wanyu/IteraTeR_v2 huggingface dataset:
Original:  This raises the question of how revolutionary theories and assumptions are evaluated and  accepted into the norm of scienceas the setting for the next science . Using two recently proposed metrics,
Revised:  This raises the question of how new and revolutionary ideas are evaluated and accepted into the norm of scienceas the setting for the next science . Using two recently proposed metrics,
Edit intent: clarity
Original:  This raises the question of how revolutionary theories and assumptions are evaluated and  accepted into the norm of scienceas the setting for the next science . Using two recently proposed metrics,
Revised:  This raises the question of how revolutionary theories and assumptions are evaluated and become accepted into the norm of scienceas the setting for the next science . Using two recently proposed metrics,
Edit intent: clarity
Original:  This raises the question of how revolutionary theories and assumptions are evaluated and  accepted into the norm of scienceas the setting for the next science . Using two recently proposed metrics,
Revised:  This raises the question of how revolutionary theories and assumptions are evaluated and accepted into the canon of science . Using two recently proposed metrics,
Edit intent: clarity
Original:  we identify the novel paper with high atypicality, which models how research draws upon unusual combinations of prior research in crafting their own contributions , and evaluate recognition to novel papers by citation and disruption, which captures the degree to which a research article creates a new direction by eclipsing citations to the prior work it builds upon.
Revised:  we identify papers with high atypicality, which models how research draws upon unusual combinations of prior research in crafting their own contributions , and evaluate recognition to novel papers by citation and disruption, which captures the degree to which a research article creates a new direction by eclipsing citations to the prior work it builds upon.
Edit intent: coherence
Original:  we identify the novel paper with high atypicality, which models how research draws upon unusual combinations of prior research in crafting their own contributions , and evaluate recognition to novel papers by citation and disruption, which captures the degree to which a research article creates a new direction by eclipsing citations to the prior work it builds upon.
Revised:  we identify the novel paper with high atypicality, which models how research draws upon novel combinations of prior research in crafting their own contributions , and evaluate recognition to novel papers by citation and disruption, which captures the degree to which a research article creates a new direction by eclipsing citations to the prior work it builds upon.
Edit intent: clarity
Original:  we identify the novel paper with high atypicality, which models how research draws upon unusual combinations of prior research in crafting their own contributions , and evaluate recognition to novel papers by citation and disruption, which captures the degree to which a research article creates a new direction by eclipsing citations to the prior work it builds upon.
Revised:  we identify the novel paper with high atypicality, which models how research draws upon unusual combinations of prior research , and evaluate recognition to novel papers by citation and disruption, which captures the degree to which a research article creates a new direction by eclipsing citations to the prior work it builds upon.
Edit intent: clarity
Original:  we identify the novel paper with high atypicality, which models how research draws upon unusual combinations of prior research in crafting their own contributions , and evaluate recognition to novel papers by citation and disruption, which captures the degree to which a research article creates a new direction by eclipsing citations to the prior work it builds upon.
Revised:  we identify the novel paper with high atypicality, which models how research draws upon unusual combinations of prior research in crafting their own contributions , and evaluate disruption, which captures the degree to which a research article creates a new direction by eclipsing citations to the prior work it builds upon.
Edit intent: coherence
Original:  we identify the novel paper with high atypicality, which models how research draws upon unusual combinations of prior research in crafting their own contributions , and evaluate recognition to novel papers by citation and disruption, which captures the degree to which a research article creates a new direction by eclipsing citations to the prior work it builds upon.
Revised:  we identify the novel paper with high atypicality, which models how research draws upon unusual combinations of prior research in crafting their own contributions , and evaluate recognition to novel papers by citation and disruption, which captures the degree to which a study creates a new direction by eclipsing citations to the prior work it builds upon.
Edit intent: clarity
Original:  A highly novel paper indeed has a much higher chance (61.3\%) to disrupt science than conventional papers (36.4\%) , but this recognition only comes from a distant future as reflected in citations, and it typically takes 10 years or longer for the disruption score of a paper to stabilize.
Revised:  A highly novel paper indeed has a much higher chance (61.3\%) to disrupt science than conventional papers , but this recognition only comes from a distant future as reflected in citations, and it typically takes 10 years or longer for the disruption score of a paper to stabilize.
Edit intent: coherence
Original:  A highly novel paper indeed has a much higher chance (61.3\%) to disrupt science than conventional papers (36.4\%) , but this recognition only comes from a distant future as reflected in citations, and it typically takes 10 years or longer for the disruption score of a paper to stabilize.
