wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our framework allows to include typical features as the Samuelson effect, seasonalities as well as a stochastic volatilityin the absence of arbitrage.
Revised:  Our framework allows for including typical features as the Samuelson effect, seasonalities as well as a stochastic volatilityin the absence of arbitrage.
Edit intent: fluency
Original:  (iii) The result of T-Test shows that SPR criteria can significantly distinguish between FR and TR, in addition, it can be useful as a new method to verify  trueness of rumors.
Revised:  (iii) The result of T-Test shows that SPR criteria can significantly distinguish between FR and TR, besides it can be useful as a new method to verify trueness of rumors.
Edit intent: coherence
Original:  (iii) The result of T-Test shows that SPR criteria can significantly distinguish between FR and TR, in addition, it can be useful as a new method to verify  trueness of rumors.
Revised:  (iii) The result of T-Test shows that SPR criteria can significantly distinguish between FR and TR, in addition, it can be useful as a new method to verify the trueness of rumors.
Edit intent: fluency
Original:  We prove that the two variants of Multistage s-t Path are already NP-hard for an input sequence of only two graphs  .
Revised:  We prove that these two variants of Multistage s-t Path are already NP-hard for an input sequence of only two graphs .
Edit intent: clarity
Original:  As a novel conceptual contribution , we then modify the multistage model by asking for dissimilar consecutive paths.
Revised:  As a further conceptual study , we then modify the multistage model by asking for dissimilar consecutive paths.
Edit intent: clarity
Original:  As one of the main results , we prove that dissimilarity allows for fixed-parameter tractability for the parameter solution size, thereby contrasting our W[1]-hardness proof of the corresponding similarity case  .
Revised:  As one of the main results , we prove that dissimilarity allows for fixed-parameter tractability for the parameter solution size, contrasting our W[1]-hardness proof of the corresponding similarity case .
Edit intent: coherence
Original:  We describe an efficient training algorithm which guarantees the correctness of the RQ-RMI-based classification.
Revised:  We describe an efficient training algorithm that guarantees the correctness of the RQ-RMI-based classification.
Edit intent: fluency
Original:  The use of RQ-RMI allows the packet rules to be compressed into model weights that fit into the hardware cache and takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector processing engines , achieving a rate of tens of nanoseconds per lookup.
Revised:  The use of RQ-RMI allows the rules to be compressed into model weights that fit into the hardware cache and takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector processing engines , achieving a rate of tens of nanoseconds per lookup.
Edit intent: clarity
Original:  The use of RQ-RMI allows the packet rules to be compressed into model weights that fit into the hardware cache and takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector processing engines , achieving a rate of tens of nanoseconds per lookup.
Revised:  The use of RQ-RMI allows the packet rules to be compressed into model weights that fit into the hardware cache . Further, it takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector processing engines , achieving a rate of tens of nanoseconds per lookup.
Edit intent: coherence
Original:  The use of RQ-RMI allows the packet rules to be compressed into model weights that fit into the hardware cache and takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector processing engines , achieving a rate of tens of nanoseconds per lookup.
Revised:  The use of RQ-RMI allows the packet rules to be compressed into model weights that fit into the hardware cache and takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector instructions , achieving a rate of tens of nanoseconds per lookup.
Edit intent: clarity
Original:  Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geomean compression factor of 4.9 X, 8X, and 82X, and an average performance improvement of 2.7X, 4.4X and 2.6 X in latency and 1.3X, 2.2X, and 1.2X in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.
Revised:  Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geometric mean compression factor of 4.9 X, 8X, and 82X, and an average performance improvement of 2.7X, 4.4X and 2.6 X in latency and 1.3X, 2.2X, and 1.2X in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.
Edit intent: fluency
Original:  Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geomean compression factor of 4.9 X, 8X, and 82X, and an average performance improvement of 2.7X, 4.4X and 2.6 X in latency and 1.3X, 2.2X, and 1.2X in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.
Revised:  Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geomean compression factor of 4.9 x, 8x, and 82x, and average performance improvement of 2.7X, 4.4X and 2.6 X in latency and 1.3X, 2.2X, and 1.2X in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.
Edit intent: fluency
Original:  We also provide evidence that the extra logarithmic term \log(T) is necessary , with a lower bound for a  variant of the problem.
Revised:  We also argue that the extra logarithmic term \log(T) is necessary , with a lower bound for a variant of the problem.
Edit intent: clarity
Original:  We also provide evidence that the extra logarithmic term \log(T) is necessary , with a lower bound for a  variant of the problem.
Revised:  We also provide evidence that the extra logarithmic term \log(T) should be necessary by proving a lower bound for a variant of the problem.
Edit intent: clarity
Original:  We continue by investigating MAX-IS and provide a series of deterministic dynamic approximation schemes .
