Implementation discuession

Hi,
I also implemented my version of SIREN according to the paper, and I have done several tests with it. My implementation is here SIREN-2d .
I haven't run your code yet but have two questions.

the weight initialization of your code applies w0 to the result of linear output link. In the paper, seems that w0 is only applied to the weight*input without the bias. I'm not sure if my understanding is correct.

In the paper, SIREN is also used to fit image gradient. It's not clear that the SIREN gradient is calculated in an analytically way or not. Since the authors explicitly give out its analytical gradient, I think it should be in an analytically way. However, I cannot converge in this setting. Could you also provide such tests, or try my code to check if my understanding is correct.

I will give more tests when I have time.

Regarding the first question, I also implemented Siren here and tried both w0 multiplying only the W matrix of the linear layer like in the paper or multiplying the whole result of the linear layer. The training results were the same in the end, and this change makes the implementation similar to other activation functions, so it's not tied to the linear layer.

Do you see faster convergence for the experiments you run? I tried it for fitting 2D shapes, and with the default setting did not get fast convergence.

Hi, I do observe some improvements in convergence and I plotted the result in my project. https://github.com/WANG-KX/SIREN-2D.
Since the official implementation is open to the public, I think further discussion can be moved under that project.


[BUG] Dead link on LSP8 ABI page

https://docs.lukso.tech/contracts/contracts/LSP8IdentifiableDigitalAsset/presets/LSP8Mintable/#constructor
This link here:

Opens a 404:
https://github.com/lukso-network/lsp-smart-contracts/blob/develop/contracts/LSP8IdentifiableDigitalAsset/presets/LSP8Mintable.sol

Just checked, solved via the linked PR above.


Avoid re-publishing duplicate versions

Also fixes #22 by using the second-last hash as @noffle suggested.

Thanks Francisco!


[stable/hazelcast] Various improvements and version bump

Following improvements has been ported the charts:

Added an option to disable Service creation and define a name to use
Added management center service annotations and https port
Bump hazelcast to 3.12.3 and management center to 3.12.6
convert README into asciidoc
nodeSelector for test scenarios

Signed-off-by: hasancelik<EMAIL_ADDRESS>Checklist
[Place an '[x]' (no spaces) in all applicable fields. Please remove unrelated fields.]

[x] DCO signed
[x] Chart Version bumped
[x] Variables are documented in the README.md
[x] Title of the PR starts with chart name (e.g. [stable/chart])

/ok-to-test

/ok-to-test

/lgtm

/approve

/lgtm


Passing invalid shader asset to ui.FragmentProgram.fromAsset crashes application

Testing on my branch where I have not yet configured the shader compiler to conditionally emit SKSL, I receieved the following error on linux desktop:
[ERROR:flutter/impeller/base/validation.cc(27)] Impeller Runtime stage has invalid magic. Perhaps the stage information is for the incorrect backend or the data is corrupted?
[ERROR:flutter/impeller/base/validation.cc(38)] Break on ImpellerValidationBreak to inspect point of failure.
Lost connection to device.

We should make sure that we can throw a Dart exception or similar.
@zanderso

Actually, I get this error anytime I pass sksl data to fromAsset, on linux desktop not targeting impeller

Compile command I am using is:
/home/jonahwilliams/flutter/bin/cache/artifacts/engine/linux-x64/impellerc, --sksl,
--spirv=/home/jonahwilliams/flutter/examples/hello_world/build/flutter_assets/shaders/ink_sparkle.frag.spirv,
--sl=/home/jonahwilliams/flutter/examples/hello_world/build/flutter_assets/shaders/ink_sparkle.frag,
--input=/home/jonahwilliams/flutter/packages/flutter/lib/src/material/shaders/ink_sparkle.frag, --input-type=frag,
--include=/home/jonahwilliams/flutter/packages/flutter/lib/src/material/shaders

Which I think is correct?

Which I think is correct?

No. Have a look at the unit tests in my Engine PR. The extension on the file specified by the --sl flag should be .iplr.


why is sunburst chart inconsistent?

The orange Azerbaijan entry should be less than 20% (7000 out of 58000 total), but is more than 50% on the visual. Is this because there are maximum numbers of entries displayed by sunburst or else?

Maybe try dropping negative values, they might be the ones causing problems?


Check for null values in TaskName

Bug :  task getting added to taskList even if there is no name

In lib>providers>task_list_provider.dart
the onAddTask function must only add the task name if its not null.

Make a PR only after you have been assigned the issue.

hello I would like to work on this can you please assign it to me?


Add google authentication

Hey, I want to add the firebase google authentication. Can you please assign it to me?

Discarded feature


Can't install_github()?

Message:

install_github("dtkaplan/checkr")
Downloading GitHub repo dtkaplan/checkr@master
from URL https://api.github.com/repos/dtkaplan/checkr/zipball/master
Installing checkr
Installation failed: cannot open the connection
Warning messages:
1: In normalizePath(path) :
path[1]="/home/docker/hms_0.3.tar.gz": No such file or directory
2: In read.dcf(path) :
cannot open compressed file '/home/docker/hms_0.3.tar.gz/DESCRIPTION', probable reason 'No such file or directory'

I was able to install on a Mac running R version 3.4.0.
Can you install other packages using install_github()?

Yup, checked before I posted :-)

Can you please include the output of sessionInfo()?
All the best,
Nick

On Jul 25, 2017, at 9:01 PM, Ken Kleinman<EMAIL_ADDRESS>wrote:
Yup, checked before I posted :-)
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or mute the thread.

Most confusingly, it installed on this fifth try.


No ui or any visualization for self-deployed rule-evaluator rules

Hi all,
i'm using self-deploy rule evaluator on my GKE cluster, how can i see all the rules -> records\alerts that i configured in the rules.yaml ?
i was able to configure the alertmanager so i can see my fired alerts, but that is not even close to what im expecting to see like the 'Alerts' or 'Rules' tab that i have in prometheus server.
is there any way to integrate between rule-evaluator and promehteus-server so i can see all of my rules, or is there any way to watch those unfired Alerts ?

We don't have this... but it's a very good idea. I'll add this towards the top of our backlog - specifically a way to query the rule-evaluator and see what rules are installed and what the current state is, like the /alerts endpoint.

That will be amazing thank you very much.

We need it too. This will be great!

hi @lyanco, any updates?

This isn't expected soon. Will post here when we start working on it.
Note that an analogue - the targets status page - is currently in review at https://github.com/GoogleCloudPlatform/prometheus-engine/pull/301

Agreed that having a streamlined solution here would be nice. Out of curiosity, what particular info from the /rules and /alerts endpoints are of interest?
Note that for pending or firing alerts, Prometheus creates synthetic time series ALERTS that are queryable alongside your regularly-ingested metrics.

@pintohutch for me, the most helpful info from those page routes is the status of the deployed rules -- we'd want to make sure that rules are correctly registered at the evaluator layer after frequent deploys

Thanks @parkedwards - that's helpful context. We'll keep this issue open as we iterate.

No movement so far. We've been focusing on delivering a fully-managed, Cloud-based alerting flow (PromQL through Cloud Alerting). This will accept rule_files format and execute them without any infrastructure running in your cluster. It also will have a rich UI to view, edit, and see the status of alerts, which should more fully solve the problems listed here.
Note that the current alerting flow isn't going to be deprecated or anything... we just expect most people to move to the fully cloud-based solution over time as it's more in line with expectations.
If you're interested in joining the preview, you can do so by filling out this form: https://docs.google.com/forms/d/e/1FAIpQLSf7XlkxiDhnlqKDtdJAOwbimGUskEP_Q2V2zqleMCP5m4C_Bg/viewform

Thanks @lyanco I guess in our case we are not using the fully-managed prometheus because we scrape GCE instances.
would this be supported for self-deployed versions as well?

PromQL in Cloud Alerting should work for all environments and all clouds, yes.
FWIW If you're using self-deployed collection, the /rules and /alerts APIs should be functional on it, but it will only show those rules and alerts that are installed in the local server and executing against local data. It won't show any rules/alerts executing against Monarch, but this might suffice for your use case.

Hey @pandrian,
Aside from leaning into the Cloud Alerting PromQL preview Lee mentioned, you can manually pull Prometheus metrics from the rule-evaluator's metrics endpoint (example) to get better signal of what it's doing with your configured Rules.
This is obviously not as nice as the Prometheus /rules API, but evaluating these along with any of its logs can be a helpful sanity check.

Hi there,
We are currently in the process of migrating from a self-hosted prometheus installation to Google Managed Prometheus. Within the scope of that migration, the only way we found that somewhat comes close to listing all rules was to pull and parse the configMap that gets created by the Operator, and that gets then ingested by the rule-evaluator.
Perhaps it helps someone. It looks something like this:
kubectl get configmap -n gmp-system rules-generated -o yaml \
| while IFS= read -r line; do echo "---"; echo "$line" \
| sed 's/^[^:]*: //' \
| base64 -d \
| gunzip \
| yq e '.groups[].rules' \
| yq e '.[] \
| [{"alert": .alert, "message": .annotations.message, "summary": .annotations.summary, "expr": .expr}]'; done \
| yq

The data is gzipped and base64-encoded in the configmap, with one file per line, hence the long pipe-train. Keep in mind that this is no guarantee that the rule is valid or actually actively being evaluated by the rule-evaluator. It's merely a list of GlobalRules, ClusterRules and Rules resources that were picked up by the gmp operator.
It kinda sorta works. But that being said, it isn't particularly user-friendly or reliable, and we would very much like to be able to add the rule-evaluator as a data-source to Grafana to properly visualize all rules as is possible using a "regular" prometheus setup.
I just forked this repo earlier today and worked on an implementation of the /api/v1/rules endpoint, making it compatible with the official prometheus one (see here: https://prometheus.io/docs/prometheus/latest/querying/api/#rules)
Here's an open PR for the endpoint. Would be happy for feedback. Unfortunately I couldn't fully make use of make presubmit and make e2e - something makes colima explode. 🤷  Please let me know if there's anything missing or not as expected.
https://github.com/GoogleCloudPlatform/prometheus-engine/pull/1149

Thanks for the contribution @yama6a!
I'm going to close this as #1149 looks like it solved it pretty nicely.


Fix the timer calculations

the time/date doesn't quite match, need to rethink the calculations.

Doesn’t match with what? I can take this on

The timer should display the date in the following format :
[day_of_week_name], the [n_day_in_month][st/nd/rd/th] of [month_name], Year [year_number]
The only measure of "time" is the number of turns that has elapsed. Each turn is 10 minutes.
The calendar can have as many months as desired, with as many days per month as deesired and so on.


