AstBuilder::copy is unsound

https://github.com/oxc-project/oxc/blob/7f7b5ea9e83f37ff666d6f33fda7a9e27ea2aa07/crates/oxc_ast/src/ast_builder.rs#L71-L79
This method can be used correctly when a node is being removed from AST anyway, but there are multiple ways in which it can lead to unsound situations:
Aliasing violation:
fn do_bad_stuff(stmt: &Statement, ast: &AstBuilder) {
  if let Statement::BlockStatement(block) = stmt {
    let box1 = ast.copy(block);
    let box2 = ast.copy(block);
    // We now have two `Box<BlockStatement>` pointing to same `BlockStatement`
  }
}

Mutation through immutable ref:
fn do_bad_stuff2(stmt: &Statement, ast: &AstBuilder) {
  if let Statement::BlockStatement(block) = stmt {
    let block = ast.copy(block);
    // We can mutate, even though we only got an immutable ref
    block.body.clear();
  }
}

The double-free which #3481 fixes was likely caused by using copy.
We could make the method unsafe and caller will have to ensure it's only used when safe to do so. However, personally I think we should remove the API entirely because (1) it is difficult to reason about when it's safe to use and is therefore a footgun and (2) the performance hit from using safe alternatives is very minimal.
Unfortunately the alternatives are likely take or mem::replace which are not so ergonomic.

@Dunqing Please don't use this API from now on 😁 .
Use move_xxx APIs instead.

I don't think it's necessarily high priority to fix this. I just wanted to request that we don't introduce more uses of it now, as it will make it more work to remove it later.

@overlookmotel Can we use the CloneIn trait over this?

I think CloneIn is something different. Usually ast.copy is used when we want to extract a node from AST, and insert it somewhere else. Cloning would be very expensive if the node is the tip of a deep tree, and isn't necessary.

I think CloneIn is something different. Usually ast.copy is used when we want to extract a node from AST, and insert it somewhere else. Cloning would be very expensive if the node is the tip of a deep tree, and isn't necessary.

If we just want to take the sub-tree out and put it somewhere else, We should be able to use the move method instead. For all other cases if we don't clone the sub-tree and actually copy it in 2 places how can we prevent aliasing?

Yes, that's true, if you want to duplicate the sub-tree and put it in AST in 2 places, it must be cloned or it's UB. But all the places I saw ast.copy being used, it was to take it out of one place, and put it back in another.

So what is stopping us from replacing all of our usages with the move method? Wouldn't it be better if we deprecate this unsound method?

So what is stopping us from replacing all of our usages with the move method?

Nothing! We just haven't done it yet.
Unfortunately, using move methods is quite unergonomic - but such is the ways of Rust.
I would be really keen to see the back of this unsound method. I remain quite anxious about the soundness of Traverse, and would like to run Miri over the transformer to test it. But right now there's no point doing that because Miri is likely to go ape over all the ast.copys, and that'll obscure any other problem.

Only 8 uses of this evil function left in codebase. 5 in JSX transform, 3 in TS namespace transform. Dunqing and I have slowly eradicated the rest of this filth over the last few months.


Package structure

Hi,
I would like to discuss the structure of the package. Wouldn't it be good to split the scripts into a _reader.py and _writer.py file in the future? Possily a _widget.py or _header_info.py as well? I used this structure in my approach and I think it might be beneficial.
Best regards,
Christopher

@grlee77 what's your oppinion?

Yes, that sounds reasonable to me.

Ok, I will include the new structure in my first development branch then.


Spent in

consolidates spent and pending_spent into a single field

All tests pass, including reload and save tests


Hot update bug

Operating System: Windows10
Node Version: 10
NPM Version: 5
webpack version: 4.15

error:

yes:

I don't know. do you understand the picture?
/* harmony default export */ var carvue_type_script_lang_js = ({

/* harmony default export */ var pages_carvue_type_script_lang_js = __webpack_exports__["default"] = (carousel["default"]);

Should not be = ( carousel [ " default" );

It looks like you just deleted our issue template. It was there for good reasons. Please help us solving your issue by answering the questions asked in this template. I'm closing this. Please open a new issue with filled issue template. Also make sure your issue is not a question. Questions should be posted on Stack Overflow.


Allow table markup to be used to render Fabric table

Looks like the biggest difference between the table structure and the current implementation is the border-collapse property needs to be set for tables.
Converting sample markup from divs to tables.
Note: leaving 'display: table' and 'display: table-cell' properties so that users can choose tables or divs.
Fixes: https://github.com/OfficeDev/Office-UI-Fabric/issues/192

Nice!
What do you think about making the classes .ms-Table-head, .ms-Table-row, and ..ms-Table-cell optional when used inside a <table class="ms-Table">? We don't usually want to tie our styles to any one HTML structure, but in this case those are the only elements you'd find in a <table> anyway. Foundation and Bootstrap do something similar and it cuts out a lot of redundant markup.

That's a good point -- I'll add table, tr, and td properties too.

Almost too good. Only a couple nits - one of them is noted above and another is on line 47 and 87 - we could be using $ms-font-s. I'm unable to check or select an item in the table on the demo page as well. It also looks like line-height is causing the row to be 31px instead of 30px. Other than that, this looks good.


[BUG] FeatureFlagConfigurationSetting overrides all of value when updating property

Describe the bug
When a set method is called such as setClientFilters it overrides anything that is stored in value that is not part of the standard Feature Flag object.
Exception or Stack Trace
N/A
To Reproduce
FeatureFlagConfigurationSetting featureFlag = new FeatureFlagConfigurationSetting("Alpha", true);
featureFlag.setValue("{\"conditions\":{\"requirement_type\":\"All\"}}");
featureFlag.setClientFilters(filters);

When client filters is set it removes the conditions I set manually.
Expected behavior
The setting a value should override another part of the json object.

As discussed via internal email, this has been investigated and determined to be the intended behavior.  Closing this out.


[KOGITO-3715] Kogito Operator installer bundle

Signed-off-by: Ricardo Zanini<EMAIL_ADDRESS>See: https://issues.redhat.com/browse/KOGITO-3715
Now the community would deploy the desired Operator version easily without OLM:
kubectl apply -f http://github.com/.../v1.0.0/kogito-operator.yaml -n mynamespace

@ibek this is my gift for you
@radtriste do you mind taking a look/testing the Jenkins side? I believe I did it right... but who knows.
Many thanks for submitting your Pull Request :heart:!
Please make sure that your PR meets the following requirements:

[x] You have read the contributors' guide
[x] Pull Request title is properly formatted: [KOGITO-XYZ] Subject
[x] Pull Request contains link to the JIRA issue
[x] Pull Request contains a description of the issue
[x] Pull Request does not include fixes for issues other than the main ticket
[x] Your feature/bug fix has a unit test that verifies it
[x] You've tested the new feature/bug fix in an actual OpenShift cluster

Tomorrow I update the install.sh file.

@ricardozanini :) thanks a lot, looking forward to trying it out on K8s

LINT error can be ignored for now since this is the first PR to introduce the file.

lint fail on kogito-operator.yaml.

lint fail on kogito-operator.yaml.

LINT error can be ignored for now since this is the first PR to introduce the file.

hehehe


Deeplabv3+ on custom dataset

I have the dataset with the image and mask pair. Dataset the as only 1 label. Can you help me with the parameters to be updated for the training Deeplabv3+ on custom dataset

maybe you can use this repo: https://github.com/SegmentationBLWX/sssegmentation


[C5][environment][api-overview] Multi-Environment API Overview

Description:
Let's assume three environments with an API, "weather API".

Figure 01: Multi-Environment API Overview for Weather API

The user has to login to an environment and clicks on a button in the API overview page to get the Multi-Environment API Overview of the API.
The UI Service make requests to all environments to get API details.
JWT Grant Type is used to get access token from environments.

API version, lifecycle state is shown in the overview page.
Affected Product Version:

API Manager 3.0.0

Related Issues:
https://github.com/wso2/product-apim/issues/2629: Multi-Environment Authentication

Below is a sample UI of an environment panel

When the user clicks on the button "more" he will direct to the particular version and environment of the API
Before switching the user the below Alert Dialog will be popped.


Issue with deployProxy when using ContractFactory instantiated directly with ABI and Bytecode

Summary:
I encountered an error while using the<EMAIL_ADDRESS>plugin's deployProxy function. The error arises when I use a ContractFactory created directly with the ABI and Bytecode, whereas using getContractFactory works correctly.
Details:

Initial Problem:

When trying to use the deployProxy function from @openzeppelin/hardhat-upgrades, I received the following error:

Error: factory runner does not support sending transactions (operation="sendTransaction", code=UNSUPPORTED_OPERATION, version=6.4.0)

Setup:

Ethers.js version initially was 6.4.0.
Hardhat version used: 2.19.0
The problematic contract factory was created as follows:const RebalancerFactory = new ethers.ContractFactory(REBALANCER_FACTORY_ABI, REBALANCER_FACTORY_BYTECODE, deployer);

Working Alternative:

When I use hardhat<EMAIL_ADDRESS>getContractFactory, the error doesn't appear and everything works as expected:const RebalancerFactory = await ethers.getContractFactory("RebalancerFactory", deployer);

However, on ethers 6.4.0(which I had installed originally) I faced an issue related to the absence of the attach method on the ContractFactory:

Property 'attach' does not exist on type 'RebalancerFactory__factory'.

Current Status:

With<EMAIL_ADDRESS>using getContractFactory works flawlessly.
However, creating the ContractFactory directly with ABI and Bytecode (as shown above) still results in the aforementioned deployProxy error.

Steps to Reproduce:

Set up a Hardhat project with<EMAIL_ADDRESS>and<EMAIL_ADDRESS>Use the @openzeppelin/hardhat-upgrades plugin.
Attempt to create a contract factory using the direct ABI and Bytecode method:const RebalancerFactory = new ethers.ContractFactory(REBALANCER_FACTORY_ABI, REBALANCER_FACTORY_BYTECODE, deployer);

Use this contract factory with the deployProxy function.
Observe the error mentioned above.

Expected Behavior:
Both methods of creating a ContractFactory (direct ABI/Bytecode instantiation and using getContractFactory) should work seamlessly with the deployProxy function without any errors.

My issue is potentially caused by having an importer file that specifically imports a contract from an installed dependency which I'm  doing to avoid the issue described in #86

Can you share what is your use case for using a ContractFactory with ABI+bytecode?
The plugin normally expects getContractFactory("ContractName"...) because that provides access to the source code of the implementation contract, which we use for upgrade safety checks prior to deployments and storage layout comparisons prior to upgrades.

@ericglau in a hardhat project I'm installing a package that has upgradeable contracts, those packages are private but for the purposes of this discussion let's say this package is an openzeppelin upgradeable version of Uniswap/v3-core. Now in my hardhat project that installs this upgradeable @uniswap/v3-core-upgradeable package I would like to import the upgradeable UniswapV3Factory abi and bytecode from the relevant @uniswap/v3-core json artifact as shown here. However, as explained in the issue description attempting this fails:
const UniswapV3FactoryContractFactory = new ethers.ContractFactory(UPGRADEABLE_FACTORY_ABI, UPGRADEABLE_FACTORY_BYTECODE, deployerSigner);

const upgradeableUniswapV3Factory = await upgrades.deployProxy(UniswapV3FactoryContractFactory, [], {
  initializer: "initialize",
});

With the error:

Error: factory runner does not support sending transactions (operation="sendTransaction", code=UNSUPPORTED_OPERATION, version=6.4.0)

@ericglau it would be great to at least have the option(say called forceDeploy) to disable/ignore those safety checks.

it would be great to at least have the option(say called forceDeploy) to disable/ignore those safety checks.

