wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our main contribution are algorithms for the enumeration of five of these six clique types , that are fixed-parameter-tractable with respect to the "degree of isolation".
Revised:  Our main contribution is the development of fixed-parameter enumeration algorithms for five of these six clique types , that are fixed-parameter-tractable with respect to the "degree of isolation".
Edit intent: clarity
Original:  Our main contribution are algorithms for the enumeration of five of these six clique types , that are fixed-parameter-tractable with respect to the "degree of isolation".
Revised:  Our main contribution are algorithms for the enumeration of five of these six clique types employing the parameter "degree of isolation".
Edit intent: clarity
Original: Computing a (shortest) path between two vertices in a graph is one of the most fundamental primitive in graph algorithmics .
Revised: Computing a (shortest) path between two vertices in a graph is one of the most fundamental primitives in graph algorithms .
Edit intent: fluency
Original:  In recent years, the study of paths in temporal graphs, that is, graphs where the vertex set remains static but the edge set may change over time, gained more and more attention.
Revised:  In recent years, the study of paths in temporal graphs, that is, graphs where the vertex set is fixed but the edge set may change over time, gained more and more attention.
Edit intent: clarity
Original:  In recent years, the study of paths in temporal graphs, that is, graphs where the vertex set remains static but the edge set may change over time, gained more and more attention.
Revised:  In recent years, the study of paths in temporal graphs, that is, graphs where the vertex set remains static but the edge set changes over time, gained more and more attention.
Edit intent: clarity
Original:  In particular, we show that problem remains computationally hard on temporal graphs with three layers and is W[1]-hard when parameterized by the feedback vertex number  of the underlying graph.
Revised:  In particular, we show that problem remains computationally hard on temporal graphs with three layers and is W[1]-hard when parameterized by feedback vertex number of the underlying graph.
Edit intent: fluency
Original: Computing a ( shortest ) path between two vertices in a graph is one of the most fundamental primitives in graph algorithms .
Revised: Computing a ( short ) path between two vertices in a graph is one of the most fundamental primitives in graph algorithms .
Edit intent: fluency
Original: Computing a ( shortest ) path between two vertices in a graph is one of the most fundamental primitives in graph algorithms .
Revised: Computing a ( shortest ) path between two vertices is one of the most fundamental primitives in graph algorithms .
Edit intent: clarity
Original: Computing a ( shortest ) path between two vertices in a graph is one of the most fundamental primitives in graph algorithms .
Revised: Computing a ( shortest ) path between two vertices in a graph is one of the most fundamental primitives in graph algorithmics .
Edit intent: clarity
Original:  Such a path is time-respecting, or temporal, if it uses edges over non-decreasing times.
Revised:  A path is time-respecting, or temporal, if it uses edges over non-decreasing times.
Edit intent: coherence
Original:  Such a path is time-respecting, or temporal, if it uses edges over non-decreasing times.
Revised:  Such a path is time-respecting, or temporal, if it uses edges with non-decreasing times.
Edit intent: clarity
Original:  In this paper, we investigate a basic constraint for temporal paths, where the time spent at each vertex must not exceed a given duration  \Delta, referred to as \Delta-restless temporal paths.
Revised:  In this paper, we investigate a basic constraint for temporal paths, where the time spent at each vertex must not exceed a given duration ~ \Delta, referred to as \Delta-restless temporal paths.
Edit intent: fluency
Original:  This constraint arises naturally in the modeling of real-world processes like infectious diseases and packet routing in communication networks .
Revised:  This constraint arises naturally in the modeling of real-world processes like packet routing in communication networks .
Edit intent: clarity
Original:  While the reachability problem for temporal paths in general is known to be polynomial-time solvable , we show that the restless version of this problem becomes computationally hard even in very restrictive settings.
Revised:  While the reachability problem for temporal paths in general is known to be doable in polynomial time , we show that the restless version of this problem becomes computationally hard even in very restrictive settings.
Edit intent: clarity
Original:  While the reachability problem for temporal paths in general is known to be polynomial-time solvable , we show that the restless version of this problem becomes computationally hard even in very restrictive settings.
Revised:  While the reachability problem for temporal paths in general is known to be polynomial-time solvable , we show that the "restless variant" of this problem becomes computationally hard even in very restrictive settings.
Edit intent: clarity
Original:  For example, it is W[1]-hard when parameterized by  feedback vertex number or  pathwidth of the underlying graph.
