wanyu/IteraTeR_v2 huggingface dataset:
Original:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both  . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Revised:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from inputs that are sequences. Interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Edit intent: clarity
Original:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both  . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Revised:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as translating natural language , engaging in dialogue, and robotic control , often demand both . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Edit intent: fluency
Original:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both  . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Revised:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and controlling a robot , often demand both . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Edit intent: fluency
Original:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both  . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Revised:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both capabilities . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Edit intent: clarity
Original:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both  . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Revised:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both . Recurrent neural networks (RNNs) are connectionist models that capture time dynamics via cycles in the graph.
Edit intent: clarity
Original:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both  . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture time dynamics via cycles in the graph.
Revised:  In other domains, such as time series prediction, video analysis, and music information retrieval, a model must learn from sequences of inputs . Significantly more interactive tasks, such as natural language translation , engaging in dialogue, and robotic control , often demand both . Recurrent neural networks (RNNs) are a powerful family of connectionist models that capture the dynamics of sequences via cycles in the graph.
Edit intent: fluency
Original:  Unlike feedforward neural networks, recurrent networks can process examples one at a time, retaining a state , or memory, that reflects an arbitrarily long context window.
Revised:  Unlike feedforward neural networks, recurrent networks retain a state that can represent information from an arbitrarily long context window.
Edit intent: clarity
Original:  While these networks have long been difficult to train  and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled  large-scale learning with recurrent nets.
Revised:  Although recurrent neural networks have traditionally been difficult to train and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled large-scale learning with recurrent nets.
Edit intent: clarity
Original:  While these networks have long been difficult to train  and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled  large-scale learning with recurrent nets.
Revised:  While these networks have long been difficult to train , and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled large-scale learning with recurrent nets.
Edit intent: fluency
Original:  While these networks have long been difficult to train  and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled  large-scale learning with recurrent nets.
Revised:  While these networks have long been difficult to train and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful large-scale learning with recurrent nets.
Edit intent: clarity
Original:  Over the past few years, systems based on state of the art long short-term memory (LSTM) and bidirectional recurrent neural network (BRNN) architectures have demonstrated record-setting performance on tasks as varied as image captioning, language translation, and handwriting recognition.
Revised:  Over the past few years, systems based on long short-term memory (LSTM) and bidirectional recurrent neural network (BRNN) architectures have demonstrated record-setting performance on tasks as varied as image captioning, language translation, and handwriting recognition.
Edit intent: clarity
Original:  Over the past few years, systems based on state of the art long short-term memory (LSTM) and bidirectional recurrent neural network (BRNN) architectures have demonstrated record-setting performance on tasks as varied as image captioning, language translation, and handwriting recognition.
Revised:  Over the past few years, systems based on state of the art long short-term memory (LSTM) and bidirectional (BRNN) architectures have demonstrated record-setting performance on tasks as varied as image captioning, language translation, and handwriting recognition.
Edit intent: clarity
Original:  Over the past few years, systems based on state of the art long short-term memory (LSTM) and bidirectional recurrent neural network (BRNN) architectures have demonstrated record-setting performance on tasks as varied as image captioning, language translation, and handwriting recognition.
Revised:  Over the past few years, systems based on state of the art long short-term memory (LSTM) and bidirectional recurrent neural network (BRNN) architectures have demonstrated ground-breaking performance on tasks as varied as image captioning, language translation, and handwriting recognition.
Edit intent: clarity
Original:  In this review of the literature we synthesize the body of research that over the past three decades has yielded and reduced to practice these powerful models.
Revised:  In this survey, we review and synthesize the research that over the past three decades has yielded and reduced to practice these powerful models.
Edit intent: clarity
Original:  In this review of the literature we synthesize the body of research that over the past three decades has yielded and reduced to practice these powerful models.
Revised:  In this review of the literature we synthesize the body of research that over the past three decades first yielded and then made practical these powerful learning models.
Edit intent: clarity
Original:  Our goal is to provide a mostly self-contained explication of  state of the art systems, together with a historical perspective and ample references to the primary research.
Revised:  Our goal is to provide a self-contained explication of state of the art systems, together with a historical perspective and ample references to the primary research.
Edit intent: clarity
Original:  Our goal is to provide a mostly self-contained explication of  state of the art systems, together with a historical perspective and ample references to the primary research.
Revised:  Our goal is to provide a mostly self-contained explication of the state of the art systems, together with a historical perspective and ample references to the primary research.
Edit intent: fluency
Original:  Our goal is to provide a mostly self-contained explication of  state of the art systems, together with a historical perspective and ample references to the primary research.
Revised:  Our goal is to provide a mostly self-contained explication of state of the art together with a historical perspective and ample references to the primary research.
