release: v1.1.5 🔧

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autoCenter: false does not seem to work correctly

Sigma with option autoRescale: false, autoCenter:false
Searching the code, I cannot find any code that is checking the autoCenter option.
Use case
A user wants to drag a icon from a toolbar into a sigma chart to create a node.
Expected behavior
When the user is dropping the icon on the chart a node is added to the chart at the position where it was dropped.
Actual behaviour
After droppping a node on the chart, Sigma automaticly centers the chart making the icon move away from the dropped position.
Possible workaround
It seems like adding the following line of code after sigma is initialized solves it but I dont know if there are any negavtive effects to it.
renderer.setCustomBBox(renderer.getBBox())

Hi @Whiteflight,
You're right, I added this setting while implementing autoRescale, but I never plugged it. I'll try to implement it as soon as I can.
About the workaround, you are using the same we use in the nodes dragging story. It should be fine, unless you have some operation that might move the graph affect its scale by a lot. In which case, calling renderer.setCustomBBox(null); will go back to the default behaviour, which is  auto rescale and auto center, basically.
Since I actually added that setting and you found it, I agree it should work :) so I just add the "bug" label. Thanks for the report!


tests folder is misleading

We have a tests folder that's largely unused now. Most of the heavy lifting handled by the scripts in this directory are now handled by the testnet test-runner.

work in progress on #79


Replace SpringPrototypeAggregateFactory BeanDefinition introspection for direct method invocation

For some reason, Spring Framework logs the following message open usages of the static factory method of the SpringPrototypeAggregateFactory inside the SpringAggregateLookup component:
LocalVariableTableParameterNameDiscoverer : Using deprecated '-debug' fallback for parameter name resolution. Compile the affected code with '-parameters' instead or avoid its introspection: org.axonframework.spring.eventsourcing.SpringPrototypeAggregateFactory
Although none of the methods are marked deprecated on the BeanDefinition API, we are, apparently, dealing with undesired behavior for Spring.
Regardless, we can easily use a Supplier<SpringPrototypeAggregateFactory> in this case since the required properties (the aggregate name and its subtypes) for constructing the SpringPrototypeAggregateFactory are known upfront.
By switching from introspection to using a Supplier, we resolve #2630.

Looking at the code, I feel this is a slight variation of the Spring issue that is referred to in the original Axon issue. In this case, it's not a constructor, but a static method that's being used. In either case, it shouldn't use parameter names, as the parameters are defined based on index name.
@smcvb , I would suggest reverting the merge of this PR. It doesn't look like our bug

Thanks for investigating the support on Spring AOT, @abuijze.
I've chipped in on the referred-to conversation following this comment of Sébastien Deleuze.
In the meantime, I think it is safe to assume the warning is not impacting our users.
As such, I have reverted the commit, as can be seen here.
Nudging @nils-christian here, as he's the constructor of the issue.

Note that a follow-up issue has just been created by Sébastien Deleuze concerning this warning, which you can find here.


Disable menu item

Hello, @kitasuke! You've made a great job!
I have to disable the last menuItem in pagingMenuController. Is there an ability to disable the last menuItem? I've created pageMenu with three pages (screenshot) but the last one should be disabled for future work. Also, is there an ability to disable user swipe to the last page? Or where I should modify the code to make it work?
Thanks a lot!

Oh, your code is perfect! It wasn't hard to do it by myself. The issue can be closed.

I had to do all this in order to disable menu items
class CustomViewController: PagingMenuController {
var isScrollEnabled: Bool = true {
didSet {
self.scrollEnabled(isScrollEnabled)
}
}
func scrollEnabled(_ enabled: Bool) {
    if let menuView = self.menuView, let gestureRecognizers = menuView.gestureRecognizers {
        self.gestureRecognizers(gestureRecognizers, enabled: enabled)

        if let containerView = menuView.subviews.first {
            for menuItemView in containerView.subviews {
                if let gestureRecognizers = menuItemView.gestureRecognizers {
                    self.gestureRecognizers(gestureRecognizers, enabled: enabled)
                }
            }
        }
    }
    if let view = self.view.subviews.last, let scrollView = view.subviews.last, let gestureRecognizers = scrollView.gestureRecognizers {
        self.gestureRecognizers(gestureRecognizers, enabled: enabled)
    }
}

fileprivate func gestureRecognizers(_ gestureRecognizers: [UIGestureRecognizer], enabled: Bool) {
    for gestureRecognizer in gestureRecognizers {
        gestureRecognizer.isEnabled = enabled
    }
}

}
Is there an easier and cleaner way to do this?

I never thought disabling some menu items, but it could be an option.


Project Architecture

Notes from our meeting with @MSblgross, @stevenvergenz, and Ken Wolfe.

Blake will be splitting his pull request into separate parts so that it is easier to review.
glTF Serialization/Deserialization will be split out into a separate project within this repo and moved to a separate Khronos repo when ready.
We'll be keeping the "UnityGLTF" name for the Unity project and using "GLTFSerialization" for the external serialization/deserialization project.

Note: Capitalization of GLTF will be kept this way for the sake of C#'s Pascal case for class names.
In the future we may try to generate the serialization code from the glTF JSON Schema, but there is additional work needed to add manual deserialization using JSON.NET.

Once Blake's architecture changes have landed we'll be breaking the importer/exporter code into classes for importing/exporting specific sets of Unity components.

An example would be MeshImporter which would generate Unity Mesh objects, cache them, and add the correct components (MeshFilter and MeshRenderer) to a node.
The importer/exporter will hold fields for the component importer/exporters to be used and can be extended as necessary.
I'll be working on a PR for these changes and have more details to discuss with the community soon.

Once the architecture changes are made the focus will be on supporting the full glTF 2.0 spec for runtime import.

@PlutoVR has been working on animation support, we'd really like to work with you to get that merged into the repository. Let us know what we can do to help.

If there are other changes you'd like to see to the architecture of the project please let us know. Ideally after making these large architectural changes we can get to a versioned, pre-alpha release and a more stable place for anyone trying to use UnityGLTF.

Does the plan include outputting animated elements (models) as glTF?

That's the plan. We'll work towards full export support as we go. However, right now we're focusing on project architecture so we can break out some of this work and hopefully clean up integration points. Hopefully we can get that done relatively soon.
After that I believe @MSblgross and I will be focusing our efforts on getting full glTF 2.0 support for import. I plan on moving back to export after import is fully featured.
However, work can be done in parallel on supporting the remaining export features. If you or anyone else want to help out it would be greatly appreciated!
I know @bghgary and the folks at @sketchfab have worked on animation export from Unity in their projects. Gary's involved already and it would be great to see if Sketchfab wants to contribute.
https://github.com/bghgary/glTF-Tools-for-Unity
https://github.com/sketchfab/Unity-glTF-Exporter

We're still trying to decide on the final namespaces for the project before we merge #50
I have a proposal here and I could use some feedback from the community.
Example of the proposed namespaces:
using UnityEngine;
using GLTF;
using UnityGLTF.Extensions;

namespace UnityGLTF {
    class GLTFComponent {
        public GLTF.Scene Scene;
        public GLTF.Math.Vector3 GLTFVector;
        public GLTF.Serialization.GLTFParser GLTFParser;
    }
}

Another issue is splitting this repo into two. Ideally the GLTF folder would be split off into its own repository. It would be responsible for the same things that glTF-CSharp-Loader is doing, but with optimized hand written JSON serialization/deserialization rather than automatically generated from the spec's JSON schema and serialized/deserialized using reflection which is considerably slower (See #10).
It would also have a name like glTF.NET or glTF-CSharp which could get confusing.
So to recap, the decisions we need to make are the following:

GLTF Serialization/Deserialization project namespace (Ex. GLTF)
Unity Import/Export project namespace (Ex. UnityGLTF)
Should we split the repositories now or keep them in folders in this repo like they are in #50
If we split the repositories, what should we name the Serialization/Deserialization project (Ex. glTF-CSharp)
What is the future plan for merging with glTF-CSharp-Loader?

What is the future plan for merging with glTF-CSharp-Loader?

same things that glTF-CSharp-Loader is doing, but with optimized hand written JSON serialization/deserialization

Is it possible to generate the optimized JSON serialization/deserialization? If so, I'm thinking we can modify glTF-CSharp-Loader to generate this optimized code instead of relying on reflection.

GLTF Serialization/Deserialization project namespace (Ex. GLTF)

Ideally, I think I would separate the serialization/deserialization from the classes that define the schema. I prefer a namespace with the word Schema on it (e.g. GLTF.Schema) for the schema. The glTF-CSharp-Loader uses glTFLoader.Schema. The serialization/deserialization code can use extension functions in a namespace with the word Serialization (e.g. GLTF.Serialization).

Some of our meeting notes from today (@robertlong, @stevenvergenz, @sbtron, @bghgary, and @MSblgross in attendance)
We decided on the following:

GLTF Serialization/Deserialization project namespace (Ex. GLTF)
GLTF for the glTF serialization/deserialization/schema project and GLTF.Schema for all the schema classes.
UnityGLTF for the Unity glTF import/export project.

Unity Import/Export project namespace (Ex. UnityGLTF)
We'll likely add this when we split the importer and exporter into classes. Still TBD.

