wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper, we propose deep learning based assessment models to predict human ratings of converted speech.
Revised:  In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech.
Edit intent: fluency
Original:  We adopt the convolutional and recurrent neural network models to build a mean opinion score (MOS) predictor, termed  MOSNet.
Revised:  We adopt the convolutional and recurrent neural network models to build a mean opinion score (MOS) predictor, termed as MOSNet.
Edit intent: fluency
Original:  The proposed models are tested on the large-scale listening test results of the Voice Conversion Challenge (VCC) 2018.
Revised:  The proposed models are tested on large-scale listening test results of the Voice Conversion Challenge (VCC) 2018.
Edit intent: fluency
Original:  Experimental results show that the predicted scores of the proposed MOSNet are highly correlated with human MOS ratings at the system level while  fairly correlated with human MOS ratings at the utterance level.
Revised:  Experimental results show that the predicted scores of the proposed MOSNet are highly correlated with human MOS ratings at the system level while being fairly correlated with human MOS ratings at the utterance level.
Edit intent: coherence
Original: Sesqui-pushout (SqPO) rewriting provides a variant of transformations of graph-like and other types of structures that fit into the framework of adhesive categories where deletion in unknown context may be implemented.
Revised: Sesqui-pushout (SqPO) rewriting is a variant of transformations of graph-like and other types of structures that fit into the framework of adhesive categories where deletion in unknown context may be implemented.
Edit intent: clarity
Original:  Extending these frameworks to accommodate the rapidly diversifying landscape of DL models and hardware platforms presents challenging tradeoffs between expressiveness , composability, and portability.
Revised:  Extending these frameworks to accommodate the rapidly diversifying landscape of DL models and hardware platforms presents challenging tradeoffs between expressivity , composability, and portability.
Edit intent: fluency
Original:  We present Relay, a new intermediate representation (IR) and compiler framework for DL models.
Revised:  We present Relay, a new compiler framework for DL models.
Edit intent: coherence
Original:  The functional, statically-typed Relay IR unifies and generalizes existing DL IRs and can express state-of-the-art models.
Revised:  The functional, statically-typed Relay IR unifies and generalizes existing DL IRs to express state-of-the-art models.
Edit intent: clarity
Original:   Relay's expressive IR required careful design of the type system, automatic differentiation, and optimizations.
Revised:  The introduction of Relay's expressive IR required careful design of the type system, automatic differentiation, and optimizations.
Edit intent: clarity
Original:   Relay's expressive IR required careful design of the type system, automatic differentiation, and optimizations.
Revised:  Relay's expressive IR requires careful design of the type system, automatic differentiation, and optimizations.
Edit intent: coherence
Original:  Relay's extensible compiler can eliminate abstraction overhead and target new hardware platforms.
Revised:  Relay's extension mechanisms. Using these extension mechanisms, Relay supports a unified compiler that can target a variety of hardware platforms.
Edit intent: clarity
Original:  The design insights from Relay can be applied to existing frameworks to develop IRs that support extension without compromising on expressivity, composibility, and portability.
Edit intent: coherence
Original:  Our evaluation demonstrates that the Relayprototype can already provide competitive performance for a broad class of models running on CPUs, GPUs, and FPGAs .
Revised:  Our evaluation demonstrates Relay's competitive performance for a broad class of models running on CPUs, GPUs, and FPGAs .
Edit intent: clarity
Original:  Our evaluation demonstrates that the Relayprototype can already provide competitive performance for a broad class of models running on CPUs, GPUs, and FPGAs .
Revised:  Our evaluation demonstrates that the Relayprototype can already provide competitive performance for a broad class of models and devices ( CPUs, GPUs, and FPGAs .
Edit intent: clarity
Original:  Typically, the mask is estimated with a deep neural network (DNN)  and element-wise applied to the complex mixture short-time Fourier transform (STFT) representation to perfom extraction.
Revised:  Typically, the mask is estimated with a deep neural network (DNN) , and element-wise applied to the complex mixture short-time Fourier transform (STFT) representation to perfom extraction.
Edit intent: fluency
Original:  Typically, the mask is estimated with a deep neural network (DNN)  and element-wise applied to the complex mixture short-time Fourier transform (STFT) representation to perfom extraction.
Revised:  Typically, the mask is estimated with a deep neural network (DNN) and element-wise applied to the complex mixture short-time Fourier transform (STFT) representation to perform the extraction.
Edit intent: fluency
Original:  Ideal mask magnitudes are zero for solely undesired signals in a TF bin and infinity for total destructive interference.
Revised:  Ideal mask magnitudes are zero for solely undesired signals in a TF bin and undefined for total destructive interference.
Edit intent: clarity
Original:  The DNN is optimized by minimizing the error between the extracted and the groundtruth desired signal allowing to train without having to specify ground-truth TF filters but learn filters by error reduction .