Revised:  For example, it is W[1]-hard when parameterized by the feedback vertex number or pathwidth of the underlying graph.
Edit intent: fluency
Original:  For example, it is W[1]-hard when parameterized by  feedback vertex number or  pathwidth of the underlying graph.
Revised:  For example, it is W[1]-hard when parameterized by feedback vertex number or the pathwidth of the underlying graph.
Edit intent: fluency
Original:  The main question thus becomes whether the problem is tractable in some natural settings.
Revised:  The main question thus is whether the problem is tractable in some natural settings.
Edit intent: coherence
Original:  The main question thus becomes whether the problem is tractable in some natural settings.
Revised:  The main question thus becomes whether the problem becomes tractable in some natural settings.
Edit intent: clarity
Original:  As of today, no reference set of parameters exist for temporal graphs.
Edit intent: coherence
Original:  We investigate a basic constraint for temporal paths, where the time spent at each vertex must not exceed a given duration ~ \Delta, referred to as \Delta-restless temporal paths.
Revised:  We investigate a basic constraint for temporal paths, where the time spent at each vertex must not exceed a given duration \Delta, referred to as \Delta-restless temporal paths.
Edit intent: fluency
Original:  Our experiments show that the introduction of genetic algorithm based method results in a significantly faster and accurate search method when compared to  brute-force parameter sweep method in both single and multi-layer optimization.
Revised:  Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to brute-force parameter sweep method in both single and multi-layer optimization.
Edit intent: clarity
Original:  Our experiments show that the introduction of genetic algorithm based method results in a significantly faster and accurate search method when compared to  brute-force parameter sweep method in both single and multi-layer optimization.
Revised:  Our experiments show that the introduction of genetic algorithm based method results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization.
Edit intent: fluency
Original:  While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100\\%DIF < accuracy in the optimized results like our %DIF > accuracy in the optimized results like our genetic algorithm.
Revised:  While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100\\%DIF < %DIF > accuracy in the optimized results like our genetic algorithm.
Edit intent: fluency
Original:  Our best case scenario was observed to utilize 57.9\% less simulations than\\%DIF > fewer  brute-force method.
Revised:  Our best case scenario was observed to utilize 57.9\% less simulations than\\%DIF > fewer simulations than the brute-force method.
Edit intent: fluency
Original: We describe a method for simultaneous visualization of multiple pairwise distances  in 3 dimensional ( 3D ) space.
Revised: We describe a method for simultaneous visualization of multiple pairwise distances in 3D ) space.
Edit intent: clarity
Original:  Our multi-perspective, simultaneous embedding (MPSE) method is based on non-linear dimensionality reduction that generalizes multidimensional scaling.
Edit intent: coherence
Original:  MPSE with fixed projections takes as input a set of pairwise distance matrices defined on the data points, along with the same number of projections and embeds the points in 3D so that the pairwise distances are preserved along the given projections.
Revised:  MPSE with fixed projections takes as input a set of pairwise distance matrices defined on the data points, along with the same number of projections and embeds the points in 3D so that the pairwise distances are preserved in the given projections.
Edit intent: clarity
Original:  We provide  extensive quantitative evaluation with datasets of different size and using different number of projections and several examples that illustrate the quality of the resulting solutions.
Revised:  We provide an extensive quantitative evaluation with datasets of different size and using different number of projections and several examples that illustrate the quality of the resulting solutions.
Edit intent: fluency
Original:  We provide  extensive quantitative evaluation with datasets of different size and using different number of projections and several examples that illustrate the quality of the resulting solutions.
Revised:  We provide extensive quantitative evaluation with datasets of different sizes and using different number of projections and several examples that illustrate the quality of the resulting solutions.
Edit intent: fluency
Original:  We provide  extensive quantitative evaluation with datasets of different size and using different number of projections and several examples that illustrate the quality of the resulting solutions.
Revised:  We provide extensive quantitative evaluation with datasets of different size and using different number of projections , as well as several examples that illustrate the quality of the resulting solutions.
Edit intent: clarity
Original:  In this work, we evaluate several data augmentation and pretraining approaches for AST,  comparing all on the same datasets.
Revised:  In this work, we evaluate several data augmentation and pretraining approaches for AST, by comparing all on the same datasets.
Edit intent: coherence
Original:  For instance, nested cross-validation is a standard procedure meant to mitigate the risk that information from held-out validation data may be reached during model selection.