We can consider adding an option similar to this.
However, I think your scenario is failing at an earlier step because the deployer that you are passing in does not seem to be a signer.  I notice the same error if I use ABI+bytecode according to your steps but do not pass in a signer when instantiating the contract factory.
If I pass in an actual signer (for example in tests, (await ethers.getSigners())[0]), I get a different error (which is the one I expect because source code is not found): The requested contract was not found. Make sure the source code is available for compilation
But aside from the signer issue, for now you may need to deploy the implementation and proxy contracts directly (using ERC1967Proxy.sol or TransparentUpgradeableProxy.sol) until we add the proposed option.

@ericglau thanks for investigating, yeah the deployer issue was a bug on my end.


rand without arguments

Rand with no arguments chrashes kookaburrashell.
Steps to reproduce the behavior:

Type "rand"

Nothing happens

OS: win10-64x
Version < 0.5.0

Fixed
Version <IP_ADDRESS>


[SPARK-49909][SQL][3.4] Fix the pretty name of some expressions

What changes were proposed in this pull request?
The pr aims to fix the pretty name of some expressions, includes: random, to_varchar, current_database, curdate, dateadd and array_agg.
(PS: The pr is backport branch-3.4, master pr is: https://github.com/apache/spark/pull/48385)
Why are the changes needed?
The actual function name used does not match the displayed name, as shown below:

Before:

After:

Does this PR introduce any user-facing change?
Yes, Make the header of the data seen by the end-user from Spark SQL consistent with the actual function name used.
How was this patch tested?

Pass GA.
Update existed UT.

Was this patch authored or co-authored using generative AI tooling?
No.

+1, LGTM. Merging to 3.4.
Thank you, @panbingkun.


Start embedded elasticsearch and setup indices during servlet initialization

With the current version, embedded elasticsearch is not started when the conductor server is started using the WAR file in the container.  Also, elasticsearch indices are not initialized in same mode.
Changes involve starting the embedded elasticsearch server and setting up indices during server context initialization. Since the same code can be used for Main and server mode, it is moved to util to avoid duplicity.

Pull Request Test Coverage Report for Build 3629

0 of 48 (0.0%)  changed or added relevant lines in 3 files are covered.
3 unchanged lines in 2 files lost coverage.
Overall coverage decreased (-0.07%) to 69.569%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

server/src/main/java/com/netflix/conductor/bootstrap/Main.java
0
7
0.0%

server/src/main/java/com/netflix/conductor/server/ServletContextListner.java
0
8
0.0%

server/src/main/java/com/netflix/conductor/bootstrap/BootstrapUtil.java
0
33
0.0%

Files with Coverage Reduction
New Missed Lines
%

server/src/main/java/com/netflix/conductor/bootstrap/Main.java
1
0.0%

server/src/main/java/com/netflix/conductor/server/ServletContextListner.java
2
0.0%

Totals

Change from base Build 3626:
-0.07%

Covered Lines:
11083

Relevant Lines:
15931

💛  - Coveralls


feat(update-notifier): replace custom update notify

Replace custom update notifier with https://www.npmjs.com/package/update-notifier. It is more efficient because it runs the update check asynchronously and has convenient configuration options to customize the alerts.
Remove unused dependencies: "i" and "request"

Example of the new update notification:

Note: The "-g" option is automatically added to the update instructions: https://github.com/yeoman/update-notifier#isglobal. It's not showing up in the screenshot above because I used this approach to test it out: https://github.com/yeoman/update-notifier/blob/main/example.js.

:tada: This PR is included in version 2.19.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


[Feature request] Allow all web requests to use a socks proxy for Tor

All wallets and other web requests (including security features like notifications) should be able to use Tor.

https://github.com/lnbits/lnbits/pull/678

I've been trying to make this work with socat and other proxy methods, in order to run LNBits on a VPS and connect it to a Start9 node, but everything I do fails (with ERROR | The backend for LndRestWallet isn't working properly: 'Unable to connect to http://<IP_ADDRESS>:9051'). I was able to previously make it all work with Umbrel and Tailscale, but that's not an option with Start9. It seems like native TOR (or at least a tor-proxy option) would be an elegant fix.


go test report empty cpuprofile file on wsl

What version of Go are you using (go version)?
in wsl:
go version go1.10.1 linux/amd64

Does this issue reproduce with the latest release?
unverified.
What operating system and processor architecture are you using (go env)?
GOARCH="amd64"
GOBIN=""
GOCACHE="/home/soren/.cache/go-build"
GOEXE=""
GOHOSTARCH="amd64"
GOHOSTOS="linux"
GOOS="linux"
GOPATH="/home/soren/workspace/golang/lib:/home/soren/workspace/golang"
GORACE=""
GOROOT="/usr/lib/go-1.10"
GOTMPDIR=""
GOTOOLDIR="/usr/lib/go-1.10/pkg/tool/linux_amd64"
GCCGO="gccgo"
CC="gcc"
CXX="g++"
CGO_ENABLED="1"
CGO_CFLAGS="-g -O2"
CGO_CPPFLAGS=""
CGO_CXXFLAGS="-g -O2"
CGO_FFLAGS="-g -O2"
CGO_LDFLAGS="-g -O2"
PKG_CONFIG="pkg-config"
GOGCCFLAGS="-fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=/tmp/go-build144534119=/tmp/go-build -gno-record-gcc-switches"

What did you do?
i was learn Go profile, and follow this chapter's step. but the cpuprofile produce a empty output file and has nothing when use go tool pprof prof command.
with the same code and command i test on windows, it works fine.
What did you expect to see?
What did you see instead?

Except

@thunayyanabdullah sorry, i cann't understand what do you mean

Dup of #22366, and looks like #22366 needs an expert on WSL and Windows internals and Linux.


chore: update release-please config

Use new repository for the action
Use nodejs-github-bot's PAT

According to the docs, it needs write permission: https://github.com/googleapis/release-please-action#workflow-permissions

According to the docs, it needs write permission: https://github.com/googleapis/release-please-action#workflow-permissions

Setting permissions: contents: read only affects permission of the GITHUB_TOKEN, which is only used by actions/checkout after this PR. The required permissions for googleapis/release-please-action are irrelevant, because it uses a different token IIUC.

I see. Updated, thanks!

RequestError [HttpError]: Error creating Pull Request: Resource not accessible by personal access token 🤔

I might have given the wrong permission. Will check when I'm on my computer unless someone else does it first

https://github.com/settings/personal-access-tokens/3911092

I forgot to give write access to the bot in https://github.com/nodejs/node-core-utils/settings/access. Now it's done.


[ocaml5-issue] Abort on Lin DSL Ephemeron test under debug runtime

I just spotted this in the CI logs after merging the Lockfree-removal PR.
The Lin DSL Ephemeron test aborts on Linux 5.0.0 when run under the debug runtime:
https://github.com/ocaml-multicore/multicoretests/actions/runs/3747248572/jobs/6363293260
random seed: 479273929
generated error fail pass / total     time test name

[ ]    0    0    0    0 / 1000     0.0s Lin DSL Ephemeron test with Domain
[ ]    0    0    0    0 / 1000     0.0s Lin DSL Ephemeron test with Domain (generating)
[00] file runtime/shared_heap.c; line 807 ### Assertion failed: hd == 0 || !Has_status_hd(hd, caml_global_heap_state.GARBAGE)
File "src/ephemeron/dune", line 13, characters 7-20:
13 |  (name lin_tests_dsl)
            ^^^^^^^^^^^^^
(cd _build/default/src/ephemeron && ./lin_tests_dsl.exe --verbose)
Command got signal ABRT.
[ ]    5    0    0    5 / 1000    61.1s Lin DSL Ephemeron test with Domain (shrinking:    6.0002)

I found another occurrence of this on main - again while testing Linux 5.0.0 debug:
https://github.com/ocaml-multicore/multicoretests/actions/runs/4494673332/jobs/7907445766
random seed: 470367559
generated error fail pass / total     time test name

[ ]    0    0    0    0 / 1000     0.0s Lin DSL Ephemeron test with Domain
[02] file runtime/domain.c; line 992 ### Assertion failed: di->backup_thread_msg == BT_INIT || di->backup_thread_msg == BT_TERMINATE
File "src/ephemeron/dune", line 13, characters 7-20:
13 |  (name lin_tests_dsl)
            ^^^^^^^^^^^^^
(cd _build/default/src/ephemeron && ./lin_tests_dsl.exe --verbose)
Command got signal ABRT.
[ ]    0    0    0    0 / 1000     0.0s Lin DSL Ephemeron test with Domain (generating)

It may be fixed on trunk though (I don't recall seeing this failure there).

The second failure looks much more like #402 which is now fixed.
The former failure we haven't seen since - partly because the Lin DSL Ephemeron test was removed ~6 months later as part of #367 to make Ephemeron test outcomes more reliable.
I'll therefore close this. We can reopen if the assertion failure shows up again.


basic_upload.py source video file missing

The video file originally hosted at https://raw.githubusercontent.com/wkaisertexas/wkaisertexas.github.io/main/upload.mp4 is currently unavailable, as such the basic_upload.py example script is currently not working.
This may just be a temporary issue but it may have been removed by accident. 😊

Oh shit, I redid my personal website and could not wonder for the life of me what that file was for. Really sorry about that. I will probably find some other video file on the internet.


release: publish release artifact

GitHub's stability guarantee for the archive is iffy, and we want metrics on downloads. See https://github.com/bazel-contrib/SIG-rules-authors/issues/11#issuecomment-1411222578

Did you want to add the bzlmod snippet to this one as well?

This repo doesn't have bzlmod at all yet. Something to add in another PR as it gets closer to 1.0.


Server Error when installing styled-components

https://bundle.run/styled-components@3.2.1?name=__npm_module_1
Command failed: /var/app/current/node_modules/.bin/npm install react@>= 0.14.0 < 17.0.0-0 /bin/sh: 17.0.0-0: No such file or directory

Thanks for the report.
It seems that I have a similar warning locally npm WARN<EMAIL_ADDRESS>requires a peer of react@>= 0.14.0 < 17.0.0-0 but none is installed. You must install peer dependencies yourself., but i'm still able to install it.
I'm not sure why this is happening yet, i'm working on it.

Ok it's live<EMAIL_ADDRESS>


Go back to using default SSL (not Wolf)

Now that Xaptum doesn't use Ed25519, we don't need to use WolfSSL over the stock SSL package.

@drbild was this fixed by PR #4 ?

Yes, thanks.


Fixed printing for DLIO output.

Currently, we print the dlp logger file even if DLIO is disabled. Also, we need to print only for rank 0—similar changes for all factories.

@zhenghh04 Can we merge this?


r18.dev not showing in Scraper list

Expected Behavior
r18.dev to show in scraper list. used Install-Module javinizer -RequiredVersion 2.5.12 to install.
Current Behavior
As title
Steps to Reproduce (for bugs)
Your Environment

Module version used: 2.5.12
Operating System and PowerShell version:7.1.3

Thanks. Will be fixed in 2.5.13.


Fix find multiple tags bug

find t/python t/java does not work as expected as reported in the PE-D.

Duplicate as closed issue #307. Please only discuss under this issue.


Update dependency versions

Address https://github.com/protocolbuffers/protobuf/security/advisories/GHSA-wrvw-hg22-4m67

@Mergifyio backport release-2.2


How to create the csv files?

Thanks for your sharing. Just want to ask how you determine which images will be included in the csv files.