Add steps to create_rf_spark_context to check for gdal

It would save end users headaches if we could check for gdal when create_rf_spark_context is called, confirm the version is compatible, and pass the library path to the JVM.

Also, if gdal can not be found and it falls back to JVM geotiff reader, surface the warning (e.g. in the notebook) to alert users that reading other types of files will not work correctly.
In the console there is a message like WARN GDALRasterSource$: GDAL native bindings are not available. Falling back to JVM-based reader for GeoTIFF format. but that might be missed.
Unless these things are not related of course.

In the end did we have success with using java options to set the library path or am i mistaken?

In my case it was solved by installing homebrew gdal on macOS. Setting the library did not help. Maybe for a future version?

Dependent upon geotrellis/gdal-warp-bindings build.
add'l context: https://gitter.im/geotrellis/geotrellis?at=5dcdbf95ea7d147cb37d438c

See also:
https://github.com/geotrellis/gdal-warp-bindings/issues/73


Feed cleanup

Switch away from blob feeds

Closing as this is a non-active repo and this is unnecessary


A short version for the image object in the schemas

Now we get the user like this
{
  "message": "Data got correctly",
  "meta": {},
  "data": {
    "id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
    "first_name": "string",
    "last_name": "string",
    .......
    "image": {
      "file_format": "string",
      "width": 0,
      "height": 0,
      "media": {
        "title": "string",
        "description": "string",
        "path": "string",
        "id": "string",
        "link": "string"
      }
    }
  }
}

But the image object takes up a lot of information.
This is especially clearly seen when we get a list of users and unnecessary fields are inserted.
it happens that need when only the url is needed from the image.
Example:
{
  "message": "Data got correctly",
  "meta": {},
  "data": {
    "id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
    "first_name": "string",
    "last_name": "string",
    .......
    "image_url": "string"
  }
}

It would be nice if there was an alternative scheme.

Hello @8thgencore I agree it seems a good idea I am going to work on that the next days

I also noticed that if we get a list of users from the server (for example, 50 people). Then for each of them there is a request to s3 storage to form a link to the image. This greatly affects the speed of data acquisition.
I am now also thinking how to speed up this process

Hello, @8thgencore that is a good insight do you have a sample code to replicate it?

Hello @8thgencore in this commit you can find how to combine pydantic models for what you are trying to do with image it uses root_validator https://github.com/jonra1993/fastapi-alembic-sqlmodel-async/commit/be0b363badd27976b0c6db147ebd4c24022d62b0
code here: https://github.com/jonra1993/fastapi-alembic-sqlmodel-async/blob/main/backend/app/app/schemas/image_media_schema.py
Image without media just link

This still grabs a link for every single item, if you needed to grab 100 users, there would be 100 seperate requests to minio to grab fresh links. I was thinking perhaps this could be stored in redis for faster retrieval, and expiration between minio urls and redis could be synchronized to expire at the same time. Otherwise, is it possible to make some kind of schema validator that looks at all the file paths and asks minio for a link on all of them at once, which can then be mapped back across the responding schema?

Hello @bazylhorsey if you want to get faster responses I see 2 options

Using fastapi-cache could be an easier solution to avoid requiring more requests it will work just in get enpoints.
Creating a dedicated cache system with Redis and minio as you suggest.


Wind

Hi, I have been trying now for a while to add "wind" to this and failed miserably.
I was wondering if this is something you could do easily?
Ideally I want to set a wind vector (direction & speed) and it effect the plane or individual airfoils. (relative velocity and the wind force)
Thanks

I believe you can easily add external wind effects by adding a vector3 to the local velocity.
Try modifying SimpleWing.cs line 37 like that?

Hi, I have been trying now for a while to add "wind" to this and failed miserably.
I was wondering if this is something you could do easily?
Ideally I want to set a wind vector (direction & speed) and it effect the plane or individual airfoils. (relative velocity and the wind force)
Thanks

I believe you can easily add external wind effects by adding a vector3 to the local velocity.
Try modifying SimpleWing.cs line 37 like that?

Hi, I have been trying now for a while to add "wind" to this and failed miserably.
I was wondering if this is something you could do easily?
Ideally I want to set a wind vector (direction & speed) and it effect the plane or individual airfoils. (relative velocity and the wind force)
Thanks

I believe you can easily add external wind effects by adding a vector3 to the local velocity.
Try modifying SimpleWing.cs line 37 like that?

Hi, i'm not so sure its that simple. Line 37 is used only for getting the AOA for display purposes (correct me if im wrong?)

Hi, I have been trying now for a while to add "wind" to this and failed miserably.
I was wondering if this is something you could do easily?
Ideally I want to set a wind vector (direction & speed) and it effect the plane or individual airfoils. (relative velocity and the wind force)
Thanks

I believe you can easily add external wind effects by adding a vector3 to the local velocity.
Try modifying SimpleWing.cs line 37 like that?

Hi, thanks for your help, however i'm not so sure its that simple. Line 37 is used only for getting the AOA for display purposes (correct me if im wrong?)

Right, my bad. I wanted to write line 104, in fixedUpdate. It works the same.

Hello! Wind is an implementation specific thing, so I didn't include it in this project, but here's how I add wind in my game.
Replace these lines in FixedUpdate:
Vector3 localVelocity = transform.InverseTransformDirection(rigid.GetPointVelocity(transform.position));
localVelocity.x = 0f;

With something like this:
// Velocity that this wing feels is the velocity at its specific position,
// and any wind that might be coming from outside.
Vector3 worldVelocity = rigid.GetPointVelocity(transform.position);
worldVelocity += -environment.WindVector;

Vector3 localVelocity = transform.InverseTransformDirection(worldVelocity);
localVelocity.x = 0.0f;

In my implementation, environment is a GameObject in the world which controls things like wind and time of day. The WindVector on it is the direction that the wind is blowing. Wind for wings is weird, because from the wing's perspective it can't tell the difference between wind and just moving through the air normally, so by adding the wind's velocity to the velocity the wing already has, you're simulating that effect per-wing, which is going to be more accurate than applying a velocity to the entire plane.

Hi, thank you. I am literally working on my project which uses simple wings in, so great timing as I still haven't got back around to implementing wind. I had several attempts and got close, similar to what you have suggested but not exactly so looking forward to trying it.
Am i correct in saying that this will add the wind velocity to each wing which will effect its lift/drag ETC. But will this actually add the forces to the overall plane in a manner that will for instance make the aircraft crab with a side wind?
Thanks

wind velocity to each wing which will effect its lift/drag ETC

Yes. The important thing is that it affects the lift and drag accordingly, which will generate unusual forces that will naturally cause the plane to crab into the wind since that's just how the physics works out. The more realistic your arrangement of wings on the plane are, the more realistic an effect you're going to get. E.g. if you have a very large vertical tail that's offset from the center of mass, not only will the plane try to weather vane into the wind, but it'll also induce a rolling moment because of the drag incurred.
Here's  a gif of my game flying through wind. The camera is looking at the velocity vector, which is direction where the plane is actually moving.

That's great, thank you. Will have a play this afternoon. It really is a great system.

Hello! Wind is an implementation specific thing, so I didn't include it in this project, but here's how I add wind in my game.
Replace these lines in FixedUpdate:
Vector3 localVelocity = transform.InverseTransformDirection(rigid.GetPointVelocity(transform.position));
localVelocity.x = 0f;

With something like this:
// Velocity that this wing feels is the velocity at its specific position,
// and any wind that might be coming from outside.
Vector3 worldVelocity = rigid.GetPointVelocity(transform.position);
worldVelocity += -environment.WindVector;

Vector3 localVelocity = transform.InverseTransformDirection(worldVelocity);
localVelocity.x = 0.0f;

In my implementation, environment is a GameObject in the world which controls things like wind and time of day. The WindVector on it is the direction that the wind is blowing. Wind for wings is weird, because from the wing's perspective it can't tell the difference between wind and just moving through the air normally, so by adding the wind's velocity to the velocity the wing already has, you're simulating that effect per-wing, which is going to be more accurate than applying a velocity to the entire plane.

3 years later and someone found use for your comment :)
Thank you for your amazing work


Feature request: Add relative paths for cache dir

On MacOS with tlrc installed via homebrew.
Neither of these work:
[cache]
dir = "~/.cache/tldr"
# OR
dir = "$HOME/.cache/tldr"

It just dumps a new cache directory into the working directory where the tldr command was used with the path specified in the dir field.
However, using an absolute path seems to work:
[cache]
dir = "/Users/<my_username>/.cache/tldr"

Would be nice to support relative paths for multi device usage.

Thanks for the issue.
Support for relative paths has been added in https://github.com/tldr-pages/tlrc/commit/4a55d5b97fabd6176adb8056e2170152dac52c61 and will be a part of the next release.


Multi statement support

Feature Request: Adding support for multi statement
At Shopify, we're exploring the work required to migrate to using the Trilogy client (and respective Active Record adapter). One pre-requisite for adoption is support for multi statement queries.
We enable Mysql2::Client::MULTI_STATEMENTS (the equivalent to TRILOGY_CAPABILITIES_MULTI_STATEMENTS) to enable some specific performance optimizations and are interested in seeing this supported in Trilogy.
Discussion
It seems there's already interest, as I was happy to stumble on this existing branch by @composerinteralia and @jhawthorn which seems to prototype some of this work.
I'm curious to understand what your thoughts are on bringing support for this. I'd love for this issue to be a place to exchange perhaps already existing context like:

Are you willing to accept this feature into Trilogy
Are there any particular blockers you've discovered?
Any particular API decisions you have considered?
a. Any thoughts on mimicking mysql2's Ruby API: client.next_result and client.store_result
b. Should multi statement be something the C library is aware of, or could this be a Ruby-binding-only abstraction

Moving forward
I'd like to note that I'm happy to build this feature out and was intending to draft out some work I have locally. I noticed your branch and figured it would be best to first solicit early feedback and share any context before presenting a solution.
Curious to hear your thoughts and context!
cc: @adrianna-chang-shopify @casperisfine

Not that it was requested (😇), but here's my own input on this:
IMHO I think this should be something that is exposed on the higher level bindings only. The C API is (or at least was) designed to be intentionally lower-level - a lot closer to the protocol - so that other bindings could be built on top in a more flexible way. The C API also intentionally doesn't perform any buffering (outside of a single packet). So anything beyond that is also up to the higher level bindings to handle (like buffering the resultset before returning to the caller). So doing something akin to libmysqlclient's store_result down in the C API feels wrong (to me). With my current understanding of the protocol, our C API already exposes everything we'd need to build this into higher level bindings (Ruby being the only one included in this repo).
Adding something like Trilogy#next_result seems like it could be made pretty clean 👍. Some of the internals of execute_read_query could probably be split out into that and then reused there as well?