Should we split the repositories now or keep them in folders in this repo like they are in #50

If we split the repositories, what should we name the Serialization/Deserialization project (Ex. glTF-CSharp)

What is the future plan for merging with glTF-CSharp-Loader?
We're going to keep both projects in the same project for now and work on adding generation of optimized serialization/deserialization code to glTF-CSharp-Loader. Additionally we will move any helper classes to this project.


comma pedal: acceleration overshoot behind a stopped car

Describe the bug
After my car almost came to a complete stop behind another stopped car, openpilot decided to unnecessarily accelerate causing me to manually brake.
How to reproduce or log data
Was driving close to 50 mph following another car, and a red light caused the cars to stop in front of me. My car also braked, but the braking started relatively late, which cause the first forward collision warning.  Ultimately, my car did seem to brake just in time for a smooth stop, but right before reaching 0mph, it randomly decided to accelerate with no movement from the car in front of me. This caused another forward collision warning before I manually braked.
One thing that maybe is relavent is that because of the late braking, I did feel as if my car stopped (or got near stopping) closer behind the car in front of me than it probably would have normally.
Expected behavior
My car should have come to a complete stop, and only accelerate once  the lead car started to move again.
Device/Version information (please complete the following information):

Device: Eon Gold with black panda
Dongle ID: 23b69975490100e4
Route: 23b69975490100e4|2020-11-30--12-14-05
Timestamp: 340 seconds into the route or end of segment 5
Version: 0.8 release2-staging fork with a few mostly pedal related changes https://github.com/doktorsleepelss/openpilot/commits/release2-staging
Car make/model: 2016 honda civic sedan

https://imgur.com/148Pjx8
https://my.comma.ai/cabana/?route=23b69975490100e4|2020-11-30--12-14-05&exp=1638398245&sig=Kef41qsWemGBZHUfY0DLyO6vIo5TmQs8PAPchMpklDQ%3D&max=27&url=https%3A%2F%2Fchffrprivate-vzn.azureedge.net%2Fchffrprivate3%2Fv2%2F23b69975490100e4%2Fca7efd06a33d9236b458b35e0b77aca3_2020-11-30--12-14-05

This looks to be caused by your tuning for the pedal. The tuning can probably improved in general, but there is a clear bug in how you cap the maximum gas value here: https://github.com/doktorsleepelss/openpilot/commit/c54202c44d548d4b479ccc69fa26a79b288e575e#diff-8f6df5d986fd4f23c0c08c3737498ef5b4a270f3919a76cd8dd9678401f8c8caR426
If you cap the actuator value you have to make sure to also cap the integral of the PID at the same value. Now the integral just goes to 1 because it tries to accelerate but you're limiting the acceleration downstream. It then takes quite a bit of time to wind down again when reach the desired speed.
In the segment you shared the integral reached 1 when pulling away at the traffic light, and was still pretty high after the braking maneuver. When it tried to move forward it overshot due to the high integral.

Thanks for the feedback. I kind of suspected it would end up being pedal related as I mentioned in the discord. But I'm glad it wasn't model related.


[Forwardport] - UI - On deploying a workload, incorrectly shows 0 pods created

Forwardport of rancher/rancher#23842

Available in latest2.

Available in latest2.

Tested with Rancher server rancher\rancher:master-head version abd6f58c6
On deployment of a workload , pod count gets updated correctly.


[SPARK-40663][SQL][FOLLOWUP] SparkIllegalArgumentException should accept cause

What changes were proposed in this pull request?
This PR proposes to enable SparkIllegalArgumentException accept cause parameter.
Why are the changes needed?
The original error message generated by IllegalArgumentException can accept the cause parameter, but SparkIllegalArgumentException cannot, so there it some regression was made that we should enable back.
e.g. https://github.com/apache/spark/pull/38123/files#r1014645753
Does this PR introduce any user-facing change?
No.
How was this patch tested?
./build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite*"

cc @MaxGekk for address the https://github.com/apache/spark/pull/38104/files#r1014645923


Camera takes long time to open

I've updated the code to the 2.6.1 build but have noticed that the camera will take 7-10 seconds to open and sometimes will not open at all. I'm testing on an iPhone 6s, iOS 11.2.5.

I'm having the same problem too. I'm testing on different iPhones, but mainly iPhone X.

@gdelarosa, @eddieespinal, @viktorgardart  Yikes, I thought we had it in https://github.com/Yummypets/YPImagePicker/issues/25.
I have trouble reproducing it on my side (iPhone 6/X).
This looks like a threading issue, which explains the randomness & freeze.
I could surely use some help if you guys are willing to investigate :)
What helped me the first time was activating the Thread Sanitizer in Xcode.
Also what is your screens config ?  mine is [.library, .photo, video]
Cheers,

Hi @s4cha , I enabled the thread sanitizer in Xcode. for my screens configuration I have,
 public var screens: [YPPickerScreen] = [.library, .photo, .video] . However, I am opening the camera (.photo) first.

Hey @s4cha
I am experiencing this also, but only when open VIDEO.
config.screens = [.library, .photo, .video]   config.startOnScreen = .video
Any idea how to get this done ?
What I observed also is that delay may be because of hiding status bar: I mean I present picker, and its up, then it takes 5-7 seconds to hide status bar, after hide , camera can record.
And in this time, if I tap record, then it crashes.
Thanks a lot

Hey @s4cha
Any update on this ? any idea when we will see this fixed ?
Thanks a lot!

@s4cha I just took a look into code for a while, and I think you should the same qos: .background in YPVideoHelper class also.
//private let sessionQueue = DispatchQueue(label: "YPVideoVCSerialQueue")     private let sessionQueue = DispatchQueue(label: "YPVideoVCSerialQueue", qos: .background)
What do you think ?

@AndreiBoariu Thanks for the suggestion, I'm going to try that, It's very hard for me to debug since it's not happening every time :)

Still having the issue :/
Maybe we should use only one AVCaptureSession that would be shared among both photo and video. I think the issue lies in the fact that two session try to run in parallel, resulting in a race condition.
Going to try an alternative, will report back.

Same crash here, if remove .video the app doesn't crash

Ok this is going to be very obscure! Get ready! 👻
I finally found a deterministic way to both trigger and solve the issue.
This is very esoteric though 👀
From my experience, it only happens with Video. And only if it is the first screen shown.
aka config.startOnScreen = .video
After much trial and error I have found that  replacing tryToStartCamera in YPVideoVC but  following implementation does the trick.
public func tryToStartCamera() {
    doAfterPermissionCheck { [weak self] in
        if #available(iOS 10.0, *) {
            Timer.scheduledTimer(withTimeInterval: 0.6, repeats: false) { _ in //0.6
                self?.videoHelper.startCamera()
            }
        }
    }
}

Essentially wrapping videoHelper.startCamera() with a Timer.
Weirdly enough (at leat on my device) a timer of 0.5 does NOT work --> camera takes 9s to load.
Using a timer of 0.6 though makes the camera preview load in 1.2s 🎉
Obviously I'd love to understand why, but it looks like a step forward in this age long issue :)
If some of you could try it out and report that would be awesome !
Cheers,

@s4cha https://www.muztang.ru/upload/resize_cache/iblock/768/370_370_1/768de3eec8b1bfeff733bed617b39f79.jpg

Too old issue. Feel free to open new.


Call Shopify Wehbook without laravel job and queue

Hii Developers,
In my application i don't want to run queue to listen continuously. So please help me how can i configure custom webhook that will run from shopify after an event like after create the order etc.

@osiset Could you please help on this.

@BhuvneshSoniApporio please move over to discussion if you want to request help or start a conversation.
Please keep issues relating to package bugs etc not for seeking help. This helps us to narrow down actual issues more quickly.

Hi @Kyon147  There is an issue with package that i facing. I have removed all the webhooks from list those are install while installing app.
Now i am getting exception message and that is
Class '\App\Jobs\ShopRedactJob' not found
I am getting this exception continuously. Could you please help me to identify this issue. Also let me know the reason of occurring this.

You need to create this job, as Shopify will fire this webhook usually 3 days after the uninstall.


Support Earthly builder

I use Earthly - Make Builds Super Simple to build images remotely with caching. It would be nice to be able to specify a command to call that builds the images.

There are lots of image builders out there. Overriding a single command invocation will be insufficient for most. Those that integrate with mainline Docker and BuildKit should "just work," though.
Recommend checking out native caching support: https://kamal-deploy.org/docs/configuration/builders/#builder-cache

Earthly performs image builds in Buildkit. But kamal wants a Dockerfile.
So it is a deployment tool that requires Docker on the host that runs kamal which would be unfortunate. Is there no support for building images outside of kamal (ie in CI with unit and integration tests that must pass before it is pushed to to production registry?

Consider pre-building the image with Earthly, tag it as Kamal would, and then kamal deploy --skip-push (or skip_push: true in config). May get you what you're looking for!


Python 3 version

"Simple" request :).
Thanks!

Hi @jonovate, we are working on that. You could also contribute if you have managed to support python 3.

I'd like to contribute :)
On Tue, 13 Mar 2018, 17:42 Daxeel Soni<EMAIL_ADDRESS>wrote:

Hi @jonovate https://github.com/jonovate, we are working on that. You
could also contribute if you have managed to support python 3.
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If you are looking for someone to contribute please tag issue with help
wanted.
On Tue, 13 Mar 2018 at 18:06, Siddharth Prajosh<EMAIL_ADDRESS>wrote:

I'd like to contribute :)
On Tue, 13 Mar 2018, 17:42 Daxeel Soni<EMAIL_ADDRESS>wrote:

Hi @jonovate https://github.com/jonovate, we are working on that. You
could also contribute if you have managed to support python 3.
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@sprajosh You are welcome to contribute for python3 support.
@smitthakkar96 Thanks. This issue is not now tagged.

I would like to contribute

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Document container probes

Please tell me if there is anything else you'd like me to include here.
Fixes #8861
cc @jayunit100

Great first start let's get it in thanks!

LGTM. Thanks for the PR!


With more than one device connected, all functions work on the wrong device (i.e. other than the selected device) in the list

As per title: when there are 2 devices connected, selecting a device in the list shows the processes for the other device. Starting the log shows log output for the other device, also.

Fixed in 2.0.3


New feature: add performane test reports like jmeter

+1. It would be usefull

Sorry guys, not in this year

+1. Any news on this?


Add profiling and benckmarking

Description
Adds:

A profiling step of pytest test suite
A profiling test with a difficult model
A benchmarking test with a difficult model.

The difficult model contains around 1,000 cells, surfaces, and materials. Each material has 92 nuclides. The actual model is utter non-sense, but is valid, and meant to stress the system. I think this model is big enough to stress the system and identify bottle necks without just being useless CPU thrashing.
Fixes #514
Checklist

[x] I have added tests that prove my fix is effective or that my feature works (if applicable)
[x] Fix line overrun warnings.

This test failed with the benchmark being too slow: https://github.com/idaholab/MontePy/actions/runs/10584100119/job/29327558717

I do plan to squash this merge to add adding the big_model to the git history multiple times.


