282 Matching Annotations
1. Last 7 days
2. cw.fel.cvut.cz cw.fel.cvut.cz
1. ∑y∈Ypθ(x,y)

$$p_\theta(x)$$

2. αx(y)

Estimated probability that the label of x is y

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3. Nov 2019
4. sgs.cvut.cz sgs.cvut.cz
1. více než 75% bylo vyplacenona stipendia studentů

Musí být více než 75 % z osobních nákladů vyplaceno na stipendia studentů, nebo 75 % z osobních nákladů akademiků, nebo 75 % z celkových nákladů na projekt?

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5. cw.fel.cvut.cz cw.fel.cvut.cz
1. Deep (convolutional) networks

Recommended literature: Deep Learning Book

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6. cw.fel.cvut.cz cw.fel.cvut.cz
1. μ

The higher mu is, the more closely aligned the true gradient and g must be.

2. comparable

[close on average]

3. stepsizeαk

learning rate

4. g(θk,sk)

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7. cw.fel.cvut.cz cw.fel.cvut.cz
1. strict concavity

$$\lambda_i > 0, \sum \lambda_i = 1: \log \sum \lambda_i x_i > \sum \lambda_i \log x_i$$

2. Pθ0(supθ∈Θ|L(θ,Tm)−L(θ)|> )m→∞−−−−→0

Starting with m, all the following approximations are strictly contained within epsilon-wide sleeve around the true L.

3. zj

$$z^j = (x^j, y^j)$$

4. consistency

The more training data we have, the closer we get to the true optimum.

5. p(y|x) =exp[y(〈w,x〉+b)]1 + exp[〈w,x〉+b]

$$p(y|x) = \frac{p(x, y)}{p(x)} = \frac{p(x|y) p(y)}{p(x)} = ...$$

6. Generative learning

We assume a certain model architecture. We need to estimate the parameters of the model.

7. x∈Rn,y∈{0,1}withy∼Bern(α)andx|y∼N(μy,V)

Unknown parameters:

• Prior: alpha
• Posterior: mu_0, mu_1, V
8. Linear Classifier

Example of discriminative learning version 2

9. Logistic Regression

Example of discriminative learning version 1

10. Gaussian Discriminative Analysis

Example of generative learning

11. Discriminative learning(1)

Example: Softmax layer for classification - estimates class probabilities

12. max

Correction: min

13. conditional

posterior

14. maximum likelihood estimator (MLE)

Why "likelihood" rather than "probability"? If the domain is real valued, p is a PDF and summing over p values does not correspond to probability.

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8. ufal.mff.cuni.cz ufal.mff.cuni.cz
1. neural network

What does the network output?

1. Value estimate (required for this assignment)
2. Policy (to be introduced in later lecture)

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9. cw.fel.cvut.cz cw.fel.cvut.cz
1. ∑j=1tjlog(pj)

This expression should be negated so that the loss increases as $$p_k$$ decreases.

2. negative log likelihood for the linearregression

Assumption: y is a linear function of x with Gaussian noise with the same standard deviation (sigma) in each dimension.

3. MLE

Maximum likelihood estimation

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10. cw.fel.cvut.cz cw.fel.cvut.cz
1. Forward Message

JD: The corresponding backward message may be one of the exam assignments.

2. Universal approximation theorem: one layer is enough

Wikipedia

the universal approximation theorem states that a feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous functions on compact subsets of Rn, under mild assumptions on the activation function.

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11. www.fel.cvut.cz www.fel.cvut.cz
1. Minimální výše měsíčního stipendia v prezenční formě při plnění studijních povinností je 15 000 Kč.

Skutečné údaje:

• Minimální stipendium za měsíc: 9 000 Kč. Zdroj: Příkaz rektora č. 11/2019
• Minimální stipendium za kalendářní rok: 12 x 12 000 Kč. Zdroj: Stipendijní řád - článek 6:1-3

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12. Oct 2019
13. cw.fel.cvut.cz cw.fel.cvut.cz
1. {trn,val,tst}_kernel_mat.svmlight

Python: Use the library svmlight. Consider using sklrean.svm.

