wanyu/IteraTeR_full_sent huggingface dataset:
Original: When designing a message transmission system  from the point of view of making sure that the information transmitted is as fresh as possible, two rules of thumb seem reasonable: use small buffers and adopt a last-in-first-out policy.
Revised: When designing a message transmission system , from the point of view of making sure that the information transmitted is as fresh as possible, two rules of thumb seem reasonable: use small buffers and adopt a last-in-first-out policy.
Edit intent: fluency
Original:  We describe four different distance measures, including (Soft) DTW and  Matrix Profile, as well as four successful semi-supervised learning methods, including the graph Allen- Cahn method and a Graph Convolutional Neural Network.
Revised:  We describe four different distance measures, including (Soft) DTW and MPDist, a distance measure based on the Matrix Profile, as well as four successful semi-supervised learning methods, including the graph Allen- Cahn method and a Graph Convolutional Neural Network.
Edit intent: meaning-changed
Original:  We describe four different distance measures, including (Soft) DTW and  Matrix Profile, as well as four successful semi-supervised learning methods, including the graph Allen- Cahn method and a Graph Convolutional Neural Network.
Revised:  We describe four different distance measures, including (Soft) DTW and Matrix Profile, as well as four successful semi-supervised learning methods, including the graph Allen--Cahn method and a Graph Convolutional Neural Network.
Edit intent: fluency
Original:  Our findings show that all measures and methods vary strongly in accuracybetween data sets and that there is no clear best combination to employ in all cases  .
Revised:  Our findings show that all measures and methods vary strongly in accuracybetween data sets and that there is no clear best combination to employ in all cases is found. Our study provides a reproducible framework for future work in the direction of semi-supervised learning for time series with a focus on graph representations .
Edit intent: meaning-changed
Original: Vision models trained on multimodal datasets have recently proved very efficient, both in terms of the wide availability of large image-caption datasets , and in terms of the resulting model 's ability to generalize to multiple downstream tasks (e.g. zero-shot learning).
Revised: Vision models trained on multimodal datasets can benefit from the wide availability of large image-caption datasets , and in terms of the resulting model 's ability to generalize to multiple downstream tasks (e.g. zero-shot learning).
Edit intent: clarity
Original: Vision models trained on multimodal datasets have recently proved very efficient, both in terms of the wide availability of large image-caption datasets , and in terms of the resulting model 's ability to generalize to multiple downstream tasks (e.g. zero-shot learning).
Revised: Vision models trained on multimodal datasets have recently proved very efficient, both in terms of the wide availability of large image-caption datasets . A recent model (CLIP) was found to generalize well in zero-shot learning).
Edit intent: meaning-changed
Original: Vision models trained on multimodal datasets have recently proved very efficient, both in terms of the wide availability of large image-caption datasets , and in terms of the resulting model 's ability to generalize to multiple downstream tasks (e.g. zero-shot learning). One might assume that these abilities are derived, at least in part, from a "semantic grounding" of the visual feature space , learning meaningful structure by mirroring the space of linguistic representations.
Revised: Vision models trained on multimodal datasets have recently proved very efficient, both in terms of the wide availability of large image-caption datasets , and in terms of the resulting model 's ability to generalize to multiple downstream tasks (e.g. zero-shot and transfer learning settings. This could imply that linguistic or "semantic grounding" of the visual feature space , learning meaningful structure by mirroring the space of linguistic representations.
Edit intent: clarity
Original:  One might assume that these abilities are derived, at least in part, from a "semantic grounding" of the visual feature space , learning meaningful structure by mirroring the space of linguistic representations.
Revised:  One might assume that these abilities are derived, at least in part, from a "semantic grounding" confers additional generalization abilities to the visual feature space , learning meaningful structure by mirroring the space of linguistic representations.
Edit intent: meaning-changed
Original:  One might assume that these abilities are derived, at least in part, from a "semantic grounding" of the visual feature space , learning meaningful structure by mirroring the space of linguistic representations. Contrary to this intuition, we show that a visual model (BiT-M) trained on a very large supervised image dataset (ImageNet-21k) can be as efficient for generalization ( few-shot learning, unsupervised clustering) as its multimodal counterpart (CLIP).
