wanyu/IteraTeR_v2 huggingface dataset:
Original:  Under the presence of disparate timescales, the linear noise approximation admits a quasi-steady-state reduction referred to as the slow scale linear noise approximation.
Revised:  In the presence of disparate timescales, the linear noise approximation admits a quasi-steady-state reduction referred to as the slow scale linear noise approximation.
Edit intent: fluency
Original:  Under the presence of disparate timescales, the linear noise approximation admits a quasi-steady-state reduction referred to as the slow scale linear noise approximation.
Revised:  Under the presence of disparate timescales, the linear noise approximation admits a quasi-steady-state reduction referred to as the slow scale linear noise approximation.
Edit intent: fluency
Original:  In so doing, we can apply for the first time  the slow scale linear noise approximation to fast/slow systems that are not of standard form.
Revised:  In so doing, we also apply, for the first time the slow scale linear noise approximation to fast/slow systems that are not of standard form.
Edit intent: clarity
Original:  In so doing, we can apply for the first time  the slow scale linear noise approximation to fast/slow systems that are not of standard form.
Revised:  In so doing, we can apply for the first time , the slow scale linear noise approximation to fast/slow systems that are not of standard form.
Edit intent: fluency
Original:  In so doing, we can apply for the first time  the slow scale linear noise approximation to fast/slow systems that are not of standard form.
Revised:  In so doing, we can apply for the first time the slow scale linear noise approximation to fast/slow systems that are not in standard form.
Edit intent: clarity
Original: In this paper, we propose the reproducing activation function to improve deep learning accuracy for various applications ranging from computer vision problems to scientific computing problems .
Revised: We propose reproducing activation functions (RAFs) to improve deep learning accuracy for various applications ranging from computer vision problems to scientific computing problems .
Edit intent: clarity
Original: In this paper, we propose the reproducing activation function to improve deep learning accuracy for various applications ranging from computer vision problems to scientific computing problems .
Revised: In this paper, we propose the reproducing activation function to improve deep learning accuracy for various applications ranging from computer vision to scientific computing problems .
Edit intent: fluency
Original: In this paper, we propose the reproducing activation function to improve deep learning accuracy for various applications ranging from computer vision problems to scientific computing problems .
Revised: In this paper, we propose the reproducing activation function to improve deep learning accuracy for various applications ranging from computer vision problems to scientific computing .
Edit intent: coherence
Original:  The idea of reproducing activation functions is to employ several basic functions and their learnable linear combination to construct neuron-wise data-driven activation functions for each neuron.
Revised:  The idea is to employ several basic functions and their learnable linear combination to construct neuron-wise data-driven activation functions for each neuron.
Edit intent: clarity
Original: Over the past years, extensive research has been dedicated to developing robust platforms and data-driven dialogue models to support long-term human-robot interactions.
Revised: Over the past years, extensive research has been dedicated to developing robust platforms and data-driven dialog models to support long-term human-robot interactions.
Edit intent: fluency
Original:  In this paper, we investigate this by involving participants in three interaction sessions with multiple days of zero exposure in between.
Revised:  In this paper, we explore this by involving participants in three interaction sessions with multiple days of zero exposure in between.
Edit intent: fluency
Original:  Furthermore, the analyses of long-term gaze patterns disclose that people's mutual gaze in a social chat develops congruently with their perceptions of the robot over time.
Revised:  Furthermore, the analyses of gaze patterns in repeated interactions disclose that people's mutual gaze in a social chat develops congruently with their perceptions of the robot over time.
Edit intent: clarity
Original: Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems.
Revised: Medical brain image analysis is a necessary step in Computer Assisted /Aided Diagnosis (CAD) systems.
Edit intent: fluency
Original:  Safer engineering designs require appropriate cost and constraint function definitions that capture the risk associated with unwanted system behavior in the presence of uncertainties.
Revised:  Safer engineering designs require appropriate cost and constraint function definitions that capture the risk associated with unwanted system behavior in the presence of uncertainties.