Add post-mortem september 2023

@hennevogel I already push all the changes you suggested. Despite the fact

@saraycp what is the state of this PR?

@saraycp what is the state of this PR?

The blog post missed the Action Items section. I needed time to do some research and testing in order to provide possible solutions. As it is not straightforward, I've decided to add a generic text and go on working on it after this post-mortem is published:
"Plan a strategy to deploy migrations. It might include separating migration deployment from the code changes deployment or splitting the migration into different steps"

"Plan a strategy to deploy migrations. It might include separating migration deployment from the code changes deployment or splitting the migration into different steps"

That is a sensible approach. First we do the required changes to the database, ensure everything keeps working as before and in a 2nd deployment we add code to use the new DB parts. Would that split happen on the git level, where the PR to use the new parts would wait longer for merge?

"Plan a strategy to deploy migrations. It might include separating migration deployment from the code changes deployment or splitting the migration into different steps"

That is a sensible approach. First we do the required changes to the database, ensure everything keeps working as before and in a 2nd deployment we add code to use the new DB parts. Would that split happen on the git level, where the PR to use the new parts would wait longer for merge?

The order migration deployment + changes deployment works for the addition of columns/tables.
For removals, the order should be  changes deployment + migration deployment, as we need the code to stop using those columns/tables before we remove them.
So far, we have relied on labels like "Reference Server" or "Do Not Merge" to avoid merging and deploying PRs that require special attention. We can use them for the cases we mentioned before until we find a better way to do that.


feat: automate package publishing using semantic-release

Description
Changes introduced are based on our internal guide on adding automatic releases to Warp packages:

trigger a release with semantic-release whenever a commit is pushed to alpha or master branch.
add support for commitizen
add a PR title validator to ensure correct commit history structure in case of squashing
automatically update alpha branch whenever a merge to master takes place

Co-authored by @magnuh and @AnnaRybkina

 Automate releases of warp packages to NPM and EIK


[PRED-2823] Additional information for release process

BATCH SCORING PULL REQUEST
This is a pull request into a public repository for Batch Scoring script maintained by DataRobot.
CHANGES

Added additional instructions for release process to be more clear to the developers.

Coverage remained the same at 84.24% when pulling 1bc402995bd2c296dffd508a71688bb99d8bfd79 on PRED-2823/Additional_info_for_release_process into e2b66e02c65804309f0e7a332c3a00fe04804e6b on master.


[VL] Use dwio::common::FileSink::create for LocalFileSink and HDFSFileSink

Description
As discussion as https://github.com/oap-project/gluten/pull/4129#discussion_r1433784703,  LocalFileSink/HDFSFileSink should use dwio::common::FileSink::create

cc @yma11


pm: power_domain: enforce strict initialisation sequencing

Enforce strict initialisation ordering on power domains that depend on each other. Domains higher in the "chain" MUST be initialised first. This is required so that dependent devices can correctly query the higher level device for their own power state.
Priority cannot be set in Kconfig as per usual because different instances of the same driver need different levels.
Signed-off-by: Jordan Yates<EMAIL_ADDRESS>

@ceolin @gmarull ping

@ceolin @gmarull ping

@ceolin @gmarull ping

Agreed as well!

Yes, I think we have a consensus that here is not the right place to tackle the problem. Also, this would be very limited to this class of power domains.

We should NOT introduce init-priority into DTS. As mentioned, we should just fix the init order using the dependency graph we have in DTS.

We need things done with SYS_INIT() that are not available from DTS

Consensus is that this is not the way to fix this issue, even as a temporary hack.
Will work on progressing a solution from DTS directly.

First steps towards fixing this are in #49318


ClientController feature?

Hello,
In deprecated library I have found example
https://github.com/brutella/hc/blob/master/_example/client.go
so I'm able to pair with remote accessory.
Is this functionality present in current package or any plans to implement it?
Thanks.

There is currently no implementation of a HAP client and no plans to add that in the future.
I don't have a use case for a client, that's why it's not implemented.


big_core - Instructions for running the build.py

Instructions for running the build.py
adding it as a discussion

Please add the instruction & examples to the python build.py -h

Refer to issue #101


Mattermost - File Upload with 0 Byte

When uploading a file (e.g *.docx) to Mattermost the file will be shown and downloaded with 0 Byte at the recipient.

We are seeing the same. When uploading a file or image the upload seems to be stuck while showing the thumbnail at about 80%.
Then when i type something and hit enter it will show the uploaded file but only to me. Other users are seeing the name with 0 Bytes.

We have the same issue. It's linked to attachment transmitted inter-team. When a user send a file (over Direct Messages) to another user member of a different team it will not receive the file (0 Byte). Both user need to switch to the SAME team before exchanging any files.

I confirm what @lordbaco said, we have the same issue and switching to the "same" team solves it.

same here. 0B files when sending from a different team.


PD didn't delete member after api return 200 success

Bug Report

What did you do?
Delete PDMember cluster1-pd-5 by http API, and it returned 200 success.
What did you expect to see?
cluster1-pd-5 member is deleted.
What did you see instead?
The member is still existed.
What version of PD are you using (pd-server -V)?
4.0.0
Here is the log from member leader, hoping this could help you.
log.txt

@HunDunDM  Hi, is there any progress now?

The following logs are printed after ETCD receives the remove-peer request:
[2020/06/16 14:21:02.641 +08:00] [INFO] [raft.go:1530] ["ae4641991849be9a switched to configuration voters=(3518178369300942391<PHONE_NUMBER>6762494618<PHONE_NUMBER>2611363816<PHONE_NUMBER>9580334064)"]
[2020/06/16 14:21:02.642 +08:00] [INFO] [cluster.go:422] ["removed member"] [cluster-id=c7c36d32c2b2aa6] [local-member-id=ae4641991849be9a] [removed-remote-peer-id=3817b582625a39f7] [removed-remote-peer-urls="[http://cluster1-pd-0.cluster1-pd-peer.ns1.svc:2380]"]
[2020/06/16 14:21:02.642 +08:00] [INFO] [peer.go:333] ["stopping remote peer"] [remote-peer-id=3817b582625a39f7]
[2020/06/16 14:21:02.642 +08:00] [WARN] [stream.go:301] ["stopped TCP streaming connection with remote peer"] [stream-writer-type="stream MsgApp v2"] [remote-peer-id=3817b582625a39f7]
[2020/06/16 14:21:02.642 +08:00] [WARN] [stream.go:301] ["stopped TCP streaming connection with remote peer"] [stream-writer-type="stream Message"] [remote-peer-id=3817b582625a39f7]
[2020/06/16 14:21:02.642 +08:00] [INFO] [pipeline.go:86] ["stopped HTTP pipelining with remote peer"] [local-member-id=ae4641991849be9a] [remote-peer-id=3817b582625a39f7]
[2020/06/16 14:21:02.642 +08:00] [INFO] [stream.go:459] ["stopped stream reader with remote peer"] [stream-reader-type="stream MsgApp v2"] [local-member-id=ae4641991849be9a] [remote-peer-id=3817b582625a39f7]
[2020/06/16 14:21:02.642 +08:00] [INFO] [stream.go:459] ["stopped stream reader with remote peer"] [stream-reader-type="stream Message"] [local-member-id=ae4641991849be9a] [remote-peer-id=3817b582625a39f7]
[2020/06/16 14:21:02.642 +08:00] [INFO] [peer.go:340] ["stopped remote peer"] [remote-peer-id=3817b582625a39f7]
[2020/06/16 14:21:02.642 +08:00] [INFO] [transport.go:369] ["removed remote peer"] [local-member-id=ae4641991849be9a] [removed-remote-peer-id=3817b582625a39f7]
[2020/06/16 14:21:02.642 +08:00] [INFO] [server.go:1944] ["applied a configuration change through raft"] [local-member-id=ae4641991849be9a] [raft-conf-change=ConfChangeRemoveNode] [raft-conf-change-node-id=3817b582625a39f7]

At this point, the member has been successfully deleted. As it happens, the PD node originally ran the dashboard. It can be observed that the dashboard is re-elected.
[2020/06/16 14:21:03.312 +08:00] [INFO] [server.go:799] ["PD server config is updated"] [new="{\"use-region-storage\":\"true\",\"max-gap-reset-ts\":\"24h0m0s\",\"key-type\":\"table\",\"runtime-services\":\"\",\"metric-storage\":\"\",\"dashboard-address\":\"http://cluster1-pd-4.cluster1-pd-peer.ns1.svc:2379\"}"] [old="{\"use-region-storage\":\"true\",\"max-gap-reset-ts\":\"24h0m0s\",\"key-type\":\"table\",\"runtime-services\":\"\",\"metric-storage\":\"\",\"dashboard-address\":\"http://cluster1-pd-0.cluster1-pd-peer.ns1.svc:2379\"}"]

Then something strange happened. After about 10 seconds, it was observed that etcd printed the following log:
[2020/06/16 14:21:12.090 +08:00] [INFO] [cluster.go:392] ["added member"] [cluster-id=c7c36d32c2b2aa6] [local-member-id=ae4641991849be9a] [added-peer-id=2cf29ef6579e861f] [added-peer-peer-urls="[http://cluster1-pd-0.cluster1-pd-peer.ns1.svc:2380]"]
[2020/06/16 14:21:12.090 +08:00] [INFO] [peer.go:128] ["starting remote peer"] [remote-peer-id=2cf29ef6579e861f]
[2020/06/16 14:21:12.090 +08:00] [INFO] [pipeline.go:71] ["started HTTP pipelining with remote peer"] [local-member-id=ae4641991849be9a] [remote-peer-id=2cf29ef6579e861f]
[2020/06/16 14:21:12.093 +08:00] [INFO] [stream.go:166] ["started stream writer with remote peer"] [local-member-id=ae4641991849be9a] [remote-peer-id=2cf29ef6579e861f]
[2020/06/16 14:21:12.096 +08:00] [INFO] [stream.go:166] ["started stream writer with remote peer"] [local-member-id=ae4641991849be9a] [remote-peer-id=2cf29ef6579e861f]
[2020/06/16 14:21:12.103 +08:00] [INFO] [peer.go:134] ["started remote peer"] [remote-peer-id=2cf29ef6579e861f]
[2020/06/16 14:21:12.103 +08:00] [INFO] [stream.go:406] ["started stream reader with remote peer"] [stream-reader-type="stream MsgApp v2"] [local-member-id=ae4641991849be9a] [remote-peer-id=2cf29ef6579e861f]
[2020/06/16 14:21:12.103 +08:00] [INFO] [transport.go:327] ["added remote peer"] [local-member-id=ae4641991849be9a] [remote-peer-id=2cf29ef6579e861f] [remote-peer-urls="[http://cluster1-pd-0.cluster1-pd-peer.ns1.svc:2380]"]
[2020/06/16 14:21:12.103 +08:00] [INFO] [stream.go:406] ["started stream reader with remote peer"] [stream-reader-type="stream Message"] [local-member-id=ae4641991849be9a] [remote-peer-id=2cf29ef6579e861f]
[2020/06/16 14:21:12.180 +08:00] [INFO] [stream.go:250] ["set message encoder"] [from=ae4641991849be9a] [to=ae4641991849be9a] [stream-type="stream Message"]
[2020/06/16 14:21:12.180 +08:00] [INFO] [peer_status.go:51] ["peer became active"] [peer-id=2cf29ef6579e861f]

I think the removed node is restarted (with --join option) and then join the cluster again (note that peer-id is changed).