Revised:  For instance, nested cross-validation is a standard procedure meant at mitigating the risk that information from held-out validation data may be reached during model selection.
Edit intent: fluency
Original:  For instance, nested cross-validation is a standard procedure meant to mitigate the risk that information from held-out validation data may be reached during model selection.
Revised:  For instance, nested cross-validation is a standard procedure meant to mitigate the risk that information from held-out validation data may be used during model selection.
Edit intent: clarity
Original:  (ii) the systematic adoption of training over collection of subregions ( tiles or patches ) extracted for the same subject.
Revised:  (ii) the systematic adoption of training over collection of subregions ( i.e. "tiles" or "patches" ) extracted for the same subject.
Edit intent: clarity
Original:  Results are replicated for 4 classification tasks  on 3 datasets, for a total of 364 subjectsand 538 total slides ( about 27, 000 tiles).
Revised:  Results are replicated for 4 classification tasks on 3 datasets, for a total of 364 subjectsand 538 total slides ( around 27, 000 tiles).
Edit intent: fluency
Original:  Impact of applying transfer learning methods is also discussed.
Revised:  Impact of applying transfer learning strategies with models pre-trained on general-purpose or digital pathology datasets is also discussed.
Edit intent: clarity
Original:  Results are replicated for 4 classification tasks in digital pathology on 3  datasets, for a total of 373 subjects, and 543 total slides (around 27,000 tiles ).
Revised:  Results are replicated for 4 classification tasks on 3 datasets, for a total of 373 subjects, and 543 total slides (around 27,000 tiles ).
Edit intent: clarity
Original:  Such models are rarely analytically tractable, meaning that extremely large numbers of expensive stochastic simulations are required for parameter inference.
Revised:  Such models are rarely analytically tractable, meaning that extremely large numbers of stochastic simulations are required for parameter inference.
Edit intent: clarity
Original: Areal datais a common data type to store information such as biodiversity inventories, socio-economic censuses or cadastral surveys.
Revised: Many relevant applications in the environmental and socioeconomic sciences use areal data, such as biodiversity inventories, socio-economic censuses or cadastral surveys.
Edit intent: clarity
Original:  Inconsistent concepts, terms, definitions, or messy tables makes data-wrangling an often tedious and error-prone process.
Revised:  Inconsistent concepts, definitions, or messy tables makes data-wrangling an often tedious and error-prone process.
Edit intent: clarity
Original:  Inconsistent concepts, terms, definitions, or messy tables makes data-wrangling an often tedious and error-prone process.
Revised:  Inconsistent concepts, terms, definitions, or messy data tables make this a tedious and error-prone process.
Edit intent: clarity
Original:  A dedicated tool that assists in organising areal data still is lacking.
Revised:  To date, a dedicated tool for organising areal data still is lacking.
Edit intent: clarity
Original:  A dedicated tool that assists in organising areal data still is lacking.
Revised:  A dedicated tool that assists in organising areal data is still lacking.
Edit intent: fluency
Original:  Here, we introduce the R package arealDB that helps to harmonise and integrate  heterogeneous areal data and associated geometries into a consistent database.
Revised:  Here, we introduce the R package arealDB heterogeneous areal data and associated geometries into a consistent database.
Edit intent: coherence
Original:  By offering easy-to-use tools for integrating areal data, arealDB promises substantial time-savings to database collation efforts, as well as  quality-improvements to downstream scientific, monitoring, and management applications  .
Revised:  By offering easy-to-use tools in quality-improvements to downstream scientific, monitoring, and management applications .
Edit intent: clarity
Original:  By offering easy-to-use tools for integrating areal data, arealDB promises substantial time-savings to database collation efforts, as well as  quality-improvements to downstream scientific, monitoring, and management applications  .
Revised:  By offering easy-to-use tools for integrating areal data, arealDB promises substantial time-savings to database collation efforts, as well as promise quality-improvements to downstream scientific, monitoring, and management applications .
Edit intent: fluency
Original:  By offering easy-to-use tools for integrating areal data, arealDB promises substantial time-savings to database collation efforts, as well as  quality-improvements to downstream scientific, monitoring, and management applications  .
Revised:  By offering easy-to-use tools for integrating areal data, arealDB promises substantial time-savings to database collation efforts, as well as quality-improvements to downstream scientific, monitoring, and management applications but also substantial time-savings to database collation efforts .