Edit intent: fluency
Original:  Satisfying these notions leads to certifiable risk-based design optimization (CRiBDO).
Revised:  Satisfying these notions leads to certifiable risk-based design optimization (CRiBDO).
Edit intent: fluency
Original:  In the context of CRiBDO, risk measures based on superquantile (a.k.a.  conditional value-at-risk) and buffered probability of failure are analyzed.
Revised:  In the context of CRiBDO, risk measures based on superquantile (a.k.a. \ conditional value-at-risk) and buffered probability of failure are analyzed.
Edit intent: fluency
Original: We study the liquid-liquid phase separation (LLPS) of a cell-free transcription-translation (TXTL) system .
Revised: Liquid-liquid phase separation (LLPS) of a cell-free transcription-translation (TXTL) system .
Edit intent: clarity
Original: We study the liquid-liquid phase separation (LLPS) of a cell-free transcription-translation (TXTL) system .
Revised: We study the liquid-liquid phase separation (LLPS) of a cell-free transcription-translation (TXTL) reaction .
Edit intent: clarity
Original:  When the TXTL reaction , composed of a large amount of proteins , is concentrated, the uniformly mixed state becomes unstable  and membrane-less droplets form spontaneously.
Revised:  When the TXTL reaction composed of many proteins is concentrated, the uniformly mixed state becomes unstable and membrane-less droplets form spontaneously.
Edit intent: clarity
Original:  When the TXTL reaction , composed of a large amount of proteins , is concentrated, the uniformly mixed state becomes unstable  and membrane-less droplets form spontaneously.
Revised:  When the TXTL reaction , composed of a large amount of proteins , is concentrated, the uniformly mixed state becomes unstable , and membrane-less droplets form spontaneously.
Edit intent: fluency
Original:  When the TXTL reaction , composed of a large amount of proteins , is concentrated, the uniformly mixed state becomes unstable  and membrane-less droplets form spontaneously.
Revised:  When the TXTL reaction , composed of a large amount of proteins , is concentrated, the uniformly mixed state becomes unstable and membrane-less phases form spontaneously.
Edit intent: clarity
Original:  This LLPS droplet formation can be induced when the TXTL reaction is enclosed in water-in-oil emulsion droplets  in which water evaporates (dehydration) from the surface.
Revised:  This LLPS droplet formation is induced when the TXTL reaction is enclosed in water-in-oil emulsion droplets in which water evaporates (dehydration) from the surface.
Edit intent: clarity
Original:  This LLPS droplet formation can be induced when the TXTL reaction is enclosed in water-in-oil emulsion droplets  in which water evaporates (dehydration) from the surface.
Revised:  This LLPS droplet formation can be induced when the TXTL reaction is enclosed in water-in-oil emulsion droplets , in which water evaporates (dehydration) from the surface.
Edit intent: fluency
Original:  This LLPS droplet formation can be induced when the TXTL reaction is enclosed in water-in-oil emulsion droplets  in which water evaporates (dehydration) from the surface.
Revised:  This LLPS droplet formation can be induced when the TXTL reaction is enclosed in water-in-oil emulsion droplets in which water evaporates from the surface.
Edit intent: clarity
Original:  As the emulsion droplets shrink, smaller LLPS droplets appear inside the emulsion droplets and coalesce into  phase-separated domains that partition the location of proteins.
Revised:  As the emulsion droplets shrink, smaller LLPS droplets appear inside the emulsion droplets and coalesce into large phase-separated domains that partition the location of proteins.
Edit intent: clarity
Original:  In this paper, we study the Reachability problem on three distinct graph families -- intersection graphs of Jordan regions, unit contact disk graphs (penny graphs), and chordal graphs.
Revised:  In this paper, we study the Reachability problem on three distinct graph families - intersection graphs of Jordan regions, unit contact disk graphs (penny graphs), and chordal graphs.
Edit intent: fluency
Original:  In order to obtain these results, we use the vertex separator of these graphs effectively, and design space-efficient algorithms to find such separators.
