wanyu/IteraTeR_v2 huggingface dataset:
Original:  Using  anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we compute network controllability underlying the site of transcranial magnetic stimulation in the IFG between administrations of two word selection tasks.
Revised:  Using anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we computed network controllability underlying the site of transcranial magnetic stimulation in the IFG between administrations of two word selection tasks.
Edit intent: fluency
Original:  Using  anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we compute network controllability underlying the site of transcranial magnetic stimulation in the IFG between administrations of two word selection tasks.
Revised:  Using anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we compute network controllability underlying the site of transcranial magnetic stimulation in the LIFG between administrations of two word selection tasks.
Edit intent: clarity
Original:  In this work, we focus on reducing the sizes of basic structures (including input updates, gates, hidden states, cell states and outputs) within Long Short-term Memory (LSTM) units, so as to learn homogeneously-sparse LSTMs. Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with illegal LSTM units.
Revised:  In this work, we focus on reducing the sizes of basic structures (including input updates, gates, hidden states, cell states and outputs) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs. Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with illegal LSTM units.
Edit intent: clarity
Original:  In this work, we focus on reducing the sizes of basic structures (including input updates, gates, hidden states, cell states and outputs) within Long Short-term Memory (LSTM) units, so as to learn homogeneously-sparse LSTMs. Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with illegal LSTM units.
Revised:  In this work, we focus on reducing the sizes of basic structures (including input updates, gates, hidden states, cell states and outputs) within Long Short-term Memory (LSTM) units, so as to learn homogeneously-sparse LSTMs. Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Edit intent: fluency
Original:  To overcome this, we propose Intrinsic Sparse Structures (ISS) in LSTMs. By reducing one component of ISS, the sizes of  basic structures are simultaneously reduced by one such that the consistency of dimensions is maintained.
Revised:  To overcome this, we propose Intrinsic Sparse Structures (ISS) in LSTMs. By reducing one component of ISS, the sizes of those basic structures are simultaneously reduced by one such that the consistency of dimensions is maintained.
Edit intent: clarity
Original:  Our method is successfully evaluated by state-of-the-art LSTMs in applications of language modeling ( of Penn TreeBank dataset ) and machine Question Answering ( of SQuAD dataset ) . Our source code is public available .
Revised:  Our method is successfully evaluated by state-of-the-art LSTMs in applications of language modeling of Penn TreeBank dataset ) and machine Question Answering ( of SQuAD dataset ) . Our source code is public available .
Edit intent: fluency
Original:  Our method is successfully evaluated by state-of-the-art LSTMs in applications of language modeling ( of Penn TreeBank dataset ) and machine Question Answering ( of SQuAD dataset ) . Our source code is public available .
Revised:  Our method is successfully evaluated by state-of-the-art LSTMs in applications of language modeling ( of Penn TreeBank dataset ) and machine Question Answering of SQuAD dataset ) . Our source code is public available .
Edit intent: fluency
Original:  Our method is successfully evaluated by state-of-the-art LSTMs in applications of language modeling ( of Penn TreeBank dataset ) and machine Question Answering ( of SQuAD dataset ) . Our source code is public available .
Revised:  Our method is successfully evaluated by state-of-the-art LSTMs in applications of language modeling ( of Penn TreeBank dataset ) and machine Question Answering ( of SQuAD dataset . Our source code is public available .
Edit intent: fluency
Original: Model compression is significant for  wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and in business clusters requiring quick responses to large-scale service requests.
Revised: Model compression is significant for the wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and in business clusters requiring quick responses to large-scale service requests.
Edit intent: fluency
Original: Model compression is significant for  wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and in business clusters requiring quick responses to large-scale service requests.
Revised: Model compression is significant for wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and business clusters requiring quick responses to large-scale service requests.