2. STATISTICAL MACHINE LEARNING (SML2019)1. COMPUTER LAB

Solution consists of:

• Source code (Python or Matlab)
• PDF with answers to questions (and nothing else)

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14. cw.fel.cvut.cz cw.fel.cvut.cz
1. 1nin

Alternative from Xavier: $$\frac{2}{n_{in} + n_{out}}$$

2. letwiandxibe independentrandom variables

Is it safe to assume that x_i are independent across i?

3. =

Independent across i

4. E(xi)

We may need to normalize the input.

5. αk>0is thelearning rateorstepsize

Learning rate may be further specialized e.g. for each layer.

6. θk−αk∇L(θk)

We move in the opposite direction of the gradient.

7. yk=σk(xTW)

Class confidence distribution

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15. cw.fel.cvut.cz cw.fel.cvut.cz
1. ULLN applies

Proof: Seminar 3, Assignment 4

1. $$R(h_m) - R_{T^m}(h_m) \leq \sup_{h \in H} |R(h) - R_{T^m}(h)|$$ - trivial
2. $$R_{T^m}(h_H) - R(h_H) \leq \sup_{h \in H} |R(h) - R_{T^m}(h)|$$ - easy to see
2. $$R_{T^m}(h_m) \leq R_{T^m}(h_H)$$ because $$h_m \in \argmin_{h \in H} R_{T^m}(h)$$.

3. Neural Networks learned by back-propagation

Neural networks need not converge to the global minimum. $$R(h_m)$$ need not equal $$R(h_H)$$.

4. Vapnik-Chervonenkis dimension

$$D_{VC}(H) = max_n . \exists x \in X^n . \forall y \subseteq x . \exists h \in H . h(x) = y$$

5. 0/1(y,y′) = [[y6=y′]

We assume 0-1 misclassification loss.

6. Rn

Space of feature vectors

7. ULLN for finite hypothesis space

and bounded loss image (bounded by [a, b])

8. sup

"Uniform" because we care about all strategies in H.

9. limm→∞P(R(hm)−R(hH)≥ε)= 0

Equivalently:

$$\forall \delta > 0, \epsilon > 0. \exists m_0 \in N. \forall m \geq m_0. P(R(h_m) - R(h_H) \geq \epsilon) < \delta$$

In general:

$$lim_{m \rightarrow \infty} f(m) = 0 \Leftrightarrow \forall \delta > 0. \exists m_0 \in N. \forall m \geq m_0. f(m) < \delta$$

10. estimation error

How to reduce estimation error?

• Increase number of samples m.
• Improve the learning algorithm to utilize T^m more efficiently.
• Shrink H to exclude strategies better than h_m.

Enlarging H typically leads to worse generalization, so estimation error increases.

11. R(hm)

R(h_m) is a random variable, because h_m is a random variable, because T^m is a random variable.

12. YX

All functions $$h: X \rightarrow Y$$

13. approximation error

How can we reduce approximation error?

Enlarge H so that it includes better rules.

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16. cw.fel.cvut.cz cw.fel.cvut.cz
1. Empirical risk minimization

An empirical risk minimization (ERM) algorithm returns $$h_m \in H$$ that minimizes $$R_{T^m}(h)$$.

• Excess error: $$R(h_m) - R^*$$
• Estimation error: $$R(h_m) - R(h_H)$$
• Approximation error: $$R(h_H) - R^*$$

An algorithm is statistically consistent in $$H$$ iff $$\forall \epsilon > 0: \lim_{m \rightarrow \infty} P(R(h_m) - R(h_H) \geq \epsilon) = 0$$.

The hypothesis space $$H$$ satisfies the uniform law of large numbers (ULLN) iff $$\forall \epsilon > 0: \lim_{m \rightarrow \infty} P(\sup_{h \in H} |R(h) - R_{T^m}(h)| \geq \epsilon) = 0$$.

Theorem 1: If $$H$$ satisfies ULLN then ERM is statistically consistent in $$H$$.

Corollary 1: The ULLN is satisfied for a finite $$H$$.

Generalization bound for finite hypothesis space $$H$$ with $$Im(l) \subseteq [a, b]$$: $$P(\max_{h \in H} |R_{T^m}(h) - R(h)| < \epsilon) \geq 1 - 2 |H| \exp(-\frac{2 m \epsilon^2}{(b - a)^2})$$

2. Pdf

Boris:

We usually publish the slides 1 hour before the lecture.