Edit intent: clarity
Original:  Our goal is to provide a mostly self-contained explication of  state of the art systems, together with a historical perspective and ample references to the primary research.
Revised:  Our goal is to provide a mostly self-contained explication of state of the art systems, together with a historical perspective and references to primary research.
Edit intent: clarity
Original: Understanding the transcriptional regulatory code, as well as other types of information encoded within biomolecular sequences, will require learning biophysical models of sequence-function relationships from high-throughput data.
Revised: A fundamental aspect of biological information processing is the ubiquity of sequence-function relationships from high-throughput data.
Edit intent: clarity
Original:  In this paper we find sufficient conditions for the desired convergence by means of the weak convergence method to a class of path-dependent stochastic differential equations  .
Revised:  In this paper we find sufficient conditions for the desired convergence of the functionals associated with a class of path-dependent stochastic differential equations .
Edit intent: clarity
Original:  The sampling accuracy improves on average by 1.0 \r{A and up to 2.2 \r{A in the most prominent example.
Revised:  The sampling accuracy improves on average by 1.0 \AA and up to 2.2 \r{A in the most prominent example.
Edit intent: fluency
Original:  The sampling accuracy improves on average by 1.0 \r{A and up to 2.2 \r{A in the most prominent example.
Revised:  The sampling accuracy improves on average by 1.0 \r{A and up to 2.2 \AA in the most prominent example.
Edit intent: fluency
Original: This paper proposes a new prediction process to explain and predict popularity evolution of Youtube videos.
Revised: This paper proposes a new prediction process to explain and predict popularity evolution of YouTube videos.
Edit intent: fluency
Original:  We exploit our recent study on the classification of Youtube videos in order to predict the evolution of videos' view-count.
Revised:  We exploit our recent study on the classification of YouTube videos in order to predict the evolution of videos' view-count.
Edit intent: fluency
Original:  Common estimates of the factors determining protein levels  do not distinguish between (i) the factors determining the variability between the abundances of different proteins and (ii) the factors determining the physiological variability of the same protein across different tissue types .
Revised:  Estimates of the factors determining protein levels do not distinguish between (i) the factors determining the variability between the abundances of different proteins and (ii) the factors determining the physiological variability of the same protein across different tissue types .
Edit intent: clarity
Original:  Common estimates of the factors determining protein levels  do not distinguish between (i) the factors determining the variability between the abundances of different proteins and (ii) the factors determining the physiological variability of the same protein across different tissue types .
Revised:  Common estimates of the factors determining protein levels in human tissues do not distinguish between (i) the factors determining the variability between the abundances of different proteins and (ii) the factors determining the physiological variability of the same protein across different tissue types .
Edit intent: clarity
Original:  We estimated the factors determining these two orthogonal sources of variability  and found that the variability between the levels of different proteins is dominated by transcriptional regulation while in contrast the physiological variability across tissue-types is dominated by post-transcriptional regulation.
Revised:  We estimated the factors determining these two orthogonal sources of variability , and found that the variability between the levels of different proteins is dominated by transcriptional regulation while in contrast the physiological variability across tissue-types is dominated by post-transcriptional regulation.
Edit intent: fluency
Original:  These results reconcile existing estimates in the literature, caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation in shaping tissue-type-specific proteomes.
Revised:  These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation in shaping tissue-type-specific proteomes.
Edit intent: clarity
Original:  These results reconcile existing estimates in the literature, caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation in shaping tissue-type-specific proteomes.
Revised:  These results reconcile existing estimates in the literature, caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes.
Edit intent: fluency
Original:  We take a structural equation modeling, and show that a large proportion of fluctuation in the average log growth rate of firms can be explained by the network and that link renewal by firms decreases the standard deviation of the log growth rate\\%DIF > of the aggregate fluctuations can be explained by the  .
Revised:  We take a structural equation modeling, and show that a large proportion of fluctuation in the average log growth rate of firms can be explained by the network and that link renewal by firms decreases the standard deviation of the log growth rate\\%DIF > of the aggregate fluctuations can be explained by the network effect .
Edit intent: clarity
Original: A firm's buyer--seller relationships can be characterized as a longitudinal network in which the connectivity patterns evolve as each firm faces productivity shocks.
Revised: Buyer--seller relationships among firms can be regarded as a longitudinal network in which the connectivity patterns evolve as each firm faces productivity shocks.
Edit intent: clarity
Original: A firm's buyer--seller relationships can be characterized as a longitudinal network in which the connectivity patterns evolve as each firm faces productivity shocks.
Revised: A firm's buyer--seller relationships can be characterized as a longitudinal network in which the connectivity pattern evolves as each firm faces productivity shocks.
Edit intent: fluency
Original: A firm's buyer--seller relationships can be characterized as a longitudinal network in which the connectivity patterns evolve as each firm faces productivity shocks.
