wanyu/IteraTeR_v2 huggingface dataset:
Original:  Willump classifies most data inputs using only high-value, low-cost features selected by a dataflow analysis algorithm and cost model , improving performance by up to 5\times without statistically significant accuracy loss.
Revised:  Willump classifies most data inputs using only high-value, low-cost features selected by a dataflow analysis algorithm and cost model , improving performance by up to 5x without statistically significant accuracy loss.
Edit intent: fluency
Original:  Second, Willump accurately approximates ML top-K queries, discarding low-scoring inputs with an automatically constructed approximate model  then ranking the remainder with a more powerful model, improving  performance by up to 10\times with minimal accuracy loss.
Revised:  Second, Willump accurately approximates ML top-K queries, discarding low-scoring inputs with an automatically constructed approximate model and then ranking the remainder with a more powerful model, improving performance by up to 10\times with minimal accuracy loss.
Edit intent: coherence
Original:  Second, Willump accurately approximates ML top-K queries, discarding low-scoring inputs with an automatically constructed approximate model  then ranking the remainder with a more powerful model, improving  performance by up to 10\times with minimal accuracy loss.
Revised:  Second, Willump accurately approximates ML top-K queries, discarding low-scoring inputs with an automatically constructed approximate model then ranking the remainder with a more powerful model, improving query performance by up to 10\times with minimal accuracy loss.
Edit intent: clarity
Original:  Second, Willump accurately approximates ML top-K queries, discarding low-scoring inputs with an automatically constructed approximate model  then ranking the remainder with a more powerful model, improving  performance by up to 10\times with minimal accuracy loss.
Revised:  Second, Willump accurately approximates ML top-K queries, discarding low-scoring inputs with an automatically constructed approximate model then ranking the remainder with a more powerful model, improving performance by up to 10x with minimal accuracy loss.
Edit intent: fluency
Original:  Both optimizations automatically tune their own parameters to maximize  performance while meeting a target accuracy level.
Revised:  Willump automatically tunes these optimizations' parameters to maximize performance while meeting a target accuracy level.
Edit intent: clarity
Original:  We show that Willump improves the end-to-end performance of real-world ML inference pipelines curated from major data science competitions by up to 16\times without statistically significant loss of accuracy.
Revised:  We show that Willump improves the end-to-end performance of real-world ML inference pipelines curated from major data science competitions by up to 16x without statistically significant loss of accuracy.
Edit intent: clarity
Original:  Previously we analyzed a regular, semi-explicit, index 1 differential-algebraic equation (DAE) model for MECs.
Revised:  In our previous work, we developed and analyzed a differential-algebraic equation (DAE) model for MECs.
Edit intent: clarity
Original:  This work characterizes asymptotic stability of equilibria in two biologically relevant versions of the model.
Edit intent: coherence
Original:  Our goal is to determine the outcome of competition between methanogenic archaea and electroactive bacteria, because only the latter contribute to  electric current and  hydrogen production.
Revised:  Our goal is to determine the outcome of competition between methanogenic archaea and electroactive bacteria, because only the latter contribute to both electric current and hydrogen production.
Edit intent: clarity
Original:  Our goal is to determine the outcome of competition between methanogenic archaea and electroactive bacteria, because only the latter contribute to  electric current and  hydrogen production.
Revised:  Our goal is to determine the outcome of competition between methanogenic archaea and electroactive bacteria, because only the latter contribute to electric current and correlated hydrogen production.
Edit intent: fluency
Original:  In this model , even if electroactive bacteria can grow at the lowest substrate concentration, a few additional conditions are required to guarantee local asymptotic stability.
Revised:  In this case , even if electroactive bacteria can grow at the lowest substrate concentration, a few additional conditions are required to guarantee local asymptotic stability.
Edit intent: clarity
Original:  In this model , even if electroactive bacteria can grow at the lowest substrate concentration, a few additional conditions are required to guarantee local asymptotic stability.
Revised:  In this model , even if electroactive bacteria can grow at the lowest substrate concentration, a few additional conditions are required to guarantee local stability.