Thanks for your interest in our work.
These splits are shared among many few-shot works, e.g. MAML and ProtoNets.
We copied these files from https://github.com/abdulfatir/prototypical-networks-tensorflow.

Thanks for your reply! I have downloaded the imgnet dataset tar file, not from the official site, but from kaggle. But it looks like all images in it are different from the ones in the csv file. Most ids in the csv file cannot be found in the original dataset. I have requested the data in the official site for about two weeks but have not received any response.
Actually I just want the original images in the csv files without resizing, and also their bounding box annotations. Do you have any solution? Thanks!

I know the official website of ImageNet is not working now. I have an alternative solution: you may download ImageNet via this link. However, I've never tried it before. So I can't ensure there are no issues for that link.
You can also send the authors an email to ask for the download link.

Yeah I have tried it, but all images in it do not overlap with mini-imgnet images. Anyway thank you!


feat(taikoon-ui): taikoon ui fixes, v3

[x] Adjust width of information section to 3/4
[x] Adjust text size on footer
[x] Include taikoon horizontal bar underneath information section

[x] ~Decide on animation (full-random-blink, coordinated-random-blink or horizontal-scroll)~

[x] Taikoon preview grid section

@bearni95 i fixed the title scope to taikoon-ui

@bearni95 i fixed the title scope to taikoon-ui

taikoon-ui is not a valid PR title anymore!


Stored Reason on HttpErrors

In the case where there are multiple reason strings for HttpErrors with the same code, end users may want to filter errors based on the reason string.

@jonparrott

Added a commit to change None handling. If reason is exposed, should use None instead of empty string.

Tests to green

@elibixby Is this PR still applicable?


typo: compatibile -> compatible

Tagging @rpetrusha since this change probably needs to be ported as well.


Add option on remote repository to propagate query params

We want to be able to control the effect of query params passed with the request to Artifactory.

I have read the CLA Document and I hereby sign the CLA

recheckcla

@jfroche - The feature you want to include is only supported on generic repos. Currently, the code doesn't make any distinction. So, it's possible to, say, create an NPM repo and use this flag. Anything other than generic repos may have issues if this is used.
To that end, I would ask that, if this flag is used, a warning is produced in terraform with:
"The flag 'forward_query_params' is only safe to use with generic repos" - or something similar. If you can do that, I can accept the PR

Quickly updated to add error when trying to set it with a repository which is not generic. Still need to test this.

I will pull this code tomorrow and give it a run

@jfroche  - So, I can this code - is your PR done?

Please update the resource docs, and I would ask that you update the sample.tf in the root of the repo.
I will be shutting down that test instance this week unless you can tell me you still need it

Please update the resource docs, and I would ask that you update the sample.tf in the root of the repo.

Done

I will be shutting down that test instance this week unless you can tell me you still need it

Ok

@chb0github is this good to go? It'd be nice to get this merged as it's a blocker on us adding some remote repos to our private instances

There is a merge conflict to be resolved. Please update, let me know and I will do it this week. We're swamped with swampup. Sorry for the delay

There is a merge conflict to be resolved. Please update, let me know and I will do it this week. We're swamped with swampup. Sorry for the delay

rebased, we should be fine

released


Docs site completely broken on IE11

On IE11 material-ui.com displays a blank page. With developer tools open, no useful error, just "Minified exception occurred; use the non-minified dev environment for the full error message and additional helpful warnings."

This was fixed. see #2845


velocity is zero in dataset

I am currently trying to fetch the velocity related to each data via:
FILENAME = 'training_0000/segment-1005081002024129653_5313_150_5333_150_with_camera_labels.tfrecord'

dataset = tf.data.TFRecordDataset(FILENAME, compression_type='')

for data in dataset:
	byte_array = bytearray(data.numpy())
	# grab the velocity
	velocity = open_dataset.Velocity()
	velocity.ParseFromString(byte_array)

however, all attributes to velocity such as velocity.v_x and velocity.w_x are all zero. Is there a reason why these attributes are zero?

you need to parse the frame and get velocity from it.
 for data in dataset: 	byte_array = bytearray(data.numpy()) 	# grab the velocity 	frame = open_dataset.Frame() 	frame.ParseFromString(byte_array)
// then iterate all labels and print their velocity.

I am not sure i follow, how does iterating the labels help in this case. Looking at https://github.com/waymo-research/waymo-open-dataset/blob/master/waymo_open_dataset/dataset.proto the velocity attribute isn't part of label.

you can real a little more about protobuf https://developers.google.com/protocol-buffers/docs/pythontutorial
A frame is a class that contains a lot of information.  Label is part of it. Label contains velocity.

Ah, my bad i didn't see that. This might be a dumb question but how do I get the velocity of the current vehicle?

First, note that the velocity we release are in global frame. In order to get velocity in vehicle frame, you need to rotate the velocity vector by pose though the norm of velocity should remain the same.
For SDC's velocity, you can either get it by taking derivates of the poses we released (we release position of the SDC at each time).
Alternatively, you can also get that by looking at this field . It is the velocity of the SDC at the image center time .

@peisun1115  How do we get the ground truth velocity for each object? Do you use some radar / estimate from hand labeled location? Thanks!
https://github.com/waymo-research/waymo-open-dataset/blob/dc5db38eb67898bf98c45e710291b62181998efe/waymo_open_dataset/label.proto#L52-L55

It is estimated from hand labeled location

thanks!

@peisun1115 Could we know the details about the velocity estimation process? Is it just calculated the distance between the centers of the same bounding box in neighboring frames and divided by the sample time interval?


Ability to pass just a single object

It could be nice if we could just pass one object, that contains a tag and children properties, like so:
h({
	tag: 'a',
	class: 'prev',
	href: prev.url,
	children: [
		h('span.icon'),
		h('span.title', prev.title)
	]
})

I'm doing a gist now to test this assumption.

i agree, both ways of writing are good. while i might tend to the former, others may prefer the latter

so I did up a gist over at: https://gist.github.com/balupton/c807a9be215723f0e849697097b99a50
I'm not actually sure this suggestion results in better code, it sure makes understanding the hierarchy easier, but it does double the lines of code

that gist makes things look more readable using the children style in my opinion

I could tolerate (what this will do to the implementation internals) if this only applies when the object is the first arg (if the tag name is not the string) and if all fields are under properties.
`` js
h({tag: ..., classes: ..., attributes: {...}, events: {...}, children: [...] })
with only a fixed number of accepted properties on the top object. then the code for handling any of those things can be refactored into a function and just called on the object.

I disagree that it's more "readable" but that is very subjective.

yes of course, so it may not be more "readable"
but there is something less subjective here if we believe in the following premise:

a function that accepts fewer arguments is simpler

also if we do tag and children, let's also do a textNode prop like
h({tag: ..., classes: ..., attributes: {...}, text: ... })

@ahdinosaur I am looking for the issue that you opened regards consolidating the hyperscript api, but can't find it.

@dominictarr thanks for the ping: https://github.com/hyperhype/hyperscript/issues/66

Passing objects is also easier from the Functional Programming perspective, if you want to compose h with some pure functions:
var link = {tag: 'a', href: 'http://check.it', children:['Check my link!']}
var applyStyle = style => elm => {...elm, style}
var styledNode = h(applyStyle(link))
  //=> h({tag: 'a', href: 'http://check.it', style: style, children:['Check my link!']})

would be easier than
var textInput = {tag: 'input, href: 'http://check.it', children:['Check my link!']}
var applyStyle = style => elm => {...elm, style}
var removeProps = props => obj => {
   ... // write function removing the props from the object
}
var toArray = obj => [obj.tag, obj, obj.children]
var styledNode = h.apply(this, toArray(removeProps(['tag', 'children'])(applyStyle(link))))
  //=> h('input', {type: 'text', style: style})

that you need now with the current implementation of h.


HDFS chart : unable to put files in the filesystem

I am trying to copy a local file on my HDFS, deployed with the helm chart.
I am doing :
helm install hdfs https://github.com/Gradiant/charts/releases/download/hdfs-0.1.0/hdfs-0.1.0.tgz -f hdfs-values.yaml
My hdfs-values file :
conf:
  coreSite:
  hdfsSite:
    dfs.replication: 2
dataNode:
  replicas: 2
  pdbMinAvailable: 2
  resources:
    requests:
      memory: "256Mi"
      cpu: "10m"
    limits:
      memory: "2048Mi"
      cpu: "1000m"

kubectl get pods show that all pods are running and ready.
NAME                                  READY   STATUS      RESTARTS   AGE
hdfs-httpfs-5686fd75df-2pgk7          1/1     Running     0          59m
hdfs-namenode-0                       2/2     Running     1          59m
hdfs-datanode-0                       1/1     Running     0          59m
hdfs-datanode-1                       1/1     Running     0          58m

I use port forward to access to the K8S HDFS from my local machine :
# namenode web UI
kubectl port-forward svc/hdfs-namenode 50070:50070

# hdfs port
kubectl port-forward hdfs-namenode-0 8020:8020

On my local machine, I have just unzipped a hadoop 2 distribution (2.10.0) and updated core-site.xml like this, to use the forwarded port :
<property>
    <name>fs.default.name</name>
    <value>hdfs://localhost:8020</value>
    <description>The name of the default file system.  Either the
      literal string "local" or a host:port for NDFS.
    </description>
    <final>true</final>
  </property>

# ok :
hdfs dfs -mkdir /jars
# not ok :
hdfs dfs -put helloSpark.jar /jars
20/12/11 09:50:53 INFO hdfs.DataStreamer: Exception in createBlockOutputStream
java.net.ConnectException: Connection refused
        at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
        at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:716)
        at org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:206)
        at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:532)
        at org.apache.hadoop.hdfs.DataStreamer.createSocketForPipeline(DataStreamer.java:259)
        at org.apache.hadoop.hdfs.DataStreamer.createBlockOutputStream(DataStreamer.java:1699)
        at org.apache.hadoop.hdfs.DataStreamer.nextBlockOutputStream(DataStreamer.java:1655)
        at org.apache.hadoop.hdfs.DataStreamer.run(DataStreamer.java:710)
20/12/11 09:50:53 WARN hdfs.DataStreamer: Abandoning BP-831521929-<IP_ADDRESS>-1607678530556:blk_1073741827_1003
20/12/11 09:50:53 WARN hdfs.DataStreamer: Excluding datanode DatanodeInfoWithStorage[<IP_ADDRESS>:50010,DS-4153f502-30da-42d7-a415-69601658066a,DISK]

I don't have any error in the datanodes logs, and in the namenode , the only error is the same as above.
Did I miss something in the configuration?
Thanks :)

It is working when :

I copy my jar into the namenode pod
I run the hdfs dfs -put command

So it must be something missing in my hadoop client configuration?

your hdfs cli must be part of the hadoop cluster. It is not enough to configure a port-forward of the namenode pod.
If your client is not a pod running in the same kuberntes cluster,  I recommend you to use the httpfs service:
https://hadoop.apache.org/docs/current/hadoop-hdfs-httpfs/index.html
Just use deployed the httpfs service:
kubectl get services

or ingress (your kubernetes cluster must have an ingress controller for this option):
kubectl get ingress

It is working, thank you!

It is working with httpfs, thank you very much!


Bug

Describe the bug
A clear and concise description of what the bug is.
Exception or Stack Trace
Add the exception log and stack trace if available
To Reproduce
Steps to reproduce the behavior:
Code Snippet
Add the code snippet that causes the issue.
Expected behavior
A clear and concise description of what you expected to happen.
Screenshots
If applicable, add screenshots to help explain your problem.
Setup (please complete the following information):

OS: [e.g. iOS]
IDE: [e.g. IntelliJ]
Version of the Library used

Additional context
Add any other context about the problem here.
Information Checklist
Please make sure that you have added all the following information above and checkoff the required fields otherwise we will treat the issuer as an incomplete report

[ ] Bug Description Added
[ ] Repro Steps Added
[ ] Setup information Added

Closing this as it seems to be a possible mistake. Please open a new issue with details if you need help.