Edit intent: fluency
Original:  In this work , we focus on reducing the sizes of basic structures ( including input updates, gates, hidden states, cell states and outputs ) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs . Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Revised:  This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures ( including input updates, gates, hidden states, cell states and outputs ) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs . Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Edit intent: clarity
Original:  In this work , we focus on reducing the sizes of basic structures ( including input updates, gates, hidden states, cell states and outputs ) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs . Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Revised:  In this work , we focus on reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden states, cell states and outputs ) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs . Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Edit intent: fluency
Original:  In this work , we focus on reducing the sizes of basic structures ( including input updates, gates, hidden states, cell states and outputs ) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs . Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Revised:  In this work , we focus on reducing the sizes of basic structures ( including input updates, gates, hidden states, cell states and outputs . Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Edit intent: coherence
Original:  In this work , we focus on reducing the sizes of basic structures ( including input updates, gates, hidden states, cell states and outputs ) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs . Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Revised:  In this work , we focus on reducing the sizes of basic structures ( including input updates, gates, hidden states, cell states and outputs ) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs . Independently reducing the sizes of basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Edit intent: clarity
Original:  In this work , we focus on reducing the sizes of basic structures ( including input updates, gates, hidden states, cell states and outputs ) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs . Independently reducing the sizes of those basic structures can result in unmatched dimensions among them, and consequently, end up with invalid LSTM units.
Revised:  In this work , we focus on reducing the sizes of basic structures ( including input updates, gates, hidden states, cell states and outputs ) within Long Short-term Memory (LSTM) units, so as to learn structurally-sparse LSTMs . Independently reducing the sizes of those basic structures can result in inconsistent dimensions among them, and consequently, end up with invalid LSTM units.
Edit intent: clarity
Original:  To overcome this , we propose Intrinsic Sparse Structures (ISS) in LSTMs. By reducing one component of ISS , the sizes of those basic structures are simultaneously reduced by one such that the consistency of dimensions is maintained .
Revised:  To overcome the problem , we propose Intrinsic Sparse Structures (ISS) in LSTMs. By reducing one component of ISS , the sizes of those basic structures are simultaneously reduced by one such that the consistency of dimensions is maintained .
Edit intent: clarity
Original:  To overcome this , we propose Intrinsic Sparse Structures (ISS) in LSTMs. By reducing one component of ISS , the sizes of those basic structures are simultaneously reduced by one such that the consistency of dimensions is maintained .
Revised:  To overcome this , we propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS , the sizes of those basic structures are simultaneously reduced by one such that the consistency of dimensions is maintained .
Edit intent: clarity
Original:  To overcome this , we propose Intrinsic Sparse Structures (ISS) in LSTMs. By reducing one component of ISS , the sizes of those basic structures are simultaneously reduced by one such that the consistency of dimensions is maintained .
Revised:  To overcome this , we propose Intrinsic Sparse Structures (ISS) in LSTMs. By reducing one component of ISS will decrease the sizes of those basic structures are simultaneously reduced by one such that the consistency of dimensions is maintained .
Edit intent: clarity
Original:  To overcome this , we propose Intrinsic Sparse Structures (ISS) in LSTMs. By reducing one component of ISS , the sizes of those basic structures are simultaneously reduced by one such that the consistency of dimensions is maintained .
Revised:  To overcome this , we propose Intrinsic Sparse Structures (ISS) in LSTMs. By reducing one component of ISS , the sizes of all basic structures by one simultaneously and thereby always maintain the dimension consistency .
Edit intent: clarity
Original:  By learning ISS within LSTM units, the eventual LSTMs are still regular LSTMs but have much smaller sizes of basic structures.
Revised:  By learning ISS within LSTM units, the obtained LSTMs remain regular while having much smaller basic structures.
Edit intent: clarity
Original:  Our method achieves 10.59 X speedup in state-of-the-art LSTMs, without losing any perplexity of language modeling of Penn TreeBank dataset.
Revised:  Our method achieves 10.59 x speedup in state-of-the-art LSTMs, without losing any perplexity of language modeling of Penn TreeBank dataset.
Edit intent: fluency
Original:  To overcome the problem, we propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS will  decrease the sizes of all basic structures by one simultaneously and thereby always maintain the dimension consistency.
Revised:  To overcome the problem, we propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS will simultaneously decrease the sizes of all basic structures by one simultaneously and thereby always maintain the dimension consistency.
