wanyu/IteraTeR_v2 huggingface dataset:
Original:  which is conjectured not to be solved by two-way quantum finite automata in polynomial time, can be solved by Las-Vegas affine finite automata in linear time . Lastly, we show that how a counter helps for affine finite automata by showing that the language MANYTWINS , which is conjectured not to be recognized by affine, quantum or classical finite state models in polynomial time, can be recognized by affine counter automata with one-sided bounded-error in realtime.
Revised:  which is conjectured not to be solved by two-way quantum finite automata in polynomial time, can be solved by Las-Vegas affine finite automata . Lastly, we show that how a counter helps for affine finite automata by showing that the language MANYTWINS , which is conjectured not to be recognized by affine, quantum or classical finite state models in polynomial time, can be recognized by affine counter automata with one-sided bounded-error in realtime.
Edit intent: clarity
Original:  Lastly, we show that how a counter helps for affine finite automata by showing that the language \mathtt{MANYTWINS , which is conjectured not to be recognized by affine, quantum or classical finite state models in polynomial time, can be recognized by affine counter automata with one-sided bounded-error in realtime.
Revised:  Lastly, we show that how a counter helps for affine finite automata by showing that the language MANYTWINS , which is conjectured not to be recognized by affine, quantum or classical finite state models in polynomial time, can be recognized by affine counter automata with one-sided bounded-error in realtime.
Edit intent: fluency
Original: Many people dream to become famous, YouTube video makers also wish their videos to have a large number of audience, and product retailers always hope to expose their products to customers as many as possible.
Revised: Many people dream to become famous, YouTube video makers also wish their videos to have a large audience, and product retailers always hope to expose their products to customers as many as possible.
Edit intent: clarity
Original:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability in some properly defined network, and all  the previously mentioned phenomena can be described as a target node wants to be discovered easily by existing nodes in the network.
Revised:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability in some properly defined network, and all of the previously mentioned phenomena can be described as a target node wants to be discovered easily by existing nodes in the network.
Edit intent: coherence
Original:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability in some properly defined network, and all  the previously mentioned phenomena can be described as a target node wants to be discovered easily by existing nodes in the network.
Revised:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability in some properly defined network, and all the previously mentioned phenomena can be stated as: a target node wants to be discovered easily by existing nodes in the network.
Edit intent: clarity
Original:  While the optimization problem is NP-hard, we find that the defined discoverability measures satisfy submodularity and supermodularity respectively, that enable us to use a greedy algorithm to find provably near-optimal solutions.
Revised:  While the optimization problem is NP-hard, we find that the defined discoverability measures satisfy good properties that enable us to use a greedy algorithm to find provably near-optimal solutions.
Edit intent: clarity
Original:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability in some properly defined network, and all of the previously mentioned phenomena can be stated as: a target node wants to be discovered easily by existing nodes in the network.
Revised:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability on some properly defined network, and all of the previously mentioned phenomena can be stated as: a target node wants to be discovered easily by existing nodes in the network.
Edit intent: clarity
Original:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability in some properly defined network, and all of the previously mentioned phenomena can be stated as: a target node wants to be discovered easily by existing nodes in the network.
Revised:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability in some properly defined network, and all of the previously mentioned phenomena can be commonly stated as a target node wants to be discovered easily by existing nodes in the network.
Edit intent: clarity
Original:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability in some properly defined network, and all of the previously mentioned phenomena can be stated as: a target node wants to be discovered easily by existing nodes in the network.
Revised:  We find that fame, popularity, or exposure, could be modeled as a node's discoverability in some properly defined network, and all of the previously mentioned phenomena can be stated as: a target node wants to be discovered easily by the other nodes in the network.
Edit intent: clarity
Original:  In this work, we explicitly define the node discoverability in a network, and formulate a general node discoverability optimization problem .
Revised:  In this work, we explicitly define a node's discoverability in a network, and formulate a general node discoverability optimization problem .
Edit intent: clarity
Original:  While the optimization problem is  NP-hard, we find that the defined discoverability measures satisfy good properties that enable us to use a greedy algorithm to find provably near-optimal solutions.