Edit intent: clarity
Original: Microbial electrolysis cells (MECs) are devices that employ electroactive bacteria to perform extracellular electron transfer, enabling hydrogen generation from biodegradable substrates.
Revised: Microbial electrolysis cells (MECs) employ electroactive bacteria to perform extracellular electron transfer, enabling hydrogen generation from biodegradable substrates.
Edit intent: clarity
Original:  In our previous work, we developed and analyzed a differential-algebraic equation (DAE) model for MECs.
Revised:  In previous work, we developed and analyzed a differential-algebraic equation (DAE) model for MECs.
Edit intent: fluency
Original:  Our goal is to determine the outcome of competition between methanogenic archaea and electroactive bacteria, because only the latter contribute to both electric current and correlated hydrogen production.
Revised:  Our goal is to determine the outcome of competition between methanogenic archaea and electroactive bacteria, because only the latter contribute to electric current and correlated hydrogen production.
Edit intent: clarity
Original:  Our goal is to determine the outcome of competition between methanogenic archaea and electroactive bacteria, because only the latter contribute to both electric current and correlated hydrogen production.
Revised:  Our goal is to determine the outcome of competition between methanogenic archaea and electroactive bacteria, because only the latter contribute to both electric current and resulting hydrogen production.
Edit intent: clarity
Original:  An important aspect of many chemostat models is the principle of competitive exclusion , which states that only the microorganisms which can grow at the lowest substrate concentration will survive \to .
Revised:  An important aspect of chemostats models is the principle of competitive exclusion , which states that only the microorganisms which can grow at the lowest substrate concentration will survive \to .
Edit intent: clarity
Original:  An important aspect of many chemostat models is the principle of competitive exclusion , which states that only the microorganisms which can grow at the lowest substrate concentration will survive \to .
Revised:  An important aspect of many chemostat models is the principle of competitive exclusion -- only microbes which grow at the lowest substrate concentration will survive \to .
Edit intent: coherence
Original:  An important aspect of many chemostat models is the principle of competitive exclusion , which states that only the microorganisms which can grow at the lowest substrate concentration will survive \to .
Revised:  An important aspect of many chemostat models is the principle of competitive exclusion , which states that only the microorganisms which can grow at the lowest substrate concentration will survive as t\to .
Edit intent: clarity
Original:  We show that if methanogens can grow at the lowest substrate concentration, then the equilibrium corresponding to competitive exclusion by methanogens is globally  stable.
Revised:  We show that if methanogens grow at the lowest substrate concentration, then the equilibrium corresponding to competitive exclusion by methanogens is globally stable.
Edit intent: clarity
Original:  We show that local asymptotic stability of competitive exclusion by electroactive bacteria is not guaranteed, even in a simplified  model.
Revised:  We show that local asymptotic stability of exclusion by electroactive bacteria is not guaranteed, even in a simplified model.
Edit intent: clarity
Original:  We show that local asymptotic stability of competitive exclusion by electroactive bacteria is not guaranteed, even in a simplified  model.
Revised:  We show that local asymptotic stability of competitive exclusion by electroactive bacteria is not guaranteed, even in a simplified version of the model.
Edit intent: clarity
Original:  Our results suggest operating conditions that are most conducive to success of electroactive bacteria and current or hydrogen production in MECs.
Revised:  Our results suggest operating conditions that are most conducive to success of electroactive bacteria and the resulting current and hydrogen production in MECs.
Edit intent: clarity
Original: We study worst-case optimal approximation of positive linear functionals in reproducing kernel Hilbert spaces (RKHSs) induced by increasingly flat Gaussian kernels.
Revised: We study worst-case optimal approximation of positive linear functionals in reproducing kernel Hilbert spaces induced by increasingly flat Gaussian kernels.
Edit intent: clarity
Original:  Based on our results, we suggest design improvements that can improve long-term stability and suggest methods for solving pricing problems that arise in existing stablecoins.
Revised:  Based on our results, we suggest design improvements that aim to improve long-term stability and suggest methods for solving pricing problems that arise in existing stablecoins.