Revised:  A highly novel paper indeed has a much higher chance (61.3\%) to disrupt science than conventional papers (36.4\%) , but this is a slow process taking ten years or longer for the disruption score of a paper to stabilize.
Edit intent: clarity
Original:  We also provide the first computational model reformulating atypicality as the distance across the latent knowledge spaces learned by neural networks , as a proxy to the socially agreed relevance between distinct fields of scientific knowledge .
Revised:  We also provide the first computational model reformulating atypicality as the distance across latent knowledge spaces learned by neural networks , as a proxy to the socially agreed relevance between distinct fields of scientific knowledge .
Edit intent: fluency
Original:  We also provide the first computational model reformulating atypicality as the distance across the latent knowledge spaces learned by neural networks , as a proxy to the socially agreed relevance between distinct fields of scientific knowledge .
Revised:  We also provide the first computational model reformulating atypicality as the distance across the latent knowledge spaces learned by neural networks .
Edit intent: clarity
Original: Significant efforts are being invested to bring the classification and recognition powers of desktop and cloud systemsdirectly to edge devices.
Revised: Significant efforts are being invested to bring the classification and recognition powers of desktop and cloud systems directly to edge devices.
Edit intent: fluency
Original:  Importantly, we do not specifically engineer thenetwork for edge devices.
Revised:  Importantly, we do not specifically engineer the network for edge devices.
Edit intent: fluency
Original:  Rather, we present a universalpipeline that converts a large deep convolutional neuralnetwork (CNN) automatically via compression and quantization into a network suitable for resource-impoverishededge devices.
Revised:  Rather, we present a universal pipeline that converts a large deep convolutional neuralnetwork (CNN) automatically via compression and quantization into a network suitable for resource-impoverishededge devices.
Edit intent: fluency
Original:  Rather, we present a universalpipeline that converts a large deep convolutional neuralnetwork (CNN) automatically via compression and quantization into a network suitable for resource-impoverishededge devices.
Revised:  Rather, we present a universalpipeline that converts a large deep convolutional neural network (CNN) automatically via compression and quantization into a network suitable for resource-impoverishededge devices.
Edit intent: fluency
Original:  Rather, we present a universalpipeline that converts a large deep convolutional neuralnetwork (CNN) automatically via compression and quantization into a network suitable for resource-impoverishededge devices.
Revised:  Rather, we present a universalpipeline that converts a large deep convolutional neuralnetwork (CNN) automatically via compression and quantization into a network suitable for resource-impoverished edge devices.
Edit intent: fluency
Original:  Using 8-bit quantization, we deploy ACD-Net on standard microcontroller units (MCUs).
Revised:  Using 8-bit quantization, we deploy ACDNet on standard microcontroller units (MCUs).
Edit intent: fluency
Original:  While this should be of interestin its own right, we believe it to be of particular impor-tance that this has been achieved with a universal conver-sion pipeline rather than hand-crafting a network for mini-mal size.
Revised:  While this should be of interestin its own right, we believe it to be of particular importance that this has been achieved with a universal conver-sion pipeline rather than hand-crafting a network for mini-mal size.
Edit intent: fluency
Original:  While this should be of interestin its own right, we believe it to be of particular impor-tance that this has been achieved with a universal conver-sion pipeline rather than hand-crafting a network for mini-mal size.
Revised:  While this should be of interestin its own right, we believe it to be of particular impor-tance that this has been achieved with a universal conversion pipeline rather than hand-crafting a network for mini-mal size.
Edit intent: fluency
Original:  While this should be of interestin its own right, we believe it to be of particular impor-tance that this has been achieved with a universal conver-sion pipeline rather than hand-crafting a network for mini-mal size.
Revised:  While this should be of interestin its own right, we believe it to be of particular impor-tance that this has been achieved with a universal conver-sion pipeline rather than hand-crafting a network for minimal size.
Edit intent: fluency
Original: Significant efforts are being invested to bring the classification and recognition powers of desktop and cloud systems directly to edge devices .
Revised: Significant efforts are being invested to bring the classification and recognition to edge devices .
Edit intent: clarity
Original:  The main challenge for deep learning on the edge is to handle extreme resource constraints (memory, CPU speed and lack of GPU support).
Revised:  The main challenge for deep learning on the edge is to handle extreme resource constraints (memory, speed and lack of GPU support).