Edit intent: fluency
Original:  To overcome the problem, we propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS will  decrease the sizes of all basic structures by one simultaneously and thereby always maintain the dimension consistency.
Revised:  To overcome the problem, we propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS will decrease the sizes of all basic structures by one and thereby always maintain the dimension consistency.
Edit intent: clarity
Original:  Our method achieves 10.59x speedup in state-of-the-art LSTMs, without losing any perplexity of  language modeling of Penn TreeBank dataset.
Revised:  Our method achieves 10.59x speedup without losing any perplexity of language modeling of Penn TreeBank dataset.
Edit intent: clarity
Original:  Our method achieves 10.59x speedup in state-of-the-art LSTMs, without losing any perplexity of  language modeling of Penn TreeBank dataset.
Revised:  Our method achieves 10.59x speedup in state-of-the-art LSTMs, without losing any perplexity of a language modeling of Penn TreeBank dataset.
Edit intent: fluency
Original:  Our  source code is public available at URL
Revised:  Our source code is publicly available at URL
Edit intent: fluency
Original: Millions of users on the Internet discuss a variety of topics on Question and Answer (Q&A) instances.
Revised: Millions of users on the Internet discuss a variety of topics on Question-and-Answer (Q&A) instances.
Edit intent: fluency
Original:  However, not all instances and topics receive the same amount of attention, as some thrive and achieve self-sustaining levels of activity  while others fail to attract users and either never grow beyond being a small niche community or become inactive.
Revised:  However, not all instances and topics receive the same amount of attention, as some thrive and achieve self-sustaining levels of activity , while others fail to attract users and either never grow beyond being a small niche community or become inactive.
Edit intent: fluency
Original:  To that end, we extract, model and cluster user activity-based time series from 50 randomly selected Q&A instances from the StackExchange network to characterize user behavior.
Revised:  To that end, we extract, model and cluster user activity-based time series from 50 randomly selected Q&A instances from the Stack Exchange network to characterize user behavior.
Edit intent: fluency
Original:  Our  categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, which can potentially help operators of such instances not only to quantitatively assess status and progress , but also allow them to optimize & community building efforts   .
Revised:  Our parsimonious categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, which can potentially help operators of such instances not only to quantitatively assess status and progress , but also allow them to optimize & community building efforts .
Edit intent: clarity
Original:  Our  categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, which can potentially help operators of such instances not only to quantitatively assess status and progress , but also allow them to optimize & community building efforts   .
Revised:  Our categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, and can potentially help operators of such instances not only to quantitatively assess status and progress , but also allow them to optimize & community building efforts .
Edit intent: coherence
Original:  Our  categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, which can potentially help operators of such instances not only to quantitatively assess status and progress , but also allow them to optimize & community building efforts   .
Revised:  Our categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, which can potentially help operators of such instances : We not only quantitatively assess progress of Q & community building efforts .
Edit intent: clarity
Original:  Our  categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, which can potentially help operators of such instances not only to quantitatively assess status and progress , but also allow them to optimize & community building efforts   .
Revised:  Our categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, which can potentially help operators of such instances not only to quantitatively assess status and progress , but also allow them to optimize &A community building efforts .
Edit intent: fluency
Original: Representing large scale motions and topological changes in the finite volume (FV) framework, while at the same time preserving the accuracy of the numerical solution, is difficult.
Revised: Representing large-scale motions and topological changes in the finite volume (FV) framework, while at the same time preserving the accuracy of the numerical solution, is difficult.
Edit intent: fluency
Original:  In order to introduce motion into the system, we rely on the classical ALE (Arbitrary Lagrangian-Eulerian) approach, but with the caveat that the non-time-dependent motion of elements instantaneously crossing the interface is handled separately from the timedependent component.
Revised:  In order to introduce motion into the system, we rely on the classical ALE (Arbitrary Lagrangian-Eulerian) approach, but with the caveat that the non-time-dependent motion of elements instantaneously crossing the interface is handled separately from the time dependent component.
Edit intent: fluency
Original: Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or forensic investigations.