Portfolio: Add contact form and finish version mobile

Add a contact form to my portfolio
Collect data by using the Formspree service.
In order to lay out the elements on the page you should use Flexbox.

Note Here all failed linter caused the GitHub I solved it by closing the pull request and opening a new


replecate.helpers.fileoutput

replecate.helpers.fileoutput
i got this error when try to use fooocus ai from replecate api
but the image is complete processing in the server

For support with Replicate please send an email to<EMAIL_ADDRESS>with more details, including the full response you're receiving, and details on how you are making the request (with the code or request if via a client library or the API directly)


[Question or request] could you add the Allocated, Allocated Ratio columns to the benchmarks

Hi, impressed by the performance of the SIMD map specifically.
Regarding the benchmarks, is it possible to add the allocation columns?

Ill add some whenever i find some time :)


A small fix for confusing Hero Modal weaopons list for #1774

As Nick says, we need to communicate that 1) the weapons are wands and staffs, 2) the hero can use magic, and 3) the hero has long-ranged attacks. Let me know what do you think @differentmatt.

Thanks for the tweak Nick!


Write a PHP program to convert time from 12-hour to 24-hour format

Description
Write a PHP program to convert time from 12-hour to 24-hour format
Input  : 12:05:45PM
Output : 12:05:45

How to contribute

Save the solution in program/convert_time_from_12-hour_to_24-hour_format.php file
Add convert_time_from_12-hour_to_24-hour_format.php file in convert-time-from-12-hour-to-24-hour-format folder

!assign


When sudo is configured to ask for a fingerprint, suda halts for a long time

Using a fingerprint "unhalts" suda.
It seems sudo is waiting for a fingerprint despite writing a password.

Need minimal vimrc and reproduction steps.


New UI to simplify Inputs and Outputs

Currently creating sensor inputs and outputs is a bit confusing. We need some sort of manager / wizard to help users add and use OPUs and OPUs

adding UI enhancement and also FEAGI dependent tag as this may be best paired with extended functionality from FEAGI about the robot

Technically we can do this without FEAGI changes but we should probably include this in the bigger rewrite (or the following one)


Update of jakarta.mail + generated files

fixes https://github.com/apache/camel-quarkus/issues/3764
Update of jakarta.mail to 1.6.7 and several generated files modified by the change.

Replaced with https://github.com/apache/camel/pull/7542


Remove serialization of parsed TLA+ expressions

tlaToAST in PGoTransIntermediateData

This maybe already be fixed. Please confirm, @fhackett.


Reconnecting ActiveRecord Inside Celluloid always fails.

Chasing an issue where my Celluloid script is unable to reconnect to the database if, in case the connection gets lost.
Found this discussion around celluloid and ActiveRecord from 2011 but nothing in it that help me here.
Tested the AR reconnect under JRuby Thread, the reconnect works there.
Here are the scripts I used to test the reconnect thing ..
With script running Celluloid (celluloid (<IP_ADDRESS>))

require 'celluloid'
require 'active_record'
require 'activerecord-jdbcpostgresql-adapter'

ActiveRecord::Base.establish_connection(adapter: 'postgresql', username: 'postgres', database: 'postgres', pool: 1, timeout: 10)

class Worker
  include Celluloid

  def start
    begin
      ActiveRecord::Base.connection_pool.with_connection do |conn|
         conn.execute('select pg_sleep(0.6);')
        conn.execute('select 1;')
        puts ".. Working .."
      end
    rescue Exception => exception
      puts "\n got exception #{exception.message}"
    end
  end
end

loop do
  Worker.new.async.start
  sleep 0.5
end

Using JRuby Thread.

require 'active_record'
require 'activerecord-jdbcpostgresql-adapter'
require 'securerandom'

ActiveRecord::Base.establish_connection(adapter: 'postgresql',username: 'postgres',database: 'postgres',encoding: 'unicode', pool: 1, timeout: 4)

loop do
  Thread.new() do
    Thread.current[:id] = SecureRandom.hex
    begin
      ActiveRecord::Base.connection_pool.with_connection do |conn|
        puts ".. #{Thread.current[:id]}"
        puts conn.to_s[0..75]
        conn.execute('select pg_sleep(0.6);')
        conn.execute('select 1;')
        puts "Working .. #{Thread.current[:id]}"
      end
    rescue Exception => exception
      puts "\ngot error from #{Thread.current[:id]} .... #{exception.message}\n"
    end
  end
  sleep 0.5
end

While I'm still searching for the reason for the above issue. Is anything specific I need to understand here.
NOTE:
All code was tested under
java version "1.8.0_144"
Java(TM) SE Runtime Environment (build 1.8.0_144-b01)
Java HotSpot(TM) 64-Bit Server VM (build 25.144-b01, mixed mode)

Hi @meetme2meat sorry for the very late reply.
I tried your script with Celluloid 0.18.0 & <IP_ADDRESS> today. Both gave me the same result
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..
.. Working ..

 got exception could not obtain a connection from the pool within 5.000 seconds (waited 5.002 seconds); all pooled connections were in use
.. Working ..

 got exception could not obtain a connection from the pool within 5.000 seconds (waited 5.003 seconds); all pooled connections were in use
.. Working ..
.. Working ..

Is this the exception you are referring to? Because could not obtain a connection from the pool within 5.000 seconds is not related to Celluloid as I understand here
I am using
gem 'activerecord', '~> 6.0.3'
gem 'activerecord-jdbcpostgresql-adapter', '~> 60.2', :platform => :jruby

&
java 11.0.2 2019-01-15 LTS
Java(TM) SE Runtime Environment 18.9 (build 11.0.2+9-LTS)
Java HotSpot(TM) 64-Bit Server VM 18.9 (build 11.0.2+9-LTS, mixed mode)

@dilumn well if it working then I'm good but as long as I remember it was not reconnecting the Database in celluloid but at the same time it was working perfectly with the traditional threading approach.
If I remember correctly I was not using 6.0.3 ActiveRecord it definitely some version 5 but not 6 so if it is working then I can close this issue.

Thanks @meetme2meat feel free to reopen this again if you face the issue again


EEI: blockhash to signal success or failure

Updated to march #77 and added a comment regarding the output memory.


Update Composer dependencies (2023-01-11-00-08)

Visual regression test passed!
View the visual regression test report


Woah! More Scrappy responses I see?

I added some more responses to mention and mentionKeyword.

hell yaaa

*hack yeah


feat: Migrate to Spring Boot 3

[x] fully migrate to Spring Boot 3
[ ] build the image using native compilation via graalvm to improve performance

Pre-Release: https://github.com/idealo/spring-endpoint-exporter/releases/tag/v1.0.0-next.1

Breaking Change: jar-path is now input-path to fit better when using scan mode FILE_SYSTEM

https://github.com/spring-projects/spring-boot/issues/33767

Replaced io.swagger.core.v3:swagger-core with org.springdoc:springdoc-openapi-starter-common to profit from existing native hints for the OpenAPI model classes. As springdoc-openapi-starter-common uses io.swagger.core.v3:swagger-core internally, no code change is necessary besides deleting the custom native hints that i introduced a few commits ago.


Feature Request: Trend Lines

Do you consider adding the trend line feature to this lib in the future? My goal is to produce trend lines as we see them on graphs like Google Fit's weight graph:

Dark blue is trend line and light blue is the actual data-points.
Looks like there is a similar implementation in Google Charts lib:
https://developers.google.com/chart/interactive/docs/gallery/trendlines
That would be a good feature to have. For now, I am going to try to calculate the trend line datapoints myself and try to use the curve feature of your lib to see if it can be done this way. But I guess the curve would look better if it was calculated in the lib :)

Hi! I'm no mathematician, and to my understanding, there are some differences in how to calculate this when we have different graphs like log/expo, but I have asked some AIs how to calculate linear trendlines and this is what I've got:
class SpotsTrendline extends DelegatingList<FlSpot> {
  SpotsTrendline(List<FlSpot> spots)
      : assert(spots.length >= 2),
        super(trendline(spots));

  static List<FlSpot> trendline(List<FlSpot> spots) {
    final mySpots = [...spots.map(MySpot.new)];
    mySpots.sort();
    final xValues = [...mySpots.map((e) => e.x)];
    final yValues = [...mySpots.map((e) => e.y)];
    final xMed = xValues.reduce((a, b) => a + b) / xValues.length;
    final yMed = yValues.reduce((a, b) => a + b) / yValues.length;
    final xSomaQuadDifMedia =
        xValues.reduce((value, element) => pow(element - xMed, 2) + value);
    final somaProdDifMedia = mySpots
        .map((e) => (e.x - xMed) * (e.y - yMed))
        .reduce((value, element) => value + element);
    final m = somaProdDifMedia / xSomaQuadDifMedia;
    final b = yMed - m * xMed;
    return [
      FlSpot(xValues.first, m * xValues.first + b),
      FlSpot(xValues.last, m * xValues.last + b),
    ];
  }
}

class MySpot extends FlSpot implements Comparable<MySpot> {
  MySpot(FlSpot spot) : super(spot.x, spot.y);

  @override
  int compareTo(MySpot other) {
    final xResult = x.compareTo(other.x);
    if (xResult != 0) return xResult;
    return y.compareTo(other.y);
  }
}

I only added a new LineChartBarData like the following:
const spots = [
  FlSpot(0, 3),
  FlSpot(2.6, 2),
  FlSpot(4.9, 5),
  FlSpot(6.8, 3.1),
  FlSpot(8, 4),
  FlSpot(9.5, 3),
  FlSpot(11, 4),
];
final data = LineChartBarData(
  spots: spots,
  isCurved: true,
  curveSmoothness: 0.18,
  color: Colors.amber,
  barWidth: 5,
  isStrokeCapRound: true,
  dotData: const FlDotData(
    show: true,
  ),
  belowBarData: BarAreaData(
    show: true,
    color: Colors.transparent,
  ),
);
final trendline = LineChartBarData(
  spots: SpotsTrendline(spots),
  isCurved: true,
  curveSmoothness: 0.18,
  color: Colors.cyan,
  barWidth: 5,
  isStrokeCapRound: true,
  dotData: const FlDotData(
    show: true,
  ),
  belowBarData: BarAreaData(
    show: true,
    color: Colors.transparent,
  ),
);

differences in how to calculate this when we have different graphs like log/expo, but I have asked some AIs how to calculate linear trendlines and this is what I've got:

Thanks for your research.
Btw, I think it can be done by the developer with any algorithm that they use. So I will close this issue as we don't need to touch the internal fl_chart codes


Alter encoding statements unnecessary.

Unless I'm missing something, all the execute(..) calls in the migrations are unneeded if you just set your database encoding correctly to start with. Plus, those types seems to be specific to MySQL and are b0rken on other databases. Works great on Postgres without them.
Thanks a ton for your efforts, and keep up the great work!

@preston Your 100% right. I can't remember why we had to add those at the time - but it's def covered by just setting encoding from the start now. Thanks.


Cache settings on page-data directory

The cache settings are not set for JSON files in the page-data directory as mentioned here:
https://www.gatsbyjs.org/docs/caching/
This fix means that old page-data files will not be returned, thus avoiding errors where new data is unavailable or old data is returned.

Must be a new addition then, haven't seen it mentioned earlier on the 'caching static sites' doc page.

