Update paper.md

Author: jan-janssen

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@@ -39,7 +39,7 @@ The convergence of artificial intelligence (AI) and high-performance computing (
 We distinguish two main use cases for such interfaces: either to request a queuing system allocation from the job scheduler and or to internally allocate computing resources to individual (sub-)tasks within an existing queuing system allocation. In the context of the SLURM job scheduler, these differences are distinguished by the `sbatch` and `srun` command. A number of Python workflow frameworks have been developed for both types of interfaces, ranging from domain-specific solutions for fields like high throughput screening in computational materials science [@fireworks; @aiida; @pyiron], to generalized Python interfaces for job schedulers [@myqueue; @psij] and task scheduling frameworks which implement their own task scheduling on top of the HPC job scheduler [@dask; @parsl; @jobflow]. While these tools can be powerful, they introduce new constructs that are not familiar to most python developers, introducing complexity and a barrier to entry. To address this limitation while at the same time leveraging the powerful novel hierarchical HPC resource managers [@flux], we introduce Executorlib, which instead leverages and naturally extends the familiar Executor interface defined by the Python standard library from single-node shared-memory operation to multi-node distributed operation on HPC platforms. In doing so, Executorlib enables the rapid development, prototyping, and deployment of heterogenous HPC workflows using only familiar and easy to maintain Python syntax, hence greatly simplifying the up-scaling of scientific workflows from laptops to very large computational scales. 
 
 # Features and Implementation
-Based on prior experience with the development of the pyiron workflow framework [@pyiron], the design philosophy of Executorlib is centered on the timeless principle of not reinventing the wheel. Rather than implementing its own job scheduler, Executorlib instead leverages existing job or task schedulers to request and manage Python processes and associated computing resources. Further, instead of defining a new syntax and concepts, Executorlib extends the existing syntax of the Executor class in the Python standard library. Taken together, this makes changing the mode of execution in Executorlib as easy as changing a single variable, with the interface remaining the same. 
+Based on prior experience with the development of the pyiron workflow framework [@pyiron], the design philosophy of Executorlib is centered on the timeless principle of not reinventing the wheel. Rather than implementing its own job scheduler, Executorlib instead leverages existing job schedulers to request and manage Python processes and associated computing resources. Further, instead of defining a new syntax and concepts, Executorlib extends the existing syntax of the Executor class in the Python standard library. Taken together, this makes changing the mode of execution in Executorlib as easy as changing a single variable, with the interface remaining the same. 
 
 ## Example
 To illustrate the usage of Executorlib and explain the technical processes occurring in the background, we consider the simple example of doubling the numbers in the range from 1 to 4. With Executorlib, this can be achieved with the following code: