h3 | Hexagonal hierarchical geospatial indexing system | Dataset library

A Flutter Android app I developed suddenly compiled wrong today.

Error:

Execution failed for task ':app:processDebugResources'.

Android resource linking failed /Users/xxx/.gradle/caches/transforms-2/files-2.1/5d04bb4852dc27334fe36f129faf6500/res/values/values.xml:115:5-162:25: AAPT: error: resource android:attr/lStar not found.

error: failed linking references.

Run with --stacktrace option to get the stack trace. Run with --info or --debug option to get more log output. Run with --scan to get full insights.

The build failed in 16 seconds.

Apparently throwError(error) is now deprecated. The IntelliSense of VS Code suggests throwError(() => new Error('error'). new Error(...) accepts only strings. What's the correct way to replace it without breaking my HttpErrorHandlerService ?

Targeting S+ (version 31 and above) requires that one of FLAG_IMMUTABLE or FLAG_MUTABLE be specified when creating a PendingIntent. I got it after updating target SDK to 31. the error always come after AlarmPingSender. But i dont know any class that used AlarmPingSender.

With regard to the Log4j JNDI remote code execution vulnerability that has been identified CVE-2021-44228 - (also see references) - I wondered if Log4j-v1.2 is also impacted, but the closest I got from source code review is the JMS-Appender.

The question is, while the posts on the Internet indicate that Log4j 1.2 is also vulnerable, I am not able to find the relevant source code for it.

Am I missing something that others have identified?

Log4j 1.2 appears to have a vulnerability in the socket-server class, but my understanding is that it needs to be enabled in the first place for it to be applicable and hence is not a passive threat unlike the JNDI-lookup vulnerability which the one identified appears to be.

Is my understanding - that Log4j v1.2 - is not vulnerable to the jndi-remote-code execution bug correct?

• Apache Log4j Security Vulnerabilities

• Zero-day in ubiquitous Log4j tool poses a grave threat to the Internet

• Worst Apache Log4j RCE Zero day Dropped on Internet

• ‘Log4Shell’ vulnerability poses critical threat to applications using ‘ubiquitous’ Java logging package Apache Log4j

This blog post from Cloudflare also indicates the same point as from AKX....that it was introduced from Log4j 2!

Update #1 - A fork of the (now-retired) apache-log4j-1.2.x with patch fixes for few vulnerabilities identified in the older library is now available (from the original log4j author). The site is https://reload4j.qos.ch/. As of 21-Jan-2022 version 1.2.18.2 has been released. Vulnerabilities addressed to date include those pertaining to JMSAppender, SocketServer and Chainsaw vulnerabilities. Note that I am simply relaying this information. Have not verified the fixes from my end. Please refer the link for additional details.

Apparently, the constexpr std::string has not been added to libstdc++ of GCC yet (as of GCC v11.2).

This code:

The standard defines several 'happens before' relations that extend the good old 'sequenced before' over multiple threads:

[intro.races]

11 An evaluation A simply happens before an evaluation B if either

(11.1) — A is sequenced before B, or
(11.2) — A synchronizes with B, or
(11.3) — A simply happens before X and X simply happens before B.

[Note 10: In the absence of consume operations, the happens before and simply happens before relations are identical. — end note]

12 An evaluation A strongly happens before an evaluation D if, either

(12.1) — A is sequenced before D, or
(12.2) — A synchronizes with D, and both A and D are sequentially consistent atomic operations ([atomics.order]), or
(12.3) — there are evaluations B and C such that A is sequenced before B, B simply happens before C, and C is sequenced before D, or
(12.4) — there is an evaluation B such that A strongly happens before B, and B strongly happens before D.

[Note 11: Informally, if A strongly happens before B, then A appears to be evaluated before B in all contexts. Strongly happens before excludes consume operations. — end note]

^{(bold mine)}

The difference between the two seems very subtle. 'Strongly happens before' is never true for matching pairs or release-acquire operations (unless both are seq-cst), but it still respects release-acquire syncronization in a way, since operations sequenced before a release 'strongly happen before' the operations sequenced after the matching acquire.

Why does this difference matter?