Slow unit tests

The following tests take >1s:

[ ] TestReleaseOnCancellation_ConfigMaps 1.00s
[ ] TestReleaseOnCancellation_Endpoints 1.00s
[ ] TestReleaseOnCancellation_Leases 1.00s
[ ] TestTokenExchangePluginWithCache 1.00s
[ ] Test_tokenSupplier_GetRequestMetadata 1.00s
[ ] TestRunComponentsAfterStart/RunComponentAsyncAndWait 1.01s
[ ] TestSleepCheckInstall 1.01s
[ ] TestStsCache 1.01s
[ ] TestGetFederatedToken 1.02s
[ ] TestRunComponentsAfterStart 1.02s
[ ] TestGetCNIConfigFilepath 1.03s
[ ] TestSendPushesManyPushes 1.03s
[ ] TestClient 1.06s
[ ] TestStsFlow 1.06s
[ ] TestTrailingWhitespace 1.06s
[ ] TestLeaderElectionNoPermission 1.08s
[ ] TestSDS 1.08s
[ ] TestWorkloadAgentGenerateSecret/no_plugin 1.09s
[ ] TestAdsPushScoping/Add_an_unmatched_Sidecar_config 1.10s
[ ] TestAdsPushScoping/Add_a_unscopedname_not_match_service 1.10s
[ ] TestAdsPushScoping/Add_a_unscopedns_not_match_service 1.10s
[ ] TestAdsPushScoping/Add_destination_rule_to_an_unscoped_service 1.10s
[ ] TestAdsPushScoping/Add_instances_to_an_unscoped_service 1.10s
[ ] TestAdsPushScoping/Add_virtual_service_to_an_unscoped_service 1.10s
[ ] TestAdsPushScoping/Delete_destination_rule_of_a_unscoped_service 1.10s
[ ] TestAdsPushScoping/Delete_virtual_service_of_a_unscoped_service 1.10s
[ ] TestAdsPushScoping/Remove_a_unscopedname_not_match_service 1.10s
[ ] TestAdsPushScoping/Remove_a_unscopedns_not_match_service 1.10s
[ ] TestTokenExchangePlugin 1.15s
[ ] TestSidecarTemplate/sidecar_template 1.18s
[ ] TestStartEnvoyShouldSucceed 1.22s
[ ] TestInstallPackagePath/install_package_path#02 1.23s
[ ] TestJwtPubKeyRefreshWithNetworkError 1.25s
[ ] TestAgent/Token_exchange_with_credential_fetcher_downtime 1.32s
[ ] TestGetProxyServiceInstancesWithMultiIPsAndTargetPorts 1.32s
[ ] TestInstallPackagePath/install_package_path 1.36s
[ ] TestWorkloadAgentGenerateSecret 1.38s
[ ] TestScrtUpdated 1.40s
[ ] TestInstallPackagePath/install_package_path#01 1.41s
[ ] TestAutoregistrationLifecycle/garbage_collected_if_pilot_stops_after_disconnect 1.50s
[ ] TestNetworkUpdateTriggers 1.51s
[ ] TestCitadelClient 1.53s
[ ] TestCitadelClient/retry 1.53s
[ ] TestInstall 1.60s
[ ] TestHotRestart 1.62s
[ ] TestExtraConfigmap 1.63s
[ ] TestEds 1.81s
[ ] TestGenerateMutatingWebhook 1.85s
[ ] TestAutoregistrationLifecycle/garbage_collected_if_pilot_and_workload_stops_simultaneously_before_pilot_can_do_anything 2.00s
[ ] TestTcpEchoServer 2.00s
[ ] TestUpdateAll 2.00s
[ ] TestWaitCmd/case_0_x_wait_--generation=2_--timeout=2s_virtual-service_foo.default 2.00s
[ ] TestGoogleCASClient 2.01s
[ ] TestIstiodCipherSuites/client_and_istiod_cipher_suites_mismatch 2.01s
[ ] TestWaitCmd/case_4_x_wait_--timeout_2s_virtual-service_bar.default 2.02s
[ ] TestWasmHTTPFetch/download_retry 2.04s
[ ] TestConfigSelectors 2.12s
[ ] TestEndpointSliceFromMCSShouldBeIgnored 2.12s
[ ] TestCheckDuplicateCSR 2.50s
[ ] TestSignCSR 2.53s
[ ] TestIstiodCipherSuites/client_and_istiod_cipher_suites_match 2.54s
[ ] TestNewServer 2.62s
[ ] TestProxyCachedToken 2.76s
[x] TestAddMeshConfigUpdate 2.92s
[x] TestGatewayHostnames 3.00s
[x] TestGatewayHostnames/re-resolve_after_TTL 3.00s
[x] TestReadSignedCertificate 3.00s
[x] TestGoogleCAClient 3.05s
[ ] TestWebhookSelector 3.18s
[ ] TestProxySTS 3.27s
[x] TestIstiodCipherSuites/default_cipher_suites 3.29s
[ ] TestGenCertKeyFromOptions 3.51s
[ ] TestAutoregistrationLifecycle 4.01s
[ ] TestWaitCmd 4.03s
[ ] TestServerCachedToken 4.49s
[ ] TestServerShortLivedCachedToken 4.55s
[x] TestK8sSign 5.00s
[x] TestWasmCache/download_failure 5.41s
[x] TestWasmCache 5.46s
[ ] TestAgent 5.74s
[x] TestWasmHTTPFetch/download_max_retry 6.02s
[ ] TestRenewToken 7.64s
[x] TestIstiodCipherSuites 7.84s
[x] TestWasmHTTPFetch 8.07s
[ ] TestTokenFetchFailureTwo 11.02s
[ ] TestTokenFetchFailureOne 11.06s
[x] TestAdsPushScoping 12.46s https://github.com/istio/istio/pull/37572/files
[ ] TestTokenFetchTimeoutTwo 14.02s
[ ] TestTokenFetchTimeoutOne 14.05s
[x] TestK8sSignWithMeshConfig 25.01s https://github.com/istio/istio/pull/37640

These should be optimized.
It is recommended to run https://github.com/istio/istio/wiki/Test-Flakes#reproducing-test-flakes locally before fixing anything to ensure that changes (such as lowering timeouts, etc) do not make things flaky.

Hi @howardjohn,
is this issue still open to help from the community? I would be interested to help here

@cebernardi Sure. Appreciate your help.

Awesome! Taking a stab..

Great! the list may be out of date though, just FYI

I opened a draft PR to check if the direction could be good (for the first 2 enumerated tests) and get some early feedback.

Hey @howardjohn, I am looking to pick up some of these tests. Is the original list up to date?

Hasn't been updated since the original issue.
I believe I found it by
test-times () {
        rg --hidden '\-\-\- PASS' | map 'i.split("PASS: ")[1].replace("(", "").replace(")", "")' | sort -k2,2 -n
}
cat build-log.txt | test-times

You can get the build log by goin to the commit, clicking the green check or red x and going to the prow page: https://prow.istio.io/view/gs/istio-prow/logs/unit-tests_istio_postsubmit/1637825841897410560 for example

test-times () {
        rg --hidden '\-\-\- PASS' | map 'i.split("PASS: ")[1].replace("(", "").replace(")", "")' | sort -k2,2 -n
}

@howardjohn
I could not locate map command on my ubuntu (v22.04) box. Which package it belongs to?

I see time taken by TestSignCSR varies a lot. On my local system, it is taking around one to one and half sec. Some times it takes less than a sec as well. On Prow CI/CD pipeline, I see two instances with 8s and 3s. Wonder why such deviation? Appreciate if someone can help me to understand.
go test ./security/pkg/pki/ca -count=1 -v -run ^TestSignCSR$
=== RUN   TestSignCSR
--- PASS: TestSignCSR (1.44s)
PASS
ok  	istio.io/istio/security/pkg/pki/ca	1.452s

go test ./security/pkg/pki/ca -count=1 -v -run ^TestSignCSR$
=== RUN   TestSignCSR
--- PASS: TestSignCSR (1.12s)
PASS
ok  	istio.io/istio/security/pkg/pki/ca	1.130s

go test ./security/pkg/pki/ca -count=1 -v -run ^TestSignCSR$
=== RUN   TestSignCSR
--- PASS: TestSignCSR (0.78s)
PASS
ok  	istio.io/istio/security/pkg/pki/ca	0.795s

Btw below links might have expire
https://prow.istio.io/log?job=unit-tests_istio_postsubmit&id=1669581982494887936
=== RUN   TestSignCSR
--- PASS: TestSignCSR (8.21s)

https://prow.istio.io/log?job=unit-tests_istio_postsubmit&id=1669650939406979072
=== RUN   TestSignCSR
--- PASS: TestSignCSR (3.60s)

Ci runs with -race, I think it makes the signing take exceptionally long

on the map command - sorry it's an alias, I can post the content when I'm at my computer

I use this to get a list of the tests with execution time:
cat build-log.txt | grep "\-\-\- PASS" | grep -v "    \-\-\- PASS:" | sort -V -k2 -t'('

I hope this helps

I wrote a lot about Go build times which is tightly coupled to test times: https://blog.howardjohn.info/posts/go-build-times/

cc

New slow list:

Test_autoAllocateIP_values 1.01s
TestFuzzers 1.01s
TestListenerGeneration 1.01s
TestSecretsController 1.01s
TestIngressController 1.02s
TestKubeConfigOverride 1.02s
TestDescribe 1.03s
TestGetCNIConfigFilepath 1.03s
TestIPAllocate 1.03s
TestSendPushesManyPushes 1.03s
TestListInvalidGroupVersionKind 1.05s
TestInjection/[76]_list.yaml.injected 1.06s
TestNewServer/default_domain 1.06s
TestWorkloadInstances/Kubernetes_only 1.06s
TestInjection/[36]_proxy-override-args-native.yaml.injected/webhook 1.07s
TestAPIGen 1.08s
TestSleepCheckInstall 1.08s
TestUpdateDataInConfigMap/non-existing_ConfigMap 1.08s
TestIstiodCipherSuites/client_and_istiod_cipher_suites_mismatch 1.10s
TestAutoregistrationLifecycle 1.12s
TestIsK8VersionSupported 1.13s
TestAgent/Unhealthy_SDS_socket 1.14s
TestEndpointsByNetworkFilter 1.15s
TestEndpointsByNetworkFilter_WithConfig/networking.istio.io/v1/DestinationRule 1.15s
TestInjection/[36]_proxy-override-args-native.yaml.injected 1.15s
TestDisablePortTranslation/multiple_target_port,_target_port_first 1.17s
TestIPTablesRuleGeneration/basic 1.17s
TestMutatingWebhookPatch 1.17s
TestAgent/Kubernetes_defaults 1.18s
TestConcurrentCreateSelfSignedIstioCA 1.18s
TestCNIPluginServer 1.19s
TestLeaderElectionNoPermission 1.21s
TestCreateSelfSignedIstioCAWithoutSecret 1.22s
TestExtractRootCertExpiryTimestamp 1.22s
TestProxyStatus 1.23s
TestUpdateDataInConfigMap 1.23s
TestAgent/credential_fetcher_downtime 1.24s
TestK8sSignWithMeshConfig 1.25s
TestDisablePortTranslation 1.27s
TestProxyConfigAnchors 1.31s
TestSingleClusterNodeAuthorization 1.31s
TestJwtPubKeyRefreshWithNetworkError 1.32s
Test_ConfigMapWatcher 1.34s
TestConfigureIstioGateway 1.41s
TestLabelPods 1.41s
TestEndpointsWithMTLSFilter 1.42s
TestBogusControlPlaneSec 1.45s
TestBareValues 1.46s
TestBareSpec 1.47s
TestTrailingWhitespace 1.51s
TestCitadelClient/retry 1.53s
TestCitadelClient 1.56s
TestGenerator_GenerateHTTP 1.62s
TestOutboundListener_PrivilegedPorts 1.63s
TestGenKeyCertK8sCA 1.65s
TestRecordCertsExipryMetrics 1.68s
TestRecordCertsExipryMetrics/test_certificate_metric_recording 1.68s
TestNewServer/override_default_secured_grpc_port 1.70s
TestIPTablesRuleGeneration 1.72s
TestNewServerCertInit 1.73s
TestAnalyzers 1.88s
TestEndpointsByNetworkFilter_WithConfig 1.88s
TestExtraConfigmap 1.88s
Test_tokenSupplier_GetRequestMetadata 1.89s
TestManifestGenerateGateway/ingressgateway_k8s_settings 1.90s
TestGreenfield 1.98s
TestInjection 2.00s
TestSidecarTemplate/sidecar_template 2.04s
TestIstiodCipherSuites 2.07s
TestProxyConfig 2.09s
TestFault 2.25s
TestManifestGenerateFlagsMinimalProfile 2.26s
TestManifestGenerateFlags/all_on 2.28s
TestMeshNetworking 2.28s
TestDependencies/exhaustive 2.57s
TestManifestGenerateFlagsSetHubTag 2.59s
TestManifestGenerateIstiodRemote 2.61s
TestManifestGenerateFlagsSetValues 2.65s
TestExtractCACertExpiryTimestamp 2.70s
TestPrune 2.82s
TestManifestGenerateComponentHubTag 2.93s
TestManifestGenerateFlags/flag_values_enable_egressgateway 2.93s
TestManifestGeneratePilot/pilot_override_kubernetes 2.97s
TestGolden 3.00s
TestManifestGeneratePilot/autoscaling_ingress_v2 3.00s
TestManifestGeneratePilot/autoscaling_v2 3.03s
TestGenCertKeyFromOptions 3.10s
TestDependencies 3.19s
TestManifestGenerateHelmValues/helm_values_enablement 3.19s
TestSignCSR 3.20s
TestManifestGenerateZtunnel/ztunnel 3.30s
TestAdsPushScoping 3.36s
TestManifestGenerateFlagAliases 3.49s
TestTrafficShifting 3.61s
TestManifestGenerateOrdered 3.99s
TestWorkloadInstances 4.07s
TestDependencies/exhaustive 4.31s
TestNewServer 4.54s
TestValidator 4.92s
TestConfigSelectors 5.09s
TestDependencies 5.26s
TestCRDs 5.34s
TestManifestGenerateWithDuplicateMutatingWebhookConfig 5.38s
TestManifestGenerateDefaultWithRevisionedWebhook 5.43s
TestManifestGenerateFailedDefaultInstallation 5.52s
TestReferencePolicy 5.65s
FuzzCRDs 5.66s
TestWebhookSelector 6.55s
TestAgent 7.99s
TestConvertResources 9.01s


Framework/Test Targets for macOS

I hope those do not confuse the Cocoapod.
This stuff makes it build as a Cocoa framework. Building things works fine, but the tests actually crash on macOS (same w/ spm test btw).

What test fails? Is it the create class in the issue you made?

Check issue #5. This is not related to Linux, it is just the same tests you run on iOS, fail on macOS. Seems to be an RC issue.
This PR is just adding a target to the Xcode project to build a macOS framework & tests. It should be fine to merge, unless they break the pods, don't know how that works.

I think I may be reverting the SPM stuff so I'm going to hold off. Not a big fan of the massive restructure. When I first looked over the PR it didn't show all the the moved files and only showed about 400 line changes but once merged I saw the full extent of the changes. My mind was also clouded by excitement of my first contributor lol. I think all of the changes minus the file restructure are good and we need them in but I would like to keep the project structure the same. In swift 4 they did a full redesign of the SPM and I believe now you don't have to have all of the /Sources folder. Take a look at ReSwift and they support SPM but have a normal more traditional fire structure. Sorry about all of the confusion this is my first open source project people want to contribute to so this is all new to me, and just trying to make sure everything is clean.

Hey, no problem, it's your very own project! ;-) I'm not entirely sure where you see a "massive restructure", the files just got moved into Sources/Tests, thats it, but sure, if you don't like it, there is no obligation to accept PRs :-)

Awesome thanks for being nice about it 🙂 I felt bad lol. Anxiety and OCD get the best of me. But I made a new develop and added the Package.swift and it builds fine for a test project as a dependency. So if you want to start fresh and branch off that and copy some of the changes for linux/test over that would be good.

BTW: This PR #4 is completely unrelated to SPM or Linux. It is about adding a regular macOS Target to the regular Xcode project you have. Do you want this? I think it would be good to get it working on macOS first, and then checkout Linux. I think it would be good to have at least a cannotCreateInstanceOnThisPlatform error.
(also: I'm personally fine w/ not having instance construction, I'm mostly interested in property write access, which does seem to work fine across platforms).

Anyways, if you reverted the directory structure, this would need an update, so do not commit merge yet ;-)

Actually, let me close this, I'm going to create a new PR.


Strange highlights and Tensor not working.

When using:
# Imports
import vapoursynth as vs
# getting Vapoursynth core
core = vs.core
import site
import os
import ctypes
# Adding torch dependencies to PATH
path = site.getsitepackages()[0]+'/torch_dependencies/'
ctypes.windll.kernel32.SetDllDirectoryW(path)
path = path.replace('\\', '/')
os.environ["PATH"] = path + os.pathsep + os.environ["PATH"]
# Loading Plugins
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/Support/fmtconv.dll")
core.std.LoadPlugin(path="i:/Hybrid/64bit/vsfilters/SourceFilter/LSmashSource/vslsmashsource.dll")
# source: 'C:\Users\Selur\Desktop\howToSharpenThis.mp4'
# current color space: YUV420P10, bit depth: 10, resolution: 3840x2160, fps: 50, color matrix: 709, yuv luminance scale: limited, scanorder: progressive
# Loading C:\Users\Selur\Desktop\howToSharpenThis.mp4 using LibavSMASHSource
clip = core.lsmas.LibavSMASHSource(source="C:/Users/Selur/Desktop/howToSharpenThis.mp4")
# Setting color matrix to 709.
clip = core.std.SetFrameProps(clip, _Matrix=1)
clip = clip if not core.text.FrameProps(clip,'_Transfer') else core.std.SetFrameProps(clip, _Transfer=1)
clip = clip if not core.text.FrameProps(clip,'_Primaries') else core.std.SetFrameProps(clip, _Primaries=9)
# Setting color range to TV (limited) range.
clip = core.std.SetFrameProp(clip=clip, prop="_ColorRange", intval=1)
# making sure frame rate is set to 50
clip = core.std.AssumeFPS(clip=clip, fpsnum=50, fpsden=1)
clip = core.std.SetFrameProp(clip=clip, prop="_FieldBased", intval=0)
# cropping the video to 820x820
clip = core.std.CropRel(clip=clip, left=1020, right=2000, top=540, bottom=800)

clip = core.resize.Bicubic(clip=clip, format=vs.RGBS, matrix_in_s="470bg", range_s="limited")
org = core.resize.Bicubic(clip=clip, width=1640, height=1640)

from vsfemasr import femasr
clip = femasr(clip)

# adjusting output color from: RGBS to YUV420P10 for x265Model
clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P10, matrix_s="470bg", range_s="limited", dither_type="error_diffusion")
org = core.resize.Bicubic(clip=org, format=vs.YUV420P10, matrix_s="470bg", range_s="limited", dither_type="error_diffusion")
clip = core.std.StackHorizontal([org.text.Text("Original"), clip.text.Text("Filtered")])

# Output
clip.set_output()

The result, looks impressive, but I see some strange highlights. (which I also see with other sources)
I also checked: enabling and disabling nvfuser, cuda_graphs does not change these highlights.
Are these to be expected, or is this a bug?
Using tensor:
clip = core.resize.Bicubic(clip=clip, format=vs.RGBH, matrix_in_s="470bg", range_s="limited")

from vsfemasr import femasr

clip = femasr(clip, trt=True, trt_cache_path=r"G:\Temp")

I also tried with

clip = femasr(clip, trt=True, trt_cache_path="G:/Temp")

both failed with:
Python exception:

Traceback (most recent call last):
File "src\cython\vapoursynth.pyx", line 2866, in vapoursynth._vpy_evaluate
File "src\cython\vapoursynth.pyx", line 2867, in vapoursynth._vpy_evaluate
File "C:\Users\Selur\Desktop\test_2.vpy", line 38, in
clip = femasr(clip, trt=True, trt_cache_path="G:/Temp")
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsfemasr\__init__.py", line 196, in femasr
module = lowerer(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 323, in __call__
return do_lower(module, inputs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\pass_utils.py", line 117, in pass_with_validation
processed_module = pass_(module, input, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 320, in do_lower
lower_result = pm(module)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__
out = _pass(out)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__
out = _pass(out)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\lower_pass_manager_builder.py", line 167, in lower_func
lowered_module = self._lower_func(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 180, in lower_pass
interp_res: TRTInterpreterResult = interpreter(mod, input, module_name)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 132, in __call__
interp_result: TRTInterpreterResult = interpreter.run(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\fx2trt.py", line 252, in run
assert engine
AssertionError

using Tensor without specifying trt_cache_path throws:
Python exception:

Traceback (most recent call last):
File "src\cython\vapoursynth.pyx", line 2866, in vapoursynth._vpy_evaluate
File "src\cython\vapoursynth.pyx", line 2867, in vapoursynth._vpy_evaluate
File "C:\Users\Selur\Desktop\test_2.vpy", line 38, in
clip = femasr(clip, trt=True)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\vsfemasr\__init__.py", line 196, in femasr
module = lowerer(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 323, in __call__
return do_lower(module, inputs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\pass_utils.py", line 117, in pass_with_validation
processed_module = pass_(module, input, *args, **kwargs)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 320, in do_lower
lower_result = pm(module)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__
out = _pass(out)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch\fx\passes\pass_manager.py", line 240, in __call__
out = _pass(out)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\passes\lower_pass_manager_builder.py", line 167, in lower_func
lowered_module = self._lower_func(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 180, in lower_pass
interp_res: TRTInterpreterResult = interpreter(mod, input, module_name)
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\lower.py", line 132, in __call__
interp_result: TRTInterpreterResult = interpreter.run(
File "I:\Hybrid\64bit\Vapoursynth\Lib\site-packages\torch_tensorrt\fx\fx2trt.py", line 252, in run
assert engine
AssertionError

I'm using  CUDA-11.7_cuDNN-8.6.0_TensorRT-<IP_ADDRESS>_win64.7z  from vs-animesr and NVIDIA Studio drivers 527.56 on a Geforce RTX 4080 on Windows 11.