Revised:  For intersection graphs of Jordan regions, we show how to obtain a "good" vertex separator in a space-efficient algorithms to find such separators.
Edit intent: clarity
Original: Finger-Fitts law (FFitts law) is a model to predict touch-pointing times that was modified from Fitts' law.
Revised: Finger-Fitts law (FFitts law) is a model to predict touch-pointing times modified from Fitts' law.
Edit intent: coherence
Original: Finger-Fitts law (FFitts law) is a model to predict touch-pointing times  modified from Fitts' law.
Revised: Finger-Fitts law (FFitts law) is a model to predict touch-pointing times , modified from Fitts' law.
Edit intent: fluency
Original:  This inconsistency could be harmful to HCI studies such as evaluating pointing techniques and comparing user groups .
Revised:  This inconsistency could be detrimental to HCI studies such as evaluating pointing techniques and comparing user groups .
Edit intent: clarity
Original:  This inconsistency could be harmful to HCI studies such as evaluating pointing techniques and comparing user groups .
Revised:  This inconsistency could be harmful to HCI studies such as pointing technique evaluations and user group comparisons .
Edit intent: clarity
Original:  By integrating the results of our 1D and 2D touch-pointing experiments and reanalyses of previous studies' data, we examined the advantages and disadvantages of each approach to compute sigma_a , and we found that using the parameter optimization method has overall the best prediction performance.
Revised:  By integrating the results of our 1D and 2D touch-pointing experiments and reanalyses of previous studies' data, we examined the advantages and disadvantages of each approach to compute sigma_a . We found that the parameter optimization method has overall the best prediction performance.
Edit intent: coherence
Original:  By integrating the results of our 1D and 2D touch-pointing experiments and reanalyses of previous studies' data, we examined the advantages and disadvantages of each approach to compute sigma_a , and we found that using the parameter optimization method has overall the best prediction performance.
Revised:  By integrating the results of our 1D and 2D touch-pointing experiments and reanalyses of previous studies' data, we examined the advantages and disadvantages of each approach to compute sigma_a , and we found that using the parameter optimization method is a suboptimal choice for predicting the performance.
Edit intent: clarity
Original:  First, we demonstrate the efficacy of the approach by designing the Future Log Replication model, a log in which follower is responsible for non-transactional log replication.
Revised:  First, we demonstrate the efficacy of the approach by designing the Future-Log Replication model, a log in which follower is responsible for non-transactional log replication.
Edit intent: fluency
Original:  First, we demonstrate the efficacy of the approach by designing the Future Log Replication model, a log in which follower is responsible for non-transactional log replication.
Revised:  First, we demonstrate the efficacy of the approach by designing the Future Log Replication model, which the followers are responsible for non-transactional log replication.
Edit intent: clarity
Original:  Secondly, we designed a Generation Re-replication strategy, which can ensure the security and consistency of future logs when the number of nodes changes.
Revised:  Secondly, we designed a Generation Re-replication strategy, which can ensure the security and consistency of future-logs when the number of nodes changes.
Edit intent: fluency
Original:  The results show that in the single-ms network latency environments, LCR-Raft can provide higher TPS ( 1.5X ~1.9X), and reduce the network traffic of the leader by 20 %DIFDELCMD < \\%%% %DIF < -30\\% with acceptable network traffic  and CPU cost on followers.
Revised:  The results show that in the single-ms network latency environments, LCR-Raft can provide 1.5X ~1.9X), and reduce the network traffic of the leader by 20 %DIFDELCMD < \\%%% %DIF < -30\\% with acceptable network traffic and CPU cost on followers.
Edit intent: clarity
Original:  Besides, since LCR does not change the number of leader and leader election process, it has good portability .
Revised:  Besides, since LCR does not change the number of leader and election process .
Edit intent: coherence
Original:  The results show that in  the single-ms network latency environments, LCR-Raft can provide 1.5X higher TPS, reduces transactional data response time  40\%-60\%, and network traffic by 20\%-30\% with acceptable network traffic and CPU cost on followers.