Revised: Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations.
Edit intent: clarity
Original:  We demonstrate that the trained networks'reconstruction error regularized by the  individual attribute probabilities of a journal entry can be interpreted as a highly adaptive anomaly assessment.
Revised:  We demonstrate that the trained networks'reconstruction error regularized by the entry's individual attribute probabilities of a journal entry can be interpreted as a highly adaptive anomaly assessment.
Edit intent: clarity
Original:  We demonstrate that the trained networks'reconstruction error regularized by the  individual attribute probabilities of a journal entry can be interpreted as a highly adaptive anomaly assessment.
Revised:  We demonstrate that the trained networks'reconstruction error regularized by the individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment.
Edit intent: clarity
Original:  Our empirical study, based on two datasets of real-world journal entries, demonstrates the effectiveness of the approach and outperforms several baseline anomaly detection methods.
Revised:  Experiments on two real-world datasets of journal entries, demonstrates the effectiveness of the approach and outperforms several baseline anomaly detection methods.
Edit intent: clarity
Original:  Our empirical study, based on two datasets of real-world journal entries, demonstrates the effectiveness of the approach and outperforms several baseline anomaly detection methods.
Revised:  Our empirical study, based on two datasets of real-world journal entries, show the effectiveness of the approach and outperforms several baseline anomaly detection methods.
Edit intent: fluency
Original:  Initial feedback received by accountants underpinned the quality of our approach capturing highly relevant anomalies in the data.
Revised:  Initial feedback received by accountants underpinned the quality of the approach in capturing highly relevant anomalies in the data.
Edit intent: fluency
Original:  We make several conceptual, algorithmic, and practical contributions: (i) Inspired by the recent advances on approximate couplings and randomness alignment, we present a new proof technique called coupling strategies, which casts differential privacy proofs as discovering a strategy in a game where we have finite privacy resources to expend.
Revised:  We make several conceptual, algorithmic, and practical contributions: (i) Inspired by the recent advances on approximate couplings and randomness alignment, we present a new proof technique called coupling strategies, which casts differential privacy proofs as a winning strategy in a game where we have finite privacy resources to expend.
Edit intent: clarity
Original:  (ii) To discover a winning strategy, we present a constraint-based formulation of the problem as solving a set of Horn modulo couplings (HMC) constraints, a novel combination of first-order Horn clauses and probabilistic constraints.
Revised:  (ii) To discover a winning strategy, we present a constraint-based formulation of the problem as a set of Horn modulo couplings (HMC) constraints, a novel combination of first-order Horn clauses and probabilistic constraints.
Edit intent: clarity
Original:  (iii) We present a technique for solving HMCconstraints by transforming probabilistic constraints into logical constraints with uninterpreted functions.
Revised:  (iii) We present a technique for solving HMC constraints by transforming probabilistic constraints into logical constraints with uninterpreted functions.
Edit intent: fluency
Original: Complex Event Processing (CEP) has emerged as the unifying field for technologies that require processing and correlating heterogeneous distributed data sources in real-time.
Revised: Complex Event Processing (CEP) has emerged as the unifying field for technologies that require processing and correlating distributed data sources in real-time.
Edit intent: clarity
Original:  However, existing CEP frameworks are based on ad-hoc solutions that do not rely on solid theoretical ground , making them hard to understand , extend or generalize.
Revised:  However, existing CEP languages lack from a clear semantics , making them hard to understand , extend or generalize.
Edit intent: clarity
Original:  However, existing CEP frameworks are based on ad-hoc solutions that do not rely on solid theoretical ground , making them hard to understand , extend or generalize.
Revised:  However, existing CEP frameworks are based on ad-hoc solutions that do not rely on solid theoretical ground , making them hard to understand and generalize.
Edit intent: coherence
Original:  As a starting point, we propose a formal language for specifying complex events, called CEPL , that contains the common features used in the literature and has a simple and denotational semantics.
Revised:  We propose a formal language for specifying complex events, called CEPL , that contains the common features used in the literature and has a simple and denotational semantics.
Edit intent: coherence