
Quota Management. Enables services to allocate and free quota on a number of dimensions, Quotas are used as a relatively simple resource management tool to provide some fairness between service consumers when contending for limited resources. Rate limits are examples of quotas.
Telemetry Reporting. Enables services to report logging and monitoring. In the future, it will also enable tracing and billing streams intended for both the service operator as well as for service consumers.
rpc Check(CheckRequest) returns (CheckResponse)
Checks preconditions and allocate quota before performing an operation. The preconditions enforced depend on the set of supplied attributes and the active configuration.
rpc Report(ReportRequest) returns (ReportResponse)
Reports telemetry, such as logs and metrics. The reported information depends on the set of supplied attributes and the active configuration.
Attributes represents a set of typed name/value pairs. Many of Mixer’s API either consume and/or return attributes.
Istio uses attributes to control the runtime behavior of services running in the service mesh. Attributes are named and typed pieces of metadata describing ingress and egress traffic and the environment this traffic occurs in. An Istio attribute carries a specific piece of information such as the error code of an API request, the latency of an API request, or the original IP address of a TCP connection. For example:
request.path: xyz/abc
request.size: 234
request.time: 12:34:56.789 04/17/2017
source.ip: <IP_ADDRESS>
target.service: example
A given Istio deployment has a fixed vocabulary of attributes that it understands. The specific vocabulary is determined by the set of attribute producers being used in the deployment. The primary attribute producer in Istio is Envoy, although specialized Mixer adapters and services can also generate attributes.
The common baseline set of attributes available in most Istio deployments is defined here.
Attributes are strongly typed. The supported attribute types are defined by ValueType. Each type of value is encoded into one of the so-called transport types present in this message.
Defines a map of attributes in uncompressed format. Following places may use this message: 1) Configure Istio/Proxy with static per-proxy attributes, such as source.uid. 2) Service IDL definition to extract api attributes for active requests. 3) Forward attributes from client proxy to server proxy for HTTP requests.
Specifies one attribute value with different type.
Defines a string map.
Used to get a thumbs-up/thumbs-down before performing an action.
parameters for a quota allocation
The response generated by the Check method.
Expresses the result of a precondition check.
Expresses the result of a quota allocation.
Defines a list of attributes in compressed format optimized for transport.
Within this message, strings are referenced using integer indices into
one of two string dictionaries. Positive integers index into the global
deployment-wide dictionary, whereas negative integers index into the message-level
dictionary instead. The message-level dictionary is carried by the
words
field of this message, the deployment-wide dictionary is determined via
configuration.
Operation on HTTP headers to replace, append, or remove a header. Header names are normalized to lower-case with dashes, e.g. “x-request-id”. Pseudo-headers “:path”, “:authority”, and “:method” are supported to modify the request headers.
Operation type.
| Name | Description |
|---|---|
REPLACE |
replaces the header with the given name |
REMOVE |
removes the header with the given name (the value is ignored) |
APPEND |
appends the value to the header value, or sets it if not present |
Describes the attributes that were used to determine the response. This can be used to construct a response cache.
Describes a single attribute match.
How an attribute’s value was matched
| Name | Description |
|---|---|
CONDITION_UNSPECIFIED |
should not occur |
ABSENCE |
match when attribute doesn’t exist |
EXACT |
match when attribute value is an exact byte-for-byte match |
REGEX |
match when attribute value matches the included regex |
Used to report telemetry after performing one or more actions.
Used to signal how the sets of compressed attributes should be reconstitued server-side.
| Name | Description |
|---|---|
DELTA_ENCODING |
Use delta encoding between sets of compressed attributes to reduce the overall on-wire request size. Each individual set of attributes is used to modify the previous set. NOTE: There is no way with this encoding to specify attribute value deletion. This option should be used with extreme caution. |
INDEPENDENT_ENCODING |
Treat each set of compressed attributes as complete - independent from other sets in this request. This will result in on-wire duplication of attributes and values, but will allow for proper accounting of absent values in overall encoding. |
Used to carry responses to telemetry reports
Expresses the routing manipulation actions to be performed on behalf of Mixer in response to a precondition check.
A map of string to string. The keys and values in this map are dictionary indices (see the Attributes message for an explanation)
The Status
type defines a logical error model that is suitable for different
programming environments, including REST APIs and RPC APIs. It is used by
gRPC. The error model is designed to be:
The Status
message contains three pieces of data: error code, error message,
and error details. The error code should be an enum value of
google.rpc.Code, but it may accept additional error codes if needed. The
error message should be a developer-facing English message that helps
developers understand and resolve the error. If a localized user-facing
error message is needed, put the localized message in the error details or
localize it in the client. The optional error details may contain arbitrary
information about the error. There is a predefined set of error detail types
in the package google.rpc
that can be used for common error conditions.
The Status
message is the logical representation of the error model, but it
is not necessarily the actual wire format. When the Status
message is
exposed in different client libraries and different wire protocols, it can be
mapped differently. For example, it will likely be mapped to some exceptions
in Java, but more likely mapped to some error codes in C.
The error model and the Status
message can be used in a variety of
environments, either with or without APIs, to provide a
consistent developer experience across different environments.
Example uses of this error model include:
Partial errors. If a service needs to return partial errors to the client,
it may embed the Status
in the normal response to indicate the partial
errors.
Workflow errors. A typical workflow has multiple steps. Each step may
have a Status
message for error reporting.
Batch operations. If a client uses batch request and batch response, the
Status
message should be used directly inside batch response, one for
each error sub-response.
Asynchronous operations. If an API call embeds asynchronous operation
results in its response, the status of those operations should be
represented directly using the Status
message.
Logging. If some API errors are stored in logs, the message Status
could
be used directly after any stripping needed for security/privacy reasons.


git-cherry-pick - Apply the changes introduced by some existing commits
git cherry-pick [--edit] [-n] [-m parent-number] [-s] [-x] [--ff] [-S[<key-id>]] <commit>... git cherry-pick --continue git cherry-pick --quit git cherry-pick --abort
Given one or more existing commits, apply the change each one introduces, recording a new commit for each. This requires your working tree to be clean (no modifications from the HEAD commit).
When it is not obvious how to apply a change, the following happens:
h'-04' 1.h'+01'c
The current branch and HEAD pointer stay at the last commit successfully made.
h'-04' 2.h'+01'c
The CHERRY_PICK_HEAD ref is set to point at the commit that introduced the change that is difficult to apply.
h'-04' 3.h'+01'c
Paths in which the change applied cleanly are updated both in the index file and in your working tree.
h'-04' 4.h'+01'c
For conflicting paths, the index file records up to three versions, as described in the "TRUE MERGE" section of git-merge(1). The working tree files will include a description of the conflict bracketed by the usual conflict markers <<<<<<< and >>>>>>>.
h'-04' 5.h'+01'c
No other modifications are made.