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17. ufal.mff.cuni.cz ufal.mff.cuni.cz
1. lunar_lander

The environment adheres to reward shaping. Each state has a hidden value and reward of an executed action is always the difference between the values of the previous and the current state. (?)

2. The tasks are evaluated automatically using the ReCodEx Code Examiner.

We can submit one or more Python source files (modules) with the .py file extension.

3. Time limit for each test is 5 minutes.

Most likely this is too short period to perform training in ReCodEx.

4. average reward of -150

Episode terminates after 1000 steps.

5. average return of 490

There are 500 steps in each episode.

Is gamma equal to 1.0 in evaluation?

6. Assignments: Best solution counts. There is no penalty for incorrect solution submission. The students can ask for increasing the submission count limit (50 by default).

7. monte_carlo

Train either in ReCodEx or offline.

If I can't get it running in an hour, I can write to MS and ask for advice.

8. states

Generalized states: R^4:

1. Cart position
2. Cart velocity
3. Pole angle
4. Pole angular velocity

For this task the states are discretized by binning (8 bins in each dimension; 8^4 states in total).

9. --steps steps of policy evaluation/policy improvement

We improve the policy steps times.

10. --iterations applications of the Bellman equation

After iterations evaluations, we improve the policy once.

11. Average value per assignment: 11 points

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18. ufal.mff.cuni.cz ufal.mff.cuni.cz
1. Bellman backup operator

Given a valuation and a state, returns the expected return provided we follow the action that maximizes the next reward plus the valuated value of the next state.

2. return

Return at time $$t$$

The smaller the discount factor, the smaller is the relative weight of the far future rewards.

3. Monte Carlo and -soft Policies

It may make sense to decrease epsilon gradually while training to explore less and exploit more (refine the policy for the successful runs).

4. Monte Carlo with Exploring Starts

Can we make the policy stochastic and choose each action with a probability that scales with its expected return?

5. greedy action

Under what conditions do we get stuck in a local optimum?

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19. www.ciirc.cvut.cz www.ciirc.cvut.cz
1. Český ústav robotiky a kybernetiky

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20. cw.fel.cvut.cz cw.fel.cvut.cz
1. regularization constantC

Finite C implies forces w to be in a hypersphere of finite radius R that depends on C.

2. (w∗,b∗) = argminw∈Rn,b∈R(12‖w‖2︸︷︷︸penaltyterm+Cm∑i=1max{0,1−yi(〈w,φ(xi)〉+b)}

Algorithm used: Matlab: Interior point

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21. ufal.mff.cuni.cz ufal.mff.cuni.cz
1. Q=n+1Q+nα(R−nQ)

Exponential moving average

$$(1 - \alpha) Q_n + \alpha R_n$$

2. Incremental Implementation

New estimate for this machine is old estimate plus relative current reward divided by number of times we tried this machine.

3. uniformlyrandom action with probability .ε1−εε

\epsilon: Rate of exploration

4. discrete

Non-differentiable

5. won 10 out of 11 StarCraft II games against two professional players

6. 9h

Very high computational demands

7. ches

Can beat the previously best program after 3h of playing

8. beat 60 professional

Nickname: master

Online game server, the program did not reveal it was a bot

9. AlphaGo

Why is go interesting?

• High branching factor
• Difficult to write a good state evaluation heuristic
10. to rule them all

Plays all the games

11. human-normalized mean: 623.0%

Questionable information value because some games can be exploited a lot

12. 29 games out of 49 comparable or better to professional game players

A separate agent trained for each game

13. first

1959

14. Trial and error learning

Learning in animals

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22. pdfs.semanticscholar.org pdfs.semanticscholar.org
1. stochastic scenario

One input may have more than one label with non-zero probabilities.

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23. cw.fel.cvut.cz cw.fel.cvut.cz
1. VGGNet

conv3-64: receptive field 3x3, stride 1, 64 output channels

To resolve boundary: pad with zeros

MP: max pooling layer. Window 2x2, stride 2, aggregate with max.

Deep layers: more channels, smaller resolution, large transitive receptive field, more abstract features

2. negative log-likelihood of class probabilities (a.k.a. cross entropy)

Surrogate loss function (or objective function) for 0-1 loss

Why? So that it is differentiable.

3. Loss function

If we used 0-1 loss, the empirical risk would be the error rate.

4. class probabilities

To choose the class, we take the class with the highest predicted probability.