Revised:  One might assume that these abilities are derived, at least in part, from a "semantic grounding" of the visual feature space . Here, we systematically evaluate various multimodal architectures and vision-only models in terms of unsupervised clustering, few-shot learning, unsupervised clustering) as its multimodal counterpart (CLIP).
Edit intent: clarity
Original:  Contrary to this intuition, we show that a visual model (BiT-M) trained on a very large supervised image dataset (ImageNet-21k) can be as efficient for generalization ( few-shot learning, unsupervised clustering) as its multimodal counterpart (CLIP). When compared to other standard visual or language models, the latent representations of BiT-M were found to be just as "linguistic" as those of CLIP. Overall, these findings suggest that the main factor driving improvements of generalization in current models is the size of the training dataset, not (solely) the multimodal grounding property .
Revised:  Contrary to this intuition, we show that a visual model (BiT-M) trained on a very large supervised image dataset (ImageNet-21k) can be as efficient for generalization ( few-shot learning, transfer learning and adversarial robustness. In each setting, multimodal training produced no additional generalization capability compared to standard supervised visual training. We conclude that work is still required for semantic grounding to help improve vision models .
Edit intent: clarity
Original:  (Policy Forum, 16 October 2020, p.298) claim that "low-carbon investments to put the world on an ambitious track toward net zero carbon dioxide emissions by mid-century are dwarfed by currently announced COVID-19 stimulus funds. " } We argue that this short-sighted and public investment-led view misrepresents the grand challenges that climate change  entails.
Revised:  (Policy Forum, 16 October 2020, p.298) claim that "low-carbon investments to put the world on an ambitious track toward net zero carbon dioxide emissions by mid-century are dwarfed by currently announced COVID-19 public stimulus packages is sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 } We argue that this short-sighted and public investment-led view misrepresents the grand challenges that climate change entails.
Edit intent: meaning-changed
Original: It has been claimed that a small subset of COVID-19 public stimulus packages is sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 \deg C target of the Paris Agreement.
Revised: It has been claimed that COVID-19 public stimulus packages is sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 \deg C target of the Paris Agreement.
Edit intent: clarity
Original: It has been claimed that a small subset of COVID-19 public stimulus packages is sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 \deg C target of the Paris Agreement.
Revised: It has been claimed that a small subset of COVID-19 public stimulus packages are sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 \deg C target of the Paris Agreement.
Edit intent: fluency
Original: It has been claimed that a small subset of COVID-19 public stimulus packages is sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 \deg C target of the Paris Agreement.
Revised: It has been claimed that a small subset of COVID-19 public stimulus packages is sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 degree C target of the Paris Agreement.
Edit intent: fluency
Original:  The experiments we performed in 5 different Atari games verify that our algorithm can outperform its competitors by achieving state-of-the-art performance, and its components themselves also provides significant advantages individually .
Revised:  The experiments we performed in 5 different Atari games verify that our algorithm either surpasses or performs on-par with its top competitors while being far simpler to be employed. Furthermore, its individual components are also found to be providing significant advantages alone .
Edit intent: clarity
Original: Most adversarial attack methods on text classification are designed to change the classifier's prediction by modifying few words or characters. Few try to attack classifiers by rewriting a whole sentence, due to the difficulties inherent in sentence-level rephrasing and the problem of maintaining high semantic similarity and sentence quality. To tackle this problem, we design a general sentence rewriting sampler ( SRS)framework, which can conditionally generate meaningful sentences.
Revised: Most adversarial attack methods on text classification are designed to change the classifier's prediction by synonym substitution. We propose the adversarial sentence rewriting sampler ( SRS)framework, which can conditionally generate meaningful sentences.
Edit intent: meaning-changed
Original:  To tackle this problem, we design a general sentence rewriting sampler ( SRS)framework, which can conditionally generate meaningful sentences. Then we customize SRS to attack text classification models. Our method can effectively rewrite the original sentence in multiple ways while maintaining high semantic similarity and good sentence quality. Experimental results show that many of these rewritten sentences are misclassified by the classifier.