Looks like the strange highlight is a defect of the pretrained model or the network itself. Clamping the tensor to 0-1 range doesn't help so it's not caused of out-of-range pixel values.
The TensorRT error is caused by engine creation failure due to OOM. Currently torch_tensorrt doesn't support all ops used in the network and has to partition the model into several subgraphs, which is the main culprit. You need to use a smaller tile for it to successfully build the engine, but the performance won't be optimal unfortunately.

You need to use a smaller tile for it to successfully build the engine, but the performance won't be optimal unfortunately.
To what, for example?
Since tile_w: int = 0, tile_h: int = 0, are the defaults and '0 denotes for do not use tile', I assumed there was no tiling, or if tiling was needed the dll would choose something that would work.

Using tile_w=420, tile_h=420, I get an output, but it's kind of broken.

Side note: tiling does lower the vram usage.
Using tile_w=200 and tile_h=200 results in: Padding size should be less than the corresponding input dimension, but got: padding (0, 220) at dimension 3 of input [1, 3, 216, 36]
Using tile_w=220 and tile_h=220 results in:

Seems like either I don't use 'unlucky' values, or tiling is somehow broken atm.

This model/network seems not suitable for tiling as it's quite sensitive about the content it sees and hence produces very inconsistent results between tiling and non-tiling.

Okay, so best I don't use tensor with this for now.
Tensor requires tiling to work, and tiling doesn't work with the model. ;)
Thanks for looking into it. :)


[VQC-CHALLENGE] [Qraiders] [Oct-30-2022]

Team members
David Liu
Yashaswini Manjunath
Yousra FARHANI
Ashmit Gupta
Zainab Bouchbouk
Hamza Kamel
Submission summary
2nd submission for VQC-CHALLENGE

Congrats! Your model achieved 100% accuracy on our judging data set and joins the top of the leaderboard. As a next step, your team is encouraged to take on the quantum kernel estimator challenge, which will be used to determine an overall winner. Happy hacking!


fix: 🐛 changing to an object with the undefined property to avoid an error on the android platform

Fix for the error that it is not possible to convert the null value to an object (Plataform: ANDROID)
Explanation:
When using the library on the Android platform, in the source property when placing an object and running the app, when rendering the pdf file, an error is returned saying that it is not possible to convert a null value to an object, as shown in this image:

This fix solved the issue. When can it be merged.? waiting for it.

Bump! I'm also waiting for this. Any idea when it will be merged?

Until now, nothing?

Hello @xcarpentier ,
can you review this PR if possible


Docs: Add documentation for how to deploy your own self-hosted version of JSON Hero on Cloudflare Workers

We currently host jsonhero.io on Cloudflare Workers, backed by KV store. We use a GitHub action to deploy on every commit to the main branch. It would be great if we had a guide called SELF_HOSTING.md at the root of the repository to guide people on how to host their own version of JSON Hero so they could use it without sharing private data with us.

I am ready to go with this issue. Can you just elaborate with the issue so i can create with .md file


[Handles] Generate suffix given a seed and range

Goals
As a network, Frequency would generate some random suffixes using PRNG.
Acceptance Criteria:

[ ] Use a PRNG (pseudo-random number generator) to generate suffixes based on a given seed and range.
[ ] In this implementation, we are using the xxHash algorithm n number of times, with the block number and owner key as inputs. To create the allowed suffix set, we use the range [0..10] and apply the xxHash algorithm again, this time with the ceiling and floor of the current block number as inputs.
[ ] The user's chosen suffix must be within the allowed suffix set in order to be considered valid.
[ ] Implement the generator function in https://github.com/libertydsnp/frequency-handles

We need to change the problem description / acceptance criteria to reflect that we are not creating random suffixes but rather a randomly shuffled sequence of suffixes.

Suffix generator is implemented for handles


Remove deprecated usages of getJsonRoute

Remove deprecated usages of ApiRoute.getJsonRoute method from Scorex. Handle exceptions by implicit ExceptionHandler (see example at https://github.com/ergoplatform/board-new/blob/master/src/main/scala/org/ergoplatform/board/App.scala#L24). Use standard circe .asJson method from EncoderOps to convert result to Json. Add CORS header directives to the resulting routes. Handle futures in responses with onSuccess directive from FutureDirectives.

@catena2w  I think this issue can be closed.


Broken link in installation docs

The link for smithy-cli installation is broken!
The latest release at the moment is https://github.com/awslabs/smithy/releases/tag/1.31.0 while the doc instructs to mkdir -p smithy-install/smithy && \     curl -L https://github.com/awslabs/smithy/releases/download/1.32.0/smithy-cli-linux-x86_64.tar.gz -o smithy-install/smithy-cli-linux-x86_64.tar.gz && \     tar xvzf smithy-install/smithy-cli-linux-x86_64.tar.gz -C smithy-install/smithy
The release assets are missing in the latest tag v1.32 and maybe a possible solution could be always fetch the latest release rather than tags.

Thank you for creating an issue. We are currently releasing that version. It should be fixed now.


Add call-out for how to use quotes with influx write

Regarding: https://docs.influxdata.com/influxdb/v2.0/write-data/developer-tools/influx-cli/
This command returns a parsing error (invalid boolean):
influx write  "test_measurement,test_tag=tv test_field='fv'<PHONE_NUMBER>"

This command works:
influx write  'test_measurement,test_tag=tv test_field="fv"<PHONE_NUMBER>'

The only difference is that the outer quotes are single and the quoting around the Field value is double.   This is finicky and will probably trip people up.

Will test this out and document any gotchas. Thanks, @samhld!

Hi @samhld. Got around to looking into this, and am running into the same issue myself. Will open an issue in influxdb before making a bit of a clunky doc change. This might be an easy fix...


cmd/compile: include name in "unexpected name" error message

Context: http://tour.golang.org/flowcontrol/8
func Sqrt(x float64) float64 {
    z := float64(1)

    z = z - (z^2 - x)/2z

    return z
}

I get the same results for:
   z = z - math.Pow(z,2) - x/2z;

results:
/tmp/sandbox898777084/main.go:10: syntax error: unexpected name, expecting semicolon or newline or }

Looks like I can't progress on the problem because I'm stuck on syntax.

CC @adg @campoy

The syntax error is 2z. You need an operator between the 2 and the z. I think what you want is 2*z.
We could probably improve the compiler error. Maybe it could say what the unexpected name was ("z"?).

With Robert's new parser the error is:
/tmp/x.go:6: syntax error: unexpected z at end of statement

Clear enough I think.


Failure to build component containing flags shape

I am using cargo-component from commit 29fea4af8a28857e6a1fee3df3cd930a3fadad10.
While trying to build a component that contains a flags shape (example here), the following error is presented (reproduced here):

error[E0433]: failed to resolve: use of undeclared crate or module wit_bindgen
--> src/lib.rs:1:1
|
1 | cargo_component_bindings::generate!();
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ use of undeclared crate or module wit_bindgen
|
= note: this error originates in the macro cargo_component_bindings::generate (in Nightly builds, run with -Z macro-backtrace for more info)
error[E0412]: cannot find type Permissions in this scope
--> src/lib.rs:1:1
|
1 | cargo_component_bindings::generate!();
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ not found in this scope
|
= help: consider importing this struct:
std::fs::Permissions
= note: this error originates in the macro cargo_component_bindings::generate (in Nightly builds, run with -Z macro-backtrace for more info)
error[E0433]: failed to resolve: use of undeclared type Permissions
--> src/lib.rs:1:1
|
1 | cargo_component_bindings::generate!();
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ use of undeclared type Permissions
|
= help: consider importing this struct:
std::fs::Permissions
= note: this error originates in the macro cargo_component_bindings::generate (in Nightly builds, run with -Z macro-backtrace for more info)

It looks like I might have missed a place where the generated code was assuming wit_bindgen for the runtime path.
I'll get this fixed in upstream wit-bindgen and resolve this issue once we get it patched.

This line is the problem. Looks like we should also be reexporting bitflags from cargo_component_bindings as well.

I've put up https://github.com/bytecodealliance/wit-bindgen/pull/645 to address this; once that merges, a small change is required to cargo-component to resolve the issue.


refactor(transformer/typescript): use TraverseCtx::generate_binding to create a symbol

Obviously, we reduce much code after using this API

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feat(cmd/gf): add ShardingPattern option for command gf gen dao to support generating dao files sharding tables

@houseme gci会把我放在import中的注释删掉呢，能避免吗？https://github.com/gogf/gf/commit/bab409df62384891195a72ed2c4e907a4ea44523


[Bug] react-static create typescript sample loads incorrect html when running serve

Environment

<EMAIL_ADDRESS>

Steps to Reproduce the problem
Base your steps off of any freshly installed react-static template!

run react-static create
choose the 'typescript' sample
run yarn build && yarn serve
open http://localhost:3000/about in url field in browser

Expected Behavior
It is expected that dist/about/index.html to be served as initial file to browser.
instead: dist/index.html is sent and then then rehydrated to the 'about' page.
This only happens in the 'typescript' sample. The 'basic' sample works as expected.

The serve script for the typescript sample and the basic sample differ.
The problem I had was solved by removing the "-s" flag in the serve script in package.json.
script should be: "serve": "serve dist -p 3000" and not "serve": "serve dist -p 3000 -s"

The serve script for the typescript sample and the basic sample differ.
The problem I had was solved by removing the "-s" flag in the serve script in package.json.
script should be: "serve": "serve dist -p 3000" and not "serve": "serve dist -p 3000 -s"

We take PRs!

We take PRs!


How to build and run the whole project?

I want to run the algorithm implemented in this repository on Linux, or MacOS. However, I can't find any documentation that tells me how to do it step by step. I've only seen compilation documentation for mpc4j-native-tool/fhe.

