publication update and hyper link fix

README.md

@@ -31,6 +31,7 @@ In particular, the tool provides the key features, typical examples, and open co
 ```Shell
 pip install neural-compressor
 ```
+> [!NOTE]
 > More installation methods can be found at [Installation Guide](./docs/source/installation_guide.md). Please check out our [FAQ](./docs/source/faq.md) for more details.
 
 ## Getting Started
@@ -137,17 +138,18 @@ q_model = fit(
   </tbody>
 </table>
 
+> [!NOTE] 
 > More documentations can be found at [User Guide](./docs/source/user_guide.md).
 
 ## Selected Publications/Events
+* Blog by Intel: [Effective Weight-Only Quantization for Large Language Models with Intel® Neural Compressor](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Effective-Weight-Only-Quantization-for-Large-Language-Models/post/1529552) (Oct 2023)
 * EMNLP'2023 (Under Review): [TEQ: Trainable Equivalent Transformation for Quantization of LLMs](https://openreview.net/forum?id=iaI8xEINAf&referrer=%5BAuthor%20Console%5D) (Sep 2023)
 * arXiv: [Efficient Post-training Quantization with FP8 Formats](https://arxiv.org/abs/2309.14592) (Sep 2023)
 * arXiv: [Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs](https://arxiv.org/abs/2309.05516) (Sep 2023)
-* Post on Social Media: [ONNXCommunityMeetup2023: INT8 Quantization for Large Language Models with Intel Neural Compressor](https://www.youtube.com/watch?v=luYBWA1Q5pQ)  (July 2023)
-* Blog by Intel: [Accelerate Llama 2 with Intel AI Hardware and Software Optimizations](https://www.intel.com/content/www/us/en/developer/articles/news/llama2.html) (July 2023)
 * NeurIPS'2022: [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) (Oct 2022)
 * NeurIPS'2022: [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114) (Oct 2022)
 
+> [!NOTE]
 > View [Full Publication List](./docs/source/publication_list.md).
 
 ## Additional Content
@@ -160,4 +162,5 @@ q_model = fit(
 ## Communication 
 - [GitHub Issues](https://github.com/intel/neural-compressor/issues): mainly for bugs report, new feature request, question asking, etc.
 - [Email](mailto:inc.maintainers@intel.com): welcome to raise any interesting research ideas on model compression techniques by email for collaborations.  
+- [Discord Channel](https://discord.com/invite/Wxk3J3ZJkU): join the discord channel for more flexible technical discussion.
 - [WeChat group](/docs/source/imgs/wechat_group.jpg): scan the QA code to join the technical discussion.

docs/source/publication_list.md

@@ -1,6 +1,7 @@
-Full Publications/Events (77)
+Full Publications/Events (78)
 ==========
-## 2023 (23)
+## 2023 (24)
+* Blog by Intel: [Effective Weight-Only Quantization for Large Language Models with Intel® Neural Compressor](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Effective-Weight-Only-Quantization-for-Large-Language-Models/post/1529552) (Oct 2023)
 * arXiv: [Efficient Post-training Quantization with FP8 Formats](https://arxiv.org/abs/2309.14592) (Sep 2023)
 * EMNLP'2023 (Under Review): [TEQ: Trainable Equivalent Transformation for Quantization of LLMs](https://openreview.net/forum?id=iaI8xEINAf&referrer=%5BAuthor%20Console%5D) (Sep 2023)
 * arXiv: [Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs](https://arxiv.org/abs/2309.05516) (Sep 2023)