I think the removed node is restarted (with --join option) and then join the cluster again (note that peer-id is changed).

Make sense. I think that's the problem. Thanks!

It seems that neither PD nor Etcd supports the re-joining of deleted nodes, it may only be caused by the joining of a new node.

This issue seems unresolved but closed?

I think it is not a bug.


Add Fact checker Task

This task measures the language model's ability to do fact-checking in the zero-shot and many-shot settings.

@googlebot I consent.

@googlebot I consent.

@googlebot I consent.

@googlebot I consent.

@nayeon7lee We are now requesting that task authors please include an explicit section in the README.md file called "data source", listing exactly where you got your data from (or explicitly state that you made it up yourself). Many thanks!

@windweller are one of the assigned reviewers for this task.  Please provide feedback and be sure to notify me with a formal accept or does not meet criteria by Tuesday June 22 (if you have not already done so).

@james-simon are one of the assigned reviewers for this task.  Please provide feedback and be sure to notify me with a formal accept or does not meet criteria by Tuesday June 22 (if you have not already done so).

Hey!  Overall, this task seems well-designed and worthy of inclusion; I just have a smattering of minor questions and comments, listed below:

How many questions are in each subtask? That should be in the README.
What does ‘unverifiable’ mean in this task? Looking at the README, the question of whether John DiMaggio’s done motion-capture is pretty verifiably true or false, right? Also, as for whether someone has a dog that’s “12.3 km2,” that seems clearly false if it’s describing, say, the ground area underneath the dog and km2 is km^2 (and if that’s not what it means, it’s more ‘unclear’ than ‘unverifiable’).
It’s cool to see the results of old language models on the task! Some of those figures seem weirdly nice, though - the tables say GPT2-medium, -large, and -XL all scored exactly the majority score on all three subtasks for the 0-shot setting, for example. Are you sure that’s right? If so, can you explain it? Also, I’m skeptical of your claim that “non-random results are shown for ten shots in Politifact” - that looks small enough to be a chance fluctuation, and on other 10-shot tasks the models do that much worse than the majority! Unless you can back up your claim that that high performance is systematic, not a chance fluctuation, you should modify your claim and mention the similar cases where the models perform worse than chance.
The README mentions this task "can also serve as a way of inferring how factual is the language model." That seems right, but it's pretty vague; what does it mean for a language model to be "factual"? That section would be better if you made a more concrete statement with details explaining what specific traits or abilities you're thinking of.
*The FEVER .json is illegible - ideally it’d be spaced out like the others
*A question about the criterion that the task not be solvable by memorizing the internet: if the FEVER dataset (and your own dataset) are available online and don’t have the BIG-Bench canary string, is it possible that they’d end up in the training corpus of a future language model?

If you have any countercomments, feel free to let me know!

Hi! I’m one of the reviewers assigned to your task. This is a really cool submission.
My review of the submission, following the provided guidelines, is a follows:
Correctness:
No issues. The submission passes all tests.
Formatting:
No issues. Formatting looks good.
Specificity:
No issues. The task is specific and well-defined in its aims.
Thoroughness:

All three tasks come from two papers which have passed the reviews of two NLP conferences. Therefore I believe they are qualified to be submitted to the BigBench challenge. https://www.aclweb.org/anthology/2021.naacl-main.158.pdf
One comment that I have is -- the paper proposes the Evidence Conditioned Perplexity  method to support or reject claims, and also, the original data format and the format submitted are different. Does this actually make the task harder or easier? Would it be a fair comparison to results in the paper? Have you guys considered instead of submitting a task.json, you should make it a "programmatic task" therefore you can implement the perplexity score evaluation methods? It's not a big problem, but just want to highlight the difference between what's submitted and what's in the original paper.
Since the task has changed, I strongly encourage authors to write in the README about when they want future NLP researchers to use their paper's original dataset and when they want NLP researchers to use this new task dataset -- as two slightly different versions of the same task create confusion.

Difficulty:
The task presents a challenge to language models.
Not solvable by memorizing the Internet:
The fact that the main sources do come from the internet (Politifact and Wikipedia), it is likely that ultra-large language models have seen the examples shown. However, the 2-shot results in paper convince me that training to memorize internet verbatim is not enough.
Novelty:
The tasks were based on two previously published papers, but one of the paper is published in 2021 and the task is very topical (COVID-19). Though fact checking is not particularly novel, inclusion of these datasets would still be a good idea.
Justification:
No issues.
Size:
No issues.
Compute Resources:
No issues. The task should be straight-forward to run on any modern machine.
I hope you found this review helpful!

Hey!  Overall, this task seems well-designed and worthy of inclusion; I just have a smattering of minor questions and comments, listed below:
* How many questions are in each subtask? That should be in the README.

* What does ‘unverifiable’ mean in this task? Looking at the README, the question of whether John DiMaggio’s done motion-capture is pretty verifiably true or false, right? Also, as for whether someone has a dog that’s “12.3 km2,” that seems clearly false if it’s describing, say, the ground area underneath the dog and km2 is km^2 (and if that’s not what it means, it’s more ‘unclear’ than ‘unverifiable’).

* It’s cool to see the results of old language models on the task! Some of those figures seem weirdly nice, though - the tables say GPT2-medium, -large, and -XL all scored exactly the majority score on all three subtasks for the 0-shot setting, for example. Are you sure that’s right? If so, can you explain it? Also, I’m skeptical of your claim that “non-random results are shown for ten shots in Politifact” - that looks small enough to be a chance fluctuation, and on other 10-shot tasks the models do that much _worse_ than the majority! Unless you can back up your claim that that high performance is systematic, not a chance fluctuation, you should modify your claim and mention the similar cases where the models perform worse than chance.

* The README mentions this task "can also serve as a way of inferring how factual is the language model." That seems right, but it's pretty vague; what does it mean for a language model to be "factual"? That section would be better if you made a more concrete statement with details explaining what specific traits or abilities you're thinking of.
  *The FEVER .json is illegible - ideally it’d be spaced out like the others
  *A question about the criterion that the task not be solvable by memorizing the internet: if the FEVER dataset (and your own dataset) are available online and don’t have the BIG-Bench canary string, is it possible that they’d end up in the training corpus of a future language model?

If you have any countercomments, feel free to let me know!

Thanks, @james-simon, for the helpful review! Let us address your concern as the following:

How many questions are in each subtask?
We added the number of samples in the README.md.

What does 'unverifiable' mean in this task?
Here, we use the dataset provided by the original FEVER paper. unverifiable means the claim cannot be verified using the information provided. The annotators' team has done the labeling in FEVER.

It's cool to see the results of old language models on the task! Some of those figures seem weirdly nice, though - the tables say GPT2-medium, -large, and -XL all scored exactly the majority score on all three subtasks for the 0-shot setting, for example. Are you sure that's right?
Indeed, that there are fluctuations in the old language models. Interestingly, there are encouraging results from the larger language models, such as gpt2-medium, gpt2-large, and gpt2-xl, which might mean that the larger language models have a larger capacity to extract and memorize more factual information.

The README mentions this task "can also serve as a way of inferring how factual is the language model." That seems right, but it's pretty vague; what does it mean for a language model to be "factual"?
We hypothesize that LMs are able to measure the likelihood of a given sentence with reference to the previously encountered text. Therefore, this task attempts to investigate this hypothesis by prompting the LM with statements and check the likelihood of different labels.

I hope it answers your questions!
@chiafullo we just added a data source section in README.md. Thanks for the suggestion!

Hi! I’m one of the reviewers assigned to your task. This is a really cool submission.
My review of the submission, following the provided guidelines, is a follows:
Correctness:
No issues. The submission passes all tests.
Formatting:
No issues. Formatting looks good.
Specificity:
No issues. The task is specific and well-defined in its aims.
Thoroughness:
* All three tasks come from two papers which have passed the reviews of two NLP conferences. Therefore I believe they are qualified to be submitted to the BigBench challenge. https://www.aclweb.org/anthology/2021.naacl-main.158.pdf

* One comment that I have is -- the paper proposes the **Evidence Conditioned Perplexity**  method to support or reject claims, and also, the original data format and the format submitted are different. Does this actually make the task harder or easier? Would it be a fair comparison to results in the paper? Have you guys considered instead of submitting a `task.json`, you should make it a "programmatic task" therefore you can implement the perplexity score evaluation methods? It's not a big problem, but just want to highlight the difference between what's submitted and what's in the original paper.

* Since the task has changed, I strongly encourage authors to write in the README about when they want future NLP researchers to use their paper's original dataset and when they want NLP researchers to use this new task dataset -- as two slightly different versions of the same task create confusion.

Difficulty:
The task presents a challenge to language models.
Not solvable by memorizing the Internet:
The fact that the main sources do come from the internet (Politifact and Wikipedia), it is likely that ultra-large language models have seen the examples shown. However, the 2-shot results in paper convince me that training to memorize internet verbatim is not enough.
Novelty:
The tasks were based on two previously published papers, but one of the paper is published in 2021 and the task is very topical (COVID-19). Though fact checking is not particularly novel, inclusion of these datasets would still be a good idea.
Justification:
No issues.
Size:
No issues.
Compute Resources:
No issues. The task should be straight-forward to run on any modern machine.
I hope you found this review helpful!

Thanks, @windweller, for the insightful review! Let us address your concern as the following:

Does this actually make the task harder or easier? Would it be a fair comparison to results in the paper?
Yes, the task is harder than what we propose in the paper. We think it would be a good opportunity to measure the language models' capability to identify factual information.

Have you guys considered instead of submitting a task.json, you should make it a "programmatic task" therefore you can implement the perplexity score evaluation methods?
We would keep the submission as it is. We want to follow the evaluation format of the big-bench benchmark to make it consistent with other tasks.

We hope that the above answers your questions satisfactorily and you found that the task is beneficial to the benchmark. Thanks a lot!

Looks good!

unverifiable means the claim cannot be verified using the information provided. The annotators' team has done the labeling in FEVER.

What does 'provided' mean here? You're not giving the model any information besides the statement to evaluate!

You didn't address my questions! Here are my points:

The performance of the larger models on the 0-shot tasks looks suspiciously similar to the "Majority" label. One would expect that the performance of a language model would contain some noise, but these accuracies are exactly those of the majority label. That is not what I would expect, and it makes me think that something weird is going on. Why is this the case? For example, how could it be that all three of these are exactly 50%? I'd like you to examine your code, look at some model outputs, and figure it out.

You say that "there are encouraging results from the larger language models." This is only true if you ignore the bad results! For example, the red-boxed number is as bad as the green-boxed number is good.

I can understand that you'd want bigger models to do better - that would be exciting and make sense - but that's not what the data show. You need to either remove or change that statement.

Sounds good - you should improve that explanation and add it to the README because the sentence I mentioned is very vague right now.

Thank you, @james-simon, for your valuable comments. Let us answer your questions like the following:

2. > `unverifiable` means the claim cannot be verified using the information provided. The annotators' team has done the labeling in FEVER.

What does 'provided' mean here? You're not giving the model any information besides the statement to evaluate!

Sorry if we didn't make it clear. provided refers to the original training data. unverifiable are claims for which the annotators could not find supporting evidence.

* The performance of the larger models on the 0-shot tasks looks _suspiciously_ similar to the "Majority" label. One would expect that the performance of a language model would contain some noise, but these accuracies are exactly those of the majority label. That is not what I would expect, and it makes me think that something weird is going on. Why is this the case? For example, how could it be that all three of these are exactly 50%? I'd like you to examine your code, look at some model outputs, and figure it out.

Thank you for mentioning this. This is the code from Big-bench that we used to run our code:
task = "Politifact/task.json"
task_to_solve =json_task.JsonTask(
    task_data=json.load(open(task,"r")),
    shot_list=[0, 1, 5])

print(f"EVALUATING {task}")
for model_name in ['openai-gpt','gpt2','gpt2-medium','gpt2-large','gpt2-xl']:
    model = huggingface_models.BIGBenchHFModel(model_name)
    score_data = task_to_solve.evaluate_model(model)
    print(task, model_name, score_data)

We re-run the code again, just to be sure, and these are the outputs from the code above (for medium and large)
Politifact/task.json gpt2-medium [ScoreData(score_dict={'multiple_choice_grade': 0.5}, preferred_score='multiple_choice_grade', number_of_shots=0, low_score=0.5, high_score=1.0, subtask_description='fact_checking_politifact_json_multiple_choice')]

Politifact/task.json gpt2-large [ScoreData(score_dict={'multiple_choice_grade': 0.5}, preferred_score='multiple_choice_grade', number_of_shots=0, low_score=0.5, high_score=1.0, subtask_description='fact_checking_politifact_json_multiple_choice')]

Politifact/task.json gpt2-xl [ScoreData(score_dict={'multiple_choice_grade': 0.5}, preferred_score='multiple_choice_grade', number_of_shots=0, low_score=0.5, high_score=1.0, subtask_description='fact_checking_politifact_json_multiple_choice')]

And these are the log probabilities for gpt2-medium, gpt2-large, gpt2-xl (for the first 11 samples)
gpt2-medium
'Rep. David Cicilline is responsible for the federal loan guarantee to Solyndra, the failed solar company\nQ: Is this claim true or false?\nA: ', [-2.4860751401419474, -0.08690521826694741],
'It was under Barack Obama and Hillary Clinton that changed the rules of engagement that probably cost (Capt. Humayun Khans) life.\nQ: Is this claim true or false?\nA: ', [-2.018693840669483, -0.1425189139116707],
'The presidents health care proposals will cause most Americans to have their premiums increased, not decreased, and hundreds of millions of people lose their current insurance coverage.\nQ: Is this claim true or false?\nA: ', [-2.8510400964076936, -0.059520931368631125],
'David Dewhurst has a record of promoting an income tax.\nQ: Is this claim true or false?\nA: ', [-2.1093964592736683, -0.12932443778929326],
'Marco Rubio is the one for an open border.\nQ: Is this claim true or false?\nA: ', [-1.5025696154492767, -0.2517456408399017],
'Ron Klein repeatedly voted in favor of amnesty for illegal immigrants.\nQ: Is this claim true or false?\nA: ', [-2.6115987202597566, -0.07625173539647534],
'The number of uninsured people in Massachusetts is about the same as it was when the mandates were passed in 2006.\nQ: Is this claim true or false?\nA: ', [-2.3656241289695643, -0.09859532037581431],
'that Starbucks took Christmas off of their brand new cups.\nQ: Is this claim true or false?\nA: ', [-1.3667077249616852, -0.2942971414655915],
'In the class of 1968 at Patrice Lumumba University in Moscow, Mahmoud Abbas was one of the members of that class, and so was Ali Khamenei. And thats where they first established relationships with the young Vladimir Putin.\nQ: Is this claim true or false?\nA: ', [-1.2767011418630414, -0.3270551457692914],
'Shes voted for 60 tax increases, hurting the middle class and small businesses.\nQ: Is this claim true or false?\nA: ', [-2.26064382047438, -0.11013112516187995],
'The unemployment rate may be as high as 42 percent.\nQ: Is this claim true or false?\nA: '] [-2.3312246643007892, -0.10222838744532048]]

gpt2-large
'Rep. David Cicilline is responsible for the federal loan guarantee to Solyndra, the failed solar company\nQ: Is this claim true or false?\nA: ',[-2.884294960157323, -0.057517025129246946],
'It was under Barack Obama and Hillary Clinton that changed the rules of engagement that probably cost (Capt. Humayun Khans) life.\nQ: Is this claim true or false?\nA: ',[-2.70053965213678, -0.06953150394342078],
'The presidents health care proposals will cause most Americans to have their premiums increased, not decreased, and hundreds of millions of people lose their current insurance coverage.\nQ: Is this claim true or false?\nA: ',[-2.7702129671826246, -0.06469710567139408],
'David Dewhurst has a record of promoting an income tax.\nQ: Is this claim true or false?\nA: ',[-2.691568995441341, -0.07018056007757156],
'Marco Rubio is the one for an open border.\nQ: Is this claim true or false?\nA: ',[-2.4282246712150286, -0.09232722553631767],
'Ron Klein repeatedly voted in favor of amnesty for illegal immigrants.\nQ: Is this claim true or false?\nA: ',[-2.724027426649884, -0.06786134998972759],
'The number of uninsured people in Massachusetts is about the same as it was when the mandates were passed in 2006.\nQ: Is this claim true or false?\nA: ',[-2.8558234197150254, -0.059228323126890636],
'that Starbucks took Christmas off of their brand new cups.\nQ: Is this claim true or false?\nA: ',[-1.8316966072901844, -0.1745220200404285],
'In the class of 1968 at Patrice Lumumba University in Moscow, Mahmoud Abbas was one of the members of that class, and so was Ali Khamenei. And thats where they first established relationships with the young Vladimir Putin.\nQ: Is this claim true or false?\nA: ',[-1.657229954318721, -0.21154409082384795],
'Shes voted for 60 tax increases, hurting the middle class and small businesses.\nQ: Is this claim true or false?\nA: ',[-2.352824478881823, -0.09993103100584655],
'The unemployment rate may be as high as 42 percent.\nQ: Is this claim true or false?\nA: '[-2.585515169296274, -0.07834782997132272]]

gpt2-xl
'Rep. David Cicilline is responsible for the federal loan guarantee to Solyndra, the failed solar company\nQ: Is this claim true or false?\nA: ', [-3.2975488835246223, -0.037674539652551964],
'It was under Barack Obama and Hillary Clinton that changed the rules of engagement that probably cost (Capt. Humayun Khans) life.\nQ: Is this claim true or false?\nA: ', [-2.328042095235644, -0.10257155232792936],
'The presidents health care proposals will cause most Americans to have their premiums increased, not decreased, and hundreds of millions of people lose their current insurance coverage.\nQ: Is this claim true or false?\nA: ', [-2.9944688690878003, -0.05135983359585694],
'David Dewhurst has a record of promoting an income tax.\nQ: Is this claim true or false?\nA: ', [-2.3675764031443176, -0.09839324426980589],
'Marco Rubio is the one for an open border.\nQ: Is this claim true or false?\nA: ', [-2.808818954165237, -0.06216921203877224],
'Ron Klein repeatedly voted in favor of amnesty for illegal immigrants.\nQ: Is this claim true or false?\nA: ', [-2.5937535724325844, -0.07767939182955708],
'The number of uninsured people in Massachusetts is about the same as it was when the mandates were passed in 2006.\nQ: Is this claim true or false?\nA: ', [-2.5860014960688247, -0.07830820535837546],
'that Starbucks took Christmas off of their brand new cups.\nQ: Is this claim true or false?\nA: ', [-2.797118220077502, -0.0629244181976194],
'In the class of 1968 at Patrice Lumumba University in Moscow, Mahmoud Abbas was one of the members of that class, and so was Ali Khamenei. And thats where they first established relationships with the young Vladimir Putin.\nQ: Is this claim true or false?\nA: ', [-1.94897714966217, -0.15364042633453323],
'Shes voted for 60 tax increases, hurting the middle class and small businesses.\nQ: Is this claim true or false?\nA: ', [-2.3946711026472487, -0.09563316929763932],
'The unemployment rate may be as high as 42 percent.\nQ: Is this claim true or false?\nA: '] [-2.410374942887043, -0.0940707360388986]]

* You say that "there are encouraging results from the larger language models." This is only true if you ignore the bad results! For example, the red-boxed number is as bad as the green-boxed number is good.
  I can understand that you'd want bigger models to do better - that would be exciting and make sense - but that's not what the data show. You need to either remove or change that statement.

In the repo we wrote: " However, no-random results are shown is shown for ten shots in Politifact, where the model achieve a 4% improvement over random." Sorry, in our comment above, we did not mean to hint at bad or good results for any model size. We agree that sometimes larger models do not perform as well as the smaller ones.
Let us know if you have any other comments or want us to run or show more results.
Thanks again!  Hope it answers your questions! :)

@chiafullo I recommend accept for this task. Authors responses have been satisfactory.