Aside from mpc4j-native-tool/fhe, this is a pure-Java library organized using Maven. We observe that the ones who are familiar with Java development can quickly run this library. For ones who are not familiar with Java, we plan to release a step-by-step guideline on how to import and run this library. I will reply when we upload such the guideline.

We thank @qxzhou1010 for writing a guideline for how to config the development environment on macOS. The details are in README.md.


[Feature] IP Prevalence

Show prevalence of communications with an IP address.  How many unique sources are talking to it?  Cross reference MDE logs and ASIM Network session to locate data?  Maybe SigninLogs to see how many people have signed in from it?  When was the first time the IP was seen?

perhaps adding a flag public/private IP? Or is that maybe something to do in the base module?


Feature/otel config apps

Issue #, if available:
Description of changes:
OTEL config changes for apps
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

LGTM. Is this tested

OTEL part works fine. Yet to be tested with RUST App


decryptData parses headers twice

When using AwsCrypto.decryptData(), the headers are parsed in the DecryptionHandler() constructor as well as in the processBytes() call for the DecryptHandler. This is less than an ideal because each time the headers are parsed the data keys are decrypted. For AWS KMS keys, this means two api calls to decrypt the same data key for each call to decryptData.

This has been fixed in the 1.3.0 release.


rename TRACE log level to DEBUG2

What is wrong?
In the original design for Py-EVM I imagined using the built-in python logging facilities to do EVM tracing #62 .  Since then the TRACE log level has evolved to just mean lower level debug logs.
Second, we've ended up needing to tune the logging level of certain noisy modules when ideally we'd be able to do this with finer grained logging levels.
How can it be fixed
Rename the TRACE to DEBUG2 and change the numeric value to be 8.  The idea here is that we can extend this to DEBUG3 and onward.

The eth.tools.logging.TraceLogger should be renamed to EthereumLogger
The eth.tools.setup_trace_logging should be renamed to setup_ethereum_logging
We should add a call eth.tools.logging.setup_ethereum_logging should be updated to call logging.setLoggerClass so that the logging.getLogger will always return an instance of our logger.
The TraceLogger.trace method should be renamed to TraceLogger.debug2
Update the Trinity CLI parser component that handles logging configuration.

Next steps would be to create DEBUG3 and to take our historically noisy loggers and set them to use this new lower level logging.

@pipermerriam, please confirm no one is working on it so I can start working on this one.

@rayrapetyan :+1: go for it.

The idea here is that we can extend this to DEBUG3 and onward.

I'd rather have TRACE2 than DEBUG3. :|


Improve performance when loading large logs

It seems that when the log file gets somewhat large, the readLastLines() call becomes very slow.
When I tried it on my SSD machine, it took about 6 sec to read a 1MB log. My SSD is actually a cheap one 😅 , but it still seemed unnatural that it would take 6 seconds to read a 1MB log file. So I rewrote the code using the bufio package to try it out, and the performance improved to about 40ms for loading the log file.

Great, thanks a lot for the PR @suitap ❤


fontのフラグを追加

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 0.00%. Comparing base (e0fc62f) to head (cb22cd1).

Additional details and impacted files
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##            main      #6   +/-   ##
=====================================
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=====================================
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  Lines          2       2
=====================================
  Misses         2       2

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Once upon a Time on Neural Coding.

Related Reference

Wu S, Nakahara H, Murata N, et al. Population decoding based on an unfaithful model.
Wu S, Nakahara H, Amari S. Population coding with correlation and an unfaithful model.
Wu S, Amari S. Neural implementation of Bayesian inference in population codes.
Oizumi M, Ishii T, Ishibashi K, et al. Mismatched decoding in the brain.
Xiao L, Zhang M, Xing D, et al. Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation.
Dong X, Ji Z, Chu T, et al. Adaptation Accelerating Sampling-based Bayesian Inference in Attractor Neural Networks.
Hoyer P, Hyvärinen A. Interpreting neural response variability as Monte Carlo sampling of the posterior.
Echeveste R, Aitchison L, Hennequin G, et al. Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference.
Qi Y, Gong P. Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits.
Zhang W H, Wu S, Josić K, et al. Recurrent circuit based neural population codes for stimulus representation and inference.
Chu T, Ji Z, Zuo J, et al. Oscillatory Tracking in Continuous Attractor Neural Networks Accounts for Theta Phase Shift of Hippocampal Place Cells.
Zhang W, Wu Y, Wu S. Equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors.

Overview
Neural coding, or how the brain encodes information, is a scientific problem at the "crown" level in computational neuroscience. Its importance is almost self-evident, as we seek to fundamentally elucidate the higher cognitive functions of the brain and inevitably answer how information is encoded by neural activity. But unfortunately, or confusingly (see more discussion later), we still don't have a clear answer to this question so far.
Broadly speaking, any neural computational modeling involves more or less encoding of information, i.e., specifying the form of input information in the model. However, the definition of neural coding (Fig. 1A) in this paper is narrower, which specifically refers to

how the spiking activity generated by the neuron group after receiving the external input encodes the input information (autoencoding?).

Our group has recently completed several works on neural coding, which prompted me to write this somewhat reminiscent academic essay for three reasons:

I have done three studies on the neural coding problem before and after, in 2000, 2010, and 2022. The time span is more than 20 years. After each completion, I completely put down the work at that time and switched to doing it. Other problems were not expected to be picked up again after more than 10 years. Personally, I feel that it is a very interesting academic experience, and I feel a little "impermanent" on the academic road, so I wrote it down and shared it with everyone, especially young scholars.
Neural coding is a particularly interesting problem in itself, and while it's very mathematical in nature, there are often philosophical questions behind it (which is why I love brain science). Many times I don't understand it, so I write it down for everyone to think about.
Brain-inspired intelligence is getting more and more attention now and is considered to be an important way for the development of artificial intelligence, but so far, it seems that few people have seriously thought about how to apply neural coding strategies to AI (the application I refer to here). Not just simply using the concept of spiking, but really drawing on the specific mechanisms by which the nervous system encodes information). Therefore, I wrote this essay in the hope that it will also be beneficial to AI workers.

Figure 1: Neural encoding background knowledge. A. External stimuli are encoded as the activity of neuron groups in the brain, which mediate our perception and cognitive behavior; B. The shape of the pulses emitted by neurons is almost unchanged, and the information is contained in the pulse sequence generated by neurons; C. A continuous variable is encoded by a group of neurons whose tuning functions overlap and cover the parameter space; the neural network underpinning this group encoding is a continuous attractor network; D. Under constant input, the neuron firing pulse trains are highly independent Rules, the results of each experiment record are different.

Background
Spike firing of neurons
The brain perceives the external world through different senses (vision, hearing, smell, etc.), and these physical or chemical signals (light, sound waves, odor molecules, etc.) that contain information about the external world are uniformly converted into the electrical activity of neurons within the brain , that is, the neuron's sequence of firings (spike train). Each discharge is called a spike or action potential (Figure 1B). The shape of the spikes is nearly constant, so from an information-encoding perspective,

it is the neuron-generated spike train rather than the spike shape that contains the input information.

Single neuron coding
Early research in the field focused on how a single neuron encodes information. There may be two extreme coding methods for the pulse sequence based on the response of a single neuron:

rate coding: the information is encoded in the firing frequency of the neuron, that is, the number of pulses per unit time. The specific temporal structure is irrelevant.
temporal coding: Information is encoded in the fine structure of the neuron's spike train. This coding strategy is employed for nighttime localization in the owl's auditory system, but is difficult to achieve in the cerebral cortex, where the noise is so full that the fine temporal structure of the pulse train is difficult to maintain.

In addition, there are some coding hypotheses between frequency and time series, which mainly consider some high-order statistical properties of neuron spike trains in the time domain. In general, it is difficult to answer the information encoding principles of the brain at the single neuron level, because the brain always has a large number of neurons involved in processing information, and these neurons interact through synaptic connections, isolated single neurons. Computation does not exist. Therefore, to decipher the laws of neural coding, we also need to start with the laws of neuron group activity (see below).

Neuron population coding
In the representation of some continuous variables, such as object orientation, head orientation, movement direction, spatial location, etc., experiments have confirmed that

the brain encodes information through the coordinated activity of a group of neurons.

Taking the head orientation as an example, its value ranges from 0 to $2\pi$, and the neural system covers the entire variable value space with the tuning function of a large number of neurons (that is, the range of stimuli values that neurons respond to): each neuron is mainly "responsible for" an angle value (preferred stimulus), that is, when the external stimulus takes this value, the neuron responds most strongly; at the same time, the tuning functions of a large number of neurons overlap each other (Fig. 1C). This way, when encoding a particular external stimulus value, a group of neurons, rather than a single one, responds, so that noise can be averaged out and the stimulus value can be decoded more accurately. The neural network structure that supports this group encoding is the Continuous Attractor Neural Network (CANN) (it is not the focus of this article and will not be described in detail here. Interested readers can refer to the WeChat public account article: Continuous Attractor Network). The neural system's group coding strategy has been supported by a large number of experiments, but

the current experimental data only statistically indicate that the neural system encodes with a large number of neurons,

and does not answer many details of the encoding process (see below).

Maybe disentangling representation has better explanation about why single neuron responds to single task factor.

Irregular firing of neurons
One of the most puzzling phenomena in neural coding is the irregular firing of neurons under constant input: experiments have found that even with the same input, each neuron fires a different sequence of spikes (Fig. High degree of irregularity (irregular), the degree of clutter even exceeds that of Poisson random processes.

This random firing is only present in living networks, isolated single neurons firing regularly under constant input, so this is a network effect.

The neural network structure underpinning this random firing is the excitation-inhibitory balance network, which has been supported by numerous neurobiological experiments and computational modeling. But what is particularly puzzling is why the brain uses this random neural activity to encode information? Intuitively, isn't the simplest and most reliable encoding a neuron responding constantly to constant input, as we do with encoding strategies in communication networks? Answering the computational mechanics behind the erratic activity of neurons could open the way for us to decipher the mysteries of neural coding.

In-context learning may provide better explanation about this issue.