'Strongly happens before' was introduced in C++20, and pre-C++20, 'simply happens before' used to be called 'strongly happens before'. Why was it introduced?

[atomics.order]/4 says that the total order of all seq-cst operations is consistent with 'strongly happens before'.

Does it mean that it's not consistent with 'simply happens before'? If so, why not?

I'm ignoring the plain 'happens before', because it differs from 'simply happens before' only in its handling of memory_order_consume, the use of which is temporarily discouraged, since apparently most (all?) major compilers treat it as memory_order_acquire.

I've already seen this Q&A, but it doesn't explain why 'strongly happens before' exists, and doesn't fully address what it means (it just states that it doesn't respect release-acquire syncronization, which isn't completely the case).

Found the proposal that introduced 'simply happens before'.

I don't fully understand it, but it explains following:

• 'Strongly happens before' is a weakened version of 'simply happens before'.
• The difference is only observable when seq-cst is mixed with aqc-rel on the same variable (I think, it means when an acquire load reads a value from a seq-cst store, or when an seq-cst load reads a value from a release store). But the exact effects of mixing the two are still unclear to me.

In this programming problem, the input is an n×m integer matrix. Typically, n≈ 10⁵ and m ≈ 10. The official solution (1606D, Tutorial) is quite imperative: it involves some matrix manipulation, precomputation and aggregation. For fun, I took it as an STUArray implementation exercise.

I have managed to implement it using STUArray, but still the program takes way more memory than permitted (256MB). Even when run locally, the maximum resident set size is >400 MB. On profiling, reading from stdin seems to be dominating the memory footprint:

Functions readv and readv.readInt, responsible for parsing integers and saving them into a 2D list, are taking around 50-70 MB, as opposed to around 16 MB = (10⁶ integers) × (8 bytes per integer + 8 bytes per link).

Is there a hope I can get the total memory below 256 MB? I'm already using Text package for input. Maybe I should avoid lists altogether and directly read integers from stdin to the array. How can we do that? Or, is the issue elsewhere?

On the pandas tag, I often see users asking questions about melting dataframes in pandas. I am gonna attempt a cannonical Q&A (self-answer) with this topic.

I am gonna clarify:

1. What is melt?

2. How do I use melt?

3. When do I use melt?

I see some hotter questions about melt, like:

• pandas convert some columns into rows : This one actually could be good, but some more explanation would be better.

• Pandas Melt Function : Nice question answer is good, but it's a bit too vague, not much expanation.

• Melting a pandas dataframe : Also a nice answer! But it's only for that particular situation, which is pretty simple, only pd.melt(df)

• Pandas dataframe use columns as rows (melt) : Very neat! But the problem is that it's only for the specific question the OP asked, which is also required to use pivot_table as well.

So I am gonna attempt a canonical Q&A for this topic.

I will have all my answers on this dataset of random grades for random people with random ages (easier to explain for the answers :D):

It seems to me that Pandas ExtensionArrays would be one of the cases where a simple example to get one started would really help. However, I have not found a simple enough example anywhere.

To create an ExtensionArray, you need to

• Create an ExtensionDtype and register it
• Create an ExtensionArray by implementing the required methods.

There is also a section in the Pandas documentation with a brief overview.

There are many examples of implementations:

• Pandas' own internal extension arrays
• Geopandas' GeometryArray
• Pandas documentation has a list of projects with extension data types
  • e.g. CyberPandas' IPArray
• Many others around the web, for example Fletcher's StringSupportingExtensionArray

Despite having studied all of the above, I still find extension arrays difficult to understand. All of the examples have a lot of specifics and custom functionality that makes it difficult to work out what is actually necessary. I suspect many have faced a similar problem.

I am thus asking for a simple and minimal example of a working ExtensionArray. The class should pass all the tests Pandas have provided to check that the ExtensionArray behaves as expected. I've provided an example implementation of the tests below.

To have a concrete example, let's say I want to extend ExtensionArray to obtain an integer array that is able to hold NA values. That is essentially IntegerArray, but stripped of any actual functionality beyond the basics of ExtensionArray.

I have used the following fixtures & tests to test the validity of the solution. These are based on the directions in the Pandas documentation