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For example, I have a code snippet below of how I load in all of my game’s text for localization (i.e. get english translations of all text in my UI). public void LoadLocalizedText(string fileName) { localizedText = new Dictionary<string, string>(); // StreamingAssetsPath will always be known to unity, regardless of hardware string filePath = Path.Combine(Application.streamingAssetsPath, fileName); if (File.Exists(filePath)) { string dataAsJson = File.ReadAllText(filePath); // deserialize text from text to a LocalizationData object LocalizationData loadedData = JsonUtility.FromJson<LocalizationData>(dataAsJson); for(int i = 0; i < loadedData.texts.Length; i++) { localizedText.Add(loadedData.texts[i].key, loadedData.texts[i].value); } } else { // ideally would handle this more gracefully, than just throwing an error (e.g. a pop up) Debug.LogError("Cannot find file"); } isReady = true; }  I’ve been looking at online examples, and there’s a lot of obsolete examples regarding the use of unity’s WWW class and php. From what I understand the UnityWebRequest is now the way to go, but I’m confused how to use it with regards to the example above. I’m also trying to figure out how to connect to my SQLite database which is also stored in streamingAssets.  public SqliteHelper(string databaseFileName) { if (Application.platform == RuntimePlatform.WebGLPlayer) { // ???????? } else { tag = databaseFileName + ":\t"; string dbPath = Path.Combine(Application.streamingAssetsPath, databaseFileName); if (System.IO.File.Exists(dbPath)) { dbConnectionString = "URI=file:" + dbPath; } else { Debug.Log ("ERROR: the file DB named " + databaseFileName + " doesn't exist anywhere"); } } }  Now that I can’t just open an SqliteConnection, I have no idea if there’s a c# only solution, or if I have to learn php as suggested from tutorials such as this one. If someone could walk me through how to solve either of these problems I would greatly appreciate it. Semi streaming algorithm for 2 vertices connectivity Let $$G=(V,E)$$ be an undirected graph. given a pair of vertices $$s,t \in V$$, how can we construct a semi-streaming algorithm which determines is $$s$$ and $$v$$ are connected? Is there any way to construct such an algorithm which scans the input stream only once? Note that a semi-streaming algorithm is presented an arbitrary order of the edges of $$G$$ as a stream, and the algorithm can only access this input sequentially in the order it is given; it might process the input stream several times. The algorithm has a working memory consisting of $$O(n⋅polylogn)$$. How much security would s3bubble using AWS DRM Protected Video streaming actually be adding? As the title says, how much security would s3bubble using AWS DRM Protected Video streaming actually be adding? I watched s3bubble’s tutorial at https://www.youtube.com/watch?v=bC-tZhlYH8o but I cannot really tell how much safer this makes things. My target audience would be software engineers so assume reasonable technical capability, although the field is not security. Considering the video will still actually play in the browser, how hard is it really to still grab it anyway even if direct download urls will be encrypted. I am not quite getting how this security is applied. Surely they could just reverse engineer the player? Note simple screen capturing is out of scope. Analyzing a counting triangles streaming algorithm which uses$\ell_0$sampling I’m trying to analyze the following streaming algorithm for counting triangles (see below). It supposedly works also for dynamic graphs (i.e. "turnstile model", where edge deletions are allowed). 1. What am I missing? According to my (buggy) analysis of the reported estimator’s variance, the variance equal to 0 (see below). 2. How should I analyze the use in an $$\ell_0$$-sampler? (Currently, my analysis ignore it) Algorithm: 1. Init: Define $$k := O(\frac {n^3} {\epsilon^2 t})$$. Pick $$k$$ random subsets $$S_1, \ldots, S_k \subset V$$ each of size 3 (with repetitions). 2. Update: For each random subset of size 3, denoted $$S_i$$, maintain a counter $$x_{S_i}$$, which represent the number of internal edges in $$S_i$$, i.e. a number in $$\{0, 1, 2, 3\}$$. 3. Output: Use an $$\ell_o$$-sampler over the vector $$x = (x_{S_1}, \ldots, x_{S_k})$$ to pick an index $$j \in [k]$$ (i.e. $$j$$ is chosen uniformly at random from $$\{i \in [k] : x_{S_i} > 0\}$$). Compute $$z := \mathbb{1}[x_{S_j} = 3], N = \|x\|_0$$. Report $$\tilde{T} = N \cdot z$$. Analysis, where $$T$$ is the number of triangles: $$\begin{split} E[\tilde T] &= E[N \cdot z] \ &= N \cdot E[z] \ &= \|x\|_0 \cdot \Pr \left[ \mathbb{1} \left[ x_{S_j} = 3 \right] \right] \ &= \|x\|_0 \cdot \frac T {\|x\|_0} \ &= T \end{split}$$ Which seems to be ok. But for the variance: $$\begin{split} Var[\tilde T] &= Var[N \cdot z] \ &= N^2 \cdot Var[z] \ \end{split}$$ Then $$\begin{split} Var[z] &= Var \left[ \mathbb{1} \left[ x_{S_j} = 3 \right] \right] \ &= E \left[ \left( \mathbb{1} \left[ x_{S_j} = 3 \right] \right)^2 \right] – \left( E \left[ \mathbb{1} \left[ x_{S_j} = 3 \right] \right] \right)^2 \ &= E \left[ \mathbb{1} \left[ x_{S_j} = 3 \right], \mathbb{1} \left[ x_{S_i} = 3 \right] \right] – \left( \frac {T} {\|x\|_0} \right)^2 \ &= \Pr \left[ \mathbb{1} \left[ x_{S_j} = 3 \right], \mathbb{1} \left[ x_{S_i} = 3 \right] \right] – \left( \frac {T} {\|x\|_0} \right)^2 \ &= \left( \Pr \left[ \mathbb{1} \left[ x_{S_j} = 3 \right] \right] \right)^2 – \left( \frac {T} {\|x\|_0} \right)^2 \ &= \left( \frac {T} {\|x\|_0} \right)^2 – \left( \frac {T} {\|x\|_0} \right)^2 \ &= 0 \end{split}$$ Streaming algorithm for counting triangles in a graph As described in the reference, the algorithm (see at the bottom) supposes to output an estimator $$\hat T$$ for the # of triangles in a given graph $$G = (V, E)$$, denoted $$T$$. It is written that "it can easily be shown" that $$E[\hat T] = T$$ But unfortunately, I’m not seeing it. Trying to analyze $$E[\hat T]$$, I think as follows: • At line 1, denote the probability to randomly (and uniformly) choose an edge which is part of a triangle as $$p$$. Since triangles can share edges, $$\frac T m \le p \le \frac {3T} m$$ For example, consider the following case: The central triangle doesn’t add new edges to the # of possibilities to choose an edge which is part of a triangle. You can imagine a different configuration, in which there are only the 3 outer triangles and they don’t touch each other (in this configuration, we won’t see the central 4th triangle). In both cases ((case i) 4 triangles as seen in the image; (case ii) 3 disjoint triangles), the probability to choose an edge which is part of a triangle is 1 (although the # of triangles is different). • At line 2, the probability to choose uniformly at random a vertex which "closes a triangle" with the edge from the previous step is exactly $$\frac 1 {n-2}$$. Therefore I only see that $$T \le E[\hat T] \le 3T$$ What am I missing? Another question I have is regarding line 3. The stream is ordered, and we first pick a random edge $$(u, v)$$ (line 1), then a random vertex $$w$$ from $$V \backslash \{u, v\}$$ (line 2). I feel that the analysis should take into account that at line 3 we check whether $$(u, w)$$ and $$(v, w)$$ appear after $$(u, v)$$ in the stream. Maybe after I’ll understand the answer to my first question, it will be clearer. Algorithm: 1. Pick an edge $$(u, v)$$ uniformly at random from the stream. 2. Pick a vertex $$w$$ uniformly at random from $$V \backslash \{u, v\}$$ 3. If $$(u, w)$$ and $$(v, w)$$ appear after $$(u, v)$$ in the stream, then output $$m(n-2)$$. Else, output $$0$$. Also, although I didn’t see it written, I believe there’s an assumption that $$V$$ is known ahead. Reference: Data streams lecture notes by Prof. Amit Chakrabarti, section "15.3 Triangle Counting", https://www.cs.dartmouth.edu/~ac/Teach/data-streams-lecnotes.pdf Best regards Established Adult streaming website$20 – $30 daily earning Why are you selling this site? I need fund for another project I built this website about a month ago using aged domain. lots of traffic coming from Indonesia more than 40k daily traffic here is the live traffic: https://www.histats.com/viewstats/?act=2&sid=4381265 this include: wp-script lifetime$ 297 value you can check here: wp-script.com/full-access/
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PostgreSQL Streaming Replication with Switchover