Revised: A firm's buyer--seller relationships can be characterized as a longitudinal network in which the connectivity patterns evolve as each firm receives productivity shocks.
Edit intent: clarity
Original:  We also investigate the role of the network in aggregate fluctuations and show that at least 37%DIFDELCMD < \\%%% %DIF < of the aggregate fluctuations can be explained by the network effect .
Revised:  We also investigate the role of networks in aggregate fluctuations and show that at least 37%DIFDELCMD < \\%%% %DIF < of the aggregate fluctuations can be explained by the network effect .
Edit intent: fluency
Original:  We also investigate the role of the network in aggregate fluctuations and show that at least 37%DIFDELCMD < \\%%% %DIF < of the aggregate fluctuations can be explained by the network effect .
Revised:  We also investigate the role of the network in aggregate fluctuations %DIFDELCMD < \\%%% %DIF < of the aggregate fluctuations can be explained by the network effect .
Edit intent: coherence
Original:  We also investigate the role of the network in aggregate fluctuations and show that at least 37%DIFDELCMD < \\%%% %DIF < of the aggregate fluctuations can be explained by the network effect .
Revised:  We also investigate the role of the network in aggregate fluctuations and show that at least 37%DIFDELCMD < \\%%% %DIF < of the aggregate fluctuations can be explained by the .
Edit intent: clarity
Original: Diffusion of information, behavioural patterns or innovations follows diverse pathways depending on a number of conditions, including the structure of the underlying social network, the sensitivity to peer pressure and the influence of media.
Revised: Diffusion of information, behavioral patterns or innovations follows diverse pathways depending on a number of conditions, including the structure of the underlying social network, the sensitivity to peer pressure and the influence of media.
Edit intent: fluency
Original:  While any constant, non-zero rate of dynamically-introduced innovators leads to global spreading, the kinetics by which the asymptotic state is approached show rich behaviour .
Revised:  While any constant, non-zero rate of dynamically-introduced spontaneous adopters leads to global spreading, the kinetics by which the asymptotic state is approached show rich behaviour .
Edit intent: clarity
Original:  While any constant, non-zero rate of dynamically-introduced innovators leads to global spreading, the kinetics by which the asymptotic state is approached show rich behaviour .
Revised:  While any constant, non-zero rate of dynamically-introduced innovators leads to global spreading, the kinetics by which the asymptotic state is approached shows rich behavior .
Edit intent: fluency
Original:  In particular we find that, as a function of the density of immune nodes , there is a transition from fast to slow spreading governed by entirely different mechanisms.
Revised:  In particular we find that, as a function of the immune node density , there is a transition from fast to slow spreading governed by entirely different mechanisms.
Edit intent: clarity
Original:  This transition happens below the percolation threshold of fragmentation of the network , and has its origin in the competition between cascading behaviour induced by innovators and blocking of adoption due to immune nodes.
Revised:  This transition happens below the percolation threshold of network fragmentation , and has its origin in the competition between cascading behaviour induced by innovators and blocking of adoption due to immune nodes.
Edit intent: clarity
Original:  This transition happens below the percolation threshold of fragmentation of the network , and has its origin in the competition between cascading behaviour induced by innovators and blocking of adoption due to immune nodes.
Revised:  This transition happens below the percolation threshold of fragmentation of the network , and has its origin in the competition between cascading behavior induced by adopters and blocking due to immune nodes.
Edit intent: clarity
Original:  Our proposed CNN provides an end-to-end framework  for learning not only the image representation, the composition model for question , but also the inter-modal interaction between the image and question, for the generation of answer.
Revised:  Our proposed CNN provides an end-to-end framework for learning not only the image and question representations , but also the inter-modal interaction between the image and question, for the generation of answer.
Edit intent: clarity
Original:  Our proposed CNN provides an end-to-end framework  for learning not only the image representation, the composition model for question , but also the inter-modal interaction between the image and question, for the generation of answer.
Revised:  Our proposed CNN provides an end-to-end framework for learning not only the image representation, the composition model for question , but also their inter-modal interaction between the image and question, for the generation of answer.
Edit intent: clarity
Original:  Our proposed CNN provides an end-to-end framework  for learning not only the image representation, the composition model for question , but also the inter-modal interaction between the image and question, for the generation of answer.
Revised:  Our proposed CNN provides an end-to-end framework for learning not only the image representation, the composition model for question , but also the inter-modal interactions to produce the answer.
Edit intent: clarity
Original:  More specifically, the proposed model consists of three components: an image CNN to extract the image representation , one sentence CNN to encode the question, and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words.
Revised:  More specifically, our model consists of three components: an image CNN to extract the image representation , one sentence CNN to encode the question, and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words.
Edit intent: clarity