Revised:  The results show that in the transactional data response time 40\%-60\%, and network traffic by 20\%-30\% with acceptable network traffic and CPU cost on followers.
Edit intent: coherence
Original:  The results show that in  the single-ms network latency environments, LCR-Raft can provide 1.5X higher TPS, reduces transactional data response time  40\%-60\%, and network traffic by 20\%-30\% with acceptable network traffic and CPU cost on followers.
Revised:  The results show that in the single-ms network latency environments, LCR-Raft can provide 1.5X higher TPS, reduces transactional data response time 40\%-60\%, and the network traffic is reduced by 20\%-30\% with acceptable network traffic and CPU cost on followers.
Edit intent: clarity
Original:  Besides, LCR can provide high portability since it does not change the number of leader and election process.
Revised:  Besides, LCR can provide high portability since it does not change the number of leaders or the election process.
Edit intent: fluency
Original: Audio Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER)  which is calculated by comparing the number of errors between the ground truth and the ASR system's transcription .
Revised: Automatic Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER) which is calculated by comparing the number of errors between the ground truth and the ASR system's transcription .
Edit intent: clarity
Original: Audio Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER)  which is calculated by comparing the number of errors between the ground truth and the ASR system's transcription .
Revised: Audio Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER) , which is calculated by comparing the number of errors between the ground truth and the ASR system's transcription .
Edit intent: fluency
Original: Audio Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER)  which is calculated by comparing the number of errors between the ground truth and the ASR system's transcription .
Revised: Audio Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER) which is calculated by comparing the number of errors between the ground truth and the transcription of the ASR system .
Edit intent: clarity
Original: This tutorialpaper presents a didactic treatment of the emerging topic of signal processing on higher-order networks.
Revised: In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on higher-order networks.
Edit intent: clarity
Original:  Drawing analogies from discrete and graph signal processing, we introduce the building blocks for processing data on simplicial complexes and hypergraphs, two common abstractions of higher-order networks that can incorporate polyadic relationships.
Revised:  Drawing analogies from discrete and graph signal processing, we introduce the building blocks for processing data on simplicial complexes and hypergraphs, two common higher-order networks that can incorporate polyadic relationships.
Edit intent: clarity
Original:  We provide basic introductions to simplicial complexes and hypergraphs, making special emphasis on the concepts needed for processing signals on them.
Revised:  We provide brief introductions to simplicial complexes and hypergraphs, making special emphasis on the concepts needed for processing signals on them.
Edit intent: clarity
Original:  We provide basic introductions to simplicial complexes and hypergraphs, making special emphasis on the concepts needed for processing signals on them.
Revised:  We provide basic introductions to simplicial complexes and hypergraphs, with a special emphasis on the concepts needed for processing signals on them.
Edit intent: fluency
Original:  We further demonstrate the potential of the proposed approach for processing meeting-like recordings, where speaker activity obtained from a diarization system is used as a speaker clue for ADEnet .
Revised:  We further demonstrate the potential of the proposed approach for processing meeting-like recordings, where the speaker activity is obtained from a diarization system is used as a speaker clue for ADEnet .
Edit intent: fluency
Original:  We further demonstrate the potential of the proposed approach for processing meeting-like recordings, where speaker activity obtained from a diarization system is used as a speaker clue for ADEnet .
Revised:  We further demonstrate the potential of the proposed approach for processing meeting-like recordings, where speaker activity obtained from a diarization system .
Edit intent: clarity
Original:  We show that this simple yet practical approach can successfully extract speakers after diarization, which leads to improved ASR performance when using a single microphone , especially in high overlapping conditions, with a relative word error rate reduction of up to 25\%.
Revised:  We show that this simple yet practical approach can successfully extract speakers after diarization, which results in improved ASR performance when using a single microphone , especially in high overlapping conditions, with a relative word error rate reduction of up to 25\%.