Revised:  We continue by investigating Max-IS and provide a series of deterministic dynamic approximation schemes .
Edit intent: fluency
Original:  We have implemented our algorithms and report the results of an experimental comparison exploring the trade-off between solution size and update time for synthetic and real-world map labeling data sets .
Revised:  We have implemented our algorithms and report the results of an experimental comparison exploring the trade-off between solution size and update time for synthetic and real-world map labeling instances .
Edit intent: clarity
Original:  This improves upon the result of Chitnis and Talmon by not requiring b to be small.
Revised:  This improves upon a result of Chitnis and Talmon by not requiring b to be small.
Edit intent: fluency
Original: We study the following algorithm synthesis question : given the description of a locally checkable graph problem \Pi for paths or cycles, determine in which instances \Pi is solvable , determine what is the distributed round complexity of solving \Pi in the usual LOCAL model of distributed computing, and construct an asymptotically optimal distributed algorithm for solving \Pi.
Revised: We study the following algorithm synthesis question : given the description of a graph problem \Pi for paths or cycles, determine in which instances \Pi is solvable , determine what is the distributed round complexity of solving \Pi in the usual LOCAL model of distributed computing, and construct an asymptotically optimal distributed algorithm for solving \Pi.
Edit intent: clarity
Original: We study the following algorithm synthesis question : given the description of a locally checkable graph problem \Pi for paths or cycles, determine in which instances \Pi is solvable , determine what is the distributed round complexity of solving \Pi in the usual LOCAL model of distributed computing, and construct an asymptotically optimal distributed algorithm for solving \Pi.
Revised: We study the following algorithm synthesis question : given the description of a locally checkable graph problem \Pi , we would like to determine if \Pi is solvable , determine what is the distributed round complexity of solving \Pi in the usual LOCAL model of distributed computing, and construct an asymptotically optimal distributed algorithm for solving \Pi.
Edit intent: clarity
Original: We study the following algorithm synthesis question : given the description of a locally checkable graph problem \Pi for paths or cycles, determine in which instances \Pi is solvable , determine what is the distributed round complexity of solving \Pi in the usual LOCAL model of distributed computing, and construct an asymptotically optimal distributed algorithm for solving \Pi.
Revised: We study the following algorithm synthesis question : given the description of a locally checkable graph problem \Pi for paths or cycles, determine in which instances \Pi is solvable and what is the distributed round complexity of solving \Pi in the usual LOCAL model of distributed computing, and construct an asymptotically optimal distributed algorithm for solving \Pi.
Edit intent: coherence
Original: We show the surprising result that the cutpoint isolation problem is decidable for probabilistic finite automata where input words are taken from a letter-monotonic context-free language.
Revised: We show the surprising result that the cutpoint isolation problem is decidable for Probabilistic Finite Automata (PFA) where input words are taken from a letter-monotonic context-free language.
Edit intent: clarity
Original:  We discuss the incomputabilty of Kolmogorov complexity, which formal loopholes this leaves us, recent approaches to compute or approximate Kolmogorov complexity, which approaches fail and which approaches are viable.
Revised:  We discuss the incomputabilty of Kolmogorov complexity, which formal loopholes this leaves us, recent approaches to compute or approximate Kolmogorov complexity, which approaches are problematic and which approaches are viable.
Edit intent: clarity
Original:  By ``hopping'' iteratively to such larger cycles, we obtain a powerful and very general inductive motor for proving and computing long cycles (we will give an algorithm with running time O(n^2)).
Revised:  By "hopping" iteratively to such larger cycles, we obtain a powerful and very general inductive motor for proving and computing long cycles (we will give an algorithm with running time O(n^2)).
Edit intent: fluency
Original:  By ``hopping'' iteratively to such larger cycles, we obtain a powerful and very general inductive motor for proving and computing long cycles (we will give an algorithm with running time O(n^2)).
Revised:  By ``hopping'' iteratively to such larger cycles, we obtain a powerful and very general inductive motor for proving long cycles and computing them (we will give an algorithm with running time O(n^2)).
Edit intent: clarity
Original:  This provides a method to prove  lower bounds on  Tutte cycles, as C' will be a Tutte cycle of G if C is.
Revised:  This provides a method to prove lower bounds on the length of Tutte cycles, as C' will be a Tutte cycle of G if C is.
Edit intent: clarity
Original:  As a sample application, we test our motor on a conjecture on essentially 4-connected graphs.
Revised:  As a sample application, we test our motor on the following conjecture about essentially 4-connected graphs.
Edit intent: clarity
Original:  Essentially 4-connected graphs have been thoroughly investigated throughout literature as the subject of Hamiltonicity studies.
Edit intent: coherence
Original:  Given such a financial system, we want to decide which of the banks are in default, and how much of their liabilities  these defaulting banks can pay.
