wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our experimental results show that our approach does not only significantly reduce network traffic but is also at least an order of magnitude faster in comparison to the state-of-the-art interfaces under high query processing load.
Revised:  Our experimental results show that our approach does not only significantly reduce network traffic but is also at least an order of magnitude faster than the state-of-the-art interfaces under high query processing load.
Edit intent: clarity
Original:  Our experimental results show that our approach does not only significantly reduce network traffic but is also at least an order of magnitude faster in comparison to the state-of-the-art interfaces under high query processing load.
Revised:  Our experimental results show that our approach does not only significantly reduce network traffic but is also at least an order of magnitude faster in comparison to the state-of-the-art interfaces under high query load.
Edit intent: clarity
Original: To gain insights into the regional inequality problem , we proposed new regional asset exchange models based on existing kinetic income-exchange models in economic physics by setting spatial exchange range and adding bias to asset fraction probability in equivalent exchanges.
Revised: To gain insights into the problem of regional inequality , we proposed new regional asset exchange models based on existing kinetic income-exchange models in economic physics by setting spatial exchange range and adding bias to asset fraction probability in equivalent exchanges.
Edit intent: clarity
Original:  Avoiding overconcentration of assets due to repeated exchanges , however, requires , thirdly, adding local support bias (distribution norm).
Revised:  However, avoiding over-concentration of assets due to repeated exchanges , however, requires , thirdly, adding local support bias (distribution norm).
Edit intent: fluency
Original:  Avoiding overconcentration of assets due to repeated exchanges , however, requires , thirdly, adding local support bias (distribution norm).
Revised:  Avoiding overconcentration of assets due to repeat exchanges requires adding a third measure; the local support bias (distribution norm).
Edit intent: clarity
Original:  In contrast to other systems supporting dependent type theory, Isabelle is based on simple type theory -- yet I show how its existing logical framework infrastructure is able to handle automationtasks that are typically implemented on the source code level of dependently-typed systems.
Revised:  In contrast to other systems supporting dependent type theory, Isabelle is based on simple type theory -- yet I show how its existing logical framework infrastructure is able to handle automationtasks that are typically implemented on the source code level of dependently typed systems.
Edit intent: fluency
Original:  I also go some way in integrating the propositions-as-types paradigm with the declarative Isar proof language .
Revised:  I also integrate the propositions-as-types paradigm with the declarative Isar proof language .
Edit intent: clarity
Original:  We present a multi-model open dataset for mental-disorder analysis.
Revised:  We present a multi-modal open dataset for mental-disorder analysis.
Edit intent: fluency
Original: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Revised: Knowledge graphs have proven extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Edit intent: fluency
Original: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Revised: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search and natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Edit intent: fluency
Original: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Revised: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Edit intent: coherence
Original: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Revised: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code is a knowledge graph about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Edit intent: clarity
Original: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Revised: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code that can similarly power diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Edit intent: clarity
Original: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation.
Revised: Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search , natural language understanding , and even image classification . Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as program search, code understanding, refactoring, bug detection, and code automation.
Edit intent: fluency
Original:  The edges in the graph are based on 1) function usage in the wild (e.g., which other function tends to call this one, or which function tends to precede this one, as gleaned from program analysis ), 2) documentation about the function (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow) , and 3) program specific features such as class hierarchies.
Revised:  The edges in the graph are based on 1) function usage in the wild (e.g., how data flows through function calls, as derived from program analysis ), 2) documentation about the function (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow) , and 3) program specific features such as class hierarchies.
Edit intent: clarity
Original: Knowledge graphs have  proven extremely useful in powering diverse applications in semantic search and natural language understanding.
Revised: Knowledge graphs have been proven extremely useful in powering diverse applications in semantic search and natural language understanding.
Edit intent: fluency
Original:  Graph4Code is a knowledge graph about program code that can similarly power diverse applications such as program search, code understanding, refactoring, bug detection, and code automation.
Revised:  Graph4Code is a knowledge graph about program code that can similarly power various applications such as program search, code understanding, refactoring, bug detection, and code automation.
Edit intent: clarity
Original:  Graph4Code is a knowledge graph about program code that can similarly power diverse applications such as program search, code understanding, refactoring, bug detection, and code automation.
Revised:  Graph4Code is a knowledge graph about program code that can similarly power diverse applications such as program search, code understanding, bug detection, and code automation.
Edit intent: clarity
Original:  The graph uses generic techniques to capture the semantics of Python code : the key nodes in the graph are classes, functions and methods in popular Python modules . Edges indicate function usage (e.g., how data flows through function calls, as derived from program analysis of real code), and documentation about functions (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow).
Revised:  The graph uses generic techniques to capture code semantics with the key nodes in the graph are classes, functions and methods in popular Python modules . Edges indicate function usage (e.g., how data flows through function calls, as derived from program analysis of real code), and documentation about functions (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow).
Edit intent: clarity
Original:  The graph uses generic techniques to capture the semantics of Python code : the key nodes in the graph are classes, functions and methods in popular Python modules . Edges indicate function usage (e.g., how data flows through function calls, as derived from program analysis of real code), and documentation about functions (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow).
