wanyu/IteraTeR_v2 huggingface dataset:
Original:  The energetic cost for kinesin-1 varies nonmonotonically with ATP concentration and load (f) , and is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably,  this local minimum vanishes and the cost increases significantly for a mutant that has a longer neck-linker , which underscores the importance of molecular structure in transport properties.
Revised:  The energetic cost for kinesin-1 varies nonmonotonically with ATP concentration and load (f) . For kinesin-1, Q is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably, this local minimum vanishes and the cost increases significantly for a mutant that has a longer neck-linker , which underscores the importance of molecular structure in transport properties.
Edit intent: coherence
Original:  Calculations show that the energetic cost and precision of motility also vary depending on the motor type.
Edit intent: coherence
Original: Molecular motors play key roles in organizing the interior of cells.
Revised: Molecular motors play pivotal roles in organizing the interior of cells.
Edit intent: clarity
Original:  An efficient motor in cargo transport would travel with a high speed and a minimal error in transport time (or distance ) while consuming  minimal amount of energy.
Revised:  A motor efficient in cargo transport would travel with a high speed and a minimal error in transport time (or distance ) while consuming minimal amount of energy.
Edit intent: fluency
Original:  An efficient motor in cargo transport would travel with a high speed and a minimal error in transport time (or distance ) while consuming  minimal amount of energy.
Revised:  An efficient motor in cargo transport would travel with a high speed and a minimal error in transport distance (or time ) while consuming minimal amount of energy.
Edit intent: fluency
Original:  An efficient motor in cargo transport would travel with a high speed and a minimal error in transport time (or distance ) while consuming  minimal amount of energy.
Revised:  An efficient motor in cargo transport would travel with a high speed and a minimal error in transport time (or distance ) while consuming a minimal amount of energy.
Edit intent: fluency
Original:  The travel distance  and its variance of motor are, however, physically constrained by energy consumption, the principle of which has recently been formulated into the thermodynamic uncertainty relation  .
Revised:  The travel distance of the motor and its variance of motor are, however, physically constrained by energy consumption, the principle of which has recently been formulated into the thermodynamic uncertainty relation .
Edit intent: clarity
Original:  The travel distance  and its variance of motor are, however, physically constrained by energy consumption, the principle of which has recently been formulated into the thermodynamic uncertainty relation  .
Revised:  The travel distance and its variance are, however, physically constrained by energy consumption, the principle of which has recently been formulated into the thermodynamic uncertainty relation .
Edit intent: clarity
Original:  The travel distance  and its variance of motor are, however, physically constrained by energy consumption, the principle of which has recently been formulated into the thermodynamic uncertainty relation  .
Revised:  The travel distance and its variance of motor are, however, physically constrained by the free energy being consumed. A recently formulated thermodynamic uncertainty relation .
Edit intent: clarity
Original:  Here, we reinterpret the uncertainty measure (Q) defined in the thermodynamic uncertainty relation such that a motor efficient in cargo transport is characterized with a small Q . Analyses on the motility data from several types of molecular motors show that \mathcal{Q} is a nonmonotic function of ATP concentration and load (f).
Revised:  Analyses on the motility data from several types of molecular motors show that \mathcal{Q} is a nonmonotic function of ATP concentration and load (f).
Edit intent: clarity
Original:  Here, we reinterpret the uncertainty measure (Q) defined in the thermodynamic uncertainty relation such that a motor efficient in cargo transport is characterized with a small Q . Analyses on the motility data from several types of molecular motors show that \mathcal{Q} is a nonmonotic function of ATP concentration and load (f).
Revised:  Here, we reinterpret the uncertainty measure (Q) defined in the thermodynamic uncertainty relation such that a motor efficient in cargo transport is characterized with a small Q . Analyses on the motility data from several types of molecular motors reveal that \mathcal{Q} is a nonmonotic function of ATP concentration and load (f).
Edit intent: fluency
Original:  Here, we reinterpret the uncertainty measure (Q) defined in the thermodynamic uncertainty relation such that a motor efficient in cargo transport is characterized with a small Q . Analyses on the motility data from several types of molecular motors show that \mathcal{Q} is a nonmonotic function of ATP concentration and load (f).