Unreliable Decoding, 2000 at the Institute of Physics and Chemistry, Japan
This is the first neural coding job I've done. Around 2000, a number of international research groups simultaneously theoretically explored the mathematical properties of neuron group coding, mainly based on three mathematical methods:

Directly compute Fisher information of $p(\mathbf{r}|x)$. According to Cramer-Rao bound, the reciprocal of Fisher information gives the lower bound of the error of any unbiased decoding method, so Fisher information measures the amount of stimulus information contained in neuron groups in a sense.
The simplest decoding method, Population Vector or Center of Mass, is used, which is equivalent to a weighted average of the preferred stimulus of all neurons according to the neuron's response strength. This method is the most intuitive, but the disadvantage is that the error is too large.
Use the maximum likelihood method $\hat{x}=Max_{x}lnp(\mathbf{r}|x)$, which is statistically optimal, the Cramer-Rao bound can be approximated asymptotically (sufficient number of neurons) when the correlation between neuronal activity is small enough.

My collaborators and I analyzed the performance of neuron group encoding when the formation of the likelihood function $p(\mathbf{r}|x)$ was different (Figure 2), especially the performance of neuron group coding when $p(\mathbf{r}|x)$ ignores the correlation of neuron activity, and the theory proved the corresponding decoding method in this case. It is pattern matching (template matching), and pattern matching can be realized naturally by biologically plausible continuous attractor network [2]. The work was submitted to the NIPS of the year (now called NeurIPS; it took only two weeks from the idea to the submission, and the NIPS emphasized the novelty of the idea rather than the completeness of the work, which is different from today's requirements), and the result is Best score of all my NIPS articles (score: 5, 5, 4; confidence is 5, 5, 5; 5 is the highest score at the time). After completing this work, I then did a group coding work, which considered how the previous decoding value can be used as a priori knowledge to continuously optimize the subsequent decoding accuracy during the decoding process iteratively over time, which is equivalent to a time dimension. Bayesian inference on [3]. Since then, my work interest has shifted to Continuous Attractor Networks (CANN), and the research on neural decoding has been around for nearly ten years.
Figure 2: Unreliable neural decoding. A. Mathematical expression of unreliable decoding, and precision calculation formula; B. Under the condition of ignoring the correlation of neuron activity, unreliable decoding realizes pattern matching, and its precision and computational complexity are both in the maximum likelihood method and Between the centroid methods, a balance between accuracy and computational complexity is achieved.

Dynamic Coding, 2010, Institute of Neurology, Chinese Academy of Sciences
My renewed interest in neural coding was around 2010. At that time, an article by a Japanese friend quoted our unreliable decoding work, and used the mathematical formula we introduced to measure the stimulus information contained in the neuronal association activity [4], an application that I had not thought of before. At that time, I happened to be collaborating with Professor Liang Peiji from Shanghai Jiaotong University. Now that we were familiar with this mathematical analysis tool, we thought of using this tool to explore an important scientific problem, namely, the coding strategy of the nervous system during the adaptation process. Adaptation is a universal behavior of the nervous system that occurs everywhere in life. Judging from the response of a single neuron, when a stimulus just appears in the receptive field of the neuron, the neuron will have a strong response, but slowly the neuron "adapts" to the stimulus, which is reflected in the response intensity returning to the stimulus. The background level before appearing. According to the traditional view of frequency encoding, this means that neurons no longer encode stimulus information, but this seems to be inconsistent with common sense in our lives. For example, for an object that appears in the field of view for a long time, the visual system does gradually "adapt" to it, but we still know that the object is still there, just no longer pay attention to it deliberately. So

how is the information of the object continuously encoded after adaptation?

To answer this question, we propose a dynamic coding hypothesis (Fig. 3), that is, during the adaptation process of neurons, the nervous system does not completely ignore the external stimulus information, but the coding strategy changes, that is, from the initial Fast frequency coding translates into energy-efficient low-frequency correlation code (ie, the nervous system encodes stimulus information with low-frequency synchronous activity of neuronal populations). The advantage of this dynamic encoding is that when a new stimulus is first presented, the nervous system can respond quickly, which is most critical to the survival of the animal; but as the stimulus is recognized, to conserve energy, the nervous system switches to More economical associative coding to maintain information. In order to test this hypothesis, Liang Peiji's research group recorded the adaptation process of the bullfrog retina to dark stimuli (the bullfrog was chosen because its adaptive response was as long as 5 seconds, which provided us with good experimental data), and analyzed the firing frequency of neurons The results support our hypothesis by the transformation of the amount of stimulus information contained in the correlation between early and late adaptation.
When this work was submitted, the reviewers all said they liked the idea of dynamic coding, but they also doubted that the amount of stimulus information we calculated was too small (only a dozen neurons could be effectively recorded in the experiment at that time), and it was not ruled out that it was the data Analyzing the possibility of errors requires us to analyze the data with simpler and more intuitive methods (classifier, synergy information). After some tossing, the work was finally published in the Journal of Neurophysiology [5]. But I have always believed that dynamic coding strategies are widespread in the nervous system, because this is very natural and effective, but more experimental data are needed to prove it. In addition, what I am satisfied with this work is that the theoretical hypothesis is in the front, and the experimental design and verification are in the back. After completing this work, my collaborators and I have done some extended work piecemeal. For more details, please refer to the WeChat work account article: Dynamic Neural Coding. Since then, my research on neural coding has been on hiatus for almost a decade.
Figure 3: Dynamic Neural Coding. A. Typical neuronal adaptive behavior, the neuronal firing frequency gradually decreases during the adaptation process; B. During the adaptation process of the bullfrog retina, the resolution of Meta-fire rate classifiers are getting lower and lower resolution; C. Schematic illustration of dynamic coding work. When the external stimuli first appeared, the bullfrog retina transmits information rapidly to subsequent neurons through the high-frequency independent firing of neurons; after adapting to the external stimulus, the bullfrog retina transmits information backward through the associated low-frequency firing between neurons.

Random sampling coding, Peking University in 2022
After completing the above two tasks, my research on neural coding stopped for a long time, and my interest was rekindled due to the influence of Dr. Zhang Wenhao, a graduate student, who invited me to discuss the irregular firing of neurons a few years ago. computational meaning. The following highlights a work we accepted at NeurIPS this year [6].
As mentioned earlier, one of the most puzzling phenomena of neural coding is the irregular firing of neurons under constant input. There are several hypotheses about the computational significance of this random distribution. The hypothesis I personally agree with the most is random sampling coding, which holds that the random firing of neuron groups is actually doing sampling-based Bayesian inference [7]. A large number of human behavior experiments and animal physiological experiments support that the brain can do Bayesian inference with statistical optimization, but to perform Bayesian inference specifically, the nervous system needs to calculate the posterior distribution of information. It is unclear whether, or whether, neural circuits represent a probability distribution.

The random sampling point of view believes that the random firing activity of neurons is to acquire knowledge of the posterior distribution by sampling (just like diffusion-model, which provides a sampling method to get more samples to approximate the true distribution, but diffusion-model never masters the whole distribution), rather than to directly represent the posterior distribution, so that we can perceive the uncertainty of the external world.

This kind of uncertainty knowledge is very important for brain cognition. For example, when recognizing objects, we must not only identify the category of the object, but also have a judgment on the reliability of the recognition result.
In the field of machine learning, approximating a complex probability distribution with random sampling is a classic problem. The traditional random sampling algorithm is MCMC (Markov Chain Monte Carlo), but this algorithm is very slow. To this end, the field of machine learning has developed an improved and sophisticated algorithm called the Hamilton Monte Carlo (HMC) method. Its basic idea is to introduce a new momentum variable (that is, the speed of the sampled variable), which together with the sampled variable constitutes a physical Hamiltonian dynamic system; through relatively simple random sampling of momentum, indirect The random change of the sampled amount is realized, thereby greatly accelerating the whole sampling process. Years of research experience in intersecting fields tell us that usually a clever algorithm of artificial intelligence is often applied to biological systems, and vice versa. Indeed, many international research groups have studied the mechanism of HMC in the nervous system, such as [8, 9]. We are also interested in this question. In the published work of NeurIPS [6], we use the familiar continuous attractor neural network with negative feedback to rigorously prove theoretically that in the presence of noise, the network can implement HMC to sample the posterior of a continuous variable distribution (Figure 4). The mechanism behind it can be intuitively understood as follows:

From the dynamic effect, the negative feedback increases the mobility of the network attractor state.
From the sampling effect, the negative feedback plays the role of momentum in the HMC
Since the negative feedback depends on the activity of the network Therefore, it effectively speeds up the random sampling of network dynamics, which is similar to the introduction of a momentum term in the gradient descent algorithm of machine learning.

For more details, please refer to our follow-up WeChat public account article (if Dong Xingsi, the first author of the article, is willing to write).
Figure 4: A neural network implements random sampling-based neural coding. A. An example of Bayesian inference; B. A continuous attractor neural network model with negative feedback; C. An example of the network sampling in the state space; D. The network implements HMC, which greatly speeds up the sampling process of the posterior distribution.

Some thoughts on neural coding
The above describes the three research experiences that my collaborators and I have conducted on neural coding in the past 20 years. You can also see that the specific mechanism of neural coding in the field is still very unclear so far, and the research progress has been slow for so many years. This also prompted me to reflect on the scientific problem of neural coding itself, which I would like to share with you below.

Is there a uniform law for neural coding?
From the perspective of a theoretical researcher, I certainly hope that there is a unified law, just as Newton's three laws of physics describe the laws of motion of nature at low speeds, but there may not be such a universal law in brain science. Sexual coding rules. The golden rule of life science is Darwin's theory of evolution. The development of the brain is more like an engineering problem. It is a long-term evolution of living organisms to adapt to the environment, not pre-designed. Different species face different computing requirements, and different organs of the brain are also processing different information. Therefore, different species or different brain regions of the same species may adopt different information encoding strategies. There is no reason to assume that the nervous system must use the same information. Laws encode information. When we study neural coding, we need to be careful to define the scope of the law to apply.
Of course, there is also a possibility, because life on earth faces a similar natural environment, neurons all use the signal representation and transmission method of pulse firing (possibly from energy saving and other needs), and the nervous system also uses the network for calculation. etc., it is not excluded that

under the constraints of these common conditions, the neural coding strategy has evolved to a mathematically unique optimal solution, so it is unified.

What is the neural code encoding?
In the traditional animal experimental paradigm (and corresponding mathematical modeling), we present the experimental animal with a stimulus variable that we "define" in advance, such as a moving grating to define an orientation variable; then we record and analyze the neuron population. activity, in the hope of deciphering how this population of neurons encodes the orientation values represented by the grating. Note that the entire research paradigm implicitly assumes a strong assumption that experimental animals are encoding an orientation variable as we wish. But this hypothesis is unprovable because animals can't speak. For the experimental animal, all it sees is a moving grating. It is very likely that the response of neurons is not encoding the orientation variable (orientation itself is actually a very abstract mathematical concept), but encoding its life in the image. A piece of information that makes sense for an activity. This is somewhat similar to the problem faced by deep learning networks in black-box training: if we always use images of horses and grass to train horse recognition, the network will most likely recognize grass as horses. Deep learning networks can try to overcome this by increasing the diversity of samples, but in animal experiments, adding too many stimulus samples is not feasible due to experimental costs (and theoretically we don't know exactly how many samples are appropriate) .