Revised:  The DNN is optimized by minimizing the error between the extracted and the ground-truth desired signal allowing to train without having to specify ground-truth TF filters but learn filters by error reduction .
Edit intent: fluency
Original:  The DNN is optimized by minimizing the error between the extracted and the groundtruth desired signal allowing to train without having to specify ground-truth TF filters but learn filters by error reduction .
Revised:  The DNN is optimized by minimizing the error between the extracted and the groundtruth desired signal allowing to learn the TF filters without having to specify ground-truth TF filters but learn filters by error reduction .
Edit intent: clarity
Original:  The DNN is optimized by minimizing the error between the extracted and the groundtruth desired signal allowing to train without having to specify ground-truth TF filters but learn filters by error reduction .
Revised:  The DNN is optimized by minimizing the error between the extracted and the groundtruth desired signal allowing to train without having to specify ground-truth TF filters .
Edit intent: coherence
Original:  We compare our approach with complex and real valued TF masks by separating speech from a variety of different sound and noise classes from the Google AudioSet corpus.
Revised:  We compare our approach with complex and real-valued TF masks by separating speech from a variety of different sound and noise classes from the Google AudioSet corpus.
Edit intent: fluency
Original:  We also process the mixture STFT with notch filters and zero whole time-frames to demonstrate the reconstruction capabilities of our approach.
Revised:  We also process the mixture STFT with notch-filters and zero whole time-frames to demonstrate the reconstruction capabilities of our approach.
Edit intent: fluency
Original:  The proposed method outperformed the baselines especially when notch filters and time-frame zeroing were applied.
Revised:  The proposed method outperformed the baselines , especially when notch-filters and time-frame zeroing were applied.
Edit intent: fluency
Original: From the perspective of a question answering system, a useful representation of a document might comprise the questions it can potentially answer.
Revised: From the perspective of a question answering system, this might comprise questions the document can potentially answer.
Edit intent: clarity
Original:  Our predictions are made with a vanilla sequence-to-sequence model trained with supervised learning using a dataset of pairs of query and relevant documents.
Revised:  Our predictions are made with a vanilla sequence-to-sequence model , trained using datasets consisting of pairs of query and relevant documents.
Edit intent: clarity
Original:  In a latency-critical regime, retrieval results alone (without the re-ranking component ) approach the effectiveness of more computationally expensive neural re-rankers while taking only a fraction of the query latency .
Revised:  In a latency-critical regime, retrieval results alone (without re-ranking component ) approach the effectiveness of more computationally expensive neural re-rankers while taking only a fraction of the query latency .
Edit intent: fluency
Original:  In a latency-critical regime, retrieval results alone (without the re-ranking component ) approach the effectiveness of more computationally expensive neural re-rankers while taking only a fraction of the query latency .
Revised:  In a latency-critical regime, retrieval results alone (without the re-ranking ) approach the effectiveness of more computationally expensive neural re-rankers while taking only a fraction of the query latency .
Edit intent: clarity
Original:  We find that relatively simple network metrics, such as adaptive degree or closeness centralities, are able to achieve performances very close to the baseline value, thus providing good support for the use of these metrics in large-scale problem settings... 
Revised:  We find that relatively simple network metrics, such as adaptive degree or closeness centralities, are able to achieve performances very close to the baseline value, thus providing good support for the use of these metrics in large-scale problem settings... .
Edit intent: fluency
Original:  MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy tradeoff on the MNIST classification task compared to previously-proposed SNNs, while keeping a competitive energy-accuracy tradeoff.
Revised:  MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy tradeoff on the MNIST classification task compared to previously-proposed SNNs, while having no penalty in the energy-accuracy tradeoff.
Edit intent: clarity
Original: We introduce DeepNovoV2, the state-of-the-art neural networks based model for de novo peptide sequencing.
Revised: We introduce DeepNovoV2, the state-of-the-art model for peptide sequencing.
Edit intent: coherence
Original:  Contrary to existing models like DeepNovo or DeepMatch which represents each spectrum as a long sparse vector, in DeepNovoV2, we propose to directly represent a spectrum as a set of (m/z, intensity) pairs .
Revised:  In DeepNovoV2, we propose to directly represent a spectrum as a set of (m/z, intensity) pairs .
Edit intent: clarity
Original:  Our experiments show that comparing to the original DeepNovo model, DeepNovoV2 has at least 15\% relative improvement on peptide accuracy.
Revised:  Our experiments on a wide variety of data from different species show that DeepNovoV2 has at least 15\% relative improvement on peptide accuracy.
Edit intent: clarity
Original:  It is applied to a test problem described by a diffusion-advection equation forced with a spectrum of fast processes.
