wanyu/IteraTeR_v2 huggingface dataset:
Original:  Besides, the original DPC algorithm requires visiting a large number of objects  making it slow.
Revised:  Besides, the original DPC algorithm requires visiting a large number of objects , making it slow.
Edit intent: fluency
Original:  (i) a simple List Index  and (ii) an advanced Cumulative Histogram Index.
Revised:  (i) a simple List Index , and (ii) an advanced Cumulative Histogram Index.
Edit intent: fluency
Original:  To remedy this for memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost provided slight inaccuracies are admissible.
Revised:  For memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost provided slight inaccuracies are admissible.
Edit intent: coherence
Original:  To remedy this for memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost provided slight inaccuracies are admissible.
Revised:  To remedy this for memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost , provided that slight inaccuracies are admissible.
Edit intent: coherence
Original:  The experimental insights obtained  help to guide in selecting the befitting index.
Revised:  The experimental insights obtained can help to guide in selecting the befitting index.
Edit intent: coherence
Original:  The experimental insights obtained  help to guide in selecting the befitting index.
Revised:  The experimental insights obtained help to guide in selecting a befitting index.
Edit intent: fluency
Original:  The inclusion problem is the following: given two DPAs P1 and P2 , determine whether the language recognized by P1 is a subset of the language recognized by P2 , and if not, return an ultimately periodic omega word accepted by P1 but not P2 . We describe a polynomial time algorithm to solve this problem  . Corollaries include polynomial time algorithms to solve the equivalence problem for DPAs  and also the inclusion and equivalence problems for deterministic Buechi and coBuechi acceptors.
Revised:  The inclusion problem is the following: given two DPAs P1 and P2 , determine whether the language recognized by A1 is a subset of the language recognized by P2 , and if not, return an ultimately periodic omega word accepted by P1 but not P2 . We describe a polynomial time algorithm to solve this problem . Corollaries include polynomial time algorithms to solve the equivalence problem for DPAs and also the inclusion and equivalence problems for deterministic Buechi and coBuechi acceptors.
Edit intent: fluency
Original:  The inclusion problem is the following: given two DPAs P1 and P2 , determine whether the language recognized by P1 is a subset of the language recognized by P2 , and if not, return an ultimately periodic omega word accepted by P1 but not P2 . We describe a polynomial time algorithm to solve this problem  . Corollaries include polynomial time algorithms to solve the equivalence problem for DPAs  and also the inclusion and equivalence problems for deterministic Buechi and coBuechi acceptors.
Revised:  The inclusion problem is the following: given two DPAs P1 and P2 , determine whether the language recognized by P1 is a subset of the language recognized by A2 , and if not, return an ultimately periodic omega word accepted by P1 but not P2 . We describe a polynomial time algorithm to solve this problem . Corollaries include polynomial time algorithms to solve the equivalence problem for DPAs and also the inclusion and equivalence problems for deterministic Buechi and coBuechi acceptors.
Edit intent: fluency
Original:  The inclusion problem is the following: given two DPAs P1 and P2 , determine whether the language recognized by P1 is a subset of the language recognized by P2 , and if not, return an ultimately periodic omega word accepted by P1 but not P2 . We describe a polynomial time algorithm to solve this problem  . Corollaries include polynomial time algorithms to solve the equivalence problem for DPAs  and also the inclusion and equivalence problems for deterministic Buechi and coBuechi acceptors.
Revised:  The inclusion problem is the following: given two DPAs P1 and P2 , determine whether the language recognized by P1 is a subset of the language recognized by P2 , and if not, return an ultimately periodic omega word accepted by A1 but not A2 . We describe a polynomial time algorithm to solve this problem . Corollaries include polynomial time algorithms to solve the equivalence problem for DPAs and also the inclusion and equivalence problems for deterministic Buechi and coBuechi acceptors.
Edit intent: clarity
Original:  The inclusion problem is the following: given two DPAs P1 and P2 , determine whether the language recognized by P1 is a subset of the language recognized by P2 , and if not, return an ultimately periodic omega word accepted by P1 but not P2 . We describe a polynomial time algorithm to solve this problem  . Corollaries include polynomial time algorithms to solve the equivalence problem for DPAs  and also the inclusion and equivalence problems for deterministic Buechi and coBuechi acceptors.
Revised:  The inclusion problem is the following: given two DPAs P1 and P2 , determine whether the language recognized by P1 is a subset of the language recognized by P2 , and if not, return an ultimately periodic omega word accepted by P1 but not P2 . We describe polynomial time algorithms to solve this problem . Corollaries include polynomial time algorithms to solve the equivalence problem for DPAs and also the inclusion and equivalence problems for deterministic Buechi and coBuechi acceptors.