When introducing random sampling coding above, it was also mentioned that the brain not only encodes the properties of objects, but also encodes other related information, such as the uncertainty of objects. For example, for a fuzzy animal image, we may recognize that it is a horse with a 60% probability, and recognize that it is not a horse but a donkey with a 40% probability. The calculation of this uncertainty requires comparison with prior knowledge (ground truth), which must be pre-stored in the brain rather than temporarily provided by external input. As seen from this example, neural coding may involve complex cognitive processes, not just direct responses to external inputs. This is also reflected in the experimental definition of neuronal receptive fields. The receptive fields of neurons in the retina, LGN, and V1 are relatively easy to determine, but the receptive fields of neurons in the V2 and above brain regions have not been determined, because the neurons in these brain regions have received a large number of feedback connections from other brain regions. Influence, their responses are no longer simply driven by external input, so the traditional definition of receptive field no longer holds.

There is a logical debate in Chinese history that "a white horse is not a horse". Applied to neural coding, when recognizing a picture of a horse, does the brain encode an abstract concept (prototype) of all horses, or does it encode the specific (white) horse that is presented in the picture? I prefer the former, because life experience tells me that we can easily identify horses, but it is difficult to describe what horses look like when we close our eyes; at the same time, from an evolutionary perspective, we do not need to encode the specific appearance of all horses. Related to this concept, Zhang Wenhao et al. did an interesting neural coding work [10]. Different from the traditional only considering $p(\mathbf{r}|x)$ as the encoding process, we consider the variable $x$ itself is generated by a latent variable $z$, ie. $p(x|z)$. Here, $x$ corresponds to a specific image of a horse that generates a neural response $\mathbf{r}$, and the hidden variable $z$ corresponds to the prototype of the horse, which is the information that the brain really needs to encode. This new coding framework can explain some experimental findings that traditional models cannot, including that the widespread lateral connections between neurons can be explained as a store of prior knowledge $p(x|z)$.

Neural coding is a false proposition?
The concept of coding, and its corresponding mathematical description - information theory, originally came from the field of communication, which considers how to encode information into a suitable signal and transmit it safely to a distant place, and then decode it back, so the research of coding is actually signal transmission instead of information processing. Thinking about it carefully, there is no simple signal transmission task in the brain. The transmission of neural activity from one brain area to another involves the extraction and processing of information; in the brain, the transmission of neural signals and information processing are intertwined. together. Therefore, research on neural coding based on signal transmission is incomplete and may even mislead our understanding of the computational function of the brain. For example, in current neural coding modeling work, we usually start by assuming an encoding process $p(r|x)$, and then analyze how neural activity characterizes and decodes this input signal $x$; but the reality may be that the brain directly computes and characterizes another variable $y(x)$.

The framework of information theory is not unusable, but we need to understand its limitations in describing the information processing mechanism of the brain.

Can neural coding be deciphered?
From the above discussion, the strategy of neural coding may not be unique, and animal experiments cannot really determine the content of neural coding. Neural coding and computation are intertwined, which seems to mean that it is difficult for us to decipher the mystery of neural coding. So is it finally possible for us to decipher the mechanisms of neural coding? I don't have an answer either, but I realize that neural coding faces the same dilemma as deep learning interpretability. As we all know, after setting a specific task (usually a classification task) and training a deep learning network with big data, it is very difficult to decipher the computing mechanism inside the network. The same is true for neural coding research: the brain adapts to its environment (task) and evolves (trains) to process information more efficiently, but it is extremely difficult to decipher how neural circuits specifically encode information. Because the structure and computational laws of brain networks are task-driven like deep learning networks, not after designing the coding strategy of neurons. This can also be reflected from the working principle of the library network (reservoir network): the external input information is projected into a high-dimensional neural activity representation space, which is convenient for the next-level neurons to read, as for how the input information is encoded. , it is difficult to express with simple rules. Therefore, I tend to think that for some simple inputs, it is possible to describe how they are encoded in the early stages of neural pathways; but for more complex inputs, and their computations in advanced brain areas process, it is difficult to quantitatively describe it with neural coding. Perhaps, like deep learning, we can often only describe the working principle of a neural system as a whole, the so-called black-box operation.


Create contribute md

🔴 Related Issue
#58

merge although test failed because its again the test with the timestamp.. which I have to fix in the near future.
In the code everything is correct but if the github servers are slow jest can't test the value in the 5ms window

merge although test failed because its again the test with the timestamp.. which I have to fix in the near future.
In the code everything is correct but if the github servers are slow jest can't test the value in the 5ms window


Updating method dependent options for the input generator

Description
small PR. mainly cosmetic for input generator.
Todos

[x] fixed some variables that are dependent on method choice (eg cp correction and fsapt stuff)
[x] added extra text to indicate what boxes are optional

Status

[x] Ready to go

Questions
Just in case I forget, I will document it here so we can discuss later. But I remembered that the current f-sapt partitions that are being made will only run for non-d3 method choices. I think there may be a bug in the handling of external potentials in the fragments when the method has a -d

In regards to -d with FSAPT partitioning, is there a way that we could raise a warning if FSAPT partition is checked and "-d" is in the method name? We can also add some statement in the FSAPT partitioning cell of the options table saying this might not be a great idea. I'll add it to the task list

yeah that is a good idea. we can definitely raise a warning if the partition box is checked and -d is in the method. ideally, i figure out the issue and fix it in psi4. but until then a warning is a good idea. and i can even write the line numbers to comment out in fsapt.py if you want to force it to run like i do and i can say that the total sapt won't equal the total-total fsapt but the functional group components are still correct. i will think about the best way to display this sentiment and probably make it a different PR


BROOKLYN-245: improve synchronization in entity

one comment to address

i've merged this, with one further small tidy-up 9720743a23b816914e66fb37ed4d783837ecca36
this and #97 have been backported to 0.9.0


remove 0

in this window yago wants to loose the two zeros after 0.25 but keep btc in the same place as the one above


Support for sandbox API endpoints

Hi,
we're starting to integrate PDL into our backend. For our local and sandbox environments, we would like to use the sandbox API endpoints (https://docs.peopledatalabs.com/docs/sandbox-apis). However it seems that this client doesn't support it out of the box, the only way seems to set a BASE_PATH environment variable (from this line). The issue with this approach is that the naming of this variable is confusing, ideally it would be something like PDL_BASE_PATH.
Actually I'd suggest prefixing by PDL_ all environment variables that would override the settings to avoid conflicts with existing env vars.
Let me know what you think,
Cheers, Olivier

Just noticed https://github.com/peopledatalabs/peopledatalabs-python/pull/15 will add sandbox support, my bad will close the issue. Thanks!

@Layvier Yup, we just release the sandbox API not even a month ago, we should be releasing an updated python package very soon :)

That's awesome, thanks a lot:)

@Layvier We just merged PR https://github.com/peopledatalabs/peopledatalabs-python/pull/15


podvm: remove CentOS support

The podvm build for CentOS has been broken for a while now (see #1558) and even on the last release (0.8.0) we didn't publish an image. After some discussions in https://github.com/confidential-containers/cloud-api-adaptor/pull/1564 and in our community's meeting today there is a consensus that CentOS support can be dropped.
Important to say that we will continue building an RPM-based distro (Fedora) but with the mkosi tool instead of packer.
Somehow-fixes #1558
Signed-off-by: Wainer dos Santos Moschetta<EMAIL_ADDRESS>

Tested this PR in https://github.com/wainersm/cc-cloud-api-adaptor/actions/runs/7120480521 . It was able to build the builder, binaries and podvm images only of Ubuntu.


java_test targets don't have source files in Eclipse, tests don't show up

Hi,
per https://github.com/salesforce/bazel-eclipse/issues/475 I had expected that java_test targets work when using project-per-target. However, for me, I only get empty eclipse projects for my java_test targets, with no sources inside. See https://github.com/criemen/eclipse-repro for an example reproducer.
See this screenshot from the package explorer:

The project-per-package strategy would probably work better for my project, but that's buggy for another reason (I'll file a bug shortly, it's not setting up the classpath correctly in our case), so I was forced to look at project-per-target first.

@criemen Not a "fix" to solve the support in IDEs but now at least the problem is surfaced. TL;DR -> the problem is that the targets are located in the workspace root. That causes some trouble in the IDE. If you create a sub-package it should work.


Bug: Codepen imports failing

It would also be nice if we could import react-annotation in Observable notebooks.

This has been resolved.
@KentDi808 I would recommend using d3-annotation instead in observable notebooks, example here made by @hydrosquall 🙌 : https://beta.observablehq.com/@hydrosquall/d3-annotation-with-d3-line-chart


Slow Barcode Reading

I am experiencing very slow barcode reading for both version 1.4.4 and 1.5.1. First it was not working at all and then got it working by setting the WidthRequest to a fixed value as suggested by a different issue here on github.
Does anyone know how to get good speed for the barcode reading (QR and 1-D Codes)? Right now the user needs to target the code for several seconds before the app picks it up. And that is in Release mode build with a Samsung Galaxy S21. Also tried on a newer iPad and same issue.

I am experiencing very slow barcode reading for both version 1.4.4 and 1.5.1. First it was not working at all and then got it working by setting the WidthRequest to a fixed value as suggested by a different issue here on github.
Does anyone know how to get good speed for the barcode reading (QR and 1-D Codes)? Right now the user needs to target the code for several seconds before the app picks it up. And that is in Release mode build with a Samsung Galaxy S21. Also tried on a newer iPad and same issue.

Current version is completely broken as far as detection is concerned, you're better off downgrading to 1.2.1 which doesn't require Camera.MAUI.ZXing, scans with that version are almost instant for me, some people completely abandoned this repo and moved on to others which I haven't tried yet since the downgrade was enough (for now).

@strajk- I attempted to downgrade to 1.3.5 and was unable to find any references on how to setup the bar code scanning. When I go to the readme from the 1.3.5 repo it still requires ZXingHelper in the code.


A mistake in README.md

In the step 1.clone the package , '.git' is missed when running git clone.

Updated! Thanks for letting us know. Closed.