Edit intent: clarity
Original:  We show that this simple yet practical approach can successfully extract speakers after diarization, which leads to improved ASR performance when using a single microphone , especially in high overlapping conditions, with a relative word error rate reduction of up to 25\%.
Revised:  We show that this simple yet practical approach can successfully extract speakers after diarization, which leads to improved ASR performance , especially in high overlapping conditions, with a relative word error rate reduction of up to 25\%.
Edit intent: clarity
Original:  In this paper, various techniques to speed up the recognition of real-world speeches are proposed and tested including parallelizing the recognition using batched beam search, detecting end-of-speech based on connectionist temporal classification (CTC) , restricting CTC prefix score and splitting long speeches into short segments.
Revised:  In this paper, various techniques to speed up the recognition of real-world speeches are proposed and tested including parallelizing the recognition using batched beam search, detecting end-of-speech based on CTC , restricting CTC prefix score and splitting long speeches into short segments.
Edit intent: clarity
Original:  Experiments are conducted with real-world Korean speech recognition task.
Edit intent: coherence
Original:  Experimental results with an 8-hour  test corpus show that the proposed system can convert speeches into text in less than 3 minutes with 10.73\% character error ratewhich is 27.1\% relatively low compared to conventional DNN-HMM based recognition system .
Revised:  Experimental results with an 8-hour test corpus show that the proposed system can convert speeches into text in less than 3 minutes with 10.73\% error rate .
Edit intent: coherence
Original:  But it is not easy to be used for long sequences.
Revised:  But it is not easy to use for long sequences.
Edit intent: clarity
Original:  In this paper, various techniques to speed up the recognition of real-world speeches are proposed and tested including parallelizing the recognition using batched beam search, detecting end-of-speech based on  CTC , restricting CTC prefix score  and splitting long speeches into short segments.
Revised:  In this paper, various techniques to speed up the recognition of real-world speeches are proposed and tested including parallelizing the recognition using batched beam search, detecting end-of-speech based on CTC prefix score and splitting long speeches into short segments.
Edit intent: coherence
Original:  In this paper, various techniques to speed up the recognition of real-world speeches are proposed and tested including parallelizing the recognition using batched beam search, detecting end-of-speech based on  CTC , restricting CTC prefix score  and splitting long speeches into short segments.
Revised:  In this paper, various techniques to speed up the recognition of real-world speeches are proposed and tested including parallelizing the recognition using batched beam search, detecting end-of-speech based on CTC , restricting CTC prefix score , and splitting long speeches into short segments.
Edit intent: fluency
Original:  Experimental results with an 8-hour real-world Korean speech test corpus show that the proposed system can convert speeches into text in less than 3 minutes with  10.73\% error rate .
Revised:  Experimental results with an 8-hour real-world Korean ASR tasks to verify the proposed methods. From the experiments, the proposed system can convert speeches into text in less than 3 minutes with 10.73\% error rate .
Edit intent: clarity
Original:  Experimental results with an 8-hour real-world Korean speech test corpus show that the proposed system can convert speeches into text in less than 3 minutes with  10.73\% error rate .
Revised:  Experimental results with an 8-hour real-world Korean speech test corpus show that the proposed system can convert speeches into text in less than 3 minutes with a 10.73\% error rate .
Edit intent: fluency
Original:  In this paper, we show that using Intelligent Reflecting Surfaces (IRS) can enhance the computing capability of a wireless network scenario.
Revised:  In this paper, we introduce the idea of using intelligent reflecting surfaces (IRSs) to enhance the computing capability of a wireless network scenario.
Edit intent: clarity
Original:  We consider a Multiple Access Channel (MAC) where a number of users aim to send data to a Base Station (BS) . The BS is interested in decoding a linear combination of the data from different users in the corresponding finite field.
Revised:  We consider a Multiple Access Channel (MAC) where a number of users aim to send data to a base station (BS) . The BS is interested in decoding a linear combination of the data from different users in the corresponding finite field.
Edit intent: fluency