Edit intent: clarity
Original: We develop a model of stable assets, including noncustodial stablecoins backed by cryptocurrencies.
Revised: We develop a model of stable assets, including non-custodial stablecoins backed by cryptocurrencies.
Edit intent: fluency
Original: One reason for the emergence of bias in AI systems is biased data -- datasets that may not be true representations of the underlying distributions -- and may over or under-represent groups with respect to protected attributes such as gender or race .
Revised: One reason for the emergence of bias in AI systems is biased data -- datasets that may not be true representations of the underlying distributions -- and may over or under-represent groups with respect to salient social attributes such as gender or race .
Edit intent: clarity
Original:  The reformulation of the minimization problem as a suitable saddle point problem allows us to obtain the optimality conditions by using differentiability properties of the min-sup formulation .
Revised:  The reformulation of the minimization problem as a suitable saddle point problem allows us to obtain the optimality conditions by using the min-sup formulation .
Edit intent: clarity
Original: We present quantum algorithms for solving finite-horizon and infinite-horizon dynamic programming problems.
Revised: We present quantum algorithms for solving finite-horizon dynamic programming problems.
Edit intent: clarity
Original:  Up to polylogarithmic factors, our quantum algorithms provide quadratic advantage in terms of the number of states |S|, and the number of actions |A|, in the Markov decision process when the transition kernels are deterministic.
Revised:  Up to polylogarithmic factors, our algorithm provides a quadratic quantum advantage in terms of the number of states |S|, and the number of actions |A|, in the Markov decision process when the transition kernels are deterministic.
Edit intent: clarity
Original:  In particular , we show that our quantum algorithm solves the travelling salesperson problem  in O^*( c \lceil \rceil ^4 2^n)  where n is the number of nodes of the underlying graph and c is the \lceil \rceil  maximum edge-weight of it.
Revised:  In particular , we show that the travelling salesperson problem in O^*( c \lceil \rceil ^4 2^n) where n is the number of nodes of the underlying graph and c is the \lceil \rceil maximum edge-weight of it.
Edit intent: clarity
Original:  In particular , we show that our quantum algorithm solves the travelling salesperson problem  in O^*( c \lceil \rceil ^4 2^n)  where n is the number of nodes of the underlying graph and c is the \lceil \rceil  maximum edge-weight of it.
Revised:  In particular , we show that our quantum algorithm solves the travelling salesperson problem in O^*( c \lceil \rceil ^4 2^n) where n is the number of nodes of the underlying graph and \lceil \rceil maximum edge-weight of it.
Edit intent: fluency
Original:  In particular , we show that our quantum algorithm solves the travelling salesperson problem  in O^*( c \lceil \rceil ^4 2^n)  where n is the number of nodes of the underlying graph and c is the \lceil \rceil  maximum edge-weight of it.
Revised:  In particular , we show that our quantum algorithm solves the travelling salesperson problem in O^*( c \lceil \rceil ^4 2^n) where n is the number of nodes of the underlying graph and c is the \lceil \rceil is its maximum edge-weight of it.
Edit intent: fluency
Original:  Finally we prove lower bounds for the query complexity of our quantum algorithms and  show that no more-than-quadratic speed-up in either of |S| or |A| can be achieved for solving dynamic programming and Markov decision problems using quantum algorithms.
Revised:  Finally we prove lower bounds for the query complexity of quantum algorithms and show that no more-than-quadratic speed-up in either of |S| or |A| can be achieved for solving dynamic programming and Markov decision problems using quantum algorithms.
Edit intent: clarity
Original:  Finally we prove lower bounds for the query complexity of our quantum algorithms and  show that no more-than-quadratic speed-up in either of |S| or |A| can be achieved for solving dynamic programming and Markov decision problems using quantum algorithms.
Revised:  Finally we prove lower bounds for the query complexity of our quantum algorithms and show that no greater-than-quadratic speedup in either the number of states or number of actions can be achieved for solving dynamic programming and Markov decision problems using quantum algorithms.
