Description

Building the R package with CMake-based installation (https://github.com/microsoft/LightGBM/blob/master/R-package/README.md#install) and gcc (or MINGW on Windows results in the following compiler warnings:

/private/var/folders/xq/wktq4zdx4jd3qdpk34d28m940000gn/T/Rtmp7sAM9p/R.INSTALL71b1ac713c8/lightgbm/src/src/lightgbm_R.cpp:722:41: warning: cast between incompa

• Source

JsPointerToString(NULL, 0, &str);

• Expected
  str = empty string

• Actual
  failed and returns JsErrorNullArgument

I think this should be possible

Right now, the validation error message is in the Example Usage box, which is what SSDT does

There's support for validation for input boxes, so it would be nicer for the error message to show up with the associated input box like this:
![image](https://user-images.githubusercontent

Description of Change

Hang or crash macOS App scaling a View to zero.

Issues Resolved

• fixes #8981

API Changes

None

Platforms Affected

• macOS

Behavioral/Visual Changes

None

Before/After Screenshots

Before

Hang in the first case and getting a crash in the second case setting the Scale to zero.

Ater

Issue Description

msbuild's code is open source under the MIT license. This allows anyone to examine, modify and distribute the sources (among other things). However, the source code repository includes a documentation file that has a "Microsoft Confidential" notice:

https://github.com/dotnet/msbuild/blob/master/src/Shared/Shared%20Code.doc

I suspect this is a mistake since there i

The getDataObject function is remaining public on RootDataObject that needs to be remove. It can simply be removed.

https://github.com/microsoft/FluidFramework/blob/main/experimental/framework/fluid-static/src/containerCode.ts

Summary

The Event Hubs client library is instrumented using the .NET EventSource mechanism for logging. When instrumenting or diagnosing issues with applications that consume the library, it is often helpful to have access to the Event Hubs logs.

Though EventSource instrumentation is

I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?