See git-merge(1) for some hints on resolving such conflicts.
<commit>...
Commits to cherry-pick. For a more complete list of ways to spell commits, see gitrevisions(7). Sets of commits can be passed but no traversal is done by default, as if the --no-walk option was specified, see git-rev-list(1). Note that specifying a range will feed all <commit>... arguments to a single revision walk (see a later example that uses maint master..next).
-e, --edit
With this option, git cherry-pick will let you edit the commit message prior to committing.
-x
When recording the commit, append a line that says "(cherry picked from commit ...)" to the original commit message in order to indicate which commit this change was cherry-picked from. This is done only for cherry picks without conflicts. Do not use this option if you are cherry-picking from your private branch because the information is useless to the recipient. If on the other hand you are cherry-picking between two publicly visible branches (e.g. backporting a fix to a maintenance branch for an older release from a development branch), adding this information can be useful.
-r
It used to be that the command defaulted to do -x described above, and -r was to disable it. Now the default is not to do -x so this option is a no-op.
-m parent-number, --mainline parent-number
Usually you cannot cherry-pick a merge because you do not know which side of the merge should be considered the mainline. This option specifies the parent number (starting from 1) of the mainline and allows cherry-pick to replay the change relative to the specified parent.
-n, --no-commit
Usually the command automatically creates a sequence of commits. This flag applies the changes necessary to cherry-pick each named commit to your working tree and the index, without making any commit. In addition, when this option is used, your index does not have to match the HEAD commit. The cherry-pick is done against the beginning state of your index.
This is useful when cherry-picking more than one commits*(Aq effect to your index in a row.
-s, --signoff
Add Signed-off-by line at the end of the commit message.
-S[<key-id>], --gpg-sign[=<key-id>]
GPG-sign commits.
--ff
If the current HEAD is the same as the parent of the cherry-pick(cqed commit, then a fast forward to this commit will be performed.
--allow-empty
By default, cherry-picking an empty commit will fail, indicating that an explicit invocation of git commit --allow-empty is required. This option overrides that behavior, allowing empty commits to be preserved automatically in a cherry-pick. Note that when "--ff" is in effect, empty commits that meet the "fast-forward" requirement will be kept even without this option. Note also, that use of this option only keeps commits that were initially empty (i.e. the commit recorded the same tree as its parent). Commits which are made empty due to a previous commit are dropped. To force the inclusion of those commits use --keep-redundant-commits.
--allow-empty-message
By default, cherry-picking a commit with an empty message will fail.  By default these redundant commits are ignored. This option overrides that behavior and creates an empty commit object. Implies --allow-empty.
--strategy=<strategy>
Use the given merge strategy. Should only be used once. See the MERGE STRATEGIES section in git-merge(1) for details.
-X<option>, --strategy-option=<option>
Pass the merge strategy-specific option through to the merge strategy. See git-merge(1) for details.
--continue
Continue the operation in progress using the information in .git/sequencer. Can be used to continue after resolving conflicts in a failed cherry-pick or revert.
--quit
Forget about the current operation in progress. Can be used to clear the sequencer state after a failed cherry-pick or revert.
--abort
Cancel the operation and return to the pre-sequence state.
git cherry-pick master
Apply the change introduced by the commit at the tip of the master branch and create a new commit with this change.
git cherry-pick ..master, git cherry-pick ^HEAD master
Apply the changes introduced by all commits that are ancestors of master but not of HEAD to produce new commits.
git cherry-pick maint next ^master, git cherry-pick maint master..next
Apply the changes introduced by all commits that are ancestors of maint or next, but not master or any of its ancestors. Note that the latter does not mean maint and everything between master and next; specifically, maint will not be used if it is included in master.
git cherry-pick master~4 master~2
Apply the changes introduced by the fifth and third last commits pointed to by master and create 2 new commits with these changes.
git cherry-pick -n master~1 next
Apply to the working tree and the index the changes introduced by the second last commit pointed to by master and by the last commit pointed to by next, but do not create any commit with these changes.
git cherry-pick --ff ..next
If history is linear and HEAD is an ancestor of next, update the working tree and advance the HEAD pointer to match next. Otherwise, apply the changes introduced by those commits that are in next but not HEAD to the current branch, creating a new commit for each new change.
git rev-list --reverse master -- README | git cherry-pick -n --stdin
Apply the changes introduced by all commits on the master branch that touched README to the working tree and index, so the result can be inspected and made into a single new commit if suitable.
The following sequence attempts to backport a patch, bails out because the code the patch applies to has changed too much, and then tries again, this time exercising more care about matching up context lines.
$ git cherry-pick topic^ (1) $ git diff (2) $ git reset --merge ORIG_HEAD (3) $ git cherry-pick -Xpatience topic^ (4)
1. apply the change that would be shown by git show topic^. In this example, the patch does not apply cleanly, so information about the conflict is written to the index and working tree and no new commit results.
2. summarize changes to be reconciled
3. cancel the cherry-pick. In other words, return to the pre-cherry-pick state, preserving any local modifications you had in the working tree.
4. try to apply the change introduced by topic^ again, spending extra time to avoid mistakes based on incorrectly matching context lines.
Part of the git(1) suite


Dataflow Diagrams (DFDs) are depictions of software systems as directed graphs. As such, they can be extracted from source code, as we did manually to create this dataset. For this, evidence for the implementation of model items needs to be found in code. The DFDs’ basic structure (its nodes and edges) show the architectural layout of the system. Added annotations provide further information about employed (security and other) features. It varies largely, what is included in a DFD and how the granularity is chosen. Here, we describe what items we include in our DFDs.
We consider services, databases, and external entities as nodes in the DFD and information flows as edges. They are annotated with stereotypes and tagged values.
Services
Services correspond to the application’s microservices. They are visualized as rectangles with rounded corners. We distinguish internal services (those realizing the business logic) from infrastructural services (those building the supporting infrastructure by using mainly existing code libraries with only marginal alterations). Databases are microservices that are used to store data. As such, they are represented as services with the stereotype (see below) –database–.
External entities
External entities are software components that are not deployed with the application, for example public mail servers, websites, or other code repositories. They are visualized as rectangles. External entities are called from the application without a deployment descriptor or other implementation in the codebase. Additionally, an external component user representing the human user is added implicitly.