Yes - you are correct.  I checked https://web.archive.org and it wasn't there in the previously checked version, which was in March.


Create a mirror tree from the given binary tree

To create a new binary tree which is a mirror image of the given binary tree.
Please assign this to me.
@HarshCasper

@Ayushee11  Just comment  " /assign " and you are good to go

/assign

@sanjitcodes Unassigned you and closing your PR. Don't take up someone else's Issue.


Still not working... user not found... someone please help

Urgent to gather some informayion

I will work on it.

The same.
It could be a great tool if it works


Fix #116: Update toAscii to accept a locale

WIP
https://github.com/danielstjules/Stringy/issues/116 brought up the issue that toAscii needs to be locale-aware when performing transliteration.
For example, Ö was added in https://github.com/danielstjules/Stringy/commit/2fe42b46a1d89dd829414f6c498c71d63e3b6db7 but a regression was later committed in https://github.com/danielstjules/Stringy/commit/ee4b89c2f8aef09d740f2dccd193fe3f8e64b33f This is because in german, Ö should map to OE, while in most other languages, it should map to O.
The regression (/breaking change) occurred in a minor release (2.2.0), but it's unclear if this PR would force a new major. Simply adding the locale parameter would indicate a minor bump. I've also been careful not to modify or remove the call to charsArray from toAscii since some users overwrite its implementation in their projects.

Closing. Will target a new major and merge into https://github.com/danielstjules/Stringy/tree/3.x-dev Will also commit a breaking change by placing the locale parameter before removeUnsupported


Settle for configuration parameter names

Now that most configuration parameters should be in place, it is time to check what comparable parameters are called in Captain as well as Polybar. If possible, we should then allow for both names to be used, in order to make for a high compatibility and easy migration between all three projects. Once that's done, all parameters should be properly documented, ideally in a Wiki here on GitHub.

See:

polybar config
Captain config

Incomplete, but a start:

polybar
Captain
i3blocks

wm-name
name
n/a

modules-left, modules-center, modules-right
format
n/a

width
?
n/a

height
height
n/a

bottom
dock
n/a

background
background
background

foreground
foreground
color

line-size
``
?

line-color
line
?

module-margin
offset
n/a

exec, command
n/a
command

interval
reload
interval

?
overline
?

?
underline
?

click-left
mouse-left
?

click-right
mouse-right
?

click-middle
mouse-middle
?

scroll-up
scroll-up
?

scroll-down
scroll-down
?

?
?
min-width

Overhauled them with v1.0.0


Update openshift starter workflow

Once we release oc-new-app action then we should update the starter workflows for the openshift.
As it is using scripts to deploy application on openshift, but after the release of oc-new-app action we can change that with this action as it satisfies our requirements.
Also, since we have released v2 version of push-to-registry action and buildah-build action, so it's worth updating these actions in the starter workflows.

PR: https://github.com/actions/starter-workflows/pull/852


Worker-Pouch not working with new pouchdb version 6.0.4

Hi,
I have updated to the latest pouchdb version & noticed that the worker-pouch which I have been using with the old pouchdb version 5.4.5 is not working with this new version.
Wanted to know what might be the problem here & when can this be fixed?
Thanks,
Ajinkya

Hi!
I also just noticed the same! Have not had time to dig into it yet.
Thanks!
On 8 September 2016 at 14:46, Ajinkya Udgirkar<EMAIL_ADDRESS>wrote:

Hi,
I have updated to the latest pouchdb version & noticed that the
worker-pouch which I have been using with the old pouchdb version 5.4.5 is
not working with this new version.
Wanted to know what might be the problem here & when can this be fixed?
Thanks,
Ajinkya
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/nolanlawson/worker-pouch/issues/27, or mute the
thread
https://github.com/notifications/unsubscribe-auth/AFU80iRla76wzXHy9QrUCZEvivRgIJFuks5qoAO_gaJpZM4J383I
.

Thanks for reporting. socket-pouch probably has this issue too.

Any update on this issue?

Nope I am swamped right now and would appreciate all the help I can get. 😅

fixed in #30


Fixes #6113 : Anchor custom link on homepage produces bad href

Can you provide a repro of the issue? We are lost in the ifs

Repro steps in the issue : #6113

Line endings in some files were fixed in the Orchard source, that's why this PR now has merge conflicts.
Please sync your fork with the Orchard repo and merge the target branch of this PR into your branch (for a guide see: https://help.github.com/articles/syncing-a-fork/ or alternatively if you use GitExtensions you can simply add https://github.com/OrchardCMS/Orchard.git under Repository/Remote repositories, do a fetch all and then merge the target branch into your branch).
If you have files appearing changed in your working directory that you didn't change but were actually touched by git to normalize line endings then you don't need to commit those (these were also fixed): clear the contents of the .gitattributes file in the repository root or remove the file, then do the merge. The changed files won't appear now (.gitattributes instructs git to care about line endings) but please don't commit the .gitattributes change (you can simply reset that change).
There shouldn't be too many merge conflicts and most of those should be resolved automatically by the merge tool (in GitExt hit Start merge tool and it will be able to do the merges).
Sorry about the inconvenience. On the upside this leftover from the hg to git conversion is now gone :-).
If you need further help just ping me here.

PR sync'd


Use Python instead of curl to download test file on validate-runtime-image target

What changes were proposed in this pull request?

Fixes https://github.com/elyra-ai/elyra/actions/runs/12429390840/job/34702792058
Apparently, the new pytorch image doesn't include curl anymore, which causes validate-runtime-image to fail.
How was this pull request tested?

Tested on my fork through this run.
Developer's Certificate of Origin 1.1
   By making a contribution to this project, I certify that:

   (a) The contribution was created in whole or in part by me and I
       have the right to submit it under the Apache License 2.0; or

   (b) The contribution is based upon previous work that, to the best
       of my knowledge, is covered under an appropriate open source
       license and I have the right under that license to submit that
       work with modifications, whether created in whole or in part
       by me, under the same open source license (unless I am
       permitted to submit under a different license), as indicated
       in the file; or

   (c) The contribution was provided directly to me by some other
       person who certified (a), (b) or (c) and I have not modified
       it.

   (d) I understand and agree that this project and the contribution
       are public and that a record of the contribution (including all
       personal information I submit with it, including my sign-off) is
       maintained indefinitely and may be redistributed consistent with
       this project or the open source license(s) involved.

looks good to me, thank you, @caponetto, I have noticed the error in my PR as well.

Elyra Tests / Lint Server is also failing on main so I think this PR is good to be merged.

I made some changes in lint server section in my PR. E226 related
https://github.com/elyra-ai/elyra/pull/3167/commits/a8722faba44fe7987db5d80fef2438712789b5ec#diff-4f0535277f973d16f4972b236075db911a09293effd07706b45fe787c133e18e


OCPBUGS-4409: Support converting HTML links to Markdown

The operator uses a handy library for converting HTML-like text to
markdown. This is nice for content text that informs users about what to
do with specific findings because it's formatted (e.g., adding lines
around formatted code snippet).
Unfortunately, the parser didn't account for links, which contain
multiple XML elements. The original  tag was preserved but the
actual link reference in href=example.com was dicarded.
This results in none of the links we put in content getting rendered in
the actual CRs where users can read and use them.
This commit adds some special handling for XML start elements to detect
cases where we're dealing with links and ensures they get propogated
into the final Markdown representation.

/retest
Issues standing up the environment.

/jira refresh

@mkumku PTAL for px-ack
@sheriff-rh PTAL for docs-ack
@xiaojiey PTAL for qe-ack


XCode 15, Firebase SPM - Fatal error "An attribute list cannot appear here"

Description
XCode 15, Firebase SPM - Fatal error "An attribute list cannot appear here"
Reproducing the issue
No response
Firebase SDK Version
9.6.0
Xcode Version
15.0
Installation Method
Swift Package Manager
Firebase Product(s)
Authentication
Targeted Platforms
iOS
Relevant Log Output
XCode 15, Firebase SPM - Fatal error "An attribute list cannot appear here"

If using Swift Package Manager, the project's Package.resolved

Expand Package.resolved snippet

Replace this line with the contents of your Package.resolved.

If using CocoaPods, the project's Podfile.lock

Expand Podfile.lock snippet

Replace this line with the contents of your Podfile.lock!

Duplicate of #11840. See the resolution and discussion there.


chore: Changed docker name format for accessbility while ensuring no duplications

Motivation
Current docker container names are barely legible on graphical and command line interfaces

Modification
Make the name start with service name, then uuid, e.g. Lobby-1_9fda2d26-f201-4505-a041-15e73937bc57
Result
A easier to manage docker name

lgtm.


NuGet error: Package is not compatible with framework version, does not support any target frameworks

Issue545.csproj.txt
When adding a 19.1.602 project, the following error is given:
error NU1202: Package eXpandSystem 19.1.602 is not compatible with net461 (.NETFramework,Version=v4.6.1).
Package eXpandSystem 19.1.602 does not support any target frameworks.

eXpandLib seems be the only package to not have this.
csproj I tested with:
<Project Sdk="Microsoft.NET.Sdk">

  <PropertyGroup>
    <TargetFramework>net461</TargetFramework>
  </PropertyGroup>

  <ItemGroup>
    <PackageReference Include="eXpandSystem" Version="19.1.602.0" />
  </ItemGroup>

</Project>

yes i see the problem, though i missed it before, maybe my clocal sources conflict somehow. I am working on it

I am not sure why it happens because I cannot repro it anymore, after Clear-NugetCache the problem stopped. I can create a new empty XAF solution and install the eXpandSystem package. Can you try it?

I just tested this on the buildserver, issue is there too. We can't use this version in our projects until there is a fix or an easy workaround.

did you clean the nuget cache in your buildserver?

Yes, I did

tried on another machine fresh install DX works also

I'll try to find the error later, if I have found the problem I'll report it.

feel free to ping me on the chat while u doing it

The DevExpress.XAF repository includes commits that relate to this task:

lab-Pipeline uses wrong artifacts #525 #545

Please update the related Nuget packages and test if issues is addressed. These are nightly nuget packages available only from our NugetServer.
If you do not use the Xpand.XAF.Modules directly but through a module of the main eXpandFramework project, please wait for the bot to notify you again when integration is finished or update the related packages manually.
Thanks a lot for your contribution.


Code Style Checkers Complain on the "Unused" Private Applier Methods

Since recent times, we agreed to use package-private (a.k.a. default) access level for event applier methods.
Though, currently, when launching a Spine-based project, the Spine Grade plugin logs following:
io.spine.server.model.MethodAccessChecker warnOnWrongModifier
WARNING: Event applier method my.aggregate.Fqn.on() must be declared 'private'.

UPD.
It is decided to:

use private access level for applier methods;
use info logging level to report the wrong access level;
develop custom validation rules for PMD and CheckStyle, which would describe the access level practices.

Also, it is required to change the doc of @Apply according to this resolution.

Which static analyzer did cause the unused problem?
Can we tune the analyzers and make the API of aggregates safer instead?

@armiol, the idea to have private appliers is to ensure that nobody can touch an aggregate from outside.  Why did we surrender that easy?

The problem is not on our side. We cannot tune the static code analisers for all our clients.

@dmdashenkov, is it solved (or at least eased) by your recent efforts related to PMD?

It is resolved now. With a bit of configuration of PMD and fix in the Spine own access level check, this is not an issue anymore.