Thanks! So far you have answered some of my questions, but have not made any of the changes I've asked for. All four responses below ask for specific changes that need to be made (or otherwise satisfactorily addressed) before I will accept this task.
(1)

provided refers to the original training data. unverifiable are claims for which the annotators could not find supporting evidence.

I got that! My question was this: how do you expect a language model that's been trained on the whole internet, not just a specific subset of information, to decide which statements are and are not verifiable from that specific subset? For example, in this question
Claim: John DiMaggio has done motion-capture roles.
Q: Is this claim true, false or unverifiable?
A: true|false|unverifiable
* Answer: unverifiable

it appears that this information was not in the "original training data." It probably is on the internet, though, so a model could easily know for certain whether it is true or false. Given that a model might actually know the answer, how does it make sense to expect it to give the label unverfiable?
(2)
Thanks for giving the logprobs of the models on some of your facts! That's quite helpful. Looking through them, it appears that false is always higher than true! If that's the case, that is important and should be mentioned in the README, right?

we did not mean to hint at bad or good results for any model size

(3)
Good! As currently written, however, your README does sound like it hints at good results. In my previous comment, I requested you make some specific changes to it, and I do not think you have made them.
(4)
Also, I've asked this a few times before, but I don't think you've actually made the changes: this sentence in the README is very vague, and you need to rewrite it to be clearer about what specific traits you mean by "factual."

Second, it can also infer how factual the language model is (the language model will have bad fact-checking performance if it does not contain any factual information).

The amount of tasks we received this round is tremendous.  With so many tasks to review we have decided to extend the review period to Tuesday, June 29.
Reviewers: if the submitter has made revisions please be sure to notify me with a formal ”accept” or ”does not meet criteria” by Tuesday, June 29  (if you haven't already done so).
this is an automated message

Thank you, @james-simon for the comments. We went through all your comments and suggestions. And, we addressed them in the last commits as the following:

(1)

provided refers to the original training data. unverifiable are claims for which the annotators could not find supporting evidence.

I got that! My question was this: how do you expect a language model that's been trained on the whole internet, not just a specific subset of information, to decide which statements are and are not verifiable from that specific subset? For example, in this question
Claim: John DiMaggio has done motion-capture roles.
Q: Is this claim true, false or unverifiable?
A: true|false|unverifiable
* Answer: unverifiable

it appears that this information was not in the "original training data." It probably is on the internet, though, so a model could easily know for certain whether it is true or false. Given that a model might actually know the answer, how does it make sense to expect it to give the label unverifiable?

Now we got what you mean, excellent suggestion. So, to avoid this ambiguity, we decided to removed samples with unverifiable labels, since we cannot make sure that this info are not present in the training data of the LM. Therefore, we focus on only true and false claims. We pushed the new data and we re-run the LMs on zero, one, and five shots.
You can find more information in the README: https://github.com/ML-Collective/BIG-bench/blob/fact_checker/bigbench/benchmark_tasks/fact_checker/README.md

(2)
Thanks for giving the logprobs of the models on some of your facts! That's quite helpful. Looking through them, it appears that false is always higher than true! If that's the case, that is important and should be mentioned in the README, right?
(3)

we did not mean to hint at bad or good results for any model size

Good! As currently written, however, your README does sound like it hints at good results. In my previous comment, I requested you make some specific changes to it, and I do not think you have made them.

Thank you for mentioning this. We explicitly mentioned this in the results in the README file.

(4)
Also, I've asked this a few times before, but I don't think you've actually made the changes: this sentence in the README is very vague, and you need to rewrite it to be clearer about what specific traits you mean by "factual."

Second, it can also infer how factual the language model is (the language model will have bad fact-checking performance if it does not contain any factual information).

We also updated the README file to the following:
... Second, it can also infer how factual the language model is. We hypothesize that LMs are able to measure the likelihood of a given sentence with reference to the previously encountered text. Therefore, this task investigates this hypothesis by prompting the LM with statements and checking the likelihood of different labels. ...

Hopefully, now it is clearer for the readers.
Please let us know if we miss anything. Thank you so much for your help!

The changes look good! Is there a reason you didn't address (2)? To reiterate my point, future researchers who read your README might wonder "how could it be that GPT-XL with 0 shots on Politifact gets EXACTLY 50% accuracy? Isn't that very unlikely?" This is a very obvious question, and so you should answer it if you know the reason. From your copy-pasted results above, it looked like the answer might be that the model always prefers the same response (e.g. it always gives "false" regardless of what the prompt was), and because you designed Politifact to be 50% true and 50% false, this naive strategy scores exactly 50%.

The changes look good! Is there a reason you didn't address (2)? To reiterate my point, future researchers who read your README might wonder "how could it be that GPT-XL with 0 shots on Politifact gets EXACTLY 50% accuracy? Isn't that very unlikely?" This is a very obvious question, and so you should answer it if you know the reason. From your copy-pasted results above, it looked like the answer might be that the model always prefers the same response (e.g. it always gives "false" regardless of what the prompt was), and because you designed Politifact to be 50% true and 50% false, this naive strategy scores exactly 50%.

Thank you for your response. Good point! We updated the README.md file as the following:
In the zero-shot setting, the models always predict the same class (e.g., "false" regardless of the prompt), thus achieving exactly 50% multiple_choice_grade (Politifact is a balanced dataset with 50% true and 50% false labels).

Please let us know if we miss anything. Thank you so much for your help!

Looks good! @chiafullo accept

Your task has been accepted in the initial review stage.  The next stage of this process is for us to assign your task a meta-reviewer for final review and merge.  An assigned meta-reviewer will follow-up by commenting on the PR should it need further revisions.

Hello, I'm not a reviewer, but doing some sanity checks I noticed that the task name in the top-level task.json is intent_recognition, which conflicts with another task, and which is probably not what you intended. If you could, please change this prior to merging. Thanks!

@ajslone thanks for spotting the typo. I have just pushed the fix :)
--Andrea

Hello, I’m the meta-reviewer for this task. Here’s my feedback with respect to our review criteria:
Overall
Overall I have some concerns about this task, in that fact-checking is not a particularly novel task, and the source datasets are likely to be a part of most future (and many current) large language models trained on a large internet corpus.
One of the reviewers (@james-simon) requested a change regarding unverifiable examples in the FEVER subtask, which the authors said they would remove, but it appears that these examples are still in place. These should be removed as the reviewer made his approval contingent on this modification, at least to my reading of the conversation. The concerns regarding these unverifiable examples are significant, and should be addressed.
Additionally I would request that the authors also reformat the json file for the FEVER set also as requested by @james-simon above.
One concern I have is that many of the examples from each of the subtasks seem to have statements whose truth may change in time. For example:

FEVER: {"id": 57085, "input": "Claim: Legendary Entertainment is the owner of Wanda Cinemas. ..."}
Politifact: {"id": 28, "input": "Ken Block supports Obamacare. ..."}
Covid: {"id": "64", "input": "Claim: The source of SARS-CoV-2, the coronavirus (CoV) causing COVID-19 is unknown. ..."}

Another concern I have is that many statements lack any way to determine their factuality and have only a true/false choice. Some of these are ill-formed statements, or contain ambiguous references and pronouns. (the Politifact set seems to have such examples in abundance). Some examples:

Politifact: {"id": 9, "input": "Shes voted for 60 tax increases, hurting the middle class and small businesses. ...}
Politifact: {"id": 2, "input": "The presidents health care proposals will cause most Americans to have their premiums increased, not decreased, and hundreds of millions of people lose their current insurance coverage....}
FEVER: {"id": 125577, "input": "Claim: Ron Dennis is unemployed. ..."} (this has a target of false)

These are serious deficiencies to my mind, and need to be addressed.
Correctness
The task is correctly formed and passes all of the tests.
Specificity
The task is specific in its goal to probe if models can differentiate factual and non-factual statements given no supporting information in context.
Thoroughness
It is difficult to assess thoroughness in such a task. The task draws from a set of three datasets, two of which are fairly domain-specific (politifact and covid), with one (FEVER) that is more general. The number of examples here is overall
reasonably large.
Difficulty
The task seems to be difficult for a range of smaller language models.
Not Solvable by Memorizing the Internet
This seems to be, in essence, the measure of the task itself. The data sources are quite probably contained in the training data for current and future language models.
Novelty
This task does not seem novel.
Justification
The justification here seems reasonable.
Compute Resources
There are no compute resource issues for this task.
Data Sources and Licensing
This task lists its data sources and there are no issues here.

Good catches, @ajslone - you've read my comments correctly, and I agree the issues you've raised should be addressed.

@ajslone I was afraid to raise this point that some of the "facts" might still be in dispute, but thank you for mentioning it first.

{"id": "65", "input": "Claim: COVID-19 has a natural animal origin and is not a constructed virus\nQ: Is this claim true or false?", "target_scores": {"true": 1, "false": 0}}

{"id": "86", "input": "Claim: The COVID-19 is originated in a laboratory in China.\nQ: Is this claim true or false?", "target_scores": {"true": 0, "false": 1}},

Here's a link to the CNN report posted about 4 days ago:
Senior Biden officials finding that Covid lab leak theory as credible as natural origins explanation
I don't want to get into a "political" debate or anything but as far as I know, some of Politifact "fact checks" might themselves be evolving over time.

One of the reviewers (@james-simon) requested a change regarding unverifiable examples in the FEVER subtask, which the authors said they would remove, but it appears that these examples are still in place. These should be removed as the reviewer made his approval contingent on this modification, at least to my reading of the conversation. The concerns regarding these unverifiable examples are significant, and should be addressed.
Additionally I would request that the authors also reformat the json file for the FEVER set also as requested by @james-simon above.

Thank you for your response @ajslone. Actually, we have updated our datasets weeks ago, but we forgot to push the changes. This is the mistake from our side. We have just fixed this and pushed the commit. We appreciate your time to improve the submission.

One concern I have is that many of the examples from each of the subtasks seem to have statements whose truth may change in time. For example:
* FEVER: `{"id": 57085, "input": "Claim: Legendary Entertainment is the owner of Wanda Cinemas. ..."}`

* Politifact: `{"id": 28, "input": "Ken Block supports Obamacare. ..."}`

* Covid: `{"id": "64", "input": "Claim: The source of SARS-CoV-2, the coronavirus (CoV) causing COVID-19 is unknown. ..."}`

Thank you for raising your concern. We also agree that the information may change over time, therefore, we have updated our task to also include the timestamp. Following Lazaridou et al., we propose to modify our task to include the timestamp as a prefix, and we will re-run the experiments with this format tomorrow.
Details about the timestamp:

FEVER: date of the Wikipedia Dump utilized for data creation.