Information flows
Information flows are connections between two nodes (services and external entities) over which information is sent. They are visualized as arrows. Information flows can be explicit requests to other services as part of the business logic as well as communication happening in the infrastructural working, for example registrations with service discovery mechanisms, sending of logs, or retrieval of configuration data.
Annotations
Annotations are made to the model items described above in the form of stereotypes and tagged values. Stereotypes (visualized as –stereotype–) enrich the model with semantic or non-functional information about items. Tagged values (visualized as key = value pairs) present additional attributes that are infeasible to capture in a finite list of stereotypes.


Lights Out is an HTML5 game. It is based on Tiger Toys electronic
Lights Out game. It is written in HTML5 canvas and JavaScript.
Game Objective:
You are presented with a board of lights. When a light is lit (on), it appears yellow.
When a new game is initiated, a random setting of lights will be lit.
Clicking on a light (whether lit or not) will toggle its state as well as the state
of the lights above and below and to the right and left of it.
Your goal is to turn-off all the lights.
To Play:
1. Select the level difficulty. (1 is easiest 5 most difficult)
2. Press the New button to initialize the game and start the timer.
3. Click lights turned on until all lights are out.
Each move you make is reported in Moves. When all lights are out the game so informs you
and the Elapsed timer is stopped.
At any time after pressing New button you can restart the game
from the beginning by pressing the Replay
Game button.
You can press See Solution to have the game step through and push the
buttons in order to turn off all the lights.
Need more help? Check Wikipedia:
Lights Out (game)


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Image result for what is css CSS stands for Cascading Style Sheets. It is the language for describing the presentation of Web pages, including colours, layout, and fonts, thus making our web pages presentable to the users.It is independent of HTML and can be used with any XML-based markup language.


To make the risotto, heat 1 tablespoon oil in a large heavy-bottomed saucepan over a medium heat. Sauté the garlic, fennel seeds and chilli flakes for 1–2 minutes. Add the finely chopped fennel and cook for 15–20 minutes, or until softened.
Meanwhile, preheat the grill to medium–high. Using half the Parmesan, spread piles of cheese onto a silicon sheet and grill until melted and golden – if the cheese is spread too thinly it will burn. Remove and leave to cool before breaking into pieces.
Heat the remaining 1 tablespoon oil in a large frying pan over a medium heat and cook the onion until softened. Stir in the rice and cook for 1–2 minutes. Pour in the wine and let it bubble until all the liquid has gone. Add the stock, ladle by ladle, stirring and cooking each ladleful until it has been absorbed before adding any more. Continue adding the stock and cooking like this until the rice is tender but still creamy (about 25–30 minutes).
Stir in the remaining cooked fennel, lemon zest and juice and plenty of seasoning. Stir in remaining Parmesan, cover and remove from the heat while you make the fritters.
To make the fritters, beat together the soda water and egg in a bowl. Place the bowl into a larger bowl full of ice (this will keep the batter as cold as possible). Slowly stir in the cayenne pepper, baking powder, cornflour and plain flour.
Heat the oil in a deep saucepan until it reaches 180°C, or a cube of bread browns in 20 seconds. (CAUTION: hot oil can be dangerous. Do not leave unattended.)
Dip the fennel wedges in the batter and deep-fry for 1–2 minutes or until golden. Drain on kitchen paper.
Garnish the risotto with the fennel fronds and Parmesan crisps and serve with the fennel fritters.


Gupter is a condensed serif font which takes inspiration from early 20th century English fonts such as Times New Roman.
The family excels in small spaces, due to its large x-height and condensed letterforms. The light stems and subtle details give it a gentle personality and a nice color balance on the page.
To contribute, see Gupter GitHub Repository


core/src/control-value-accessors/text-cell-control-value-accessor.directive.ts
The ControlValueAccessor that is instantiated for the following usage, with type='text'
or omitting the type
:
<tr teRow *ngFor=let row in rows>
....
<td teCell type=text ([ngModel])=row.someText>
<ng-container teAnchor></ng-container>
</td>
<td teCell ([ngModel])=row.someOtherText>
<ng-container teAnchor></ng-container>
</td>
....
</tr>
Do not forget to put <ng-container teAnchor></ng-container>
inside the table elements. This is required because at this moment, it is not possible for the Angular renderer to insert elements into another element, only append as a sibling. It is thus not possible to insert into the nativeElement
associated with and injected into the teCell
directive.
For this reason, we need to create a ViewContainer
inside the HTMLTableCellElement
and get a reference to it, which is done by NgContainer
and teAnchor
respecively. See this issue for a lengthy discussion.
| Providers |
TE_TEXT_CELL_ACCESSOR
|
| Selector | td[type=text][teCell][ngModel],td:not([type])[teCell][ngModel],td[type=text][teCell][formControl],td:not([type])[teCell][formControl],td[type=text][teCell][formControlName],td:not([type])[teCell][formControlName] |
import { Directive, forwardRef } from '@angular/core';
import { NG_VALUE_ACCESSOR } from '@angular/forms';
import { AbstractTableCell } from './abstract-table-cell';
const TE_TEXT_CELL_ACCESSOR = {
provide: NG_VALUE_ACCESSOR,
useExisting: forwardRef(() => TextCellControlValueAccessor),
multi: true
};
/**
* The ControlValueAccessor that is instantiated for the following usage, with `type='text'` or omitting the `type`:
* ```html
<tr teRow *ngFor=let row in rows>
....
<td teCell type=text ([ngModel])=row.someText>
<ng-container teAnchor></ng-container>
</td>
<td teCell ([ngModel])=row.someOtherText>
<ng-container teAnchor></ng-container>
</td>
....
</tr>
```
* Do not forget to put `<ng-container teAnchor></ng-container>` inside the table elements. This is required because at this moment, it is not possible for the Angular renderer to **insert** elements into another element, only append as a sibling. It is thus not possible to insert into the `nativeElement` associated with and injected into the `teCell` directive.
* For this reason, we need to create a `ViewContainer` inside the `HTMLTableCellElement` and get a reference to it, which is done by `NgContainer` and `teAnchor` respecively. [See this issue for a lengthy discussion]{@link https://github.com/angular/angular/issues/9035}.