Revised:  It is applied to a test problem described by an advection-diffusion equation forced with a spectrum of fast processes.
Edit intent: fluency
Original:  We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized It\^o correction helps to substantially reduce time-stepping error and significantly improve the convergence rate of the numerical solutions when forcing in the governing equation is rough (fast varying);
Revised:  We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized It\^o correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when forcing in the governing equation is rough (fast varying);
Edit intent: clarity
Original:  We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized It\^o correction helps to substantially reduce time-stepping error and significantly improve the convergence rate of the numerical solutions when forcing in the governing equation is rough (fast varying);
Revised:  We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized It\^o correction helps to substantially reduce time-stepping error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying);
Edit intent: clarity
Original:  alternatively, for the same target accuracy, the generalized It\^o correction allows for the use of significantly longer step size and hence helps to reduce the computational cost of the numerical simulation.
Revised:  alternatively, for the same target accuracy, the generalized It\^o correction allows for the use of significantly longer time steps and hence helps to reduce the computational cost of the numerical simulation.
Edit intent: clarity
Original:  It is applied to a test problem described by an advection-diffusion equation forced with a spectrum of fast processes.
Revised:  The formulation is applied to a test problem described by an advection-diffusion equation forced with a spectrum of fast processes.
Edit intent: clarity
Original:  This is also known as the conflict-free chromatic guarding problem with vertex guards (which is quite different from point guards considered in  other papers).
Revised:  This is also known as the conflict-free chromatic guarding problem with vertex guards (which is quite different from point guards considered in some other papers).
Edit intent: clarity
Original:  In the first scenario , we assume that the viewers are all points of the polygon.
Revised:  In the first one , we assume that the viewers are all points of the polygon.
Edit intent: clarity
Original:  We  solve the related problem of minimizing the number of guards and approximate (up to only an additive error) the number of colours in the special case of funnels.
Revised:  We efficiently solve the related problem of minimizing the number of guards and approximate (up to only an additive error) the number of colours in the special case of funnels.
Edit intent: clarity
Original: We study the problem of colouring the vertices of a polygon, such that every viewer  can see a unique colour.
Revised: We study the problem of colouring the vertices of a polygon, such that every viewer in it can see a unique colour.
Edit intent: clarity
Original:  The goal is to minimize the number of colours used.
Revised:  The goal is to minimise the number of colours used.
Edit intent: fluency
Original:  We efficiently solve the related problem of minimizing the number of guards and approximate (up to only an additive error) the number of colours  in the special case of funnels.
Revised:  We efficiently solve the related problem of minimising the number of guards and approximate (up to only an additive error) the number of colours in the special case of funnels.
Edit intent: clarity
Original:  We efficiently solve the related problem of minimizing the number of guards and approximate (up to only an additive error) the number of colours  in the special case of funnels.
Revised:  We efficiently solve the related problem of minimizing the number of guards and approximate (up to only an additive error) the number of colours required in the special case of funnels.
Edit intent: clarity
Original:  In the second scenario, we assume that the viewers are only the vertices of the polygon.
Revised:  In the second scenario, in which the viewers are only the vertices of the polygon.
Edit intent: clarity
Original:  Over the last decade, terrain visibility graphs attracted quite some attention in the context of time series analysis with various practical applications in areas such as physics, geography and medical sciences.
Revised:  Over the last decade, terrain visibility graphs attracted attention in the context of time series analysis with various practical applications in areas such as physics, geography and medical sciences.
Edit intent: clarity
Original:  Moreover, we obtain two algorithmic results which are interesting from a practical perspective .
Revised:  Moreover, we obtain two algorithmic results .
Edit intent: clarity
Original:  We devise a fast  shortest path algorithm on arbitrary induced subgraphs of terrain visibility graphs and a polynomial-time algorithm for Dominating Set on special terrain visibility graphs (called funnel visibility graphs).
Revised:  We devise a fast shortest path algorithm on arbitrary induced subgraphs of terrain visibility graphs and a polynomial-time algorithm for Dominating Set on special terrain visibility graphs (called funnel visibility graphs).
Edit intent: fluency
Original:  We devise a fast output-sensitive shortest path algorithm on arbitrary induced subgraphs of terrain visibility graphs and a polynomial-time algorithm for Dominating Set on special terrain visibility graphs (called funnel visibility graphs).
Revised:  We devise a fast output-sensitive shortest path algorithm on terrain-like graphs and a polynomial-time algorithm for Dominating Set on special terrain visibility graphs (called funnel visibility graphs).
Edit intent: clarity
Original: We investigate different notions of arbitrage in a multiple-priors setting in discrete time .
Revised: In a discrete time and multiple-priors setting in discrete time .
Edit intent: clarity
Original:  We  build upon and extend an existing generalisation of the Nerode equivalence to a categorical setting , and relate it to the existence of minimal automata.
