fixed bib

_includes/latest_posts.html

@@ -2,7 +2,7 @@
           <div class="news">
             {% if site.latest_posts != blank -%}
             {%- assign latest_posts_size = site.posts | size -%}
-            <div class="table-responsive" {% if site.latest_posts.scrollable and latest_posts_size > 3 %}style="max-height: 60vw"{% endif %}>
+            <div class="table-responsive" {% if site.latest_posts.scrollable and latest_posts_size > 5 %}style="max-height: 88vw"{% endif %}>
               <table class="table table-sm table-borderless">
               {%- assign latest_posts = site.posts -%}
               {% if site.latest_posts.limit %}

_pages/about.md

@@ -21,7 +21,7 @@ social: true  # includes social icons at the bottom of the page
 
 Hello World! I'm An Wang Xuelong, a junior researcher interested in the [consilience of knowledge](https://en.wikipedia.org/wiki/Consilience_%28book%29), with a particular focus within the field of artificial intelligence and socio-biological science. 
 
-I aim to strike the balance the scientific pursuit to understand intelligence and technological endeavor to adopt what we learn in the research of AI in applications for social good, such as to [accelerate scientific discovery](https://www.youtube.com/watch?v=Ds132TzmLRQ)
+I aim to strike the balance the scientific pursuit to understand intelligence and the technological endeavor to adopt what we learn in the research of AI in applications for social good, namely to [accelerate scientific discovery in biology](https://www.youtube.com/watch?v=Ds132TzmLRQ)
 
 <!-- Write your biography here. Tell the world about yourself. Link to your favorite [subreddit](http://reddit.com). You can put a picture in, too. The code is already in, just name your picture `prof_pic.jpg` and put it in the `img/` folder. -->
 <!-- 

_posts/2021-08-06-kaifu-toc.md

@@ -1,5 +1,5 @@
 ---
-layout: post
+layout: distill
 title: "Saving the Future: Comment on AI·Future (AI·未来) and Artificial Intelligence (人工智能) " #a post with table of contents
 date: 2021-08-06 11:42:00-0400
 description: my review on two books AI·Future (AI·未来) and Artificial Intelligence (人工智能) authored by Dr. Lee Kai-Fu. 
@@ -28,7 +28,7 @@ Cover of the book AI·Future (AI·未来) by Kai-Fu Lee。
 
 # Narrow AI vs. General AI: the future of AI progress
 
-A common starting point to address AI might be establishing with what perspective to view AI. Given the influence of fictitious movies and novels, it might be worth pointing out some of the exaggerations regarding the current and near-future progress on AI. Despite since 2012 with the release of Prof. Geoffrey Hinton’s paper about Deep Learning[1] which has “revolutionized” the field of AI, such optimistic environment has also morphed into fear given that AI may one day surpass humans in every task we do, with such worry being augmented by how Alpha-Go defeated the world champion in the game of Go, with books like Superintelligence by philosopher Nick Bostrom or predictions made by Prof. Stephen Hawking regarding a dystopian future caused by AI. However, from the point of view of Kai-Fu Lee, such incidents should be addressed cautiously and without spreading unnecessary fear. 
+A common starting point to address AI might be establishing with what perspective to view AI. Given the influence of fictitious movies and novels, it might be worth pointing out some of the exaggerations regarding the current and near-future progress on AI. Despite since 2012 with the release of Prof. Geoffrey Hinton’s paper about Deep Learning[^1] which has “revolutionized” the field of AI, such optimistic environment has also morphed into fear given that AI may one day surpass humans in every task we do, with such worry being augmented by how Alpha-Go defeated the world champion in the game of Go, with books like Superintelligence by philosopher Nick Bostrom or predictions made by Prof. Stephen Hawking regarding a dystopian future caused by AI. However, from the point of view of Kai-Fu Lee, such incidents should be addressed cautiously and without spreading unnecessary fear. 
 