*
*/
@Directive({
selector:
'td[type=text][teCell][ngModel],td:not([type])[teCell][ngModel],td[type=text][teCell][formControl],td:not([type])[teCell][formControl],td[type=text][teCell][formControlName],td:not([type])[teCell][formControlName]',
providers: [TE_TEXT_CELL_ACCESSOR],
host: {
'(input)': '$any(this).onChange($event.target.value)'
}
})
export class TextCellControlValueAccessor extends AbstractTableCell {
/** @internal for documentation see {@link AbstractTableCell} */
public onChange: (_: any) => void;
/** @internal for documentation see {@link AbstractTableCell} */
public writeValue(modelVal: any): void {
super.setCellValue(modelVal);
}
/** @internal for documentation see {@link AbstractTableCell} */
public registerOnChange(fn: (_: any) => void): void {
this.onChange = fn;
}
/** @internal for documentation see {@link AbstractTableCell} */
public registerOnTouched(fn: () => void): void {
// this.onTouched = fn;
}
/** @internal for documentation see {@link AbstractTableCell} */
public parser(value: any) {
return value;
}
/** @internal for documentation see {@link AbstractTableCell} */
public cellValueFormatter(value: any) {
return (value = value === null ? '' : value);
}
/** @internal for documentation see {@link AbstractTableCell} */
public inputValueFormatter(value: any) {
return value;
}
}


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This recipe is the starting point for all my cheesecake recipes,
before adding flavorings or toppings.
Pumpkin Pie Chai Masala Caramel Swirl
Cheesecake
A fresh take on pumpkin pie, with a spiced Chai infused caramel
swirl and no whipped cream necessary.
A decadent dark chocolate cheesecake with ganache topping and a
touch of Bailey's to make it merrier.
Poached Pear and Mulled Wine on Vanilla Cheesecake
A spiced mulled wine sauce and poached pear slices top this festive
cheesecake with holiday spirits.
Maple and Apple Butter Cheesecake
Maple syrup flavors this cheesecake, which is complemented by the
layer of spiced apple butter atop the crust.


|PyPi version| |PyPi downloads|
May the LINE be with you...
>>> from line import LineClient
>>> client = LineClient("carpedm20", "xxxxxxxxxx")
Enter PinCode '9779' to your mobile phone in 2 minutes
>>> client =<EMAIL_ADDRESS>"xxxxxxxxxx")
Enter PinCode '7390' to your mobile phone in 2 minutes
>>> print client.getProfile()
First, you need to install Apache Thrift. Install instructions are here. (This might take some time...)
Next:
$ pip install line
Or, you can use:
$ easy_install line
Or, you can also install manually:
$ git clone git://github.com/carpedm20/line.git
$ cd LINE
$ python setup.py install
First, you need to create a LineClinet object with YOUR_ID and YOUR_PASSWORD. YOUR ID can be LINE email address or NAVER id.
>>> from line import LineClient
>>> client = LineClient(YOUR_ID, YOUR_PASSWORD)
Enter PinCode '9779' to your mobile phone in 2 minutes
After you enter PinCode to your mobile phone, you will get authToken.
>>> authToken = client.authToken
>>> print authToken
With authToken, you don’t have to enter PinCode to your phone anymore!
>>> client = LineClient(authToken=authToken)
>>> print client.getProfile()
Source codes are distributed under BSD license.
Taehoon Kim / @carpedm20


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Notes from Jan. 29, 2019 to Feb. 15, 2019
| Date: | Jan 29, 2019 |
|---|---|
| Last Updated: | Feb 15, 2019 |
| Categories: | |
| Tags: |
Contents
Introduction
In this article we would summarize some popular solutions for the inverse problem. To be specific, here we only discuss the methods of inverse problems. Although when introducing some algorithms, we may need to explain what problem they work on, the various topics about which problem we solve is not what we concentrate on in this article.
Generally the solutions could be divided into 2 parts: optimizing methods and stochastic approaches. In the first part, i.e. the optimizing methods, we would introduce different iterative algorithms which are used to find the optimal solution of the problem. In most cases, these algorithms are based on deterministic methods, i.e. the derivatives. In the second part, we would introduce some stochastic methods especially some heuristic methods where we do not need to calculate the gradient but only need to evaluate the loss function. In those 2 parts, we would also talks about machine learning, regularization and some other related topics which have been applied to enhance the performance of the plain algorithms.
In the following parts, we will show not only the methods and ideas but also the state-of-art progress on inverse problem. Our literature resource is mainly from NIPS and IEEE Xplore.
In the last section, we attach 3 groups of slides which are related to this article. The slides include the weekly report from 02/01/2019 to 02/15/2019.
Optimizing methods
Optimizing methods for solving non-linear inverse problem
Conjugate gradient descent
- Title: A new family of conjugate gradient methods for unconstrained optimization
- Author: Mohd Rivaie, Muhammad Fauzi, and Mustafa Mamat
- Year: 2011
- Theory level: Theoretical
- Theory type: Gradient based algotirhm
- Used data: Standard testing benchmarks
- Source: International Conference on Modeling, Simulation and Applied Optimization
- External Reference: A modified PRP conjugate gradient method with Armijo line search for large-scale unconstrained optimization.
Learning abstract:
This paper proposes a new conjugate gradient coefficient which is used in conjugate gradient descent method. As an alternative of Newton's method, conjugate gradient method aims at approximating the Hessian matrix with first order gradient to avoid the computationally expensive second-order term. Regardless the computational cost, this method is slower than Newton's method but faster than first-order gradient descent.
Denote that we need to solve such an unconstrained problem which could be non-linear,
The algorithm could be described as:
- Initialize the input parameter $\mathbf{x}_0$, $k=0$.
- Calculate first-order gradient $\mathbf{g}_k = \nabla f(\mathbf{x}_k)$.
- Compute $\boldsymbol{\beta}_k$ which is the conjugate gradient coefficient.
- Update descent direction: when $k=0$, let $\mathbf{d}_k=-\mathbf{g}_k$; when $k > 0$, $\mathbf{d}_k=-\mathbf{g}_k+\boldsymbol{\beta}_k\mathbf{d}_{k-1}$.
- Use line search to find the best update parameter: $\alpha_k=\arg \min_{\alpha}f(\mathbf{x}_k+\alpha \mathbf{d}_k)$.
- Let $\mathbf{x}_{k+1} = \mathbf{x}_k+\alpha_k \mathbf{d}_k$. If $f(\mathbf{x}_{k+1}) < f(\mathbf{x}_k)$ and $\lVert \mathbf{g}_k \rVert < \varepsilon$, stop; otherwise get back to step 2.
The author also proposes a new conjugate gradient coefficient and proves the convergence of the new coefficient.