Revised:  The graph uses generic techniques to capture the semantics of Python code : the key nodes in the graph representing classes, functions and methods in popular Python modules . Edges indicate function usage (e.g., how data flows through function calls, as derived from program analysis of real code), and documentation about functions (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow).
Edit intent: clarity
Original:  The graph uses generic techniques to capture the semantics of Python code : the key nodes in the graph are classes, functions and methods in popular Python modules . Edges indicate function usage (e.g., how data flows through function calls, as derived from program analysis of real code), and documentation about functions (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow).
Revised:  The graph uses generic techniques to capture the semantics of Python code : the key nodes in the graph are classes, functions , and methods . Edges indicate function usage (e.g., how data flows through function calls, as derived from program analysis of real code), and documentation about functions (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow).
Edit intent: clarity
Original:  We make extensive use of named graphs in RDF to make the knowledge graph extensible by the community.
Revised:  Our toolkit uses named graphs in RDF to make the knowledge graph extensible by the community.
Edit intent: clarity
Original:  We describe a set of generic extraction techniques that we applied to over 1.3 M Python files drawn from GitHub, over 2,300 Python modules, as well as 47M forum poststo generate a graph with over 2 billion triples.
Revised:  We describe a set of generic extraction techniques that we applied to over 1.3 million Python files drawn from GitHub, over 2,300 Python modules, as well as 47M forum poststo generate a graph with over 2 billion triples.
Edit intent: fluency
Original:  We describe a set of generic extraction techniques that we applied to over 1.3 M Python files drawn from GitHub, over 2,300 Python modules, as well as 47M forum poststo generate a graph with over 2 billion triples.
Revised:  We describe a set of generic extraction techniques that we applied to over 1.3 M Python files drawn from GitHub, 2,300 Python modules, as well as 47M forum poststo generate a graph with over 2 billion triples.
Edit intent: fluency
Original:  Finally, and most importantly, our method is strongly-local, meaning that its runtime depends only on the size of an input set.
Revised:  Finally, and most importantly, our algorithm is strongly-local, meaning that its runtime depends only on the size of an input set.
Edit intent: clarity
Original:  Our hypergraph objective is related to higher-order notions of modularity and normalized cut , and is amenable to approximation algorithms via hypergraph expansion techniques.
Revised:  Our hypergraph objective is related to higher-order notions of normalized cut and modularity in hypergraphs. It is further amenable to approximation algorithms via hypergraph expansion techniques.
Edit intent: clarity
Original:  Our hypergraph objective is related to higher-order notions of modularity and normalized cut , and is amenable to approximation algorithms via hypergraph expansion techniques.
Revised:  Our hypergraph objective is related to higher-order notions of modularity and normalized cut , and is amenable to approximation algorithms via hyperedge expansion techniques.
Edit intent: fluency
Original:  The problem in general is NP-hard, but we show that in a certain parameter regime it is equivalent to a bipartite matching problem , meaning that it is polynomial timesolvable in this regime .
Revised:  The problem in general is NP-hard, but we show that in a certain parameter regime it is equivalent to a bipartite matching problem and thus can be solved in polynomial time .
Edit intent: clarity
Original:  We illustrate the flexibility of our framework in several experiments .
Revised:  We illustrate the flexibility of our framework and the diversity of results that can be obtained in different parameter settings .
Edit intent: clarity
Original:  We develop a conformal mapping formulation of the water wave equations that employs a quasi-periodic variant of the Hilbert transform to compute the normal velocity of the fluid from its velocity potential on the free surface.
Revised:  We propose a conformal mapping formulation of the water wave equations that employs a quasi-periodic variant of the Hilbert transform to compute the normal velocity of the fluid from its velocity potential on the free surface.
Edit intent: clarity
Original:  The objective function that we define for the inverse problem is comprised of multiple data misfit terms: one for each geophysical survey and one for the petrophysical properties and geological information.
Revised:  The objective function that we define for the inverse problem , is necessary. Second, we address the practical issues of simultaneously inverting data from multiple surveys and finding a solution that acceptably reproduces each one, along with the petrophysical and geological information.
Edit intent: coherence
Original:  The pipe contains two distinct kimberlite facies embedded in a host rock.
Revised:  The pipe contains two distinct facies embedded in a host rock.
Edit intent: clarity
Original:  We show that inverting the datasets individually , even with petrophysical information, leads to a binary geologic modelconsisting of background or kimberlite.
Revised:  Inverting the datasets individually , even with petrophysical information, leads to a binary geologic modelconsisting of background or kimberlite.
Edit intent: coherence
Original:  We show that inverting the datasets individually , even with petrophysical information, leads to a binary geologic modelconsisting of background or kimberlite.
Revised:  We show that inverting the datasets individually leads to a binary geologic modelconsisting of background or kimberlite.
Edit intent: clarity
Original:  We show that inverting the datasets individually , even with petrophysical information, leads to a binary geologic modelconsisting of background or kimberlite.