Revised:  Given such a financial system, we want to decide which of the banks are in default, and how much of their liabilities can these defaulting banks can pay.
Edit intent: fluency
Original:  Given such a financial system, we want to decide which of the banks are in default, and how much of their liabilities  these defaulting banks can pay.
Revised:  Given such a financial system, we want to decide which of the banks are in default, and how much of their liabilities these defaulting banks pay.
Edit intent: clarity
Original:  There can easily be multiple different solutions to this problem, leading to a situation of default ambiguity  and a range of possible solutions to implement for a financial authority.
Revised:  There can easily be multiple different solutions to this problem, leading to a situation of default ambiguity , and a range of possible solutions to implement for a financial authority.
Edit intent: fluency
Original:  In this paper, we study the general properties of the solution space of such financial systems, and analyze a wide range of reasonable objective functions for selecting from the set of solutions.
Revised:  In this paper, we study the properties of the solution space of such financial systems, and analyze a wide range of reasonable objective functions for selecting from the set of solutions.
Edit intent: coherence
Original:  Examples of such objective functions include minimizing the number of defaulting banks, minimizing the amount of unpaid debt, maximizing the number of satisfied banks, maximizing the equity of a specific bank, finding the most balanced distribution of equity, and many others.
Revised:  Examples of such objective functions include minimizing the number of defaulting banks, minimizing the amount of unpaid debt, maximizing the number of satisfied banks, and many others.
Edit intent: clarity
Original:  We show that for all of these objective functions , it is not only NP-hard to find the optimal solution , but it is also NP-hard to approximate this optimum: for each objective function, we show an inapproximability either to an n ^{ 1/2- \epsilon} or to an n^{1/4-\epsilon factor for any \epsilon>0, with n denoting the number of banks in the system.
Revised:  We show that for all of these objectives , it is not only NP-hard to find the optimal solution , but it is also NP-hard to approximate this optimum: for each objective function, we show an inapproximability either to an n ^{ 1/2- \epsilon} or to an n^{1/4-\epsilon factor for any \epsilon>0, with n denoting the number of banks in the system.
Edit intent: fluency
Original:  We show that for all of these objective functions , it is not only NP-hard to find the optimal solution , but it is also NP-hard to approximate this optimum: for each objective function, we show an inapproximability either to an n ^{ 1/2- \epsilon} or to an n^{1/4-\epsilon factor for any \epsilon>0, with n denoting the number of banks in the system.
Revised:  We show that for all of these objective functions , it is NP-hard to find the optimal solution , but it is also NP-hard to approximate this optimum: for each objective function, we show an inapproximability either to an n ^{ 1/2- \epsilon} or to an n^{1/4-\epsilon factor for any \epsilon>0, with n denoting the number of banks in the system.
Edit intent: clarity
Original:  We show that for all of these objective functions , it is not only NP-hard to find the optimal solution , but it is also NP-hard to approximate this optimum: for each objective function, we show an inapproximability either to an n ^{ 1/2- \epsilon} or to an n^{1/4-\epsilon factor for any \epsilon>0, with n denoting the number of banks in the system.
Revised:  We show that for all of these objective functions , it is not only NP-hard to approximate the optimal solution , but it is also NP-hard to approximate this optimum: for each objective function, we show an inapproximability either to an n ^{ 1/2- \epsilon} or to an n^{1/4-\epsilon factor for any \epsilon>0, with n denoting the number of banks in the system.
Edit intent: clarity
Original:  We show that for all of these objective functions , it is not only NP-hard to find the optimal solution , but it is also NP-hard to approximate this optimum: for each objective function, we show an inapproximability either to an n ^{ 1/2- \epsilon} or to an n^{1/4-\epsilon factor for any \epsilon>0, with n denoting the number of banks in the system.
Revised:  We show that for all of these objective functions , it is not only NP-hard to find the optimal solution to an n ^{ 1/2- \epsilon} or to an n^{1/4-\epsilon factor for any \epsilon>0, with n denoting the number of banks in the system.
Edit intent: clarity
Original:  After a decade of continuous progress, the currently fastest algorithm for N-fold integer programming by Jansen et al. (2019) has a running time of (rs\Delta) ^{O(r^2s + s^2) \phi%DIFDELCMD < }%%% ^2 \cdot nt \log^{O(1) \phi%DIFDELCMD < } %%% is the largest binary encoding length of a number in the input.
Revised:  After a decade of continuous progress, the currently fastest algorithm for N-fold integer programming by Jansen et al. (2019) has a running time of (rs\Delta) ^{O(r^2s + s^2) %DIFDELCMD < }%%% ^2 \cdot nt \log^{O(1) \phi%DIFDELCMD < } %%% is the largest binary encoding length of a number in the input.