Frank–Wolfe Algorithm
- Title: Decentralized Frank–Wolfe Algorithm for Convex and Nonconvex Problems
- Author: Hoi-To Wai, Jean Lafond, Anna Scaglione, and Eric Moulines
- Year: 2017
- Theory level: Theoretical
- Theory type: Gradient based algotirhm
- Used data: Standard testing benchmarks
- Source: IEEE Transactions on Automatic Control
Learning abstract:
This paper introduce a decentralized version of Frank–Wolfe Algorithm which is used to solve a strictly constrained optimizing problem. In this paper, the author assume that there a series of functions $f_i (\mathbf{x})$ which might not be convex, then the problem is
Denote that the adjacent matrix $\mathbf{W}$ has such condition: $\sum_{j} W_{ij} = 1$. Then the algorithm could be described as:
- For each agent, calculate the local average iterate among its neighbor: $\bar{\mathbf{x}}_i = \sum_{j} W_{ij} \mathbf{x}_j$, where $W_{ij}$ is an element of the adjacent matrix.
- For each agent, calculate the local average gradient among its neighbor: $\overline{\nabla F}_i= \sum_{j} W_{ij} \nabla f_j (\mathbf{x}_j)$.
- Let $\boldsymbol{\alpha}_i = \arg \min\limits_{\boldsymbol{\alpha}_i \in \mathcal{D}} \boldsymbol{\alpha}_i^T \overline{\nabla F}_i$.
- Update iterate: $\mathbf{x}_{i+1} = (1-\gamma) \bar{\mathbf{x}}_i + \gamma \boldsymbol{\alpha}_i$.
Note that the domain $\mathcal{D}$ is a strict constraint. For example, we could set that $\mathcal{D} = \{ \mathbf{x} | \lVert \mathbf{x} \rVert_1 < \lambda \}$. Then the problem would become a lasso problem.
The author also discuss the convergence when $f_i$ is convex functions, and the bound when $f_i$ is non-convex. Compared to the conventional FW algorithm, the proposed one is decentralized, which means it could make use of multiple agents and the parallel computing. The author has proved that although in the algorithm we only calculate the "local average" for each agent, when considering the whole system product, the "local average" would converge to the real one.
Optimizing methods for solving linear inverse problem
LISTA-CPSS Algorithm
- Title: Theoretical Linear Convergence of Unfolded ISTA and its PracticalWeights and Thresholds
- Author: Xiaohan Chen, Jialin Liu, Zhangyang Wang and Wotao Yin
- Year: 2018
- Theory level: Theoretical
- Theory type: Machine learning
- Used data: 11 test images for sparse coding
- Source: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings
Learning abstract:
This paper concentrates on the inspection on LISTA (Learning-ISTA). To check the basic idea of LISTA and LAMP, please check this article:
LISTA which is adapted from ISTA, has 2 weight matrices in each layer. The network structure could be described in the following figure.
The network still preserves the workflow of ISTA. The observed data is the input for each layer, while we still need an initial guess for the prediction. The initial guess is passed to the first layer and adjusted in each layer until we get the accurate prediction.
In this paper, the author propose two improvements on the original LISTA:
- Partial weight coupling (CP): The author proves that the primal LISTA could not converge unless $\mathbf{W}_{k2} = \mathbf{I} - \mathbf{W}_{k1} \mathbf{A}$. Hence we would only has one weight matrix in each layer.
- Support selection technique: The author proposes that after we calculate the $\mathbf{v}_k = \mathbf{W}_{k1} \mathbf{b} + \mathbf{W}_{k2} \mathbf{x}_k$, we could use a new threshold technique. When $\mathbf{v}_k$ is small, use soft thresholding; when $\mathbf{v}_k$ is large, use hard thresholding.
With the two above methods equipped, the convergence speed would be accelerated.
Inf-ADMM-ADNN
- Title: An inner-loop free solution to inverse problems using deep neural networks
- Author: Kai Fan, Qi Wei, Lawrence Carin and Katherine A. Heller
- Year: 2017
- Theory level: Application
- Theory type: Machine learning
- Used data: Deblurring, super resolution and colorization
- Source: Advances in Neural Information Processing Systems 30 (NIPS 2017)
- External Reference: Distributed optimization and statistical learning via the alternating direction method of multipliers.
Learning abstract:
This paper aims at solve a general form of liner inverse problem:
By using Lagrange multiplier method, we could decompose this optimization into 3 steps in each iteration. We call this method alternating direction method of multipliers (ADMM) framework:
In practice, the matrix $\mathbf{A}$ may be a very large one. To solve this problem, we have overcome two main challenges. The first one is the solution for $\mathbf{z}^{k+1}$. Although it has a closed-form solution, we need to calculate the inverse of $\mathbf{B} = \left( \beta\mathbf{I} + \mathbf{A}\mathbf{A}^T \right)^{-1}$. Thus the author proposes a network which is used to learn $\mathbf{C}_{\phi} \rightarrow \mathbf{B}^{-1}$
In some cases, for example, if $\mathcal{R} = \lVert \cdot \rVert_1$, $\mathbf{x}^{k+1}$ which is deduced from proximal operator could get the closed-form solution. However, if $\mathcal{R}$ is in a generalized form, for the proximal operator $\arg\min\limits_{\mathbf{x}} \frac{1}{2} \lVert \mathbf{x} - \mathbf{v} \rVert_2^2 + \mathcal{R}(\mathbf{x},~\mathbf{y})$, we could find that
Thus we know that $\mathbf{v} = \mathcal{F} (\mathbf{x})$. And we use the following network to learn the inverse $\mathbf{x} = \mathcal{F}^{-1}(\mathbf{v})$.
VAMP equipped with non-separable denoiser
- Title: Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis
- Author: Alyson K. Fletcher, Parthe Pandit, Sundeep Rangan, Subrata Sarkar and Philip Schniter
- Year: 2018
- Theory level: Theoretical
- Theory type: Gradient based algorithm
- Used data: Compressive Image Recovery and Lifting
- Source: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings
- External Reference: To learn more about AMP, please check this note:
Learning abstract:
This paper extends the Vector-AMP (VAMP) to a wider case. First, let us introduce VAMP. VAMP extend the conventional AMP into a two-part iteration structure. In the first part, we use a denoising operator $\mathbf{g}_1(\cdot)$ as we have done in AMP. In the other part, we use a linear minimum mean-squared error (LMMSE) operator to ensure a state evolution (SE) analysis. This technique extend the algorithm to a case that $\mathbf{A}$ is not required to be in the i.i.d. sub-Gaussian distribution but only need to be an arbitrary right rotationally invariant matrix.