Try without reflection when using data augmentation

Reflection is leaving us with images that have Sorghum heads but no corresponding bounding boxes. We should either:

Remove reflection
Correct the bounding boxes when we use reflection


Embedded watcher only working if lib declares its own embedded_watcher feature

Bevy version
0.12.1
[Optional] Relevant system information
$ cargo --version
cargo 1.75.0 (1d8b05cdd 2023-11-20)
$ uname -a
Darwin Marks-MacBook-Pro.local 22.6.0 Darwin Kernel Version 22.6.0: Wed Jul  5 22:21:56 PDT 2023; root:xnu-8796.141.3~6/RELEASE_X86_64 x86_64

If your bug is rendering-related, copy the adapter info that appears when you run Bevy.
`AdapterInfo { name: "Intel(R) Iris(TM) Plus Graphics", vendor: 0, device: 0, device_type: IntegratedGpu, driver: "", driver_info: "", backend: Metal }`

What you did

Create a new library for bevy

mkdir repro_lib && cd repro_lib
cargo init --lib
cargo add bevy --no-default-features --features bevy_asset,bevy_pbr,bevy_core_pipeline,bevy_render,multi-threaded

Create a plugin with an extended material with a custom fragment shader

lib.rs
use bevy::{
    asset::embedded_asset,
    pbr::{ExtendedMaterial, MaterialExtension},
    prelude::*,
    render::render_resource::AsBindGroup,
};

pub struct MyMaterialPlugin;

impl Plugin for MyMaterialPlugin {
    fn build(&self, app: &mut App) {
        // Explicit `src/` required because path is `src/frag.wgsl` so default
        // `/src/` doesn't work
        embedded_asset!(app, "src/", "frag.wgsl");
        app.add_plugins(MaterialPlugin::<MyMaterial>::default());
    }
}

pub type MyMaterial = ExtendedMaterial<StandardMaterial, MyMaterialExtension>;

#[derive(Asset, Default, Debug, Clone, TypePath, AsBindGroup)]
pub struct MyMaterialExtension {}

impl MaterialExtension for MyMaterialExtension {
    fn fragment_shader() -> bevy::render::render_resource::ShaderRef {
        "embedded://repro_lib/frag.wgsl".into()
    }
}

frag.wgsl
#import bevy_pbr::forward_io::{VertexOutput, FragmentOutput};

@fragment
fn fragment(in: VertexOutput) -> FragmentOutput {
    var out: FragmentOutput;
    out.color = vec4(0.2);
    return out;
}

Create an example for the library to help with prototyping and iterating:

cargo add --dev bevy --feature embedded_watcher,dynamic_linking
mkdir examples

examples/example.rs
use bevy::{prelude::*, render::mesh::shape::Plane};
use repro_lib::{MyMaterial, MyMaterialPlugin};

fn main() {
    App::new()
        .add_plugins(DefaultPlugins)
        .add_plugins(MyMaterialPlugin)
        .add_systems(Startup, setup)
        .run();
}

fn setup(
    mut commands: Commands,
    mut meshes: ResMut<Assets<Mesh>>,
    mut materials: ResMut<Assets<MyMaterial>>,
) {
    commands.spawn(Camera3dBundle {
        transform: Transform::from_translation((0.0, 3.0, 10.0).into())
            .looking_at(Vec3::ZERO, Vec3::Y),
        ..default()
    });
    commands.spawn(MaterialMeshBundle {
        mesh: meshes.add(Plane::from_size(10.0).into()),
        material: materials.add(MyMaterial {
            // no Default for ExtendedMaterial
            base: default(),
            extension: default(),
        }),
        ..default()
    });
}

Run the example

cargo run --example example

Change the fragment shader while the example is running, e.g. changing the value assigned to out.color.

What went wrong
When making changes to the fragment shader with the example running, I would expect the fragment shader to be hot-reloaded, due to the embedded_watcher feature - but it isn't. Even moving the feature setting from [dev-dependencies] to [dependencies] does not fix this.
Additional information
I've debugged the code and the cause seems to be that in the embedded_asset macro, the feature attribute for embedded_watcher is evaluating to it being disabled.
        #[cfg(feature = "embedded_watcher")]
        let full_path = std::path::Path::new(file!()).parent().unwrap().join($path);
        #[cfg(not(feature = "embedded_watcher"))]
        let full_path = std::path::PathBuf::new();

I confirmed this by hacking print statements for the value of full_path and of cfg!(feature="embedded_watcher") to the macro code in bevy_asset. The output showed full_path was empty, and the feature off.
My suspicion is that that the cfg attribute in the macro is being evaluated in the context of the library. I think I've confirmed this by adding the it as a feature on the library itself, and enabling it for the example, which fixes the issue:

Change the library's Cargo.toml to include

[features]
embedded_watcher = []

Run the example with cargo run --example example --features embedded_watcher

Modify the shader - it is now reloaded.

This looks like it was already fixed in https://github.com/bevyengine/bevy/pull/11370


Introduce services attribute to the IdP template list model

Is your feature request related to a problem? Please describe.
An IdP template describes a specific Identity Provider configuration. Now this configuration can support various features; authentication, provisioning, etc. It makes sense to have this information while listing templates, in order to choose a template to cater to certain requirements.
Describe the solution you would prefer
Introduce a services attribute to the existing IdP template model. One suggested services object is described below.
{
    name: "Authentication",
    displayName: "Authentication Capability",
    logo: "authentication"
}

When the above model is embedded into the template model, it would look like this.
{
    name: "Google"
    ...
    services: [
        {
            name: "Authentication",
            displayName: "Authentication Capability",
            logo: "authentication"
        }
    ]
    ...
}

If a template is configured to use a specific authenticator / provisioning connector, can't we derive the template service type from that? what is the purpose of having a seperate field? can there be any other service types?

@emswbandara

If a template is configured to use a specific authenticator / provisioning connector, can't we derive the template service type from that? what is the purpose of having a separate field? can there be any other service types?

The reason is both authenticator and provisioning connector information is a part of the full template response. Therefore during the listing, we don't have enough information to interpret services information unless we make additional API calls per each template, which would be unnecessary.

@tharindu-bandara in that case, we can process the template object from the backend and if there is a authenticator configured, we can add a service attribute to the template list model. However i feel its redundant to have the displayName - Authentication Capability  and logo - authentication. Can't you derive that from the UI itself if we send the service as 'authentication'?
services: [
    {
        name: "Authentication",
        displayName: "Authentication Capability",
        logo: "authentication"
    }
]

@tharindu-bandara in that case, we can process the template object from the backend and if there is a authenticator configured, we can add a service attribute to the template list model. However i feel its redundant to have the displayName - Authentication Capability and logo - authentication. Can't you derive that from the UI itself if we send the service as 'authentication' as follows?
services: [
       "Authentication", "Provisioning"
]

+1. We can derive it from the UI side, as the services are common for all templates.


Add LB IPv6 to the outputs

First of all a word of thanks, this is project is awesome!
This PR simply adds the IPv6 address of the load balancer to the outputs.

Great work folks, and awesome first contribution @captnCC! Just released under v1.5.4 🚀


feature request: export an unregister method for returning to Node's require methods

In my Jest config I have import '@esbuild-kit/cjs-loader'; so that the config can load node modules that use ESM.
However, because we don't have a way to unregister the loader, it continues to be used through all of the tests, slowing down Jest and double-compiling those that we want transformed.
Ideally, it would be something like:
import { unregisterLoader } from '@esbuild-kit/cjs-loader';
import { } from 'esm module 1';
import { } from 'esm module 2';

unregisterLoader();

// rest of Jest config.

Why would it be double compiled? And what's the slowdown you're experiencing?

We have a jest transform transforming the code.
So esbuild-kit is transforming code when it is requireed, AND jest is transforming the code through it's module loader.

Hi @privatenumber -
I decided that our goals aren't the same and it would be best to build our own ESM loader using babel-core so you don't need to make changes you don't want and we're not blocked by bugs introduced by nuances in ESBuild.
I truly appreciate your hard work on this project. You helped us finally be able to use third-party ESM modules. I hope we can use your work again in the future.


Training code implementation

`
import matplotlib.pyplot as plt
import cv2
import sys
import os
from PIL import Image, ImageDraw
from utils.utils import fan_NME, show_landmarks, get_preds_fromhm
import numpy as np
from skimage import io
import shutil
from torch.autograd import Variable
import time
import copy
from torch import nn
import torch
import math
import matplotlib
matplotlib.use('Agg')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

class AdaptiveWingLoss(nn.Module):
    def __init__(self, omega=14, theta=0.5, epsilon=1, alpha=2.1):
        super(AdaptiveWingLoss, self).__init__()
        self.omega = omega
        self.theta = theta
        self.epsilon = epsilon
        self.alpha = alpha

    def forward(self, pred, weight_map, target):
        y = target
        y_hat = pred
        delta_y = (y - y_hat).abs()
        delta_y1 = delta_y[delta_y < self.theta]
        delta_y2 = delta_y[delta_y >= self.theta]
        y1 = y[delta_y < self.theta]
        y2 = y[delta_y >= self.theta]
        loss1 = self.omega * torch.log(1 + torch.pow(
            delta_y1 / self.omega, self.alpha - y1)) * weight_map[delta_y < self.theta]
        A = self.omega * (1 / (1 + torch.pow(self.theta / self.epsilon, self.alpha - y2))) * (self.alpha - y2) * (
            torch.pow(self.theta / self.epsilon, self.alpha - y2 - 1)) * (1 / self.epsilon)
        C = self.theta * A - self.omega * \
            torch.log(1 + torch.pow(self.theta / self.epsilon, self.alpha - y2))
        loss2 = (A * delta_y2 - C) * weight_map[delta_y >= self.theta]
        return (loss1.sum() + loss2.sum()) / (len(loss1) + len(loss2))

def train_model(model, dataloaders, dataset_sizes, use_gpu=True, epoches=5,
                save_path='./', num_landmarks=68, start_epoch=0):
    best_acc = 100
    optimizer = torch.optim.RMSprop(
        model.parameters(), lr=0.0000001, weight_decay=0)
    loss_AW = AdaptiveWingLoss()
    for epoch in range(start_epoch, epoches + start_epoch):
        running_loss = 0
        step = 0
        total_nme = 0
        total_count = 0
        fail_count = 0
        nmes = []
        # running_corrects = 0
        step_start = time.time()

        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            # Iterate over data.
            # with torch.set_grad_enabled(True):
            for data in dataloaders[phase]:
                optimizer.zero_grad()
                total_runtime = 0
                run_count = 0

                step += 1
                # get the inputs
                inputs = data['image'].type(torch.FloatTensor)
                labels_heatmap = data['heatmap'].type(torch.FloatTensor)
                labels_boundary = data['boundary'].type(torch.FloatTensor)
                gt_landmarks = data['landmarks'].type(torch.FloatTensor)
                loss_weight_map = data['weight_map'].type(torch.FloatTensor)
                # wrap them in Variable
                if use_gpu:
                    inputs = inputs.to(device)
                    labels_heatmap = labels_heatmap.to(device)
                    labels_boundary = labels_boundary.to(device)
                    loss_weight_map = loss_weight_map.to(device)
                else:
                    inputs, labels_heatmap = Variable(
                        inputs), Variable(labels_heatmap)
                    labels_boundary = Variable(labels_boundary)
                labels = torch.cat((labels_heatmap, labels_boundary), 1)
                single_start = time.time()
                with torch.set_grad_enabled(phase == 'train'):
                    outputs, boundary_channels = model(inputs)
                    pred_labels = torch.cat(
                        (outputs[-1][:, :-1, :, :], boundary_channels[-1][:, :-1, :, :]), 1)
                    ###
                    loss_total = loss_AW(
                        pred_labels, loss_weight_map * 10 + 1, labels)
                    ###
                    #print("Batch Loss: {:.6f}".format(loss.item()))
                    if phase == 'train':
                        loss_total.backward()
                        optimizer.step()
                batch_nme = fan_NME(
                    outputs[-1][:, :-1, :, :].detach().cpu(), gt_landmarks, num_landmarks)
                #print("Batch NME: {:.6f}".format(batch_nme))
                # batch_nme = 0
                total_nme += batch_nme
            epoch_nme = total_nme / dataset_sizes[phase]
            step_end = time.time()
            print(phase + ' NME: {:.6f}'.format(epoch_nme))
            if phase == 'val' and epoch_nme < best_acc:
                state = {
                    'next_epoch': epoch+1,
                    'epoch_total_nme': epoch_nme,
                    'state_dict': model.state_dict(),
                    # 'scheduler' : scheduler.state_dict(),
                    'optimizer': optimizer.state_dict()
                }
                torch.save(state, save_path+'{:02d}'.format(epoch)+'.pth')
        #nme_save_path = os.path.join(save_path, 'nme_log.npy')
        #np.save(nme_save_path, np.array(nmes))
        #print('NME: {:.6f} Failure Rate: {:.6f} Total Count: {:.6f} Fail Count: {:.6f}'.format(epoch_nme, fail_count/total_count, total_count, fail_count))