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24. cw.fel.cvut.cz cw.fel.cvut.cz
1. Empirical Risk Minimization

Optimization problem

2. Testing: confidence intervals

Filip: Is the estimation based on Hoeffding inequality commonly used in practice (research)?

A: Yes.

3. Hoeffding inequality

Strength: Valid irrespective of the underlying distribution

What distribution maximizes the error?

4. 1−2e−2lε2(b−a)2=γ

This equality binds the variables \epsilon, \gamma, l and (b-a).

5. [a,b]

The variables are bounded interval by an interval $$[a, b]$$.

6. Example (rolling a die):μ= 3.5,zi∈[1,6],ε= 0.5.
• Left graph
• Red line: True expectation (3.5)
• Blue band: \epsilon band around assumed expectation
• Right graph:
• Red curve: Portion of experiments that estimate a mean outside the blue band in the left graph
• Blue curve: Upper bound of the empirical established by Hoeffding inequality
7. how the interval widthεdepends onlandδ

3 interdependent variables:

• $$\epsilon$$
• $$l$$
• $$\delta$$

1. Given l and \delta, minimize \epsilon.
2. Given \epsilon and l, maximize \delta.
3. Given \epsilon and \delta, minimize l.
8. (RSl(h)−ε,RSl(h)+ε)

Confidence interval

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25. Sep 2019
26. cw.fel.cvut.cz cw.fel.cvut.cz
1. NP-complete

BF:

NP complete or NP hard

2. Seminars

Theoretical assignments every other week published one week in advance.

No evaluation, just discussion.

3. Written exam

90 minutes. Assignments similar to those discussed in the labs.

4. The course will run irrespective of the number of students. The lecture format will only be held if at least approx. 5 students come consistently.

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27. cw.fel.cvut.cz cw.fel.cvut.cz
1. maximum likelihood estimator

Boris:

2. unsupervised learning

Note that the validation set must still be labeled.

3. Bayes inference rule

Note that this only works for classification.

4. inf

Is this infimum?

5. how strong can they deviate from each other?

Example: For 0-1 loss, using Chebyshev inequality, using the loss bound of 1, we get the bound: $$\frac{1}{\epsilon^2 m}$$

6. drawn fromp(x,y)

The evaluation distribution must match the application distribution. In other words, we evaluate the inference with respect to p.

7. i.i.d.

independent identically distributed

8. R(h)

In case of 0-1 loss in classification, this is the error rate of h.

9. E(x,y)∼p

Expectation

10. minimises the loss

The distribution of (x, y) will be specified later.

11. related by an unknownjoint p.d.f.p(x,y)

We assume that x and y are interdependent.

12. loss function:Y×Y →R+

Simplest loss for classification: 0-1 loss: l(a,b) = 1 iff a != b

13. real valued variable

possibly a vector

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28. cw.fel.cvut.cz cw.fel.cvut.cz
1. Labs/Seminars: Thursday 9:15-10:45, 11:00-12:30 and 12:45-14:15, all in KN:E-112

Capacity: 30 people

2. Boris:

Don't hesitate to ask questions during the lectures.

Immediately after each lecture we will go to the cafeteria where you can contact us privately.

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29. cw.fel.cvut.cz cw.fel.cvut.cz
1. practical labs

Each task: Around 10 points plus up to 3 bonus points

2. before the class

Boris:

3. Seminar

Introductory lab session. Neither theoretical, nor practical.

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30. Aug 2019
31. cluster.ciirc.cvut.cz cluster.ciirc.cvut.cz
1. public key

To have a personal certificate generated by CIIRC, follow the instructions in the wiki.

2. Everyone with CIIRC account can access master node of the cluster via ssh at cluster.ciirc.cvut.cz

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32. internal.ciirc.cvut.cz internal.ciirc.cvut.cz
1. Ubuntu

Which version of Ubuntu is this guide intended for?

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33. Jul 2019
34. vprover.github.io vprover.github.io
1. All other usage is reserved.

Does this mean that modification of Vampire for research purposes is prohibited?

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35. wwwconference.org wwwconference.org
1. max

According to the paper (Cohen 1998), maximizing the permutation score is an NP-hard problem. The paper proposes a 2-approximation greedy algorithm.