Edit intent: clarity
Original:  Finally we prove lower bounds for the query complexity of our quantum algorithms and  show that no more-than-quadratic speed-up in either of |S| or |A| can be achieved for solving dynamic programming and Markov decision problems using quantum algorithms.
Revised:  Finally we prove lower bounds for the query complexity of our quantum algorithms and show that no more-than-quadratic speed-up in either of |S| or |A| can be achieved for solving dynamic programming problems using quantum algorithms.
Edit intent: clarity
Original:  As one application, we show that the travelling salesperson problem can be solved in O^*(%DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil%%% ^4 \sqrt{2^n ) on a quantum computer , where n is the number of vertices of the underlying graph and%DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil %%% is its maximum edge-weight.
Revised:  As one application, we show that the travelling salesperson problem can be solved in O^*(%DIFDELCMD < \lceil %%% %DIFDELCMD < \rceil%%% ^4 \sqrt{2^n ) on a quantum computer , where n is the number of vertices of the underlying graph and%DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil %%% is its maximum edge-weight.
Edit intent: fluency
Original:  As one application, we show that the travelling salesperson problem can be solved in O^*(%DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil%%% ^4 \sqrt{2^n ) on a quantum computer , where n is the number of vertices of the underlying graph and%DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil %%% is its maximum edge-weight.
Revised:  As one application, we show that the travelling salesperson problem can be solved in O^*(%DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil%%% ^4 \sqrt{2^n ) on a quantum computer %DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil %%% is its maximum edge-weight.
Edit intent: coherence
Original:  As one application, we show that the travelling salesperson problem can be solved in O^*(%DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil%%% ^4 \sqrt{2^n ) on a quantum computer , where n is the number of vertices of the underlying graph and%DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil %%% is its maximum edge-weight.
Revised:  As one application, we show that the travelling salesperson problem can be solved in O^*(%DIFDELCMD < \lceil %%% c%DIFDELCMD < \rceil%%% ^4 \sqrt{2^n ) on a quantum computer , where n is the number of vertices of the underlying graph and%DIFDELCMD < \lceil %%% %DIFDELCMD < \rceil %%% is its maximum edge-weight.
Edit intent: fluency
Original:  we show that the minimum set-cover problem can be solved in O(\sqrt{2^n (m, n)), where m is the number of sets used to cover a universe of size n. Finally, we prove lower bounds for the query complexity of quantum algorithms and classical randomized algorithms for solving dynamic programming problems , and show that no greater-than-quadratic speedup in either the number of states or number of actions can be achieved for solving dynamic programming problemsusing quantum algorithms .
Revised:  we show that the minimum set-cover problem (m, n)), where m is the number of sets used to cover a universe of size n. Finally, we prove lower bounds for the query complexity of quantum algorithms and classical randomized algorithms for solving dynamic programming problems , and show that no greater-than-quadratic speedup in either the number of states or number of actions can be achieved for solving dynamic programming problemsusing quantum algorithms .
Edit intent: clarity
Original:  we show that the minimum set-cover problem can be solved in O(\sqrt{2^n (m, n)), where m is the number of sets used to cover a universe of size n. Finally, we prove lower bounds for the query complexity of quantum algorithms and classical randomized algorithms for solving dynamic programming problems , and show that no greater-than-quadratic speedup in either the number of states or number of actions can be achieved for solving dynamic programming problemsusing quantum algorithms .
Revised:  we show that the minimum set-cover problem can be solved in O(\sqrt{2^n (m, n)), where m is the number of sets used to cover a universe of size n. Finally, we prove lower bounds for the query complexity of quantum algorithms and classical randomized algorithms for DP , and show that no greater-than-quadratic speedup in either the number of states or number of actions can be achieved for solving dynamic programming problemsusing quantum algorithms .
Edit intent: clarity
Original:  we show that the minimum set-cover problem can be solved in O(\sqrt{2^n (m, n)), where m is the number of sets used to cover a universe of size n. Finally, we prove lower bounds for the query complexity of quantum algorithms and classical randomized algorithms for solving dynamic programming problems , and show that no greater-than-quadratic speedup in either the number of states or number of actions can be achieved for solving dynamic programming problemsusing quantum algorithms .