Revised:  While the optimization problem is proven to be NP-hard, we find that the defined discoverability measures satisfy good properties that enable us to use a greedy algorithm to find provably near-optimal solutions.
Edit intent: clarity
Original:  While the optimization problem is  NP-hard, we find that the defined discoverability measures satisfy good properties that enable us to use a greedy algorithm to find provably near-optimal solutions.
Revised:  While the optimization problem is NP-hard, we find that the defined discoverability measures have good properties that enable us to use a greedy algorithm to find provably near-optimal solutions.
Edit intent: clarity
Original:  The computational complexity of a greedy algorithm is dominated by the time cost of an oracle call, i.e., calculating the marginal gain of a given node.
Revised:  The computational complexity of a greedy algorithm is dominated by the time cost of an oracle call, i.e., calculating the marginal gain of a node.
Edit intent: clarity
Original:  Experiments conducted on real graphs demonstrate that our method is hundreds of times faster than an exact method using dynamic programming, thereby allowing us to solve the  optimization problem on large networks.
Revised:  Experiments conducted on real-world networks demonstrate that our method is hundreds of times faster than an exact method using dynamic programming, thereby allowing us to solve the optimization problem on large networks.
Edit intent: clarity
Original:  Advantages of the approach relative to typical spatial-projection-based nonlinear model reduction methods such as Galerkin projection and least-squares Petrov-Galerkin projection include: (1) a reduction of both the spatial and temporal dimensions of the dynamical system, (2) the removal of spurious temporal modes (e.g., unstable growth) from the state space, and ( 2 ) error bounds that exhibit slower growth in time.
Revised:  Advantages of the approach relative to typical spatial-projection-based nonlinear model reduction methods such as Galerkin projection and least-squares Petrov-Galerkin projection include: (1) a reduction of both the spatial and temporal dimensions of the dynamical system, (2) the removal of spurious temporal modes (e.g., unstable growth) from the state space, and ( 3 ) error bounds that exhibit slower growth in time.
Edit intent: clarity
Original: In this paper, we consider the strict self-assembly of fractals in one of the most well-studied models of tile based self-assembling systems known as the Two-handed Tile Assembly Model (2HAM).
Revised: We consider the self-assembly of fractals in one of the most well-studied models of tile based self-assembling systems known as the Two-handed Tile Assembly Model (2HAM).
Edit intent: clarity
Original:  We are particularly interested in a class of fractals called discrete self-similar fractals (a class of fractals that includes the discrete Sierpinski's carpet).
Revised:  In particular, we focus our attention on a class of fractals called discrete self-similar fractals (a class of fractals that includes the discrete Sierpinski's carpet).
Edit intent: clarity
Original:  We are particularly interested in a class of fractals called discrete self-similar fractals (a class of fractals that includes the discrete Sierpinski's carpet).
Revised:  We are particularly interested in a class of fractals called discrete self-similar fractals (a class of fractals that includes the discrete Sierpi\'nski carpet).
Edit intent: fluency
Original:  Finally, we give an example of a 3-sided fractal (which is not a tree fractal) that cannot be strictly self-assembled by any 2HAM system.
Revised:  Finally, we show that there exists a 3-sided fractal (which is not a tree fractal) that cannot be strictly self-assembled by any 2HAM system.
Edit intent: clarity
Original: We introduce a unified framework for  a class of optimization based statistical learning problems used by LogicBlox retail-planning and forecasting applications, where the input data is given by queries over relational databases .
Revised: We introduce a unified framework for a class of statistical learning models inside a relational database .
Edit intent: clarity
Original:  The algorithms and models proposed in the paper have already been implemented inside the LogicBlox database engine and used in retail-planning and forecasting applications, with significant performance benefits over out-of-database solutions that require the costly data-export loop.
Revised:  The algorithms and models proposed in the paper have already been implemented and deployed in retail-planning and forecasting applications, with significant performance benefits over out-of-database solutions that require the costly data-export loop.
Edit intent: clarity
Original: In-database analytics is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features , export the data, convert data format , train models using an external tool, reimport the parameters.
Revised: Solving analytics inside the database is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features , export the data, convert data format , train models using an external tool, reimport the parameters.