Edit intent: fluency
Original:  In the heavy-tailed case \% (i.e., \alpha\in(0,1)), we characterize the optimal threshold level for both policies.
Revised:  In the heavy-tailed case , we characterize the optimal threshold level for both policies.
Edit intent: coherence
Original:  \%although they do not attain the performance of our loose upper bound.
Edit intent: coherence
Original:  We also study the utility threshold policy in an unbalanced matching market with heavy-tailed matching utilities , and find that the buyers and sellers have the same asymptotically optimal utility threshold  .
Revised:  We also study the utility threshold policy in an unbalanced matching market with heavy-tailed matching utilities and find that the buyers and sellers have the same asymptotically optimal utility threshold .
Edit intent: fluency
Original: We believe that this provides a simpler explanation for the good performance of FTL in this context .  - We provide an efficient procedure for approximating convex bodies by curved ones , smoothly trading off approximation error and curvature , allowing one to extend the applicability of algorithms for curved set to non-curved ones .
Revised: We believe that this provides a simpler explanation for the good performance of FTL in this context . - We provide an efficient procedure for approximating convex bodies by curved ones , smoothly trading off approximation error and curvature . This allows one to extend the applicability of algorithms for curved set to non-curved ones .
Edit intent: coherence
Original:  For the parallel computing, both the input data and the Vandermonde matrix are partitioned and distributed across TPU cores .
Revised:  For the parallel computing, both the input data .
Edit intent: clarity
Original:  The one-shuffle scheme is designed for the interconnect topology of TPU clusters, requiring minimal communication time among TPU cores.
Revised:  The one-shuffle scheme is designed for the interconnect topology of TPU clusters, minimizing the time required by the communication among TPU cores.
Edit intent: clarity
Original:  Through the data decomposition, the dense matrix multiplications in KDFT and the Cooley-Tukey-algorithm-based transform in FFT are kept local within TPU cores and can be performed completely in parallel.
Revised:  Through the data decomposition, the dense matrix multiplications in KDFT and FFT are kept local within TPU cores and can be performed completely in parallel.
Edit intent: clarity
Original:  Through the data decomposition, the dense matrix multiplications in KDFT and the Cooley-Tukey-algorithm-based transform in FFT are kept local within TPU cores and can be performed completely in parallel.
Revised:  Through the data decomposition, the dense matrix multiplications in KDFT and the Cooley-Tukey-algorithm-based transform in FFT are kept local within TPU cores , which can be performed completely in parallel.
Edit intent: coherence
Original:  Both parallel algorithms, namely, KDFT and FFT , are implemented in TensorFlow owing to its rich set of functionalities for scientific computing and simplicity in realizing parallel computing algorithms .
Revised:  Both KDFT and FFT , are implemented in TensorFlow owing to its rich set of functionalities for scientific computing and simplicity in realizing parallel computing algorithms .
Edit intent: coherence
Original:  Both parallel algorithms, namely, KDFT and FFT , are implemented in TensorFlow owing to its rich set of functionalities for scientific computing and simplicity in realizing parallel computing algorithms .
Revised:  Both parallel algorithms, namely, KDFT and FFT are implemented in TensorFlow owing to its rich set of functionalities for scientific computing and simplicity in realizing parallel computing algorithms .
Edit intent: fluency
Original:  Both parallel algorithms, namely, KDFT and FFT , are implemented in TensorFlow owing to its rich set of functionalities for scientific computing and simplicity in realizing parallel computing algorithms .
Revised:  Both parallel algorithms, namely, KDFT and FFT , are implemented in TensorFlow .
Edit intent: clarity
Original:  Strong and weak scaling analyses are provided to demonstrate the high parallel efficiency of the two DFT implementations on TPUs.
Revised:  Scaling analysis is provided to demonstrate the high parallel efficiency of the two DFT implementations on TPUs.
Edit intent: clarity
Original:  Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and  properties and structure varying over time.
Revised:  Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and their properties and structure varying over time.
Edit intent: clarity
Original:  Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and  properties and structure varying over time.
Revised:  Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and properties and structure vary over time.
Edit intent: fluency
Original:  These are seen in social, telecom and transit networks.
Revised:  These are seen in social, telecom , transit and epidemic networks.
Edit intent: clarity
Original:  We  offer sub-second query latencies in most cases , which is 154x-1786x faster compared to industry-leading Neo4J shared-memory graph database and the JanusGraph / Spark distributed graph query engine.
Revised:  We often offer sub-second query latencies in most cases , which is 154x-1786x faster compared to industry-leading Neo4J shared-memory graph database and the JanusGraph / Spark distributed graph query engine.
Edit intent: clarity
Original:  The benchmark, including dataset, code library, evaluation metrics, and results is available upon request.
Edit intent: coherence
Original: In a general one-sided limit order book  where the unaffected price process follows a Levy process, we consider the problem for an investor with constant absolute risk aversion to optimally liquidate a given large position of shares.