This is the algorithm of VAMP:
The sub-structure in each part is the same as AMP. However, in this algorithm, we have 2 function vectors $\mathbf{g}_1(\cdot)$ and $\mathbf{g}_2(\cdot)$, where $\mathbf{g}_2(\cdot)$ needs to be a solution to the L2-penalized linear inverse problem:
However, $\mathbf{g}_1(\cdot)$ could be many kinds of denoisers. For example, soft thresholding is the solution derived from L1 norm penalty, which is a separable denoiser. In this article, the author propose that with the ground truth $\mathbf{x}^{\ast}$ companied by Gaussian noise, both of the errors $\mathbf{p}_k = \mathbf{r}_{1k} - \mathbf{x}^{\ast}$ and $\mathbf{q}_k = \mathbf{V}^T \left(\mathbf{r}_{1k} - \mathbf{x}^{\ast}\right)$
converge to a vector with each element in the Gaussian distribution with zero mean and $\tau_{1k}$, $\tau_{2k}$ variance respectively.Regularization / Penalty
Adversarial Regularizers
- Title: Adversarial Regularizers in Inverse Problems
- Author: Sebastian Lunz, Carola Schoenlieb and Ozan Öktem
- Year: 2018
- Theory level: Theoretical
- Theory type: Machine learning
- Used data: image inverse, the data is from BSDS500 dataset
- Source: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings
Learning abstract:
Consider such a problem:
This paper proposes that we could use a network $\Psi_{\boldsymbol{\Theta}}(\cdot)$ to replace the regularization term $f$. Since the network could learn from the distribution of the data, it could serve better than a specially designed regularization.
Considering that $\mathbf{y} = \mathbf{A} \mathbf{x}$, we may have a direct inverse method that $\mathbf{x} = \mathbf{A}^{\ast}\mathbf{x}$. Note that because $\mathbf{A}$ may not be a square matrix, it would not have inverse in most cases. However, $\mathbf{A}^{\ast}$ could be viewed as a pseudo-inverse of $\mathbf{A}$. There exists some techniques where we could calculate $\mathbf{A}^{\ast}$ efficiently. And a research also discloses that such conclusion also holds when we consider a pseudo-inverse of a convolution.
Considering that we have ground truth set $\mathbb{P}_r$, and the observation set $\mathbf{P}_y$. By $\mathbf{A}^{\ast}$ we could project the observation into a pseudo-inverse set $\mathbf{P}_n$. Then we could use such loss function to train network:
where $\mathbf{x}_r \sim \mathbb{P}_r$, $\mathbf{x}_n \sim \mathbb{P}_n$ and $\mathbf{x}_i = \varepsilon \mathbf{x}_r + (1 - \varepsilon \mathbf{x}_n)$. $\varepsilon$ is a random variable in the uniform distribution $U(0,~1)$. The last term in the loss function is used to enforce $\Psi_{\boldsymbol{\Theta}}$ to be Lipschitz continuous. During the training, we sample $\mathbf{x}_r$ and $\mathbf{x}_n$ randomly. Since they are not coupled, the loss function is essentially the Wasserstein distance which makes the network learn the minimal distance between the two distribution $\mathbb{P}_r$ and $\mathbb{P}_n$.
After the network getting trained, the network parameter $\boldsymbol{\Theta}$ would be fixed. Then we could use gradient descent to solve the inverse problem:
Proximal gradient method
- Title: Parallel proximal methods for total variation minimization
- Author: Ulugbek S. Kamilov
- Year: 2017
- Theory level: Theoretical
- Theory type: Regularization
- Used data: Shepp-Logan images
- Source: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Learning abstract:
In this paper, the author gives the general formulation of the proximal algorithm where we need to solve that
where $\mathcal{D},~\mathcal{R}_k$ are convex. Such problem often appears when solving dictionary learning. To get the solution, we need to apply ISTA-based optimization:
To learn the details about what is proximal operator ($\mathbf{prox}(\cdot)$) and how to solve it, please check:
Landweber iteration
- Title: Learning Model-Based Sparsity via Projected Gradient Descent
- Author: Sohail Bahmani, Petros T. Boufounos, and Bhiksha Raj
- Year: 2017
- Theory level: Theoretical
- Theory type: Regularization
- Used data: None (pure theoretical)
- Source: IEEE Transactions on Information Theory
Learning abstract:
This paper gives the proof of how the Landweber iteration (Projected Gradient Descent) converges, and it also verify some other features like Stable Model-Restricted Hessian (SMRH) condition.
The Landweber iteration is used when there is a strict constraint accompanied with the optimization.
Different from the plain gradient descent, it needs to project the updated parameter to the strictly limited space:
Note that to solve this problem, we need to use the project function $\mathcal{P}$ which means we find a solution $\mathbf{x}_{t+1} \in \mathcal{C}$ that has the smallest distance to $\mathbf{x}_t - \alpha \nabla f(\mathbf{x})$.
Stochastic methods
Sampling methods
Metropolis-Hastings
- Title: Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm
- Author: T. Cui, C. Fox, and M. J. O'Sullivan
- Year: 2011
- Theory level: Algorithm
- Theory type: Stochastic sampling algorithm
- Used data: Geothermal reservoir modeling
- Source: Water Resources Research
Learning abstract:
Metropolis-Hastings method is widely used in Monte-Carlo-Markov-Chain (MCMC) methods. This paper proposes an improvement for solving inverse problems with a highly complicated forward model. Suppose we have a model function $\mathbf{f}$, the likelihood function derived from Bayesian method could be formulated as
where $\Sigma_e$ is the covariance matrix of the misfit vector $\mathbf{e} = \mathbf{y} - \mathbf{f}(\mathbf{x})$. Note that if we have a coarse model $\mathbf{f}^{\ast}(\mathbf{x})$ which is derived by reducing the order of $\mathbf{f}$, then we would have $\mathbf{e} = \mathbf{y} - \mathbf{f}_d(\mathbf{x}) + \left(\mathbf{f}_d(\mathbf{x}) - \mathbf{f}(\mathbf{x}) \right)$. Denote that $\mathbf{d} = \mathbf{f}_d(\mathbf{x}) - \mathbf{f}(\mathbf{x})$, and suppose that $\mathbf{d} \sim \mathcal{N}(\mu_d, \Sigma_d)$. Then we would have a new likelihood function
Suppose that we have a probability function $\mathbf{g}(\cdot)$, and random vector $\boldsymbol{\lambda}$ follows the distribution of $\mathbf{g}$. Then we could update a model parameter $\mathbf{x}_k$ by updating function $\mathbf{x}^{\dagger} = \boldsymbol{\eta}(\mathbf{x}_k,~\boldsymbol{\lambda})$. Consider another probablity function $\mathbf{g}_r(\cdots)$ which generates another random vector $\boldsymbol{\lambda}_r$. We may have another updating function $\mathbf{x}_k = \boldsymbol{\eta}_r(\mathbf{x}^{\dagger},~\boldsymbol{\lambda}_r)$. Hence we know $\boldsymbol{\eta},~\boldsymbol{\eta}_r$ are reverse transitions compared to each other. After that, we could define the proposal distribution as
Then, the Metropolis-Hastings algorithm could be described as
- Generate a random update $\mathbf{x}^{\dagger}$ and calculate its bi-directional transitions $q(\mathbf{x}_k \rightarrow \mathbf{x}^{\dagger}) ,~ q(\mathbf{x}^{\dagger} \rightarrow \mathbf{x}_k)$.