Politifact: the date when claims were fact-checked by journalists.

Covid-social: the date when the data was constructed and released.

Another concern I have is that many statements lack any way to determine their factuality and have only a true/false choice. > Some of these are ill-formed statements or contain ambiguous references and pronouns. (the Politifact set seems to have such examples in abundance). Some examples:

Politifact: {"id": 9, "input": "Shes voted for 60 tax increases, hurting the middle class and small businesses. ...}

Politifact: {"id": 2, "input": "The presidents health care proposals will cause most Americans to have their premiums increased, not decreased, and hundreds of millions of people lose their current insurance coverage....}

FEVER: {"id": 125577, "input": "Claim: Ron Dennis is unemployed. ..."} (this has a target of false)

Once again, thanks for identifying and spotting some unresolved ambiguity in our dataset. For Politifact, we have fixed all the ambiguous references and pronouns based on the information available at Politifact.com. Regarding FEVER, we would like to point out that we have adopted a well-known fact-checking benchmark dataset. However, as mentioned earlier, we have removed the ‘unverifiable’ examples and added timestamps, which we believe will help improve the robustness of the dataset for the intended BigBench purpose.

Thanks for all of the updates and addressing my concerns. I still have a couple of things that I think need addressing:

One of the reviewers (@james-simon) requested a change regarding unverifiable examples in the FEVER subtask, which the authors said they would remove, but it appears that these examples are still in place. These should be removed as the reviewer made his approval contingent on this modification, at least to my reading of the conversation. The concerns regarding these unverifiable examples are significant, and should be addressed.
Additionally I would request that the authors also reformat the json file for the FEVER set also as requested by @james-simon above.

Thank you for your response @ajslone. Actually, we have updated our datasets weeks ago, but we forgot to push the changes. This is the mistake from our side. We have just fixed this and pushed the commit. We appreciate your time to improve the submission.

====================================================================
Thanks for fixing this!

One concern I have is that many of the examples from each of the subtasks seem to have statements whose truth may change in time. For example:
* FEVER: `{"id": 57085, "input": "Claim: Legendary Entertainment is the owner of Wanda Cinemas. ..."}`

* Politifact: `{"id": 28, "input": "Ken Block supports Obamacare. ..."}`

* Covid: `{"id": "64", "input": "Claim: The source of SARS-CoV-2, the coronavirus (CoV) causing COVID-19 is unknown. ..."}`

Thank you for raising your concern. We also agree that the information may change over time, therefore, we have updated our task to also include the timestamp. Following Lazaridou et al., we propose to modify our task to include the timestamp as a prefix, and we will re-run the experiments with this format tomorrow.
Details about the timestamp:

FEVER: date of the Wikipedia Dump utilized for data creation.
Politifact: the date when claims were fact-checked by journalists.
Covid-social: the date when the data was constructed and released.

====================================================================
I think this is a good addition and it should resolve many of the task ambiguities. However, looking at the task.json files, it seems that you simply added a date keyword to each example. This won't get recognized or used in the benchmark like this. What you need to do here is to update the input prompt to include this information. An example could be:
"On , the following claim was made: . Was this claim true or false?"

Another concern I have is that many statements lack any way to determine their factuality and have only a true/false choice. > Some of these are ill-formed statements or contain ambiguous references and pronouns. (the Politifact set seems to have such examples in abundance). Some examples:

Politifact: {"id": 9, "input": "Shes voted for 60 tax increases, hurting the middle class and small businesses. ...}
Politifact: {"id": 2, "input": "The presidents health care proposals will cause most Americans to have their premiums increased, not decreased, and hundreds of millions of people lose their current insurance coverage....}
FEVER: {"id": 125577, "input": "Claim: Ron Dennis is unemployed. ..."} (this has a target of false)

Once again, thanks for identifying and spotting some unresolved ambiguity in our dataset. For Politifact, we have fixed all the ambiguous references and pronouns based on the information available at Politifact.com. Regarding FEVER, we would like to point out that we have adopted a well-known fact-checking benchmark dataset. However, as mentioned earlier, we have removed the ‘unverifiable’ examples and added timestamps, which we believe will help improve the robustness of the dataset for the intended BigBench purpose.

====================================================================
Thanks very much for updating these examples. This definitely improves the quality of the task.
With regards to FEVER, while I agree that this is a well-known benchmark, this does not remove the responsibility to inspect and analyze the examples. Adding the date that the claim was made goes a long way in addressing the ambiguities in many of these examples. There will always be some level of ambiguity in such tasks, but we must make every effort to identify and remove any egregious cases. Some very quick searching of the task yields many examples that are very questionable.
One source of ambiguity is an assumption of common agreement on the specific identity of the subject. Often the ambiguity can be resolved by the subject being a highly-visible, famous individual, or by the subject having an uncommon name. Otherwise, disambiguation is made difficult due to a lack of any context. For example:
99511: "Claim: The Times has been printed since the American Revolution."
In this case, does "The Times" refer to the New York Times? The Times of London? The Times of India?
48028: "Claim: Sarah Hyland is from Idaho."
215874: "Claim: Jerome Flynn was born on March 28."
The assumption here is that these refer to the actors, but this is very ambiguous without context or a qualifier.
139994: "Claim: Scandal is from the 1950's."
"Scandal" is very ambiguous here. Is this the television show? Some additional context would greatly disambiguate this, like "The 2017 American television show Scandal is set in the 1950s."
70574: "Claim: Bonobos live south of the statue."
"Statue" is completely ambiguous here.
Other examples in FEVER are, for lack of a better term, somewhat odd :
121472: "Claim: Golf is played in a place."
6650: "Claim: The Icelandic Coast Guard is responsible for something."
87019: "Claim: Theresa May served."
212039: "Claim: A Pet helps those who live."
99461: "Claim: Personality is affected by the hindrance of values."
227098: "Claim: Blank Space is on a list in a country."
Some examples are incorrect:
219688: "Claim: Corsica is an book." (the task lists the correct answer as "False", despite several books by that title)
126799: "Claim: Alex Jones is a novelist." (listed as "False", despite there being at least one easily found novelist by that name who published a novel in 2015 (the claim date is 2017))
130325: "Claim: Dark matter is different from neutrinos." (listed as False)
These examples are not meant to invalidate this task, but to point out that even existing, public datasets have incorrect or poorly-chosen elements, and that taking a minimal amount of time to manually check these can identify and
eliminate them.
My concrete requests:

Remove the date keyword from the examples as described above, and modify the prompt to include this information.
Remove at least the highlighted examples above.
(optional) Perhaps do another manual scan of these tasks for quality control to remove incorrect examples.

Thanks again for submitting this task and for all of your hard work in making updates and responding to
the reviewer feedback. I think with these last changes the task will be in good shape for merging.
====================================================================

@googlebot I fixed it.

Thanks again @ajslone for spending your time to improve our submission! We followed your requests to update our task:

Included the date information to the input as the context (i.e., using: On <date>, the following claim was made: <claim>. Q: Was this claim true or false?”).

Removed/ fixed the examples highlighted by you.

Spent some time trying to further remove/update incorrect/ambiguous examples. To be more specific,

Provided additional information in the form of brackets - e.g., Chinatown —> Chinatown (1974 film), Hebe —> Hebe (goddess), Chris Bosh —> Chris Bosh (basketball player).
Provided full-name - e.g., Timur —> Timur Gurkani, Nero —> Nero Claudius Caesar
Removed some weird examples - e.g., "<Name> is a person.", "Elizabeth of York was someone's niece", "Planet Hollywood Las Vegas is operated by someone.".
Fixed sentences containing the ambiguous terms ("something") - e.g.,  Sacre-Coeur, Paris is publicly dedicated to something. —> Sacre-Coeur, Paris is publicly dedicated to Sacratissimum Cor Iesu. | The Icelandic Coast Guard is responsible for something. —> The Icelandic Coast Guard is responsible for search and rescue in the seas surrounding Iceland.

Sorry for taking so long to get back to you on this. Thanks very much for all of your updates and all of the hard work you've put into this task. I think with these latest changes things look good and I'll merge.


Create the syntax for the  data flow utility, and publish it as the README/–help

GIVEN that the flow of our data requires many steps
AND these steps will be implemented as different arguments for our tool
WHEN a user of the tool is curious for how it works
AND WHEN a developer of the tool is looking for direction
THEN either can consult a readme to see the syntax to build or use
AND can add the --help argument to find the same syntax
AND our development will follow the already made documentation.

closed with https://github.com/cncf/apisnoop/pull/97/commits/c1434c9c5f99fb2839995b4bc2a62bb4a0a0a834


No Device found in nodered

I have a problem with the alexa skill.
I added a device under https://alexa-node-red.bm.hardill.me.uk/devices in my account and installed the alexa skill in the alexa app.

After searching for devices in the alexa App, the 2 new devices are found.

but the nodered plugin doesn't find any device, even after refresh. The node is connected, but the device list stays empty.

Do you have any suggestions for me?
Thanks and best regards,
Andreas

I'll try and have a look tonight

Can you check the node-red logs to see if there is any output when you hit the refresh button

Also what version of Node-RED and NodeJS are you using

Good morning and thank you for your repley.
22 Mar 08:07:05 - [info] Node-RED version: v0.18.3
22 Mar 08:07:05 - [info] Node.js  version: v6.13.0
22 Mar 08:07:05 - [info] Linux 4.14.18-v7+ arm LE
The Log, even in trace mode dosn't contain any entrys. There is no output from the plugin.

I extended the log at line 327 a little bit:
err: null
id: 7d5d9396.22e9bc
devicesURL: https://alexa-node-red.bm.hardill.me.uk/api/v1/devices
username: SpeedyRS2
password: xxxxxxxxx
res: [object Object]
body: Unauthorized
ok, i found the problem. I used a password with special characters like ":" and so on. This was the problem. I registred a new user, with another password (without  special characters) and the device was found from the plugin.
Thank you very much for your help.