Revised:  We then build upon and extend an existing generalisation of the Nerode equivalence to a categorical setting , and relate it to the existence of minimal automata.
Edit intent: coherence
Original:  We  build upon and extend an existing generalisation of the Nerode equivalence to a categorical setting , and relate it to the existence of minimal automata.
Revised:  We build upon and extend an existing generalisation of the Nerode equivalence to a categorical setting and relate it to the existence of minimal automata.
Edit intent: fluency
Original:  Lastly , we show that generalised types of side-effects, such as non-determinism, can be captured by this framework, which leads to a general determinisation procedure.
Revised:  Finally , we show that generalised types of side-effects, such as non-determinism, can be captured by this framework, which leads to a general determinisation procedure.
Edit intent: fluency
Original:  Lastly , we show that generalised types of side-effects, such as non-determinism, can be captured by this framework, which leads to a general determinisation procedure.
Revised:  Lastly , we show that generalised types of side-effects, such as non-determinism, can be captured by this categorical framework, leading to a general determinisation procedure.
Edit intent: clarity
Original:  The first is based on a multi-task neural network that uses logs of diffusion cascades to embed diffusion probabilities between nodes as well as the ability of a node to create massive cascades .
Revised:  The first is based on a multi-task neural network that learns embeddings of nodes that initiate cascades (influencer vectors) and embeddings of nodes that participate in them (susceptible vectors). The norm of an influencer vector captures the ability of a node to create massive cascades .
Edit intent: clarity
Original:  The first is based on a multi-task neural network that uses logs of diffusion cascades to embed diffusion probabilities between nodes as well as the ability of a node to create massive cascades .
Revised:  The first is based on a multi-task neural network that uses logs of diffusion cascades to embed diffusion probabilities between nodes as well as the ability of the node to create massive cascades .
Edit intent: fluency
Original: We apply our method in three sizable networks accompanied by diffusion cascades and evaluate it using unseen diffusion cascades from future time steps.
Revised: We apply our method in three sizable networks with diffusion cascades and evaluate it using unseen diffusion cascades from future time steps.
Edit intent: clarity
Original: We apply our method in three sizable networks accompanied by diffusion cascades and evaluate it using unseen diffusion cascades from future time steps.
Revised: We apply our method in three sizable networks accompanied by diffusion cascades and evaluate it using cascades from future time steps.
Edit intent: clarity
Original:  We observe that our method outperforms various competitive algorithms and metrics from the diverse landscape of influence maximization , in terms of prediction precision and seed set quality.
Revised:  We observe that our method outperforms various competitive algorithms and metrics from the diverse landscape of influence maximization in terms of prediction precision and seed set quality.
Edit intent: fluency
Original:  One,  it reduces the computational effort to construct the correction , especially if the nonlinearity is numerically heavy to compute .
Revised:  One, it reduces the computational effort to construct the correction .
Edit intent: coherence
Original:  In this paper, we propose a SQL clause-wise decoding neural architecture with a self-attention based database schema encoder to address  Spider task.
Revised:  In this paper, we propose a SQL clause-wise decoding neural architecture with a self-attention based database schema encoder to address the Spider task.
Edit intent: fluency
Original:  The experimental result shows that our model outperforms the previous state-of-the-art model by 9.8\% in the exact matching accuracy on the Spider dev dataset.
Revised:  When evaluated on the Spider dev dataset.
Edit intent: clarity
Original:  In addition, we show that our model is significantly more effective to predict complex and nested queries than previous works .
Revised:  In addition, we show that our model is significantly more effective at predicting complex and nested queries than previous works .
Edit intent: fluency
Original:  In addition, we show that our model is significantly more effective to predict complex and nested queries than previous works .
Revised:  In addition, we show that our model is significantly more effective to predict complex and nested queries than previous work .
Edit intent: fluency
Original: How epithelial cells coordinate their polarity to form complex tissues and organs remains a fundamental question in biology.
Revised: How epithelial cells coordinate their polarity to form functional tissues is an open question in cell biology.
Edit intent: clarity
Original:  Here, we characterize a unique type of polarity found in liver tissue - nematic cell polarity, which is different from the common vectorial cell polarity in simple  epithelia.
Revised:  Here, we characterize a unique type of polarity found in liver tissue , nematic cell polarity, which is different from the common vectorial cell polarity in simple epithelia.
Edit intent: fluency
Original:  Here, we characterize a unique type of polarity found in liver tissue - nematic cell polarity, which is different from the common vectorial cell polarity in simple  epithelia.
Revised:  Here, we characterize a unique type of polarity found in liver tissue - nematic cell polarity, which is different from vectorial cell polarity in simple epithelia.
Edit intent: clarity