Revised:  Here, we reinterpret the uncertainty measure (Q) defined in the thermodynamic uncertainty relation such that a motor efficient in cargo transport is characterized with a small Q . Analyses on the motility data from several types of molecular motors show that \mathcal{Q} is a complex function of ATP concentration and load (f).
Edit intent: clarity
Original:  For the biological motors studied here , their value of \mathcal{Q semi-optimized under the cellular condition ([ATP] \approx 1 mM, f=0-1 pN).
Revised:  For the biological motors studied here are semi-optimized under the cellular condition ([ATP] \approx 1 mM, f=0-1 pN).
Edit intent: clarity
Original:  We find that among the motors, kinesin-1 at single molecule level is the most efficient in cargo transport .
Revised:  Our study indicates that among many possible directions of optimization, cytoskeletal motors are designed to operate at a high speed with a minimal error while leveraging their energy resources .
Edit intent: clarity
Original:  We generalize the notion of extensivity of restriction categories to one that may be accommodated by inverse categories, and use the resulting decision maps to give a reversible representation of predicates and assertions.
Revised:  We generalize the notion of extensivity of restriction categories to one that may be accommodated by inverse categories, and use the resulting decisions to give a reversible representation of predicates and assertions.
Edit intent: clarity
Original:  This leads to a categorical semantics for structured reversible flowcharts, which we show to be both sound , adequate, and fully abstract with respect to the operational semantics under certain conditions.
Revised:  This leads to a categorical semantics for structured reversible flowcharts, which we show to be computationally sound and adequate, as well as equationally fully abstract with respect to the operational semantics under certain conditions.
Edit intent: clarity
Original:  By allowing for finite cancellation and deposition rates within a continuous reaction-diffusion setup, we account for finite memory effects on the dynamics of  latent order book.
Revised:  By allowing for finite cancellation and deposition rates within a continuous reaction-diffusion setup, we account for finite memory effects on the dynamics of the latent order book.
Edit intent: fluency
Original: We consider a practical top-k route  problem: given a collection of points of interest (POIs) with rated features and traveling costs between POIs, a user wants to find k routes from a source to a destination  , that maximally match her needs on feature preferences and can be completed within a travel cost budget .
Revised: We consider a practical top-k route problem: given a collection of points of interest (POIs) with rated features and traveling costs between POIs, a user wants to find k routes from a source to a destination , that maximally match her needs on feature preferences .
Edit intent: coherence
Original:  One challenge is dealing with the personalized diversity requirement where each user has a different trade-off between quantity (the number of POIs with a specified feature) and variety (the coverage of specified features).
Revised:  One challenge is dealing with the personalized diversity requirement where users have various trade-off between quantity (the number of POIs with a specified feature) and variety (the coverage of specified features).
Edit intent: clarity
Original:  Another challenge is the large scale of the POI network and the great many alternative routes to search.
Revised:  Another challenge is the large scale of the POI map and the great many alternative routes to search.
Edit intent: clarity
Original:  We model the personalized diversity requirement by the whole class of submodular functions, and present an optimal solution to the top-k route problem through an index structure for retrieving relevant POIs in both feature and route spaces and various strategies for pruning the search space using user preferences and constraints.
Revised:  We model the personalized diversity requirement by the whole class of submodular functions, and present an optimal solution to the top-k route search problem through indices for retrieving relevant POIs in both feature and route spaces and various strategies for pruning the search space using user preferences and constraints.
Edit intent: clarity
Original:  We also present  heuristic solutions and evaluate all the solutions on real life POI network data.
Revised:  We also present heuristic solutions and evaluate all the solutions on real life data.
Edit intent: clarity
Original:  Our findings suggest that our computationally tractable approach can be used to directly evaluate the performance sensitivity of a market making strategy to classifier error and can augment traditional market simulation based testing.
Revised:  Our findings suggest that our computationally tractable approach can be used to directly evaluate the performance sensitivity of a market making strategy to classifier error and augments traditional market simulation based testing.