- Use Metropolis-Hastings algorithm to calculate acception rate over the coarse likelihood:
\begin{align} \alpha(\mathbf{x}_k ,~ \mathbf{x}^{\dagger}) = \min \left( 1,~ \frac{p_d(\mathbf{x}^{\dagger}|\mathbf{y}) q(\mathbf{x}^{\dagger} \rightarrow \mathbf{x}_k)}{p_d(\mathbf{x}_k|\mathbf{y}) q(\mathbf{x}_k \rightarrow \mathbf{x}^{\dagger})} \right). \end{align}
- If $\mathbf{x}^{\dagger}$ could be accept according to the acception rate $\alpha$, then calculate another accpetion rate over the detail likelihood:
\begin{align} \beta(\mathbf{x}_k ,~ \mathbf{x}^{\dagger}) = \min \left( 1,~ \frac{p(\mathbf{x}^{\dagger}|\mathbf{y}) \alpha(\mathbf{x}^{\dagger} ,~ \mathbf{x}_k) q(\mathbf{x}^{\dagger} \rightarrow \mathbf{x}_k)}{p(\mathbf{x}_k|\mathbf{y}) \alpha(\mathbf{x}_k ,~ \mathbf{x}^{\dagger}) q(\mathbf{x}_k \rightarrow \mathbf{x}^{\dagger})} \right). \end{align}
- If $\mathbf{x}^{\dagger}$ could be accept according to the acception rate $\beta$, then perform update $\mathbf{x}_{k+1} = \mathbf{x}^{\dagger}$.
The idea of this algorithm is, if the sample is rejected by the coarse likelihood, then we do not need to calculate the complicated forward model. Although calculating the coarse model would reduce the efficiency, if the sample is easy to be rejected, then we would gain better performance by this method.
Gibbs sampling
- Title: A Bayesian inference approach to the inverse heat conduction problem
- Author: Jingbo Wang, and Nicholas Zabaras
- Year: 2004
- Theory level: Algorithm
- Theory type: Stochastic sampling algorithm
- Used data: Two-dimensional inverse heat conduction problem (IHCP)
- Source: International Journal of Heat and Mass Transfer
- External Reference: This is another article about solving multi-frequency problem (i.e. multiple forward model) with Gibbs sampling:
Learning abstract:
In this paper, we have $y_j \sim \mathbf{T}(\mathbf{x},t_j)$, and $k \frac{\partial T(\mathbf{x}, t)}{\partial \mathbf{n}} = \sum \theta_i \mathbf{w}_i (\mathbf{x},~t)$. Hence we define that vector $\mathbf{y}$ should fulfil that $\mathbf{y} \sim \mathbf{f} (\boldsymbol{\theta})$, where $\mathbf{f}(\cdot)$ is known by some prior work.
According to Bayesian method, $p(\boldsymbol{\theta}|\mathbf{y}) \propto p(\mathbf{y}|\boldsymbol{\theta}) p(\boldsymbol{\theta})$, hence we have
To specify the prior $p(\boldsymbol{\theta})$, we use the random field, which means
Then in each iteration, we could use Gibbs sampling method, which means in the kth iteration, if we have $\boldsymbol{\theta}^{(k)}$, then we perform that
- $\theta^{(k+1)}_1 \sim p\left(\theta_1 \left| \theta_2=\theta^{(k)}_2,~ \theta_3=\theta^{(k)}_3,~ \theta_n=\theta^{(k)}_n \right.\right)$
- $\theta^{(k+1)}_2 \sim p\left(\theta_2 \left| \theta_1=\theta^{(k+1)}_1,~ \theta_3=\theta^{(k)}_3,~ \theta_n=\theta^{(k)}_n \right.\right)$
- $\theta^{(k+1)}_3 \sim p\left(\theta_3 \left| \theta_1=\theta^{(k+1)}_2,~ \theta_2=\theta^{(k+1)}_2,~ \theta_n=\theta^{(k)}_n \right.\right)$
- ...
- $\theta^{(k+1)}_n \sim p\left(\theta_n \left| \theta_1=\theta^{(k+1)}_1,~ \theta_2=\theta^{(k+1)}_2,~ \theta_n=\theta^{(k+1)}_{n-1} \right.\right)$
Heuristic methods
Ant colony algorithm
- Title: Application of homogenous continuous Ant Colony Optimization algorithm to inverse problem of one-dimensional coupled radiation and conduction heat transfer
- Author: Biao Zhang, Hong Qi, Ya-Tao Ren, Shuang-Cheng Sun, and Li-Ming Ruan
- Year: 2013
- Theory level: Algorithm
- Theory type: Stochastic heruistic algorithm
- Used data: Steady-state coupled radiation and conduction heat transfer in an absorbing, emitting and scattering plane-parallel slab
- Source: International Journal of Heat and Mass Transfer
- External Reference: To learn a visual demo for ant colony optimization, please check this link:
Learning abstract:
This paper introduce a method where we could enhance the optimization results by ant colony optimization (ACO). First, this paper introduce the basic-ACO (BACO), which shrinks the search scope in each iteration to reduce the calculation complexity. However, because of the grid-scheme and the scope-shrinking strategy, BACO may converge to the local minimum in some cases. To solve this problem, in this paper the author proposes the homogeneous ACO (HACO).