Revised:  To tackle this problem, we design a general sentence rewriting sampler ( ASRS), which rewrites the whole sentence to generate more similar and higher-quality adversarial examples.
Edit intent: clarity
Original:  Our method achieves a better attack success rate on 4 out of 7 datasets, as well as significantly better sentence quality on all 7 datasets  .
Revised:  Our method achieves a better attack success rate on 4 out of 7 datasets, as well as significantly better sentence quality on all 7 datasets . ASRS is an indispensable supplement to the existing attack methods, because classifiers cannot resist the attack from ASRS unless they are trained on adversarial examples found by ASRS .
Edit intent: meaning-changed
Original:  This is optimal in the sense that as long as the exponent of T is independent of n, T, it cannot be improved while retaining the dependency on n , as follows from the lower bound of Eden and Rosenbaum [APPROX/RANDOM 2018].
Revised:  This is optimal in the sense that as long as the exponent of T is independent of n, T, it cannot be improved while retaining the dependency on n ; this as follows from the lower bound of Eden and Rosenbaum [APPROX/RANDOM 2018].
Edit intent: coherence
Original:  The design uses a human-centric approach to information filtering, selectively visualising only parts of the virtual environment related to the interactive range and possibilities of a virtual reality user.
Revised:  We explore an additional two conditions, Context and Spotlight, in a follow-up study. Our design uses a human-centric approach to information filtering, selectively visualising only parts of the virtual environment related to the interactive range and possibilities of a virtual reality user.
Edit intent: meaning-changed
Original:  DART uses careful trajectory design to find a sweet spot in this constraint space, a fast reconnaissance flight to narrow the search area for structures, and offloads expensive computations to the cloud by streaming compressed LiDAR data over LTE.
Revised:  ARES uses careful trajectory design to find a sweet spot in this constraint space, a fast reconnaissance flight to narrow the search area for structures, and offloads expensive computations to the cloud by streaming compressed LiDAR data over LTE.
Edit intent: clarity
Original:  Especially we show that a probability of a tropical line segment of randomly chosen two trees going thorough the origin (the star tree) is zero and we also show that if two given trees differ only one nearest neighbor interchange (NNI) move, then the tree topology of a tree in the tropical line segment between them is the same tree topology of one of these given two trees with possible zero branch lengths.
Revised:  Specifically we show that a probability of a tropical line segment of randomly chosen two trees going thorough the origin (the star tree) is zero and we also show that if two given trees differ only one nearest neighbor interchange (NNI) move, then the tree topology of a tree in the tropical line segment between them is the same tree topology of one of these given two trees with possible zero branch lengths.
Edit intent: fluency
Original:  Especially we show that a probability of a tropical line segment of randomly chosen two trees going thorough the origin (the star tree) is zero and we also show that if two given trees differ only one nearest neighbor interchange (NNI) move, then the tree topology of a tree in the tropical line segment between them is the same tree topology of one of these given two trees with possible zero branch lengths.
Revised:  Especially we show that a probability of a tropical line segment of two randomly chosen trees going thorough the origin (the star tree) is zero and we also show that if two given trees differ only one nearest neighbor interchange (NNI) move, then the tree topology of a tree in the tropical line segment between them is the same tree topology of one of these given two trees with possible zero branch lengths.
Edit intent: fluency
Original:  Data was analyzed from 3262 head impacts from head model simulations, on-field data from American footballand mixed martial arts (MMA) using our instrumented mouthguard , and publicly available car crash data.
Revised:  Data was analyzed from 3,262 head impacts from head model simulations, on-field data from American footballand mixed martial arts (MMA) using our instrumented mouthguard , and publicly available car crash data.
Edit intent: fluency
Original:  Data was analyzed from 3262 head impacts from head model simulations, on-field data from American footballand mixed martial arts (MMA) using our instrumented mouthguard , and publicly available car crash data.
Revised:  Data was analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts (MMA) using our instrumented mouthguard , and publicly available car crash data.