    #print('Everage runtime for a single batch: {:.6f}'.format(total_runtime/run_count))
    return model

`
@protossw512   code you please check if my training implementation is correct

@HassanAbbas92 Thanks for posting this, I was looking for something to get started with training. I found a couple pretty small issues:

In the Adaptive Wing Loss calculation, there is one equation that should be changed to match the paper:
loss1 = self.omega * torch.log(1 + torch.pow(           delta_y1 / self.omega, self.alpha - y1)) * weight_map[delta_y < self.theta]
should be
loss1 = self.omega * torch.log(1 + torch.pow(           delta_y1 / self.epsilon, self.alpha - y1)) * weight_map[delta_y < self.theta]
In the main training loop, total_nme is reset at the start of the epoch, not at the start of each phase, so your train_nme will be correct but your val_nme will be off because it didn't zero out the train error.
Some of the hyperparameters are different than what is listed in the paper, but I think it will still train somewhat with what is present...

@mustangchavez Thanks for correction, did you start training from scratch and achieve same results in the papper on WFLW dataset?

Sorry, I haven't tried to reproduce the results as I am actually working on a different problem domain.
I found what seems to be another correction, I believe the correct boundary map to use is already part of the first output and not the "boundary_channels" output. So
pred_labels = torch.cat(
                        (outputs[-1][:, :-1, :, :], boundary_channels[-1][:, :-1, :, :]), 1)

should be
pred_labels = outputs[-1]

@mustangchavez are you sure?
because i thought @protossw512 talk about it in https://github.com/protossw512/AdaptiveWingLoss/issues/12

I'm not sure, no! Haha.
But I think that each "boundary_channels" is intended to be passed into the next HourGlass module, whereas the "outputs" contains the final boundary heatmap prediction. That's why the shape of the last tensor tensor in the "outputs" list is [batch_size, num_landmarks + 1, heatmap_size, heatmap_size]

You are correct @mustangchavez
But also i will try to visualize both of them to see the difference

You are correct @mustangchavez
But also i will try to visualize both of them to see the difference

Hello @HassanAbbas92 , Thanks for sharing training code imlementation, how is it going? I am reading the paper and just inference the result and think about training from scratch.
Have u managed to reproduce the result?

`
import matplotlib.pyplot as plt
import cv2
import sys
import os
from PIL import Image, ImageDraw
from utils.utils import fan_NME, show_landmarks, get_preds_fromhm
import numpy as np
from skimage import io
import shutil
from torch.autograd import Variable
import time
import copy
from torch import nn
import torch
import math
import matplotlib
matplotlib.use('Agg')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

class AdaptiveWingLoss(nn.Module):
    def __init__(self, omega=14, theta=0.5, epsilon=1, alpha=2.1):
        super(AdaptiveWingLoss, self).__init__()
        self.omega = omega
        self.theta = theta
        self.epsilon = epsilon
        self.alpha = alpha

    def forward(self, pred, weight_map, target):
        y = target
        y_hat = pred
        delta_y = (y - y_hat).abs()
        delta_y1 = delta_y[delta_y < self.theta]
        delta_y2 = delta_y[delta_y >= self.theta]
        y1 = y[delta_y < self.theta]
        y2 = y[delta_y >= self.theta]
        loss1 = self.omega * torch.log(1 + torch.pow(
            delta_y1 / self.omega, self.alpha - y1)) * weight_map[delta_y < self.theta]
        A = self.omega * (1 / (1 + torch.pow(self.theta / self.epsilon, self.alpha - y2))) * (self.alpha - y2) * (
            torch.pow(self.theta / self.epsilon, self.alpha - y2 - 1)) * (1 / self.epsilon)
        C = self.theta * A - self.omega * \
            torch.log(1 + torch.pow(self.theta / self.epsilon, self.alpha - y2))
        loss2 = (A * delta_y2 - C) * weight_map[delta_y >= self.theta]
        return (loss1.sum() + loss2.sum()) / (len(loss1) + len(loss2))

def train_model(model, dataloaders, dataset_sizes, use_gpu=True, epoches=5,
                save_path='./', num_landmarks=68, start_epoch=0):
    best_acc = 100
    optimizer = torch.optim.RMSprop(
        model.parameters(), lr=0.0000001, weight_decay=0)
    loss_AW = AdaptiveWingLoss()
    for epoch in range(start_epoch, epoches + start_epoch):
        running_loss = 0
        step = 0
        total_nme = 0
        total_count = 0
        fail_count = 0
        nmes = []
        # running_corrects = 0
        step_start = time.time()

        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            # Iterate over data.
            # with torch.set_grad_enabled(True):
            for data in dataloaders[phase]:
                optimizer.zero_grad()
                total_runtime = 0
                run_count = 0

                step += 1
                # get the inputs
                inputs = data['image'].type(torch.FloatTensor)
                labels_heatmap = data['heatmap'].type(torch.FloatTensor)
                labels_boundary = data['boundary'].type(torch.FloatTensor)
                gt_landmarks = data['landmarks'].type(torch.FloatTensor)
                loss_weight_map = data['weight_map'].type(torch.FloatTensor)
                # wrap them in Variable
                if use_gpu:
                    inputs = inputs.to(device)
                    labels_heatmap = labels_heatmap.to(device)
                    labels_boundary = labels_boundary.to(device)
                    loss_weight_map = loss_weight_map.to(device)
                else:
                    inputs, labels_heatmap = Variable(
                        inputs), Variable(labels_heatmap)
                    labels_boundary = Variable(labels_boundary)
                labels = torch.cat((labels_heatmap, labels_boundary), 1)
                single_start = time.time()
                with torch.set_grad_enabled(phase == 'train'):
                    outputs, boundary_channels = model(inputs)
                    pred_labels = torch.cat(
                        (outputs[-1][:, :-1, :, :], boundary_channels[-1][:, :-1, :, :]), 1)
                    ###
                    loss_total = loss_AW(
                        pred_labels, loss_weight_map * 10 + 1, labels)
                    ###
                    #print("Batch Loss: {:.6f}".format(loss.item()))
                    if phase == 'train':
                        loss_total.backward()
                        optimizer.step()
                batch_nme = fan_NME(
                    outputs[-1][:, :-1, :, :].detach().cpu(), gt_landmarks, num_landmarks)
                #print("Batch NME: {:.6f}".format(batch_nme))
                # batch_nme = 0
                total_nme += batch_nme
            epoch_nme = total_nme / dataset_sizes[phase]
            step_end = time.time()
            print(phase + ' NME: {:.6f}'.format(epoch_nme))
            if phase == 'val' and epoch_nme < best_acc:
                state = {
                    'next_epoch': epoch+1,
                    'epoch_total_nme': epoch_nme,
                    'state_dict': model.state_dict(),
                    # 'scheduler' : scheduler.state_dict(),
                    'optimizer': optimizer.state_dict()
                }
                torch.save(state, save_path+'{:02d}'.format(epoch)+'.pth')
        #nme_save_path = os.path.join(save_path, 'nme_log.npy')
        #np.save(nme_save_path, np.array(nmes))
        #print('NME: {:.6f} Failure Rate: {:.6f} Total Count: {:.6f} Fail Count: {:.6f}'.format(epoch_nme, fail_count/total_count, total_count, fail_count))

    #print('Everage runtime for a single batch: {:.6f}'.format(total_runtime/run_count))
    return model

`
@protossw512 code you please check if my training implementation is correct

Hi. Thanks for your code and idea for the loss function. I have a question. Is there correct about the calculation method of loss1 and loss2. More specifically, * weight_map[delta_y < self.theta] is correctly? Thanks.

`
import matplotlib.pyplot as plt
import cv2
import sys
import os
from PIL import Image, ImageDraw
from utils.utils import fan_NME, show_landmarks, get_preds_fromhm
import numpy as np
from skimage import io
import shutil
from torch.autograd import Variable
import time
import copy
from torch import nn
import torch
import math
import matplotlib
matplotlib.use('Agg')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

class AdaptiveWingLoss(nn.Module):
    def __init__(self, omega=14, theta=0.5, epsilon=1, alpha=2.1):
        super(AdaptiveWingLoss, self).__init__()
        self.omega = omega
        self.theta = theta
        self.epsilon = epsilon
        self.alpha = alpha

    def forward(self, pred, weight_map, target):
        y = target
        y_hat = pred
        delta_y = (y - y_hat).abs()
        delta_y1 = delta_y[delta_y < self.theta]
        delta_y2 = delta_y[delta_y >= self.theta]
        y1 = y[delta_y < self.theta]
        y2 = y[delta_y >= self.theta]
        loss1 = self.omega * torch.log(1 + torch.pow(
            delta_y1 / self.omega, self.alpha - y1)) * weight_map[delta_y < self.theta]
        A = self.omega * (1 / (1 + torch.pow(self.theta / self.epsilon, self.alpha - y2))) * (self.alpha - y2) * (
            torch.pow(self.theta / self.epsilon, self.alpha - y2 - 1)) * (1 / self.epsilon)
        C = self.theta * A - self.omega * \
            torch.log(1 + torch.pow(self.theta / self.epsilon, self.alpha - y2))
        loss2 = (A * delta_y2 - C) * weight_map[delta_y >= self.theta]
        return (loss1.sum() + loss2.sum()) / (len(loss1) + len(loss2))

def train_model(model, dataloaders, dataset_sizes, use_gpu=True, epoches=5,
                save_path='./', num_landmarks=68, start_epoch=0):
    best_acc = 100
    optimizer = torch.optim.RMSprop(
        model.parameters(), lr=0.0000001, weight_decay=0)
    loss_AW = AdaptiveWingLoss()
    for epoch in range(start_epoch, epoches + start_epoch):
        running_loss = 0
        step = 0
        total_nme = 0
        total_count = 0
        fail_count = 0
        nmes = []
        # running_corrects = 0
        step_start = time.time()