 Yes, AI is advancing very rapidly, with achievements such as being better at image recognition than humans across different fields such as in that of security or medicine, predictions in the field of economics or simulating physical phenomena, among others. This holds true especially given the extraordinarily amount of training data that is being generated which will be able to further train neural networks at much more diverse tasks. Nonetheless, it’s worth pointing out that such AI shouldn’t be the source of fear given how limited is the task it can perform, i.e., Alpha Go can defeat the world champion in Go, but definitely can’t at the same time generate a short poem, a convolutional neural network can excel radiologists in identifying, say, a pulmonary disease like pneumonia, but it can’t simultaneously device a therapeutic strategy to deal with such disease like a doctor would. All in all, AI still cannot perform tasks across fields, or do multitasking, which means that it is still what’s commonly known as narrow AI. Given such premise, a fundamental idea that has put forward is to not fall down into ungrounded panic that may result in the halting of study of AI in order to prevent a perceived destruction of humanity (a sort of subconscious self-preservation bias). 
 

_posts/2023-11-2-mechanistic-subtypes-parkinson.md → _posts/2023-11-2-mechanistic-subtypes-parkinson-copy.md

@@ -1,18 +1,18 @@
 ---
 layout: distill
-title: "Review of the paper Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models"
-#a post with bibliography
-date: 2023-11-02 14:42
-description: Thoughts and comments on the paper "Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models" #an example of a blog post with bibliography 
+title: "Review of the paper Prediction of mechanistic subtypes of Parkinson's using patient derived stem cell models"
+date: 2023-11-02 19:42
+description: comments on a paper that leverages deep learning to classify cells into Parkinson's subtypes 
 tags: research
 categories: blog-post
 disqus_comments: true
-thumbnail: /assets/img/parkinson.png
+related_posts: true
 authors:
-    - name: An Xuelong
+  - name: Xuelong An
 toc:
-    -name: Brief summary
-    -name: My comments and future research directions
+  - name: Brief summary
+  - name: My comments and future research directions
+thumbnail: /assets/img/parkinson.png
 bibliography: deep-med.bib
 ---
 
@@ -42,6 +42,6 @@ One source of inspiration is from <d-cite key="li_2023_v1t"></d-cite>, where the
   <figcaption id="bottleneck">A depiction of the pipeline of a concept-bottleneck model. The first half outputs a set of concepts given an image, which can be learnt from intricate annotations, or metadata, of the image. The second half outputs a classification label. Figure extracted from the original paper </figcaption>
 </figure>
 
-With regards to the work by <d-cite key="dsa_2023_prediction"></d-cite>, one interesting extension to their CNN is to have it not predict a Parkinson subtype, but rather learn to predict the cell's physiological features stored as tabular data given image input. Subsequently, use the features to train a multi-class regressor using standard softmax to output a classification label. The prospect is that this hybrid model can leverage the high accuracy prediction of the CNN, whilst being explainable thanks to the logistic regressor. 
+Altogether, with regards to the work by <d-cite key="dsa_2023_prediction"></d-cite>, one interesting extension to their CNN is to have it not predict a Parkinson subtype, but rather learn to predict the cell's physiological features stored as tabular data given image input. Subsequently, use the features to train a multi-class regressor using standard softmax to output a classification label. The prospect is that this hybrid model can leverage the high accuracy prediction of the CNN, whilst being explainable thanks to the logistic regressor. 
 
 As a further improvement, we can use a [Slot Transformer](https://arxiv.org/abs/2210.11394) instead of the CNN with the hope of learning a disentangled representation given the image with its annotations. However, the architecture will be more computationally expensive. A pretrained Slot Transformer that already learnt to disentangle CLEVR-Scenes may be more powerful than training it from scratch.    

_posts/insights-nesy.md → _posts/insights-nesy copy.md

@@ -1,3 +1,5 @@
+
 # On the philosophical insights of neurosymbolic AI
 
-Divide and conquer instead of delegating problem solving to a monolith model. 
+Divide and conquer instead of delegating problem solving to a monolith model. 
+

assets/bibliography/deep-med.bib

@@ -67,7 +67,7 @@ @book{wang_2020_blockchain
 @article{carvalho_2020_selfregulated,
   author = {Carvalho, Paulo F. and Sana, Faria and Yan, Veronica X.},
   month = {03},
-  pages = {1–7},
+  pages = {1-7},
   title = {Self-regulated spacing in a massive open online course is related to better learning},
   doi = {10.1038/s41539-020-0061-1},
   url = {https://www.nature.com/articles/s41539-020-0061-1},
@@ -138,7 +138,7 @@ @book{clark_2010_naturalborn
 