Edit intent: fluency
Original:  After a decade of continuous progress, the currently fastest algorithm for N-fold integer programming by Jansen et al. (2019) has a running time of (rs\Delta) ^{O(r^2s + s^2) \phi%DIFDELCMD < }%%% ^2 \cdot nt \log^{O(1) \phi%DIFDELCMD < } %%% is the largest binary encoding length of a number in the input.
Revised:  After a decade of continuous progress, the currently fastest algorithm for N-fold integer programming by Jansen et al. (2019) has a running time of (rs\Delta) ^{O(r^2s + s^2) \phi%DIFDELCMD < }%%% ^2 \cdot nt \log^{O(1) %DIFDELCMD < } %%% is the largest binary encoding length of a number in the input.
Edit intent: fluency
Original:  In this paper we propose a different approach that is not based on augmentation .
Revised:  In this paper we propose a different approach .
Edit intent: clarity
Original:  This relaxation can be solved in polynomial time with parameter dependence (s \Delta%DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution x^* of this relaxation, there exists an optimal integer solution z^* that is within short \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Revised:  This relaxation can be solved \Delta%DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution x^* of this relaxation, there exists an optimal integer solution z^* that is within short \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Edit intent: clarity
Original:  This relaxation can be solved in polynomial time with parameter dependence (s \Delta%DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution x^* of this relaxation, there exists an optimal integer solution z^* that is within short \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Revised:  This relaxation can be solved in polynomial time with parameter dependence (s %DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution x^* of this relaxation, there exists an optimal integer solution z^* that is within short \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Edit intent: clarity
Original:  This relaxation can be solved in polynomial time with parameter dependence (s \Delta%DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution x^* of this relaxation, there exists an optimal integer solution z^* that is within short \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Revised:  This relaxation can be solved in polynomial time with parameter dependence (s \Delta%DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution of this relaxation, there exists an optimal integer solution z^* that is within short \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Edit intent: fluency
Original:  This relaxation can be solved in polynomial time with parameter dependence (s \Delta%DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution x^* of this relaxation, there exists an optimal integer solution z^* that is within short \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Revised:  This relaxation can be solved in polynomial time with parameter dependence (s \Delta%DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution x^* of this relaxation, there is an optimal integer solution z^* that is within short \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Edit intent: clarity
Original:  This relaxation can be solved in polynomial time with parameter dependence (s \Delta%DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution x^* of this relaxation, there exists an optimal integer solution z^* that is within short \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Revised:  This relaxation can be solved in polynomial time with parameter dependence (s \Delta%DIFDELCMD < }%%% )^{O(s^2) show that, for any given optimal vertex solution x^* of this relaxation, there exists an optimal integer solution within \ell_1-distance , namely \|x^* - z^*\|_{1 \leq (rs\Delta)^{O(rs)}.
Edit intent: clarity
Original:  With dynamic programming one can then }  find an optimal integer solution of the N-fold IP in time (rs\Delta)^{O(r^2s + s^2) \,nt . This, together with an off-the-shelf-method from convex optimization, results in the currently fastest algorithm for N-fold integer programming}  .
Revised:  With dynamic programming one can then } find an optimal integer solution \,nt . This, together with an off-the-shelf-method from convex optimization, results in the currently fastest algorithm for N-fold integer programming} .
Edit intent: clarity
Original: The efficiency of a spatial DNN accelerator depends heavily on the compiler 's ability to generate optimized mappings for a given DNN's operators (layers) on to the accelerator's compute and memory resources.
Revised: The efficiency of a spatial DNN accelerator depends heavily on the compiler 's ability to generate optimized mappings for various operators of DNN models on to the accelerator's compute and memory resources.
Edit intent: clarity
Original:   Searching for the optimal mapping is challenging because of a massive space of possible data-layouts and loop transformations for the DNN layers.
Revised:  Searching for the optimal mappings is challenging because of a massive space of possible data-layouts and loop transformations for the DNN layers.
Edit intent: fluency
Original:  This challenge gets exacerbated with new layer types (e.g., depth-wise and point-wise convolutions) and diverse hardware accelerator configurations.
Revised:  This challenge gets exacerbated with new operators and diverse accelerator configurations.
Edit intent: clarity
Original:  Harshaw\emph{et al. , ICML'19) .
Revised:  Harshaw\emph{ , ICML'19) .
Edit intent: clarity
Original:  In this paper, we generalize this problem formulation and set as our objective to optimize a function that maximizes the coverage of skills minus the cost of the corresponding team .
Revised:  In this paper, we consider a generalization of the above formulation where the goal is to optimize a function that maximizes the coverage of skills minus the cost of the corresponding team .
Edit intent: clarity
Original:  This formulation appears as a more natural one, particularly in cases where one needs to strike a balance between the coverage achieved and the cost being paid .
Revised:  This formulation appears as a more natural one, particularly when one needs to strike a balance between the coverage achieved and the cost being paid .
Edit intent: clarity