Edit intent: clarity
Original:  This paper introduces a unified framework for training and evaluating a class of statistical learning models inside a relational database.
Revised:  This article introduces a unified framework for training and evaluating a class of statistical learning models inside a relational database.
Edit intent: fluency
Original:  We show that, by synergizing key tools from relational database theory such as schema information, query structure,  recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database learning problems and design efficient algorithms to solve them.
Revised:  We show that, by synergizing key tools from relational database theory such as schema information, query structure, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database analytics problems and design efficient algorithms to solve them.
Edit intent: clarity
Original: Solving analytics inside the database is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations;
Revised: Integrated solutions for analytics over relational databases are of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations;
Edit intent: clarity
Original: Solving analytics inside the database is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations;
Revised: Solving analytics inside the database is of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations;
Edit intent: clarity
Original:  These in-database analytics problems are also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models.
Revised:  These integrated solutions are also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models.
Edit intent: clarity
Original:  This article introduces a unified framework for training and evaluating a class of statistical learning models inside a relational database .
Revised:  This article introduces a unified framework for training and evaluating a class of statistical learning models over relational databases .
Edit intent: clarity
Original:  We show that, by synergizing key tools from relational database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database analytics problems and design efficient  algorithms to solve them.
Revised:  We show that, by synergizing key tools from database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database analytics problems and design efficient algorithms to solve them.
Edit intent: clarity
Original:  We show that, by synergizing key tools from relational database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database analytics problems and design efficient  algorithms to solve them.
Revised:  We show that, by synergizing key tools from relational database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as tensor and matrix operations, one can formulate in-database analytics problems and design efficient algorithms to solve them.
Edit intent: clarity
Original:  We show that, by synergizing key tools from relational database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database analytics problems and design efficient  algorithms to solve them.
Revised:  We show that, by synergizing key tools from relational database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate relational analytics problems and design efficient algorithms to solve them.
Edit intent: clarity
Original:  Increasingly complex systems have been conceived without comparison to a simpler neural baseline system that would justify their complexity.
Revised:  Increasingly complex systems have been conceived without comparison to simpler neural baseline system that would justify their complexity.
Edit intent: fluency
Original:  Increasingly complex systems have been conceived without comparison to a simpler neural baseline system that would justify their complexity.
Revised:  Increasingly complex systems have been conceived without comparison to a simpler neural baseline systems that would justify their complexity.
Edit intent: fluency
Original:  In this work, we propose a simple heuristic that guided the development of FastQA, an efficient end-to-end neural model for question answering that is very competitive with existing models.
Revised:  In this work, we propose a simple heuristic that guides the development of FastQA, an efficient end-to-end neural model for question answering that is very competitive with existing models.
Edit intent: fluency
Original:  The overall strong performance of FastQA puts results of existing, more complex models into perspective.
Revised:  The overall strong performance of FastQA puts results of previous systems and the complexity of recent QA datasets into perspective.
Edit intent: clarity
Original: This paper describes our approach for the triple scoring task at  WSDM Cup 2017.
Revised: This paper describes our approach for the triple scoring task at the WSDM Cup 2017.
Edit intent: fluency
Original:  The task aims to assign a relevance score for each pair of entities and their types in a knowledge base in order to enhance the ranking results in entity retrieval tasks.
Revised:  The task required participants to assign a relevance score for each pair of entities and their types in a knowledge base in order to enhance the ranking results in entity retrieval tasks.
Edit intent: clarity
Original:  The experimental results show that our proposed method achieves the best performance in one out of three measures , and performs competitively in the other two measures  .
Revised:  The experimental results showed that our proposed method achieves the best performance in one out of three measures , and performs competitively in the other two measures .
Edit intent: fluency
Original:  The experimental results show that our proposed method achieves the best performance in one out of three measures , and performs competitively in the other two measures  .
Revised:  The experimental results show that our proposed method achieved the best performance in one out of three measures , and performs competitively in the other two measures .
Edit intent: fluency
Original:  Additionally, we prove the hierarchies for sublinear width bounded error quantum k-OBDDs using our lower bounds for k=o(n).