        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            # Iterate over data.
            # with torch.set_grad_enabled(True):
            for data in dataloaders[phase]:
                optimizer.zero_grad()
                total_runtime = 0
                run_count = 0

                step += 1
                # get the inputs
                inputs = data['image'].type(torch.FloatTensor)
                labels_heatmap = data['heatmap'].type(torch.FloatTensor)
                labels_boundary = data['boundary'].type(torch.FloatTensor)
                gt_landmarks = data['landmarks'].type(torch.FloatTensor)
                loss_weight_map = data['weight_map'].type(torch.FloatTensor)
                # wrap them in Variable
                if use_gpu:
                    inputs = inputs.to(device)
                    labels_heatmap = labels_heatmap.to(device)
                    labels_boundary = labels_boundary.to(device)
                    loss_weight_map = loss_weight_map.to(device)
                else:
                    inputs, labels_heatmap = Variable(
                        inputs), Variable(labels_heatmap)
                    labels_boundary = Variable(labels_boundary)
                labels = torch.cat((labels_heatmap, labels_boundary), 1)
                single_start = time.time()
                with torch.set_grad_enabled(phase == 'train'):
                    outputs, boundary_channels = model(inputs)
                    pred_labels = torch.cat(
                        (outputs[-1][:, :-1, :, :], boundary_channels[-1][:, :-1, :, :]), 1)
                    ###
                    loss_total = loss_AW(
                        pred_labels, loss_weight_map * 10 + 1, labels)
                    ###
                    #print("Batch Loss: {:.6f}".format(loss.item()))
                    if phase == 'train':
                        loss_total.backward()
                        optimizer.step()
                batch_nme = fan_NME(
                    outputs[-1][:, :-1, :, :].detach().cpu(), gt_landmarks, num_landmarks)
                #print("Batch NME: {:.6f}".format(batch_nme))
                # batch_nme = 0
                total_nme += batch_nme
            epoch_nme = total_nme / dataset_sizes[phase]
            step_end = time.time()
            print(phase + ' NME: {:.6f}'.format(epoch_nme))
            if phase == 'val' and epoch_nme < best_acc:
                state = {
                    'next_epoch': epoch+1,
                    'epoch_total_nme': epoch_nme,
                    'state_dict': model.state_dict(),
                    # 'scheduler' : scheduler.state_dict(),
                    'optimizer': optimizer.state_dict()
                }
                torch.save(state, save_path+'{:02d}'.format(epoch)+'.pth')
        #nme_save_path = os.path.join(save_path, 'nme_log.npy')
        #np.save(nme_save_path, np.array(nmes))
        #print('NME: {:.6f} Failure Rate: {:.6f} Total Count: {:.6f} Fail Count: {:.6f}'.format(epoch_nme, fail_count/total_count, total_count, fail_count))

    #print('Everage runtime for a single batch: {:.6f}'.format(total_runtime/run_count))
    return model

`
@protossw512 code you please check if my training implementation is correct

Hi. Thanks for your code and idea about the loss function. I have a question. Is there correct about the calculation method of loss1 and loss2. More specifically, * weight_map[delta_y < self.theta] is correct? Thanks.


about trajectory prediction

hey! I appreciate that your work on the transportation open dataset(citysim)，especially task of trajectory prediction. In fact, I have
read your paper called "Trajectory Prediction for Vehicle Conflict Identification at Intersections Using Sequence-to-Sequence Recurrent Neural Networks", and I see that you've already done the work of dealing with raw data, eliminating stationary tracks and dividing the dataset according to 0.7/0.3 respectively. But I cannot find available code anywhere. I would really appreciate it if you shared your code with me! And it can also help me avoid doing repetitive work.

Hello, you can contact @AmrARaouf about that paper and code


apollo dreamview frontend：yarn install 报错

node版本：12.18.1 (14.15.1也是报错)
安装报错具体如下：
[6/19] ⠄ sharp
[11/19] ⡀ cwebp-bin
[9/19] ⡀ optipng-bin
[8/19] ⡀ mozjpeg
warning Error running install script for optional dependency: "/home/nvidia/newApollo/apollo/modules/dreamview/frontend/node_modules/optipng-bin: Command failed.
Exit code: 1
Command: node lib/install.js
Arguments:
Directory: /home/nvidia/newApollo/apollo/modules/dreamview/frontend/node_modules/optipng-bin
Output:
⚠ connect ECONNREFUSED <IP_ADDRESS>:443
⚠ optipng pre-build test failed
ℹ compiling from source
✖ Error: Command failed: /bin/sh -c make install
pngrtran.c:99:1: warning: ‘png_rtran_ok’ defined but not used [-Wunused-function]
png_rtran_ok(png_structrp png_ptr, int need_IHDR)
^~~~~~~~~~~~
ar: u' modifier ignored since D' is the default (see U') ar: u' modifier ignored since D' is the default (see U')
ar: u' modifier ignored since D' is the default (see U') ar: u' modifier ignored since D' is the default (see U')
pngxmem.c: In function ‘pngx_malloc_rows_extended’:
pngxmem.c:38:34: warning: comparison is always false due to limited range of data type [-Wtype-limits]
(pngx_alloc_size_t)height > (pngx_alloc_size_t)(-1) / sizeof(png_bytep))
^
ar: u' modifier ignored since D' is the default (see U') ../libpng/libpng.a(pngrutil.o): In function png_read_filter_row':
pngrutil.c:(.text+0x20c4): undefined reference to `png_init_filter_functions_neon'
collect2: error: ld returned 1 exit status
make[1]: *** [optipng] Error 1
make: *** [install] Error 2

It's been solved

How did you solve the problem? I have the same troubles

It's been solved

@wdhxs


[Addition]: Hummingbird selfwitnessing cluster

Hotspot b58 Addresses
1124bXuCJ1zPgsdwkTZesdupNEnfxEpn7rkD5aodAbaQNeVop6ts
1124q2Vi9h8XxMJQE9UdafmEke7kMAhQW6QxkJNmCk21WHB2hCBj
11266Xk2n73HFugwdghx4k8Rj8VPHQ82vv6L1dm2JxkbBa9Smtrz
1129TLbs3fCSxxwBSq9Askxb5EZRLhqq7wcTghWswRfxGRCD7kr8
1129Vzfi96ejS2xBNjVXxbPJUGmvtAicEqv1GYgUWKajGFCCZDXv
112AohmzRHJezDjSAsfNJV6fAL1VSNM6PpGzf4mv2oNmdgqGMqoq
112b7VbxnbEWdAWeqkbUWpot7zBy1peyhdiCMF93iaXzh5Xgc4YS
112bJGwzpbkXkHVfN8BxWdjzb2E7qCqpmBkAh4RHRgibVmpQHWkx
112DbcuME7Jdygt8zvzsYjMWYEN7QVBYfHTj2r2FoYAYPVMUCUtB
112dE3S9C65qs8K2x2rRybEnuLQJm2eA1xPF6T17a5WdFzQgCHwJ
112ENVbiRnA3LwvRQ6eAkZpNKEK7BNebPjzvMuKXEJxv4xvA631n
112evpyxgxHK8VQRadaULfSAWGZrCuFwGv938eyz7JFXBqpheygu
112F917gADn2RnDMqKs5m8TD8n3FJSnQp76aB36v2MAxXRRm8Kz9
112ftWEHGeJ26ZQJstv8erWzgRUVqkwY6vQvB2XozrNjf6X2zTBa
112h6jSeX1NGiziwHbwLXqeuJmUc6bugraAqG6bPaEXr1ksJR2pM
112jMDPzLq8Yx12iFYALq9j7JeYTnSo3jq8UD3GPCCCTaELHiHGY
112jzg2ca7KVn3YRbEyF9YVMwtjsPBey71M3MdfSh5Pie7B4K3Yj
112PSpV3zAWAzsEuefEmrP1muoCDMvKvazrfTPSEp8NXkqLWukjB
112QuvRQM2QatufcRMsgB3tbtgZTNmoxyrvfCRDegETiAdn7yGML
112rm1CTpdxwNoe9Ft6bqFexoqvfKcibEE7i2DgjafZcUTFtpsHj
112SfZr2BP4qtYnqE1uhhcfCETMhoR6tdXV7vUg4uNFAkEpwndez
112TgBQ4HNYHMm8VrBrp6zeLUg8KdpyVrd3Ri6TS37HdqRV94wyn
112vJo1b3qLTmu6tr6cpQFQ8LoyzrS3Bn6Tt7sM6GUEoSvmHfzi7
112VLJ8y3K7j9H3zdHAybyEuLQJjgVaRPTea8Ho6qU2DCW1MqgY7
112vptKcRf3mbfoF2gbTH66TQMVnxKQfLPkfZ3e8UjGf9ayJQGrz
112wBHEayfgBujGkJTjmWK9aLytq49vA9u7hn3UucUnAtVQGVrwP
112XsGBEPJ3HnNwj6qGhZpnz8mHf5JQ3jQ7jLVAJ4pohyu4kELGR
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11ThgBRsEqc8UfVLeoAUyLRfqDnXisDZMxv8L6p1Vvz8Ti7ydUQ
11TmMqM8AbS9LJyWM5rFTnDRhJhqfpmr8vBXvpj1pTztZ8hmRtc
11Tx5sntNRk8BLWj5PuG9sAvFbYy6jwR9KJhvo9ncatCWwghdTf
11UMPz3mfWq2JJXWRZVaQGQNSmHrAeyB6NcYxJZ3YnGo8ATEt8n
11wJPNA5gBDrvtWhKEsypWe6bQzUnLEUEaJvqQVfKZ5A4t3jR5j
11YSnXjQ9XpF66RmjYh6dvrg7WaUtAjDHunsaMLQbcuxHCS33pC
11Yz7vbH4sNUxRu44BML1fR1aBkFiDjNxStfrbokkEud7CiX5ib
Discord Handle
No response
Reason(s)

All but 1 account for all Hummingbird gateway's didn't show any data transfers.
So data transfer is possible! only 109 out of 3853 showed packet transfer data, all these 109 are in same account.
the gateway's in that account are part of a selfwitnessing cluster so they probably all heared the same
signal.

Most of all Hummingbird gateway's witness only Hummingbird gateway's.

We iterated the witnesses of the witnesses to find the clusters.
We used the node-red server we setup for this to get the results out of api.

All HNT stolen with the total amount of 21067 HNT as by todays date/time.

We conclude that out of the 3852 gateway's we inspected only 109 gateway's in 1 account showed accidental
data transfer. so data transfer for gateway's Hummingbird is possible.

We conclude that out of the 3852 gateway's we inspected only a couple of accounts showed communication with other makers. so communication with other makers for Hummingbird gateway's is possible.

Rssi/ distance / SNR calculations are to perfect, we have found some invalids -59 dbm but we suspect they are from cables touching eachother.

We haven't targetted any maker, it was the result of a count WHERE actors1.actor_role = 'witness'
AND actors2.actor_role = 'challengee' are in same account.

My gut feeling says that this could be genuine. Based on the witness graph
https://www.crowdspot.io/hotspots/11266Xk2n73HFugwdghx4k8Rj8VPHQ82vv6L1dm2JxkbBa9Smtrz
Although some of those listed seem to be in a cluster.  11BNdWbNc65U8RCRnTvH9GALyrnbLLvRpQLwuP8kvHCcsUprdNG


HSEARCH-2108 Elasticsearch analyzers v2

https://github.com/hibernate/hibernate-search/pull/1017 Follows up
https://hibernate.atlassian.net/browse/HSEARCH-2108
I've tried to follow @gunnarmorling suggestion, this patch will add a new AnalyzerReference class to keep track of analyzers, locals or remotes. A remote analyzer is represented using the class RemoteAnalyzer.
It seems to work with custom-analyzers defined on Elasticsearch and the built-in one.

interface AnalyzerReference {
String getName(); // if that's needed here at all?
void close();
}

Analyzers can be lazy initialized, at the moment this is done using the DelegateNamedAnalyzer.
My plan was to assume that a reference to an analyzer can be lazy and therefore remove the need for DelegateNamedAnalyzer and therefore an additional wrapper.
As for the other remarks, I'll try to apply them.