Edit intent: coherence
Original:  We apply the  execution model and trade information matrix to Level II E-mini S&P 500 futures history and demonstrate an estimation approach for measuring the sensitivity of the P&L to classification error .
Revised:  We apply the trade execution model and trade information matrix to Level II E-mini S&P 500 futures history and demonstrate an estimation approach for measuring the sensitivity of the P&L to classification error .
Edit intent: fluency
Original:  Our approach directly evaluates the performance sensitivity of a market making strategy to classifier error and augments traditional market simulation based testing.
Revised:  Our approach directly evaluates the performance sensitivity of a market making strategy to prediction error and augments traditional market simulation based testing.
Edit intent: clarity
Original:  The semantics of the language is given in terms of monotonic property transformers, an extension to reactive systems of monotonic predicate transformers  which have been used to give compositional semantics to sequential programs.
Revised:  The semantics of the language is given in terms of monotonic property transformers, an extension to reactive systems of monotonic predicate transformers , which have been used to give compositional semantics to sequential programs.
Edit intent: fluency
Original: We prove that the B\"uchi topology , the automatic topology , the alphabetic topology and the strong alphabetic topology are Polish, and provideconsequences of this .
Revised: We prove that the B\"uchi topology and the automatic topology , the alphabetic topology and the strong alphabetic topology are Polish, and provideconsequences of this .
Edit intent: fluency
Original: We prove that the B\"uchi topology , the automatic topology , the alphabetic topology and the strong alphabetic topology are Polish, and provideconsequences of this .
Revised: We prove that the B\"uchi topology , the automatic topology are Polish .
Edit intent: coherence
Original:  in particular the B\"uchi and the Muller topologies in that case are not Polish  .
Revised:  in particular the B\"uchi and the Muller topologies are not Polish .
Edit intent: clarity
Original:  Earlier, we have demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIS in commonly-used brain atlases.
Revised:  Earlier, we have demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIs in commonly-used brain atlases.
Edit intent: fluency
Original:  Further, we see time-dependent changes in the ROIs' network neighborhoods , resulting in high network turnover.
Revised:  Further, we see time-dependent changes in the network neighborhoods of the ROIs, reflected in high network turnover.
Edit intent: clarity
Original:  This turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover.
Revised:  Network turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover.
Edit intent: fluency
Original:  Finally, we reveal rich internal, voxel-level correlation structure inside ROIs.
Revised:  Finally, we reveal that there is rich voxel-level correlation structure inside ROIs.
Edit intent: clarity
Original:  Because the internal structure and  connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate.
Revised:  Because the internal structure and the connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate.
Edit intent: fluency
Original:  V1 supports property graphs with mixed (both directed and undirected) edges and half-edges, with enumerated, multivalued and composite properties, and with empty property values.
Revised:  V1 supports property graphs with mixed (both directed and undirected) edges and half-edges, with multivalued and composite properties, and with empty property values.
Edit intent: clarity
Original:  V1 is a declarative visual  query language for schema-based property graphs.
Revised: The property graph is an increasingly popular data model. An important task when dealing with property graphs is pattern matching. Given a property graph schema S, a property graph G and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics. Calculated properties may be defined for vertices, edges, and subgraphs, and constraints may be imposed on their evaluation result. Query-posers would like to pose complex queries in a manner that is coherent with the way they think. They want to do it with minimal technical training, minimal effort, and minimal trial and error. The ability to express patterns in a way that is aligned with their mental processes is crucial to the flow of their work, and to the quality of the insights they can draw. Since the capabilities of the human visual system with respect to pattern perception are remarkable, it is a matter of course that query patterns were to be expressed visually. Visual query languages have the potential to be much more 'user-friendly' than their textual counterparts in the sense that patterns may be constructed and understood much more quickly and with much less mental effort. A long-standing challenge is to design a visual query language that is generic, has rich expressive power, and is highly receptive and productive. V1 is a declarative visual query language for schema-based property graphs.
