Merge pull request #52 from uddaloksarkar/master

Update README.md

Author: msoos

README.md

@@ -6,7 +6,7 @@
 # ApproxMC4: Approximate Model Counter
 ApproxMCv4 is a state-of-the-art approximate model counter utilizing an improved version of CryptoMiniSat to give approximate model counts to problems of size and complexity that were not possible before.
 
-This work is by Mate Soos, Stephan Gocht, and Kuldeep S. Meel, as [published in AAAI-19](https://www.comp.nus.edu.sg/~meel/Papers/aaai19-sm.pdf) and [in CAV2020](https://www.comp.nus.edu.sg/~meel/Papers/cav20-sgm.pdf). A large part of the work is in CryptoMiniSat [here](https://github.com/msoos/cryptominisat).
+This work is by Mate Soos, Stephan Gocht, and Kuldeep S. Meel, as [published in AAAI-19](https://www.cs.toronto.edu/~meel/Papers/aaai19-sm.pdf) and [in CAV2020](https://www.cs.toronto.edu/~meel/Papers/cav20-sgm.pdf). A large part of the work is in CryptoMiniSat [here](https://github.com/msoos/cryptominisat).
 
 ApproxMC handles CNF formulas and performs approximate counting. 
 
@@ -186,18 +186,18 @@ int main() {
 ```
 
 ### ApproxMC5: Sparse-XOR based Approximate Model Counter
-Note: this is beta version release, not recommended for general use. We are currently working on a tight integration of sparse XORs into ApproxMC based on our [LICS-20](http://comp.nus.edu.sg/~meel/Papers/lics20-ma.pdf) paper. You can turn on the sparse XORs using the flag "sparse" but beware as reported in LICS-20 paper, this may slow down in some cases; it is likely to give a significant speedup if the number of solutions is very large. 
+Note: this is beta version release, not recommended for general use. We are currently working on a tight integration of sparse XORs into ApproxMC based on our [LICS-20](http://www.cs.toronto.edu/~meel/Papers/lics20-ma.pdf) paper. You can turn on the sparse XORs using the flag "sparse" but beware as reported in LICS-20 paper, this may slow down in some cases; it is likely to give a significant speedup if the number of solutions is very large. 
 
 
 ### Issues, questions, bugs, etc.
 Please click on "issues" at the top and [create a new issue](https://github.com/meelgroup/mis/issues/new). All issues are responded to promptly.
 
 ## How to Cite
-If you use ApproxMC, please cite the following papers: [CAV20](https://dblp.uni-trier.de/rec/conf/cav/SoosGM20.html?view=bibtex), [AAAI19](https://www.comp.nus.edu.sg/~meel/bib/SM19.bib) and [IJCAI16](https://www.comp.nus.edu.sg/~meel/bib/CMV16.bib).
+If you use ApproxMC, please cite the following papers: [CAV20](https://dblp.uni-trier.de/rec/conf/cav/SoosGM20.html?view=bibtex), [AAAI19](https://www.cs.toronto.edu/~meel/bib/SM19.bib) and [IJCAI16](https://www.cs.toronto.edu/~meel/bib/CMV16.bib).
 
-If you use sparse XORs, please also cite the [LICS20](https://www.comp.nus.edu.sg/~meel/bib/MA20.bib) paper. 
+If you use sparse XORs, please also cite the [LICS20](https://www.cs.toronto.edu/~meel/publications/AM20.bib) paper. 
 
-ApproxMC builds on a series of papers on hashing-based approach: [Related Publications](https://www.comp.nus.edu.sg/~meel/publications.html)
+ApproxMC builds on a series of papers on hashing-based approach: [Related Publications](https://www.cs.toronto.edu/~meel/publications.html)
 
 The benchmarks used in our evaluation can be found [here](https://zenodo.org/records/10449477).