I’m trying to provide streaming replication between a master (T1) and slave (T2) and swapping their roles when necessary (i.e letting T1 be a slave to T2). So far I am able to get this working if I’m able to shut down the T1 cleanly, as it undergoes the following process:

1. Shut down T1
2. Promote T2
3. Configure T1 to work as a slave by configuring recovery.conf
4. Startup T1.

I would also like to account for a scenario where T1 is unable to shut down cleanly (e.g a crash). When T1 is back up, I would like to use this as the master again. Since T1 and T2 may not have been in total sync before the crash (as there may have been some WAL records not sent by T1), I assume one way of getting T1 back up would be to

1. Disable writing to T2.
2. Create a base backup of T2 on T1.
3. Shut down T2 and configure it to be a slave.
4. Start T1
5. Start T2

My questions about the above steps are as follows:

• Would streaming replication work if I do not disable writing on T2?

• Must two clusters be completely consistent for streaming replication to start? If I make some writes to T1 before starting T2, how would T2 know which WAL segments it needs to catch up to T1? What if I make writes to both T1 and T2 before configuring T2 to be a slave?

• Assuming T1 and T2 were in sync before T1 crashed, and assuming that WAL Archiving was enabled, would I be able to place T1 in recovery mode and replay all the WAL segments generated by T2?

• Is there a better way to approach this problem?

Thanks!