Revised:  Additionally, we prove the hierarchies for sublinear width bounded error k-QOBDDs, for k=o(n).
Edit intent: clarity
Original:  Also we prove  hierarchy for polynomial size bounded error quantum k-OBDDs constant k, and it differs from  situation with unbounded error where  known that increasing of k does not gives any advantages.
Revised:  Also we prove a hierarchy for polynomial size bounded error quantum k-OBDDs constant k, and it differs from situation with unbounded error where known that increasing of k does not gives any advantages.
Edit intent: fluency
Original:  Also we prove  hierarchy for polynomial size bounded error quantum k-OBDDs constant k, and it differs from  situation with unbounded error where  known that increasing of k does not gives any advantages.
Revised:  Also we prove hierarchy for polynomial size bounded error k-QOBDDs for constant k, and it differs from situation with unbounded error where known that increasing of k does not gives any advantages.
Edit intent: clarity
Original:  Also we prove  hierarchy for polynomial size bounded error quantum k-OBDDs constant k, and it differs from  situation with unbounded error where  known that increasing of k does not gives any advantages.
Revised:  Also we prove hierarchy for polynomial size bounded error quantum k-OBDDs constant k, and it differs from the situation with unbounded error where known that increasing of k does not gives any advantages.
Edit intent: fluency
Original:  Also we prove  hierarchy for polynomial size bounded error quantum k-OBDDs constant k, and it differs from  situation with unbounded error where  known that increasing of k does not gives any advantages.
Revised:  Also we prove hierarchy for polynomial size bounded error quantum k-OBDDs constant k, and it differs from situation with unbounded error where it is known that increasing of k does not gives any advantages.
Edit intent: clarity
Original:  It is restricted version of multi-round quantum communication protocols.
Revised:  These are restricted version of multi-round quantum communication protocols.
Edit intent: fluency
Original:   " Automata " term means that players forget history from previous rounds, and its behavior is obtained only by input and message from the opposite player.
Revised:  The " Automata " term means that players forget history from previous rounds, and its behavior is obtained only by input and message from the opposite player.
Edit intent: fluency
Original:   " Automata " term means that players forget history from previous rounds, and its behavior is obtained only by input and message from the opposite player.
Revised:  " Automata " term means that players forget history from previous rounds, and their behavior is obtained only by input and message from the opposite player.
Edit intent: fluency
Original:  This model is interesting because it allows to get lower bounds for models like automata, Ordered Binary Decision Diagrams and streaming algorithms.
Revised:  The model is interesting because it allows to get lower bounds for models like automata, Ordered Binary Decision Diagrams and streaming algorithms.
Edit intent: fluency
Original:  This model is interesting because it allows to get lower bounds for models like automata, Ordered Binary Decision Diagrams and streaming algorithms.
Revised:  This model is interesting because this allows us to get lower bounds for models like automata, Ordered Binary Decision Diagrams and streaming algorithms.
Edit intent: fluency
Original:  In the same time, we can prove stronger results with this restriction.
Revised:  At the same time, we can prove stronger results with this restriction.
Edit intent: fluency
Original:  Additionally, we show  lower bound for Disjointness function for this model.
Revised:  Additionally, we show a lower bound for Disjointness function for this model.
Edit intent: fluency
Original:   As an application of communication complexity results, we consider a quantum Ordered Read-k-times Branching Programs (k-QOBDD).
Revised:  \% As an application of communication complexity results, we consider a quantum Ordered Read-k-times Branching Programs (k-QOBDD).
Edit intent: fluency
Original:   As an application of communication complexity results, we consider a quantum Ordered Read-k-times Branching Programs (k-QOBDD).
Revised:  As an application of communication complexity results, we consider Quantum Ordered Read-k-times Branching Programs (k-QOBDD).
Edit intent: fluency
Original:  Our communication complexity result allows us to get lower bound for k-QOBDD and  hierarchies for sublinear width bounded error k-QOBDDs, for k=o(\sqrt{n}).
Revised:  Our communication complexity result allows us to get lower bound for k-QOBDD and to prove hierarchies for sublinear width bounded error k-QOBDDs, for k=o(\sqrt{n}).
Edit intent: coherence