Revised: In a general one-sided limit order book where the unaffected price process follows a Levy process, we consider a market agent that wants to liquidate a large position of shares.
Edit intent: clarity
Original:  We can reduce the optimisation problem to a deterministic two-dimensional singular problem, to which we are able to derive an explicit solution in terms of the model data.
Revised:  We reduce the optimisation problem to a deterministic two-dimensional singular problem, to which we are able to derive an explicit solution in terms of the model data.
Edit intent: coherence
Original:  Since the resulting learning rule seems not to require learning rate grid search , it may unlock a simpler workflow for training deeper and more complex neural networks.
Revised:  Since the resulting learning rule seems to require little to no learning rate tuning , it may unlock a simpler workflow for training deeper and more complex neural networks.
Edit intent: clarity
Original:  Please find the Python code used in this paper  here: URL
Revised:  The Python code used in this paper here: URL
Edit intent: clarity
Original:  Please find the Python code used in this paper  here: URL
Revised:  Please find the Python code used in this paper is here: URL
Edit intent: clarity
Original: Text-to-pattern distance is a fundamental problem in string matching, where given a pattern of length m and a text of length n, over  integer alphabet, we are asked to compute the distance between pattern and  text at every location.
Revised: Text-to-pattern distance is a fundamental problem in string matching, where given a pattern of length m and a text of length n, over an integer alphabet, we are asked to compute the distance between pattern and text at every location.
Edit intent: fluency
Original: Text-to-pattern distance is a fundamental problem in string matching, where given a pattern of length m and a text of length n, over  integer alphabet, we are asked to compute the distance between pattern and  text at every location.
Revised: Text-to-pattern distance is a fundamental problem in string matching, where given a pattern of length m and a text of length n, over integer alphabet, we are asked to compute the distance between pattern and the text at every location.
Edit intent: fluency
Original: Point-of-interest (POI) recommendation is one of the most important location-based services to help people discover interesting venues or services.
Revised: Point-of-Interest (POI) recommendation is one of the most important location-based services to help people discover interesting venues or services.
Edit intent: fluency
Original: Point-of-interest (POI) recommendation is one of the most important location-based services to help people discover interesting venues or services.
Revised: Point-of-interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services.
Edit intent: fluency
Original:  However, the extreme user-POI matrix sparsity and the varying spatial-temporal context create challenges for POI recommendation , which affects the performance of POI recommendation quality .
Revised:  However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI recommendation , which affects the performance of POI recommendation quality .
Edit intent: clarity
Original:  However, the extreme user-POI matrix sparsity and the varying spatial-temporal context create challenges for POI recommendation , which affects the performance of POI recommendation quality .
Revised:  However, the extreme user-POI matrix sparsity and the varying spatial-temporal context create challenges for POI systems , which affects the performance of POI recommendation quality .
Edit intent: clarity
Original:  However, the extreme user-POI matrix sparsity and the varying spatial-temporal context create challenges for POI recommendation , which affects the performance of POI recommendation quality .
Revised:  However, the extreme user-POI matrix sparsity and the varying spatial-temporal context create challenges for POI recommendation , which affects the quality of POI recommendations .
Edit intent: clarity
Original:  Our approach encodes the temporal and geographic information, as well as semantic content information effectively in a low-dimension relation space by using knowledge graph embedding techniques.
Revised:  Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimension relation space by using knowledge graph embedding techniques.
Edit intent: fluency
Original:  Our approach encodes the temporal and geographic information, as well as semantic content information effectively in a low-dimension relation space by using knowledge graph embedding techniques.
Revised:  Our approach encodes the temporal and geographic information, as well as semantic content information effectively in a low-dimensional relation space by using knowledge graph embedding techniques.
Edit intent: fluency
Original:  Our approach encodes the temporal and geographic information, as well as semantic content information effectively in a low-dimension relation space by using knowledge graph embedding techniques.
Revised:  Our approach encodes the temporal and geographic information, as well as semantic content information effectively in a low-dimension relation space by using Knowledge Graph Embedding techniques.
Edit intent: fluency
Original:  To further alleviate the issue of user-POI matrix sparsity, a fused matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information.
Revised:  To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information.
Edit intent: fluency
Original: In this paper, we consider the stress of a hole with the given fourfold shape (with corner) on an infinite plane under uniaxial tension.
Revised: We consider the elastic stress near a hole with the given fourfold shape (with corner) on an infinite plane under uniaxial tension.
Edit intent: clarity
Original:  We apply this technique to the Legendre Memory Unit (LMU) to obtain the first known example of a hybrid SNN outperforming SotA recurrent architectures---including the LSTM, GRU, and NRU---in accuracy, while reducing activities to at most 3.74 bits on average with 1.26 significant bits multiplying each weight.