Edit intent: clarity
Original:  We present an edge solution for audio classification that achieves close to state-of-the-art performance on ESC-50, the same benchmark used to assess large, non resource-constrained networks.
Revised:  Here, we demonstrate the first deep network for acoustic recognition that is small enough for an off-the-shelf microcrocontroller, yet achieves state-of-the-art performance on ESC-50, the same benchmark used to assess large, non resource-constrained networks.
Edit intent: clarity
Original:  Rather , we present a universal pipeline that converts a large deep convolutional neural network (CNN) automatically via compression and quantization into a network suitable for resource-impoverished edge devices.
Revised:  Rather , we present a universal pipeline that converts a large deep convolutional network automatically via compression and quantization into a network suitable for resource-impoverished edge devices.
Edit intent: clarity
Original:  Rather , we present a universal pipeline that converts a large deep convolutional neural network (CNN) automatically via compression and quantization into a network suitable for resource-impoverished edge devices.
Revised:  Rather , we present a universal pipeline that converts a large deep convolutional neural network (CNN) automatically via compression and quantization into a network for resource-impoverished edge devices.
Edit intent: clarity
Original:  We first introduce a new sound classification architecture, ACDNet, that produces above state-of-the-art accuracy on both ESC-10  and ESC-50 which are 96.75\%and 87.05\% respectively.
Revised:  After introducing ACDNet, which produces above state-of-the-art accuracy on both ESC-10 and ESC-50 which are 96.75\%and 87.05\% respectively.
Edit intent: clarity
Original:  We first introduce a new sound classification architecture, ACDNet, that produces above state-of-the-art accuracy on both ESC-10  and ESC-50 which are 96.75\%and 87.05\% respectively.
Revised:  We first introduce a new sound classification architecture, ACDNet, that produces above state-of-the-art accuracy on ESC-10 and ESC-50 which are 96.75\%and 87.05\% respectively.
Edit intent: clarity
Original:  Despite 97.22\% size reduction and 97.28\% reduction in FLOPs, the compressed network still achieves 82.90\% accuracy on ESC-50, staying close to the state-of-the-art . Using 8-bit quantization, we deploy ACDNet on standard microcontroller units (MCUs).
Revised:  Despite 97.22\% size reduction and 97.28\% FLOP reduction while maintaining close to state-of-the-art . Using 8-bit quantization, we deploy ACDNet on standard microcontroller units (MCUs).
Edit intent: clarity
Original:  Participants favored the paths that had a one meter per second forward speed and rated the path with the least amount of turns as the most comfortable .
Revised:  Participants favored the paths that had a one meter per second forward speed and rated the path with the least amount of turns as the most comfortable 
Edit intent: fluency
Original: We present a multilingual end-to-end Text-To-Speech framework that maps byte inputs to spectrograms, thus allowing arbitrary input scripts.
Revised: We present a multilingual end-to-end framework that maps byte inputs to spectrograms, thus allowing arbitrary input scripts.
Edit intent: clarity
Original:  Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording  without the need of  lexicon, extra corpus, auxiliary models, or particular linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models.
Revised:  Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording without the need of lexicon, extra corpus, auxiliary models, or particular linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models.
Edit intent: clarity
Original:  Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording  without the need of  lexicon, extra corpus, auxiliary models, or particular linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models.
Revised:  Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording , without the need of lexicon, extra corpus, auxiliary models, or particular linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models.
Edit intent: fluency
Original:  Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording  without the need of  lexicon, extra corpus, auxiliary models, or particular linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models.
Revised:  Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording without the need of lexicon, extra corpus, auxiliary models, or linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models.
Edit intent: clarity
Original:  Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording  without the need of  lexicon, extra corpus, auxiliary models, or particular linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models.
Revised:  Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording without the need of lexicon, extra corpus, auxiliary models, or particular linguistic expertise, thus ensuring scalability. While it retains satisfactory intelligibility and naturalness matching rich-resource models.
Edit intent: clarity
Original:  Exhaustive comparative  studies are performed to reveal the potential of the framework for low-resource application and the impact of various factors contributory to adaptation .
Revised:  Exhaustive comparative studies are performed to reveal the potential of the framework for low-resource languages .
Edit intent: clarity
Original:  Furthermore, we propose a novel method to extract language-specific sub-networks  for a better understanding of the mechanismof multilingual models .
Revised:  Furthermore, we propose a novel method to extract language-specific sub-networks in a multilingual model for a better understanding of the mechanismof multilingual models .