Cool, you should have been able to change your password (by saying you forgot it on the login page). But glad it's all working now.
I'll have look at the version of request I'm using because this shouldn't be a problem


ROX-13585: Delete non-postgres conditional code in Access Control UI

Description

deprecating our Postgres feature flag

Checklist

[ ] Investigated and inspected CI test results
[ ] Edited integration tests

Testing Performed
Manual testing

Visit /main/access-control/roles

Click Create role, type None Unrestricted, click None, and then click Save

Click Create role, type None Deny All, click None, click Deny All, and then click Save

Integration testing

yarn cypress-open in ui/apps/platform

accessControl/accessControlAccessScopes.test.js
accessControl/accessControlPermissionSets.test.js
accessControl/accessControlRoles.test.js

/retest

ocp-4-12-ui-e2e-tests failure:

Clusters should redirect from Dashboard when no secured clusters have been added (only applies to Cloud Service)

+ expected - actual

- '/main/dashboard'
+ '/main/clusters'

Did it timeout while waiting for the mocked response? Or timing problem with a real request?


Does this work with dark: "media" in a tailwind.config.js?

I can't seem to use this no matter how many ways I fiddle with it.
I'm using a mono-repo and need to use media preference for dark / light mode, and this addons seems to just... not work.

@Julianwan This seems to work for me.
Have you checked if  is getting a class of "dark"?

@renatomoor thank you.
My situation is unique. It's a monorepo using Next.js to host a static site, and building stories alongside my components.
The site itself uses media class, but I have been trying to get storybook to use native "class" based theme, for design and quality assurance purposes.
I can always toggle my system settings but it's not easy to explain how to do that for other team members, so I wanted a native toggle within the storybook ui.

That you need to have it in class mode, should probably be mentioned in the readme. Would have saved me time, as that's not the default (anymore?)


Upgrade tikv-client-java version from 3.2.0-SNAPSHOT to 3.2.0

Upgrade tikv-client-java version from 3.2.0-SNAPSHOT to 3.2.0

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.frank.liu seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.You have signed the CLA already but the status is still pending? Let us recheck it.


use core.ssl_verify config in dcos.http

We use the ssl_verify config property to set the corresponding env_var in dcoscli.main. However, when someone uses our http module outside of the dcos-cli, the config property doesn't work.
#632

Closing this in favor of #643


Parallel install

Installs Epinio in 3 minutes on a warmed up cluster:
real    3m8.877s
user    0m15.747s
sys     0m3.206s

May need some improvements on how we print messages from the various go routines.

A similar approach can be taken for uninstall although special care is needed to not remove things that act as finalizers too early thus preventing other objects from being deleted.

I'm amazed by how experiment sometimes contradicts the theory's expectations :D . I tried installing from main on a warmed up cluster and it was actually faster:
real    2m20.477s
user    0m14.101s
sys     0m2.304s

I don't know how to interpret this. Generally the time it takes is not deterministic but I expected some improvement when doing things in parallel. On the other hand, the difference may be noticeable on fresh cluster so I will try that too.

I tried again from this branch (with a warmed up cluster) and these are today's results:
real    0m48.312s
user    0m14.945s
sys     0m2.726s

definitely faster than the main branch. I'm using a VM which is running on a machine with shared resource so maybe generally today things are faster. I'd say now theory and experiment agree :).

Fresh cluster, parallel install:
real    2m39.405s
user    0m15.617s
sys     0m2.983s

Fresh cluster, main branch (non parallel install):
real    6m6.295s
user    0m14.434s
sys     0m2.916s


head: use std::io::copy() with TakeLines reader

Replace the custom split::walk_lines() function with a call to
std::io::copy(), using a new TakeLines reader as the source and
stdout as the destination. The TakeLines reader is an adaptor that
scans the bytes being read for line ending characters and stops the
reading after a given number of lines has been read (similar to the
std::io::Take adaptor).
This change

makes the read_n_lines() function more concise,
allows it to mirror the implementation of read_n_bytes(),
increases the speed of head -n NUM.

Here's my speed benchmark. The file shakespeare.txt contains about 170,000 lines, each less than 100 characters, comprising the collected works of Shakespeare. head is the GNU version, head-master is the version from the current master branch (11ca4be1aa466deec1633b98316ee2a7b8b675e8), and head-reader is the version from this branch. We read the first 100,000 lines from the file with each version of head.
$ hyperfine --warmup=100 --runs=500 \
> "head -n100000 shakespeare.txt > /dev/null" \
> "./head-master -n100000 shakespeare.txt > /dev/null" \
> "./head-reader -n100000 shakespeare.txt > /dev/null"
Benchmark #1: head -n100000 shakespeare.txt > /dev/null
  Time (mean ± σ):       5.3 ms ±   2.6 ms    [User: 4.4 ms, System: 1.0 ms]
  Range (min … max):     2.6 ms …  13.3 ms    500 runs

  Warning: Command took less than 5 ms to complete. Results might be inaccurate.

Benchmark #2: ./head-master -n100000 shakespeare.txt > /dev/null
  Time (mean ± σ):      18.7 ms ±   2.1 ms    [User: 8.2 ms, System: 10.5 ms]
  Range (min … max):    15.1 ms …  24.4 ms    500 runs

Benchmark #3: ./head-reader -n100000 shakespeare.txt > /dev/null
  Time (mean ± σ):       5.7 ms ±   3.1 ms    [User: 3.9 ms, System: 1.8 ms]
  Range (min … max):     1.6 ms …  10.4 ms    500 runs

  Warning: Command took less than 5 ms to complete. Results might be inaccurate.

Summary
  'head -n100000 shakespeare.txt > /dev/null' ran
    1.07 ± 0.78 times faster than './head-reader -n100000 shakespeare.txt > /dev/null'
    3.50 ± 1.76 times faster than './head-master -n100000 shakespeare.txt > /dev/null'

I have added the BENCHMARKING.md document and rebased on master.

Concerning the

Warning: Command took less than 5 ms to complete. Results might be inaccurate.

warnings issued by Hyperfine, the Wikipedia data dumps, e.g. https://dumps.wikimedia.org/wikidatawiki/latest/wikidatawiki-latest-pages-logging.xml.gz, provide text files with on the order of 100M lines which could make for a larger and easier to measure test case?

I rebased and squashed the commits


3.1.0 - Addition of "summary" to the API info is confusing

What does "summary" give us that "description" does not? Perhaps "summary" could just be an alias for "description"?

See #832 and #1779 for the rationale for this field being added and the differences between title, summary and description. The OAS does not have the concept of aliases, and I for one would not like to see them introduced.
What do you find confusing?

I think that some of the context from those two issues could help clarify in the 3.1.0 document. Right now, I did not understand that from what I read in the standard. If it is explained somewhere else, then perhaps a link to that explanation?

The line is always between conciseness, clarity, consistency and completeness. The summary and description fields have their different usage descriptions framed in the same manner as for other objects (such as pathItem and operation). If you can formulate a wording improvement to either or both field descriptions, a PR would be welcome.

@InfoSec812 When presenting information it is common to have a list view of data and a detail view.  summary is optimized to represent data in a list view, description is optimized for a detail view.  description enables rich text which is useful in documentation.  summary does not support rich text as that can cause presentation issues when presenting a list of summary values.
summary is an optional field so you can choose not to use it if it is not useful for your scenario.  Adding summary to info was a request from the community and it created consistency with other objects in the specification.  This felt like sufficient justification for its inclusion.

@darrelmiller Now that I have had it explained by @MikeRalphson I agree. What I would like to do, and thus the PR, is make that obvious to everyone who reads the specification/standard by adding the definition and providing links to that information.

@philsturgeon  is going to remove the word "short" from the description of description.  Hopefully this is more descriptive.

https://github.com/OAI/OpenAPI-Specification/pull/2408

#2408 was merged, closing this. Thanks all.


zq/fix device_configs for remainder

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Motivation and Context

fix ascend device_config for remainder
Description

fix ascend device_config for remainder
Use cases (Optional)

BC-breaking (Optional)

Checklist
Before PR:

[x] I have read and followed the workflow indicated in the Contributors.md to create this PR.
[x] Pre-commit or linting tools indicated in Contributors.md are used to fix the potential lint issues.
[x] Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
[x] New functionalities are covered by complete unit tests. If not, please add more unit test to ensure the correctness.
[x] The documentation has been modified accordingly, including docstring or example tutorials.

After PR:

[x] CLA has been signed and all committers have signed the CLA in this PR.

在fix_prod中已经将remainder修复


e621 search throws "no results"

Bug description
A clear and concise description of what the bug is.
Steps to reproduce

Go to '...'
Click on '....'
Scroll down to '....'
See error

Expected behavior

See search results

Context
main.log
settings.txt
Screenshots

System information

OS: Windows 10
Grabber version: 7.5.1

Additional context
I'm gonna assume that this has something to do with the captcha on e621

Same. And my site login isn't working either. says

definitely due their new captcha. how annoying

Same here...

It is fixed now, issue can be closed, error was on e621 side. You can have bypassed the capcha by validating one on your browser then grabber would have been fine for 1 hour or so.

Bypassing it by filling the capatcha on the site didn't work before, but I'm closing this as things work now.


android vs. ios cookie encoding inconsistency

When I make a POST request to authenticate a user, I get back a header (for both iOS and Android) that looks like:
Set-Cookie: MYDOMAIN_SESSION=“mycookieinfo”; Expires=Fri, 8-Jul-16 20:03:49 GMT; Path=/; Domain=.mydomain.com; Secure; HTTPOnly
Later, when I make a GET request, android encodes the related header as:
Cookie: MYDOMAIN_SESSION=mycookieinfo
while iOS encodes the header as
Cookie: MYDOMAIN_SESSION=“mycookieinfo”
The lack of quotes results in an error when I make subsequent requests (post authentication) on android. I am not sure why this is happening, as both of the responses from the initial authentication come back with correct (quoted) Set-Cookies.
This is causing the endpoint FOR ANDROID not to recognize the user (iOS works fine). I am getting a server error, which further enforces that this is a cookie issue.
How do I add the quotes back?
I have tried using the react-native-cookies (https://github.com/joeferraro/react-native-cookies) library to inspect the cookies, and I cannot access the session cookie. So, to inspect the network requests (and the cookie), I used Charles Proxy.

Quotes were not the issue. However, it does look like there is some issue with parsing cookies in android that are surrounded by quotes and have commas within.


GitHub action migration

Changes for migration from CircleIC to Github Actions.
For updates related to Publish to PyPi for took reference from https://github.com/splunk/addonfactory-ucc-generator
Added pre-commit lint
Added review secrets in the build-test-release workflow.
Added compliance-copyright and compliance-dependency in required for a build similar to UCC
Updated license in all required files and also updated licenserc.yaml as ignore-list file to similar to UCC
Verified the generated build by comparing with CircleCI main build only version diff.
Updated Submodules

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

:tada: This PR is included in version 2.1.2 :tada:
The release is available on:

v2.1.2
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