Revised:  We show that inverting the datasets individually , even with petrophysical information, leads to a binary geological model: background or kimberlite.
Edit intent: clarity
Original:  A joint inversion, with petrophysical information, can differentiate the two main kimberlite facies of the pipe  .
Revised:  A multi-physics inversion, with petrophysical information, can differentiate the two main kimberlite facies of the pipe .
Edit intent: clarity
Original:  A joint inversion, with petrophysical information, can differentiate the two main kimberlite facies of the pipe  .
Revised:  A joint inversion, with petrophysical information, differentiates between the two main kimberlite facies of the pipe .
Edit intent: clarity
Original: A system's heterogeneity ( equivalently, diversity)
Revised: A system's heterogeneity ( diversity)
Edit intent: coherence
Original:  Since the average heterogeneity of a component subsystem should not be greater than that of the pooled assemblage , we require that \alpha \leq \gamma .
Revised:  Since the average heterogeneity of a component subsystem should not be greater than that of the pooled system , we require that \alpha \leq \gamma .
Edit intent: clarity
Original:  Since the average heterogeneity of a component subsystem should not be greater than that of the pooled assemblage , we require that \alpha \leq \gamma .
Revised:  Since the average heterogeneity of a component subsystem should not be greater than that of the pooled assemblage , we require that \gamma \geq \alpha .
Edit intent: clarity
Original:  This paper therefore describes multiplicative decomposition of the R\'enyi heterogeneity for continuous mixture distributions under parametric and non-parametric pooling assumptions.
Revised:  We therefore describe multiplicative decomposition of the R\'enyi heterogeneity for continuous mixture distributions under parametric and non-parametric pooling assumptions.
Edit intent: clarity
Original:  By providing conditions under which the decomposability axiom of heterogeneity measurement holds, our findings further advance the understanding of heterogeneity measurement in non-categorical systems.
Revised:  Our findings will further contribute to heterogeneity measurement in non-categorical systems.
Edit intent: clarity
Original:  By providing conditions under which the decomposability axiom of heterogeneity measurement holds, our findings further advance the understanding of heterogeneity measurement in non-categorical systems.
Revised:  By providing conditions under which the decomposability axiom of heterogeneity measurement holds, our findings further advance the understanding of heterogeneity measurement in continuous systems.
Edit intent: clarity
Original: We propose novel connection between several neural network architectures and viscosity solutions of some Hamilton--Jacobi (HJ) partial differential equations (PDEs) whose Hamiltonian is convex and only depends on the spatial gradient of the solution.
Revised: We propose novel connections between several neural network architectures and viscosity solutions of some Hamilton--Jacobi (HJ) partial differential equations (PDEs) whose Hamiltonian is convex and only depends on the spatial gradient of the solution.
Edit intent: fluency
Original:  Note that these neural network representations can avoid curve of dimensionality for certain HJ PDEs, since they do not involve grid or discretization.
Revised:  Note that these neural network representations can avoid curve of dimensionality for certain HJ PDEs, since they do not involve neither grids nor discretization.
Edit intent: clarity
Original: The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced representation ability in numerous applications involving mixing of signals of   rank higher than one (blocks) .
Revised: The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced ability of rank higher than one (blocks) .
Edit intent: clarity
Original: The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced representation ability in numerous applications involving mixing of signals of   rank higher than one (blocks) .
Revised: The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced representation ability in numerous applications involving mixing of signals of blocks rank higher than one (blocks) .
Edit intent: fluency
Original: The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced representation ability in numerous applications involving mixing of signals of   rank higher than one (blocks) .
Revised: The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced representation ability in numerous applications involving mixing of signals of of rank higher than one (blocks) .
Edit intent: fluency
Original:  Nevertheless, the  problem of estimating the BTD model structure, namely the number of block terms and their individual ranks, has only recently started to attract significant attention , as it is more challenging compared to more classical tensor models such as canonical polyadic decomposition (CPD) and Tucker decomposition (TD).
Revised:  Nevertheless, the challenging problem of estimating the BTD model structure, namely the number of block terms and their individual ranks, has only recently started to attract significant attention , as it is more challenging compared to more classical tensor models such as canonical polyadic decomposition (CPD) and Tucker decomposition (TD).
Edit intent: clarity
Original:  The idea is to impose column sparsity jointly on the factors and successively estimate  the ranks as the numbers of factor columns of non-negligible magnitude , with the aid of alternating iteratively reweighted least squares ( IRLS) . Simulation results are reported that demonstrate the effectiveness of our method in accurately estimating both the ranks and the factors of the least squares BTD approximation, and in a computationally efficient manner .
Revised:  The idea is to impose column sparsity jointly on the factors and in a the ranks as the numbers of factor columns of non-negligible magnitude , with the aid of alternating iteratively reweighted least squares ( IRLS) . Simulation results are reported that demonstrate the effectiveness of our method in accurately estimating both the ranks and the factors of the least squares BTD approximation, and in a computationally efficient manner .
Edit intent: clarity