Edit intent: coherence
Original:  Here, we introduce the R package arealDB that integrates heterogeneous areal data and associated geometries into a consistent database.
Revised:  Here, we introduce the R package arealDB that integrates heterogeneous areal data and associated geometries into a consistent database.
Edit intent: fluency
Original:  The easy-to-use tools in arealDB promise quality-improvements to downstream scientific, monitoring, and management applications but also substantial time-savings to database collation efforts.
Revised:  The easy-to-use tools in arealDB promise quality-improvements to downstream scientific, monitoring, and management applications but also substantial time-savings to database collation efforts.
Edit intent: fluency
Original:  To date, a dedicated tool for organising areal data is still lacking.
Revised:  To date, a dedicated tool to address these challenges is still lacking.
Edit intent: clarity
Original:  The easy-to-use tools in arealDB promise quality-improvements to downstream scientific, monitoring, and management applications but also substantial time-savings to database collation efforts.
Revised:  arealDB promises quality-improvements to downstream scientific, monitoring, and management applications but also substantial time-savings to database collation efforts.
Edit intent: clarity
Original:  The lesson is that highly-reliable computation under time-symmetric control cannot reach, and is often far above, the Landauer limit.
Revised:  The lesson is that computation under time-symmetric control cannot reach, and is often far above, the Landauer limit.
Edit intent: clarity
Original:  We show that the accuracy can be improved even without increasing the number of devices.
Revised:  Importantly, we show that the accuracy can be improved even without increasing the number of devices.
Edit intent: coherence
Original: Artificial neural networks (ANNs) are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems.
Revised: Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems.
Edit intent: clarity
Original:  One of the most often suggested solutions is the physical implementation of ANNs in which their synaptic weights are realised with analogue resistive devices, such as resistive random-access memory (RRAM) . However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy.
Revised:  One of the most often suggested solutions is the physical implementation of artificial neural networks in which their synaptic weights are realised with analogue resistive devices, such as resistive random-access memory (RRAM) . However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy.
Edit intent: clarity
Original:  One of the most often suggested solutions is the physical implementation of ANNs in which their synaptic weights are realised with analogue resistive devices, such as resistive random-access memory (RRAM) . However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy.
Revised:  One of the most often suggested solutions is the physical implementation of ANNs in which their synaptic weights are realised with analogue resistive devices, such as resistive random-access memory . However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy.
Edit intent: clarity
Original:  We suggest applying a well-known concept in computer science -- committee machine (CM) -- in the context of RRAM-based neural networks.
Revised:  We suggest applying a well-known concept in computer science -- committee machine -- in the context of RRAM-based neural networks.
Edit intent: clarity
Original:  Using simulations and experimental data from three different types of RRAM devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities , random telegraph noise , cycle-to-cycle variability and line resistance.
Revised:  Using simulations and experimental data from three different types of memristive devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities , random telegraph noise , cycle-to-cycle variability and line resistance.
Edit intent: clarity
Original:  Using simulations and experimental data from three different types of RRAM devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities , random telegraph noise , cycle-to-cycle variability and line resistance.
Revised:  Using simulations and experimental data from three different types of RRAM devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities , random telegraph noise , cycle-to-cycle variability and line resistance.
Edit intent: clarity
Original:  Using simulations and experimental data from three different types of RRAM devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities , random telegraph noise , cycle-to-cycle variability and line resistance.
Revised:  Using simulations and experimental data from three different types of RRAM devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability , random telegraph noise , cycle-to-cycle variability and line resistance.
Edit intent: clarity
Original:  Using simulations and experimental data from three different types of RRAM devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities , random telegraph noise , cycle-to-cycle variability and line resistance.
Revised:  Using simulations and experimental data from three different types of RRAM devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities , random telegraph noise and line resistance.
Edit intent: clarity
Original:  Importantly, we show that the accuracy can be improved even without increasing the number of devices .
Revised:  Importantly, we show that the accuracy can be improved even without increasing the total number of memristors .
Edit intent: clarity
Original:  Recent years have seen an emergence of research in  hardware that strives to break the bottleneck of von Neumann architecture and optimise the data flow, namely, to bring memory and computing closer together.
Revised:  Consequently, recent years have seen an emergence of research in hardware that strives to break the bottleneck of von Neumann architecture and optimise the data flow, namely, to bring memory and computing closer together.
Edit intent: coherence