Revised:  we show that the minimum set-cover problem can be solved in O(\sqrt{2^n (m, n)), where m is the number of sets used to cover a universe of size n. Finally, we prove lower bounds for the query complexity of quantum algorithms and classical randomized algorithms for solving dynamic programming problems , and show that no greater-than-quadratic speedup can be achieved for solving dynamic programming problemsusing quantum algorithms .
Edit intent: clarity
Original:  we show that the minimum set-cover problem can be solved in O(\sqrt{2^n (m, n)), where m is the number of sets used to cover a universe of size n. Finally, we prove lower bounds for the query complexity of quantum algorithms and classical randomized algorithms for solving dynamic programming problems , and show that no greater-than-quadratic speedup in either the number of states or number of actions can be achieved for solving dynamic programming problemsusing quantum algorithms .
Revised:  we show that the minimum set-cover problem can be solved in O(\sqrt{2^n (m, n)), where m is the number of sets used to cover a universe of size n. Finally, we prove lower bounds for the query complexity of quantum algorithms and classical randomized algorithms for solving dynamic programming problems , and show that no greater-than-quadratic speedup in either the number of states or number of actions can be achieved for solving DP problems .
Edit intent: clarity
Original:  On the positive side, we provide the first nearly linear-time algorithm for maximization of non-monotone submodular functions over p-extendible independence systems , which are a subclass of p-systems .
Revised:  On the positive side, we provide the first nearly linear-time algorithm for maximization of non-monotone submodular functions over p-extendible independence systems .
Edit intent: clarity
Original: This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), termed DRL-MOA.
Revised: This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), that we call DRL-MOA.
Edit intent: clarity
Original:  The idea of decomposition is adopted to decompose a MOP into a set of scalar optimization subproblems.
Revised:  The idea of decomposition is adopted to decompose the MOP into a set of scalar optimization subproblems.
Edit intent: fluency
Original:  In specific, the multi-objective travelling salesman problem (MOTSP) is solved in this work using the DRL-MOA framework by modelling the subproblem as the Pointer Network.
Revised:  In specific, the multi-objective travelling salesman problem (MOTSP) is solved in this work using the DRL-MOA method by modelling the subproblem as the Pointer Network.
Edit intent: clarity
Original:  In specific, the multi-objective travelling salesman problem (MOTSP) is solved in this work using the DRL-MOA framework by modelling the subproblem as the Pointer Network.
Revised:  In specific, the multi-objective travelling salesman problem (MOTSP) is solved in this work using the DRL-MOA framework by modelling the subproblem as a Pointer Network.
Edit intent: fluency
Original:   It is found that, once the trained model is available, it can scale to MOTSPs of any number of cities, e.g., 70-city, 100-city, even the 200-city MOTSP, without re-training the model.
Revised:  It is found that, once the trained model is available, it can scale to newly encountered problems with no need of re-training the model.
Edit intent: clarity
Original:  The Pareto Front can be directly obtained by a simple feed-forward of the network;
Revised:  The solutions can be directly obtained by a simple feed-forward of the network;
Edit intent: clarity
Original:  The Pareto Front can be directly obtained by a simple feed-forward of the network;
Revised:  The Pareto Front can be directly obtained by a simple forward calculation of the neural network;
Edit intent: clarity
Original:  Most current approaches to scalable training of such models either don't converge to the correct target, or are not data-parallel .
Revised:  In this work, we study data-parallel .
Edit intent: clarity
Original:  We benchmark our method on a well-known corpora (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under three days.
Revised:  We benchmark our method on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under three days.
Edit intent: fluency
Original:  In this setting, we show that the pricing function is the solution to a path-dependent PDE , for which we develop a numerical scheme based on Deep Learning techniques.
Revised:  In this setting, we show that the pricing function is the solution to a path-dependent PDE , for which we develop a numerical scheme based on deep learning techniques.
Edit intent: fluency