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36. www.inference.vc www.inference.vc
1. latent representation

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37. Jun 2019
38. on.ipaslovakia.sk on.ipaslovakia.sk
1. Juraj Rosa

Bratr Marka Rosy z GoodAI

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39. May 2019
40. users.cs.duke.edu users.cs.duke.edu
1. \Uk7!U"shouldbe\U7!Uk"

The inverse is true. The text contains "U -> U^k".

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41. www.tptp.org www.tptp.org
1. Problem Generators

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42. qtube.eu qtube.eu
1. Moc emocí, málo informací a argumentů.

2. Vyber si niečo, za čím sa budeš hnať, až dokým ťa nezradí vlastné telo.

Myslím, že tento postoj je zároveň součástí ideologie autora tohoto textu.

3. Ľudí, ktorí chápu že spôsob, akým funguje naša spoločnosť, je chorý! Ľudí, ktorí vidia riešenia, nie problémy! Ľudí, ktorí kladú zaujímavé otázky a majú zaujímavé odpovede! Ľudí, ktorí sa neboja povedať svoj názor v spoločnosti, ktorá nové názory odsudzuje, no zároveň je na nich závislá!!!

Apel na emoce

4. si môžeme písať čo chceme

Tohle mi připomíná manipulativní heslo Parlamentních listů:

Nikdo nám nediktuje, o čem smíme psát.

Kritická analýza: Jeden svět na školách

5. Nikto nás nevlastní.

Zatím se mi zdá, že qTube vlastní níže podepsaný autor tohoto textu.

6. Epidémia depresie, ktorú zažívajú “rozvinuté štáty” je spôsobená nezdravým spôsobom myslenia a inherentne nesprávnymi konceptami, ktoré považujeme za pravdivé.

Chybí zdroje.

7. Ak nás za našu kontroverziu a excentrické vystupovanie odsúdite, je to v poriadku. Je to spôsob, ktorým filtrujeme tých správnych ľudí.

Co když vás odsoudím za manipulativní rétoriku?

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43. www.techlib.cz www.techlib.cz
1. HP Color LaserJet 6040 MFP

Color LaserJet cm6040 MFP

Viz screenshot v bodu 6 níže.

2. System -> Správa -> Tisk

Ubuntu 18: Settings -> Devices -> Printers -> Additional Printer Settings...

3. Server -> Nová -> Tiskárna

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44. Apr 2019
45. www.spiked-online.com www.spiked-online.com
1. Of course, it’s okay for them to fly – Emma Thompson jetted first-class from LA to London to lecture us plebs about all our eco-destructive holidaymaking.

This reminds me of the numerous shots of Al Gore at an airport or on an airplane in the 2006 environmentalist film An Inconvenient Truth.

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46. www.fel.cvut.cz www.fel.cvut.cz
1. 13136

Katedra počítačů

2. 13133

Katedra kybernetiky

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47. vprover.github.io vprover.github.io
1. SMT

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48. www.tptp.org www.tptp.org
1. a property of a person that is true for Andrew but not for Betty

Such property is implicit: = @ andrew

2. thf(heated_coffee_mix,axiom, ! [S: syrup] : ( ( heated_mix @ coffee @ S ) = coffee ) ).

A heated mixture of coffee and any syrup is still coffee.

3. thf(hot_mixture,axiom, ! [B: beverage,S: syrup] : ( hot @ ( heated_mix @ B @ S ) ) ).

A heated mixture is hot!

4. thf(heated_mix_type,type,heated_mix: beverage > syrup > beverage).

A heated mix is the application of heat to the mixture of a beverage with a syrup.

5. thf(heated_mix,axiom, heated_mix = ( ^ [B: beverage,S :syrup] : ( heat @ ( mix @ B @ S ))) ).

A heated mix is the application of heat to the mixture of a beverage with a syrup.

6. thf(mix_type,type,mix: beverage > syrup > beverage).

A mixture of a beverage with a syrup is a beverage.

7. thf(heat_type,type,heat: beverage > beverage ).

A heated beverage is a beverage.

8. thf(coffee_type,type,coffee: beverage).

Coffee is a beverage.

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49. en.wikipedia.org en.wikipedia.org

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50. www.topcoder.com www.topcoder.com
1. Nondeterministic Polynomial (NP)

Judging from the context, the author meant "NP hard".

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51. gamedev.cuni.cz gamedev.cuni.cz
1. various items to various characters

one item to one character

2. Magic Dance Dance Carpet

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52. Mar 2019
53. www.topcoder.com www.topcoder.com
1. Learning Challenge 1

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54. www.topcoder.com www.topcoder.com
1. same density

The density in the current test cases is no more than 0.2.

2. You can call the function solveDA(rule, da_params) multiple times

According to songzy12, it is necessary to call the function at least once to get a score. See discussion in the forum.

3. The edge weights are all 1.

There may be multiple edges between any pair of nodes.

I think you should accept multiple edges between the same pair of nodes.

4. T = (actual execution time of your script) - (DA API wait time) + (only the annealing time within DA)

"Total execution time(ms)" in the email is the time we are using for scoring that counts towards 180 sec. limit.

-- Meaning "Total execution time(ms)" in the email is the T, and "DA API wait time" is already excluded from there.

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55. www1.cenia.cz www1.cenia.cz
1. nařízení vlády č. 341/2017 Sb.

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1. 582Prague Pandas800521

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57. Feb 2019
58. tc3-japan.github.io tc3-japan.github.io
1. Each column-j cannot have any duplicate number

Moreover, this constraint ensures that each column has at least one number of each in $$K$$. The constraint ensures that each column contains each number in $$K$$ exactly once.

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59. Jan 2019
60. vyhodny-software.cz vyhodny-software.cz
1. Část A: Vytvoření bootovacího USB

Instrukce v této sekci jsou platné pouze pro Windows. Pokud máte Linux, následujte instrukce v jednom z následujících návodů:

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61. www.livenowthrivelater.co.uk www.livenowthrivelater.co.uk
1. it is most likely that these effects come from the breathing techniques

Why do you think so?

2. An example of why testing should always be blinded!

How exactly would that help in this case?

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62. Dec 2018
63. portal.mpsv.cz portal.mpsv.cz
1. 16. Jak vysoká bude podpora v nezaměstnanosti a jak dlouho ji mohu pobírat?

maximální výše PvN činí 0,58násobek průměrné mzdy v národním hospodářství za první až třetí čtvrtletí kalendářního roku předcházející kalendářnímu roku, ve kterém byla podána žádost o PvN.

Průměrná mzda v národním hospodářství v roce 2017: 28 761 Kč

Zdroj: Sdělení MPSV

Maximum v roce 2018: 0,58 * 28 761 = 16 681,38 Kč

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64. Aug 2018
65. www.ceskaposta.cz www.ceskaposta.cz
1. odměnu za předložení zboží a zastupování v celním řízení

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66. Jul 2018
67. drmemory.org drmemory.org
1. the Dr. Memory directory inside your profile directory

%APPDATA%\Dr. Memory

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68. tedfelix.com tedfelix.com
1. Add Users To "audio" Group

Why should I add myself to "audio" group?

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69. May 2018
70. github.com github.com
1. Hollywood principle

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71. stackify.com stackify.com
1. You can not only apply it to classes, but also to software components and microservices.

The principle can also be applied to source code files.

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72. Apr 2018
73. www.duolingo.com www.duolingo.com
1. plural

singular

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74. Feb 2018
75. blogs.msdn.microsoft.com blogs.msdn.microsoft.com
1. edge probes

It seems that the edge probe is a special case of the value probe.

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76. www.draconianoverlord.com www.draconianoverlord.com
1. you’ll need to know up front that you want your commit applied into multiple places, so that you can place it on its own branch

More specifically, you'll need to know up front all the places you want your patch commit applied into so that you can determine where to start the patch branch from.

2. if you can anticipate where a commit may/will be need to applied

This is an important assumption.

The way I understand it, cherry picking is intended to be used in case of unanticipated migration of code.

3. its own branch

The patch branch should start at a common ancestor of all the target branches. Since the broken code is in all the target branches, it must be in at least one of their common ancestors.

If we don't start from a common ancestor and merge the patch into all the target branches, at least one of the target branches will get some extra change along with the patch.

#### URL

77. Jan 2018
78. www.quora.com www.quora.com
1. call to a function defined in a DLL file

Is it really possible to get an "undefined variable" compilation error from a function defined in a DLL?

First of all, the calls to the DLL are only resolved by the linker. Second, I don't think the linker analyzes the definitions of the functions at all. Third, I don't think there are any "variables" in native binary code; there are just memory addresses and registers in play.