Edit intent: coherence
Original:  An important task when dealing with property graphs is pattern matching . Given a property graph schema S, a property graph G  and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging  and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics.
Revised:  An important task when dealing with property graphs . Given a property graph schema S, a property graph G and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics.
Edit intent: coherence
Original:  An important task when dealing with property graphs is pattern matching . Given a property graph schema S, a property graph G  and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging  and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics.
Revised:  An important task when dealing with property graphs is pattern matching . Given a property graph schema S, a property graph G , and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics.
Edit intent: fluency
Original:  An important task when dealing with property graphs is pattern matching . Given a property graph schema S, a property graph G  and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging  and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics.
Revised:  An important task when dealing with property graphs is pattern matching . Given a property graph schema S, a property graph G and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging , and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics.
Edit intent: fluency
Original:  An important task when dealing with property graphs is pattern matching . Given a property graph schema S, a property graph G  and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging  and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics.
Revised:  An important task when dealing with property graphs is pattern matching . Given a property graph schema S, a property graph G and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints , property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics.
Edit intent: fluency
Original:  An important task when dealing with property graphs is pattern matching . Given a property graph schema S, a property graph G  and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging  and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, as well as negation, quantification, grouping, aggregation, and path semantics.
Revised:  An important task when dealing with property graphs is pattern matching . Given a property graph schema S, a property graph G and a query pattern P, all expressed in language L, pattern matching is the process of finding, merging and annotating subgraphs of G that match P. Expressive pattern languages support topological constraints and property values constraints, negations, quantifications, aggregations, and path semantics.
Edit intent: clarity
Original:  Query-posers would like to pose complex queries in a manner that is coherent with the way they think.
Revised:  Query posers would like to pose complex queries in a manner that is coherent with the way they think.
Edit intent: clarity
Original:  Query-posers would like to pose complex queries in a manner that is coherent with the way they think.
Revised:  Query-posers would like to construct patterns with minimal effort, minimal trial and error, and in a manner that is coherent with the way they think.
Edit intent: clarity
Original:  They want to do it with minimal technical training, minimal effort, and minimal trial and error.
Edit intent: coherence
Original:  The ability to express patterns in a way that is aligned with their mental processes is crucial to the flow of their work , and to the quality of the insights they can draw.
Revised:  The ability to express patterns in a way that is aligned with their mental processes is crucial to the flow of their work and to the quality of the insights they can draw.
Edit intent: fluency
Original:  In this paper, we show that already the containment problem for  linear sets is \log -hard (and therefore also \log -complete ) in \Pi_2^p . This holds even when we use a unary encoding for the numerical input parameters or when we restrict the problem to 1-dimensional linear sets ( i.e, linear sets in%DIFDELCMD < \nats%%% _0) . However, combining both restrictions  , the problem becomes solvable in polynomial time.
Revised:  It had been shown quite recently that already the containment problem for linear sets is \log -hard (and therefore also \log -complete ) in \Pi_2^p . This holds even when we use a unary encoding for the numerical input parameters or when we restrict the problem to 1-dimensional linear sets ( i.e, linear sets in%DIFDELCMD < \nats%%% _0) . However, combining both restrictions , the problem becomes solvable in polynomial time.
Edit intent: coherence
Original:  In this paper, we show that already the containment problem for  linear sets is \log -hard (and therefore also \log -complete ) in \Pi_2^p . This holds even when we use a unary encoding for the numerical input parameters or when we restrict the problem to 1-dimensional linear sets ( i.e, linear sets in%DIFDELCMD < \nats%%% _0) . However, combining both restrictions  , the problem becomes solvable in polynomial time.
Revised:  In this paper, we show that already the containment problem for linear sets is \log -complete ) in \Pi_2^p . This holds even when we use a unary encoding for the numerical input parameters or when we restrict the problem to 1-dimensional linear sets ( i.e, linear sets in%DIFDELCMD < \nats%%% _0) . However, combining both restrictions , the problem becomes solvable in polynomial time.
Edit intent: coherence