Edit intent: clarity
Original:  Furthermore, we propose a novel method to extract language-specific sub-networks  for a better understanding of the mechanismof multilingual models .
Revised:  Furthermore, we propose a novel method to extract language-specific sub-networks for a better understanding of its mechanism .
Edit intent: clarity
Original:  Therefore, this problem is usually resolved by simulating the entire load path for each optimization step .
Revised:  Therefore, this problem is usually resolved by recomputing the physical behavior for each optimization step .
Edit intent: clarity
Original:  To avoid this time-consuming procedure, we develop a  surrogate material model that yields identical results for the loading case but does not dissipate energy during the virtual unloading.
Revised:  To avoid this time-consuming procedure, we develop a novel surrogate material model that yields identical results for the loading case but does not dissipate energy during the virtual unloading.
Edit intent: clarity
Original:  Since the majority of dementia patients cannot be cured, being able to diagnose them before the onset of the symptoms can prevent the rapid progression of the  cognitive impairment.
Revised:  Since the majority of dementia patients cannot be cured, being able to diagnose them before the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment.
Edit intent: clarity
Original:  Our results give essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients.
Revised:  Our results yielded essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients.
Edit intent: clarity
Original: We explore the utility of harnessing auxiliary labels (e.g., facial expression) to impose ] ] geometric structure when training embedding models for one-shot learning (e. g., for face verification).
Revised: We explore the utility of harnessing auxiliary labels (e.g., facial expression) to impose ] ], provide geometric structure when training embedding models for one-shot learning (e. g., for face verification).
Edit intent: clarity
Original:  It does not require any changes to the  backbone of the embedding functions .
Revised:  It does not require any changes to the backbone of the embedding function .
Edit intent: fluency
Original:  There is a plethora of generative models available, which build molecules either atom-by-atom and bond-by-bond or fragment-by-fragment.
Revised:  A plethora of generative models available, which build molecules either atom-by-atom and bond-by-bond or fragment-by-fragment.
Edit intent: coherence
Original:  There is a plethora of generative models available, which build molecules either atom-by-atom and bond-by-bond or fragment-by-fragment.
Revised:  There is a plethora of generative models is available, building molecules either atom-by-atom and bond-by-bond or fragment-by-fragment.
Edit intent: clarity
Original:  Many drug discovery projects also require a fixed scaffold to be present in the generated molecule, and incorporating that constraint has been recently explored.
Revised:  However, many drug discovery projects also require a fixed scaffold to be present in the generated molecule, and incorporating that constraint has been recently explored.
Edit intent: coherence
Original:  Many drug discovery projects also require a fixed scaffold to be present in the generated molecule, and incorporating that constraint has been recently explored.
Revised:  Many drug discovery projects require a fixed scaffold to be present in the generated molecule, and incorporating that constraint has been recently explored.
Edit intent: clarity
Original:  Many drug discovery projects also require a fixed scaffold to be present in the generated molecule, and incorporating that constraint has been recently explored.
Revised:  Many drug discovery projects also require a fixed scaffold to be present in the generated molecule, and incorporating that constraint has only recently been explored.
Edit intent: clarity
Original:  Extending a scaffold is implemented by using it as the initial partial graph , which is possible because our model does not depend on generation history.
Revised:  Extending a scaffold is implemented by using it as the initial partial graph , which is possible because our model is not conditioned on the generation history.
Edit intent: clarity
Original:   We show that training using a randomized generation order is necessary for good performance when extending scaffolds, and that the results are further improved by increasing  fragment vocabulary size.
Revised:  We show that training using a randomized generation order is necessary for good performance when extending scaffolds, and that the results are further improved by increasing the fragment vocabulary size.
Edit intent: fluency
Original:  Triggered by a sensory conflict between the visual and vestibular systems, cybersickness symptoms cause discomfort and hinder the immersive VR experience.
Revised:  Cybersickness symptoms cause severe discomfort and hinder the immersive VR experience.
Edit intent: clarity
Original:  In 360-degrees VR experiences,  movement in the real world is not reflected in the virtual world, and therefore self-motion information is not corroborated by matching visual and vestibular cues, which may potentially induce cybersickness.
Revised:  In traditional 360-degree VR experiences, movement in the real world is not reflected in the virtual world, and therefore self-motion information is not corroborated by matching visual and vestibular cues, which may potentially induce cybersickness.
Edit intent: clarity