#### URL

79. holos.cz holos.cz
1. zavolám za 24 hod. jak se mi daří.

Komu?

#### URL

80. Dec 2017
81. chris.beams.io chris.beams.io
1. picking one and sticking to it is far better than the chaos that ensues when everybody does their own thing

#### URL

82. www.penwatch.net www.penwatch.net
1. The PowerShell equivalent to the Unix which command is called get-command.

Also try the command where in Windows command prompt. It does not only run in PowerShell.

#### URL

83. Nov 2017
84. agilemanifesto.org agilemanifesto.org
1. Individuals and interactions over processes and tools

Do not give up personal interaction in favor of a tool-supported process. "Když tomu nerozumím, tak se jdu zeptat."

#### URL

85. blog.lenss.nl blog.lenss.nl
1. /dev/sda

On my computer the device with the unused space is /dev/sdb. You can see it in the graphical utility Disks.

#### URL

86. blog.jdpfu.com blog.jdpfu.com
1. Chipset – ICH9 this is important

Why?

#### URL

87. docs.vmware.com docs.vmware.com
1. /dev/cdrom on /mnt/cdrom type iso9660 (ro,nosuid,nodev)

VMware Workstation Player 12:

/dev/sr0 on /media/ubuntu/VMware Tools type iso9660 (ro,nosuid,nodev,relatime,uid=1000,gid=1000,iocharset=utf8,mode=0400,dmode=0500,uhelper=udisks2)

#### URL

88. docs.vmware.com docs.vmware.com
1. The name to use to register the guest operating system

What does this mean?

#### URL

89. kb.vmware.com kb.vmware.com
1. Go to Control Panel > Uninstall a Program > Turn Windows features on or off to turn off Hyper-V.

This step was sufficient on my computer.

#### URL

90. nvie.com nvie.com
1. exists as long as the feature is in development

When the development of a feature takes a long time, it may be useful to continuously merge from develop into the feature branch. This has the following advantages:

• We can use the new features introduced in develop in the feature branch.
• We simplify the integration merge of the feature branch into develop that will happen at a later point.

#### URL

91. unspecified.wordpress.com unspecified.wordpress.com
1. ignorance rather than malice

When the author prefers to explain a phenomenon by ignorance rather than malice, he is applying Hanlon's razor.

#### URL

92. Jul 2017
93. bobah.net bobah.net
1. .

This character should be escaped by a backslash. The complete command would then be:

strings $PWD/bin/myapp | egrep '\.gcda$'


#### URL

94. Jun 2017
95. doc.froglogic.com doc.froglogic.com
1. We must ensure that precompiled headers are disabled when the code is instrumented.

Why?

#### URL

96. May 2017
97. www.thoughtworks.com www.thoughtworks.com
1. This could even have been done without assertions!

What does this sentence mean?

#### URL

98. tldp.org tldp.org
1. Every shared library has a special name called the soname''. The soname has the prefix lib'', the name of the library, the phrase .so'', followed by a period and a version number that is incremented whenever the interface changes (as a special exception, the lowest-level C libraries don't start with lib''). A fully-qualified soname includes as a prefix the directory it's in; on a working system a fully-qualified soname is simply a symbolic link to the shared library's real name''.Every shared library also has a real name'', which is the filename containing the actual library code. The real name adds to the soname a period, a minor number, another period, and the release number. The last period and release number are optional. The minor number and release number support configuration control by letting you know exactly what version(s) of the library are installed. Note that these numbers might not be the same as the numbers used to describe the library in documentation, although that does make things easier.

#### URL

99. Mar 2017
100. doc.qt.io doc.qt.io
1. OpenGL ES

#### URL

101. Feb 2017
102. bezicispici.blogspot.com bezicispici.blogspot.com
1. Ale je mi jasné, že to nejde, že bych jim to tam nakonec asi narušovala.

Jestli bys to narušovala záleží na povaze akce. Tvá účast v mužském kruhu by, myslím, vyžadovala, aby účastníci potlačili svou tendenci považovat Tě za ženu. Mohl by to umožnit rituál (samotný mužský kruh je rituálem; třeba by to stačilo) v kombinaci s vědomým odhodláním účastníků.

#### URL

103. git-scm.com git-scm.com
1. branch

source