Suppose that we need to solve $\arg \min\limits_{\mathbf{x}} \lVert \mathbf{y} - \mathbf{f}(\mathbf{x})\rVert_2^2$, where $\mathbf{f}$ is a highly nonlinear model. Then we could initialize $M$ solutions $\mathbf{x}^{(1)},\mathbf{x}^{(2)},\cdots,\mathbf{x}^{(M)}$, which is also called "ants" in ACO. Suppose that the metric function $\mathcal{L}(\mathbf{x})=\frac{1}{N}\left\lVert \mathbf{y} - \mathbf{f}\left(\mathbf{x}^{(k)}\right) \right\rVert_2$ is used to evaluate the solution $\mathbf{x}^{(k)}$. For each parameter of any $\mathbf{x}^{(k)}=\begin{bmatrix}x^{(k)}_1 & x^{(k)}_2 & \cdots & x^{(1)}_n \end{bmatrix}$, we set the initial searching scope $x^{(k)}_i \in [L_i, S_i]$ and divide the scope into $N_i$ intervals. Then we could get a series of discrete positions. Initialize that $\tau_{ij} = Q$ and $\eta_{ij} = \frac{1}{N}$. After that we could perform such iterations:
- For each solution $k$, $p^{(k)}_{ij} = \frac{\zeta}{N_i} + \left( 1 - \zeta \right)\frac{ \tau^{\alpha}_{ij} \eta^{\beta}_{ij} }{ \sum_{j=1}^{N_i} \tau^{\alpha}_{ij} \eta^{\beta}_{ij} }$, where $\alpha,~\beta > 0$ and $\zeta \in [0,~1]$. $p^{(k)}_{ij}$ is the probability that how possible $x^{(k)}_i$ would transfer into $x^{(k)}_j$. Since when $\zeta \rightarrow 1$, the move tends to be random, we call such update "homogeneous updating".
- After we update the new solutions according to $p^{(k)}_{ij}$, we could search a local minimum for each solution in a local area. This search could be totally random, i.e. we just adjust the solutions by adding random noise. And we could also use some deterministic methods. If we calculate $\mathcal{L}(\mathbf{x}^{(k)})$ for each solution, the local minimum should satisfy that $\mathcal{L}(\mathbf{x}^{(k)} + \boldsymbol{\varepsilon}^{(k)}) < \mathcal{L}(\mathbf{x}^{(k)})$. This step is designed for reducing the discrete sampling effect. Record and update the historical best solution $\mathbf{x}^{\ast}$ and the best 3 solutions in this step: $\mathbf{x}^{(s_1)},~\mathbf{x}^{(s_2)},~\mathbf{x}^{(s_3)}$.
- Calculate the increments for each solution $k$, which means in step 1, if $x^{(k)}_i$ changes to $x^{(k)}_j$, then we have $\Delta \tau^{(k)}_{ij} = Q$ and $\Delta \eta^{(k)}_{ij} = \frac{1}{N \mathcal{L}(\mathbf{x}_j)}$.
- Update the information variables: \begin{align*} \tau_{ij} &= (1-\rho) \tau_{ij} + \sum_{k=1}^{M} \Delta \tau^{(k)}_{ij},\\ \eta_{ij} &= \max \left( \eta_{ij},~ \sum_{k=1}^{M} \Delta \eta^{(k)}_{ij} \right). \end{align*}
- Defining $\gamma \in (0,1)$ which indicates the shrinking ratio. Then we could shrink the searching scopes according to such equations: \begin{align*} L_i &= \max \left( \min \left( x^{\ast}_i,~ x^{(s_1)}_i,~ x^{(s_2)}_i,~ x^{(s_3)}_i \right) - \gamma \frac{S_i - L_i}{2},~ L_i \right),\\ S_i &= \min \left( \max \left( x^{\ast}_i,~ x^{(s_1)}_i,~ x^{(s_2)}_i,~ x^{(s_3)}_i \right) + \gamma \frac{S_i - L_i}{2},~ S_i \right). \end{align*}
- Resampling the new $N_i$ intervals according to new scope $x^{(k)}_i \in [L_i, S_i]$. Note that we need to interpolate $\tau_{ij},~\eta_{ij}$ for new intervals due to the resampling. Then start a new loop beginning from step 1.
Bat algorithm
- Title: A New Metaheuristic Bat-Inspired Algorithm
- Author: Xin-She Yang
- Year: 2010
- Theory level: Algorithm
- Theory type: Stochastic heruistic algorithm
- Used data: Standard benchmark non-linear functions
- Source: Nature Inspired Cooperative Strategies for Optimization
Learning abstract:
Bat algorithm could be viewed as an adaptation of particle swarm optimizaion (PSO). The author of this paper has proposed several algorithms in this kind, for example, the firefly algorithm. This algorithm imitates bats' behavior. When they have not hunt the best solution, they tend to move close to the current optimal solution; once they get the best solution, they would try to stay in the local position.
Consider we have the loss function $\mathcal{L}(\mathbf{x})$, and we initialize a series of solutions randomly $\mathbf{x}_1,~\mathbf{x}_2,~\mathbf{x}_n$, where each solution could be viewed as a bat. Attach each bat with a velocity $\mathbf{v}_i$, a frequency $f_i$, a pulse rate $r_i=0$, and a loudness $A_i=1$. Set $\alpha < 1$. Then perform such iterations:
- In iteration $t$, mark the current optimal solution as $\mathbf{x}^{\ast}$. For each bat (solution) $\mathbf{x}_i$, generate a new solution $\mathbf{x}'_i$:
- Update its frequency in uniform distribution $f_i \sim U(0,~\alpha)$.
- Update the velocity $\mathbf{v}'_{i} = \mathbf{v}_{i} (1-f_i) + \left( \mathbf{x}^{\ast} - \mathbf{x}_i\right) f_i$.
- Update its position $\mathbf{x}'_i = \mathbf{x}_i + \mathbf{v}'_{i}$.
- Generate $r \sim U(0,1)$. If $r > r_i$, select a solution from the M best solutions. And use a random vector $\boldsymbol{\epsilon}$ to update it, i.e. $\mathbf{x}'_i = \mathbf{x}_{\mathrm{selected}} + \mathrm{diag}(\boldsymbol{\epsilon})\mathbf{A}$.
- Adjust $\mathbf{x}_i$ slightly and randomly.
- Generate $a \sim U(0,1)$. If $a < A_i$, and $\mathcal{L}(\mathbf{x}'_i) < \mathcal{L}(\mathbf{x}_i)$, then let \begin{align*} \mathbf{x}_i &= \mathbf{x}'_i,\\ r_i &= 1 - e^{\lambda t},\\ A_i &= \beta A_i. \end{align*}
- After iteration $t$, sort all solutions $\{\mathbf{x}_i\}$, then we could update the optimal solution $\mathbf{x}^{\ast}$.
In the beginning, $r$ is small while $A$ is large, the algorithm tends to abandon the solutions which are not good and accept new solutions. When the algorithm is nearly converged, the new solutions would not be accepted.


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