 @article{clark_1998_the,
   author = {Clark, Andy and Chalmers, David},
-  pages = {7–19},
+  pages = {7-19},
   title = {The Extended Mind},
   url = {https://www.jstor.org/stable/3328150},
   volume = {58},
@@ -214,7 +214,7 @@ @article{savage_2020_the
   journal = {Nature}
 }
 
-@article{songchun_2017_qiantan,
+@article{songzhuchun_2017_qiantan,
   author = {Song-Chun , Zhu},
   month = {11},
   title = {Qiantan rengongzhineng: xianzhuang, renwu, goujia yu tongyi [AI: The Era of Big Integration Unifying Disciplines within Artificial Intelligence]},
@@ -243,7 +243,7 @@ @misc{wilson_2011_embodied
 @proceedings{liu_2023_characteraware,
   author = {Liu, Rosanne and Garrette, Dan and Chitwan Saharia and Chan, William and Roberts, Adam and Narang, Sharan and Blok, Irina and Mical, Rj and Norouzi, Mohammad and Constant, Noah},
   month = {01},
-  pages = {16270–16297},
+  pages = {16270-16297},
   publisher = {    Association for Computational Linguistics},
   title = {Character-Aware Models Improve Visual Text Rendering},
   doi = {10.18653/v1/2023.acl-long.900},
@@ -281,7 +281,7 @@ @article{zhu_2020_dark
 @misc{deutsch_2012_how,
   author = {Deutsch, David },
   month = {10},
-  title = {How close are we to creating artificial intelligence? – David Deutsch | Aeon Essays},
+  title = {How close are we to creating artificial intelligence? - David Deutsch | Aeon Essays},
   url = {https://aeon.co/essays/how-close-are-we-to-creating-artificial-intelligence},
   year = {2012},
   organization = {Aeon}
@@ -306,10 +306,10 @@ @article{takagi_2022_highresolution
 }
 
 @article{dsa_2023_prediction,
-  author = {D’Sa, Karishma and Evans, James R. and Virdi, Gurvir S. and Vecchi, Giulia and Adam, Alexander and Bertolli, Ottavia and Fleming, James and Chang, Hojong and Leighton, Craig and Horrocks, Mathew H. and Athauda, Dilan and Choi, Minee L. and Gandhi, Sonia},
+  author = {D'Sa, Karishma and Evans, James R. and Virdi, Gurvir S. and Vecchi, Giulia and Adam, Alexander and Bertolli, Ottavia and Fleming, James and Chang, Hojong and Leighton, Craig and Horrocks, Mathew H. and Athauda, Dilan and Choi, Minee L. and Gandhi, Sonia},
   month = {08},
-  pages = {933–946},
-  title = {Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models},
+  pages = {933-946},
+  title = {Prediction of mechanistic subtypes of Parkinson's using patient-derived stem cell models},
   doi = {10.1038/s42256-023-00702-9},
   url = {https://www.nature.com/articles/s42256-023-00702-9},
   volume = {5},
@@ -322,19 +322,17 @@ @article{li_2023_v1t
   month = {05},
   title = {V1T: large-scale mouse V1 response prediction using a Vision Transformer},
   url = {https://openreview.net/forum?id=qHZs2p4ZD4},
-  urldate = {2023-11-02},
   year = {2023},
   journal = {Transactions on Machine Learning Research}
 }
 
 @article{koh_2020_concept,
   author = {Koh, Pang Wei and Nguyen, Thao and Tang, Yew Siang and Mussmann, Stephen and Pierson, Emma and Kim, Been and Liang, Percy},
   month = {11},
-  pages = {5338–5348},
+  pages = {5338-5348},
   publisher = {PMLR},
   title = {Concept Bottleneck Models},
   url = {https://proceedings.mlr.press/v119/koh20a.html},
-  urldate = {2023-11-02},
   volume = {119},
   year = {2020},
   journal = {Proceedings of Machine Learning Research}