anchor.diff:  
   === HPO Workflow
   
  -. Edit `Supervisor/workflows/cp1/data/studies1.txt` and `Supervisor/workflows/cp1/data/studies2.txt`. These two study files specify the cross correlation between studies where each study in `studies1.txt` is cross correlated with each study in 
  -`studies2.txt` except where they are the same. Add or remove (or comment out)
  -study names in these files to omit that cross-correlation from the HPO instances.
  +. Edit `Supervisor/workflows/cp1/data/studies1.txt` and `Supervisor/workflows/cp1/data/studies2.txt`. 
  +These two study files specify the cross correlation between studies where each study in `studies1.txt` 
  +is cross correlated with each study in `studies2.txt` except where they are the same. Add or remove 
  +(or comment out) study names in these files to omit that cross-correlation from the HPO instances.
  +For example, if study1 contains CTRP and study2 contains CCLE and GDSC, then the workflow
  +will run two HPOs: one for the CTRP_CCLE and one for the CTRP_GDSC cross-correlations.
   
   . Create a set of _cfg_ and _test_ scripts for an experiment run by
  -copying an existing set (i.e., _cfg-prm-1.sh_, _cfg-sys-1.sh_, and _test-1.sh_) 
  -in the `test` directory. Be sure to update the lines in your _test-n.sh_ that 
  -export the _cfg-prm-N.sh_, and _cfg-sys-N.sh_ scripts to point to your scripts.
  +copying an existing set, i.e., `cfg-prm-1.sh`, `cfg-sys-1.sh`, and `test-1.sh`
  +from the `test` directory. Be sure to update the lines in your `test-N.sh` that 
  +export the `cfg-prm-N.sh`, and `cfg-sys-N.sh` scripts to point to your _cfg_ scripts.
   Namely,
   +
   ----
  
positive.code: export CFG_PRM=$THIS/cfg-prm-N.sh
  ----
  +
  See the comments in _cfg-prm-1.sh_, and _cfg-sys-1.sh_ for additional
  information on the various configuration parameters and how to edit them.
  
  . Launch the run using your _test-N.sh_ script, passing SITE, and optional experiment id as arguments (e.g., `./test-10.sh <site> [expid]`) where 
  site can be one of local, cori, theta, summit etc.
  
  All the output from running the workflow appears in a directory named with the
  experiment id, either your specified one or the auto-generated one. Each Uno
  model run launched by the mlrMBO instances runs in own directory: 
  `exp_id/run/W_X_Y_Z` where _W_ is the id of the mlrMBO instance that launched the run, _X_ is the restart number (almost always 1 here), _Y_ is the 
  iteration of the mlrMBO instance, and _Z_ is the id of the hyper parameter set
  with which Uno was launched
  
  A summary of each Uno for organized by mlrMBO instance and iteration will be 
  output in `exp_id/hpo_log/X_Y_hpo_runs.txt` where _X_ is the mlrMBO instance
  id, and _Y_ is the mlrMBO instance's iteration. Each row of this hpo log output
  contains info for a single Uno run and has the following format:
  
  `row_index | run_id | hpo parameters | run directory | timestamp | run val loss`
  
  ==== Associated Files
  
  * Configuration and launch scripts in `test/` (e.g. _cfg-prm-1.sh_, _test-1.sh_, etc.)
  * `swift/workflow.swift` - swift file that executes the workflow
  * `swift/workflow.sh` - launch script for the swift file. This script is 
  configured and launched from the scripts in `test/`.
  
negative.code: 
  = Mini Challenge Problem
  
  The Mini Challenge Problem has 4 distinct stages: 1) preprocessing feature selection; 2) HPO optimization using mlrMBO; 3) additional
  training of the best models identified in 2; and 4) performing inference
  on the trained models from 3. 
  
  ==== Feature Selection
  
  Rather than using all of the ~17,000 genes for prediction of drug response for Uno, we used the COXEN approach (see the _xcorr_ README) to select genes for building the prediction model in a scenario of model adaptation between studies/datasets. The COXEN approach performs statistical analysis to identify predictive and generalizable genes for prediction. We have five public cancer cell line drug response datasets, i.e., NCI-60, CTRP, GDSC, CCLE, and gCSI. These five studies may have common cancer cell lines and drugs included in their experiments, but each of them may also include unique cell lines, drugs, or combinations of the two. Thus, the COXEN approach targeting genes that are both predictive and generalizable can be helpful in the model adaptation between studies. The approach produces _cross correleated feature set_ files
  that are used as input to the model. A preprocessed data file will have a file name like `CTRP_CCLE_2000_1000_features.txt`
  where the two studies are `CTRP` and `CCLE`, and the cross correlation 
  coefficients are 2000 and 1000
  
  ==== HPO using mlrMBO
  
  The HPO workflow runs N number of concurrent mlrMBO hyperparameters optimizations (HPO) of the
  Uno benchmark using the cross correlated feature sets as input. A
  mlrMBO instance will be run for each cross correlated feature set.
  
  
  ==== Further Training
  
  The further training workflow selects N number of the best models from those trained
  in the HPO workflow. An input file contains the parameters for each model run (one per row). The workflow
  reads this file and launches the model runs with as much concurrency is available.
  
  
  ==== Inference
  
  The inference workflow run inference on the trained models from the further training workflow. It is similar to the further training workflow, but the
  parameters in the input file are those relevant for performing model inference rather than training.
  
  == Requirements
  
  === Code
  
  * The workflows. Clone git@github.com:ECP-CANDLE/Supervisor.git and switch to the develop branch. The
  workflows code is in `Supervisor/workflows/cp1`.
  * The Uno benchmark. Clone git@github.com:ECP-CANDLE/Benchmarks.git and switch to the
  develop branch. The Uno benchmarks is in `Benchmarks/Pilot1/Uno`.
  
  === Data
  
  *Download the raw Uno feature data to the `Benchmarks/Data/Pilot1` directory.*
  Note the data is quite large so, depending on the file system and machine, a better 
  choice may be to download and symlink to `Benchmarks/Data/Pilot1`.
  
  ----
  $ cd Benchmarks
  