Revised:  We apply this technique to the Legendre Memory Unit (LMU) to obtain the first known example of a hybrid SNN outperforming SotA recurrent architectures -- including the LSTM, GRU, and NRU---in accuracy, while reducing activities to at most 3.74 bits on average with 1.26 significant bits multiplying each weight.
Edit intent: fluency
Original:  We apply this technique to the Legendre Memory Unit (LMU) to obtain the first known example of a hybrid SNN outperforming SotA recurrent architectures---including the LSTM, GRU, and NRU---in accuracy, while reducing activities to at most 3.74 bits on average with 1.26 significant bits multiplying each weight.
Revised:  We apply this technique to the Legendre Memory Unit (LMU) to obtain the first known example of a hybrid SNN outperforming SotA recurrent architectures---including the LSTM, GRU, and NRU -- in accuracy, while reducing activities to at most 3.74 bits on average with 1.26 significant bits multiplying each weight.
Edit intent: fluency
Original: In the paper, we aim to train a unified model that performs three tasks: Facial Action Units (FAU) prediction, seven basic facial expressions prediction, as well as valence and arousal prediction.
Revised: We train a unified model that performs three tasks: Facial Action Units (FAU) prediction, seven basic facial expressions prediction, as well as valence and arousal prediction.
Edit intent: coherence
Original: In the paper, we aim to train a unified model that performs three tasks: Facial Action Units (FAU) prediction, seven basic facial expressions prediction, as well as valence and arousal prediction.
Revised: In the paper, we aim to train a unified model to perform three tasks: Facial Action Units (FAU) prediction, seven basic facial expressions prediction, as well as valence and arousal prediction.
Edit intent: clarity
Original:  The algorithm has two steps : first , we train a teacher model to perform all three tasks, where each instance is trained by the ground truth label of its corresponding task.
Revised:  The algorithm has two steps . We first train a teacher model to perform all three tasks, where each instance is trained by the ground truth label of its corresponding task.
Edit intent: fluency
Original:  Second , we refer to the outputs of the teacher model as the soft labels.
Revised:  Secondly , we refer to the outputs of the teacher model as the soft labels.
Edit intent: coherence
Original:  We use the soft labels and the ground truths to train the student model.
Revised:  We use the soft labels and the ground truth to train the student model.
Edit intent: fluency
Original:  We find that the student model outperforms the teacher model on all the tasks, possibly due to the exposure to the full set of labels .
Revised:  We find that most of the student models outperform their teacher model on all the tasks, possibly due to the exposure to the full set of labels .
Edit intent: clarity
Original:  We find that the student model outperforms the teacher model on all the tasks, possibly due to the exposure to the full set of labels .
Revised:  We find that the student model outperforms the teacher model on all the three tasks .
Edit intent: clarity
Original:  Finally, we use ensemble modeling to boost the performance further on the three tasks.
Revised:  Finally, we use model ensembling to boost performance further on the three tasks.
Edit intent: clarity
Original:  In order to further tackle the obstacle, we have captured the synchronized music-dance pairs by professional dancers, and thus build a high-quality music-dance pair dataset.
Revised:  In order to further tackle the obstacle, we have captured the synchronized music-dance pairs by professional dancers, and build a high-quality music-dance pair dataset.
Edit intent: coherence
Original:  Experiments have demonstrated the performance of the proposed system and the proposed method can achieve the state-of-the-art results.
Revised:  Experiments have demonstrated that the proposed method can achieve the state-of-the-art results.
Edit intent: clarity
Original:  System I enjoys the strong normalization property.
Revised:  System I enjoys the strong normalisation property.
Edit intent: fluency
Original:  This is sufficient to prove the existence of empty types, but not to prove the introduction property (every normal closed term is an introduction).
Revised:  This is sufficient to prove the existence of empty types, but not to prove the introduction property (every closed term in normal form is an introduction).
Edit intent: clarity
Original:  We show here that adding \eta-expansion rules to System I permit to drop this restriction and to retrieve full introduction property.
Revised:  We show here that adding \eta-expansion rules to System I permits to drop this restriction and to retrieve full introduction property.
Edit intent: fluency
Original: The difficulty of deploying various deep learning (DL) models on diverse DL hardwares has boosted the research and development of DL compilers in the community.
Revised: The difficulty of deploying various deep learning (DL) models on diverse DL hardware has boosted the research and development of DL compilers in the community.
Edit intent: fluency
Original:  Similarly, the DL compilers take the DL models described in different DL frameworks as input, and then generate optimized codes for diverse DL hardwares as output.
Revised:  Similarly, the DL compilers take the DL models described in different DL frameworks as input, and then generate optimized codes for diverse DL hardware as output.
Edit intent: fluency