Edit intent: meaning-changed
Original:  A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify different types of head impacts (e.g., football , MMA ), reaching a median accuracy of 96\% over 1000 random partitions of training and test sets.
Revised:  A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football , MMA ), reaching a median accuracy of 96\% over 1000 random partitions of training and test sets.
Edit intent: clarity
Original:  A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify different types of head impacts (e.g., football , MMA ), reaching a median accuracy of 96\% over 1000 random partitions of training and test sets.
Revised:  A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify different types of head impacts (e.g., football ), reaching a median accuracy of 96\% over 1000 random partitions of training and test sets.
Edit intent: coherence
Original:  A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify different types of head impacts (e.g., football , MMA ), reaching a median accuracy of 96\% over 1000 random partitions of training and test sets.
Revised:  A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify different types of head impacts (e.g., football , MMA ), reaching a median accuracy of 96\% over 1,000 random partitions of training and test sets.
Edit intent: fluency
Original:  Furthermore, to test the classifier on data from different measurement devices, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards with the classifier reaching over 96\% accuracy from these devices .
Revised:  To test the classifier on data from different measurement devices, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards with the classifier reaching over 96\% accuracy from these devices .
Edit intent: coherence
Original:  Furthermore, to test the classifier on data from different measurement devices, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards with the classifier reaching over 96\% accuracy from these devices .
Revised:  Furthermore, to test the classifier on data from different measurement devices, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards with the classifier reaching over 96\% accuracy .
Edit intent: clarity
Original:  It was found that different head impact types had different distributions of spectral densities in low-frequency and high-frequency ranges (e.g., the spectral densities of MMA impacts were higher in high-frequency range than in the low-frequency range).
Revised:  Different head impact types had different distributions of spectral densities in low-frequency and high-frequency ranges (e.g., the spectral densities of MMA impacts were higher in high-frequency range than in the low-frequency range).
Edit intent: coherence
Original:  Finally, with head impact classification , type-specific, nearest-neighbor regression models were built for 95th percentile maximum principal strain, 95th percentile maximum principal strain in corpus callosum, and cumulative strain damage (15th percentile).
Revised:  Finally, with the classifier , type-specific, nearest-neighbor regression models were built for 95th percentile maximum principal strain, 95th percentile maximum principal strain in corpus callosum, and cumulative strain damage (15th percentile).
Edit intent: clarity
Original: While common image object detection tasks focus on bounding boxes or segmentation masks as object representations, we  propose a novel method , named TetraPackNet, using fourcorner based object representations .
Revised: While common image object detection tasks focus on bounding boxes or segmentation masks as object representations, we consider the problem of finding objects based on four arbitrary vertices. We propose a novel method , named TetraPackNet, using fourcorner based object representations .
Edit intent: meaning-changed
Original:  Baselined against a previous solution, consisting of a a Mask R-CNN model and suitable post-processing steps, TetraPackNet achieves superior results ( 6 \% higher in accuracy) in the application of four-corner based transport unit side detection.
Revised:  Baselined against a previous solution, consisting of a Mask R-CNN model and suitable post-processing steps, TetraPackNet achieves superior results ( 6 \% higher in accuracy) in the application of four-corner based transport unit side detection.
Edit intent: fluency
Original:  Baselined against a previous solution, consisting of a a Mask R-CNN model and suitable post-processing steps, TetraPackNet achieves superior results ( 6 \% higher in accuracy) in the application of four-corner based transport unit side detection.
Revised:  Baselined against a previous solution, consisting of a a Mask R-CNN model and suitable post-processing steps, TetraPackNet achieves superior results ( 9 \% higher in accuracy) in the application of four-corner based transport unit side detection.
Edit intent: meaning-changed
Original:  Baselined against a previous solution, consisting of a a Mask R-CNN model and suitable post-processing steps, TetraPackNet achieves superior results ( 6 \% higher in accuracy) in the application of four-corner based transport unit side detection.
Revised:  Baselined against a previous solution, consisting of a a Mask R-CNN model and suitable post-processing steps, TetraPackNet achieves superior results ( 6 \% higher in accuracy) in the sub-task of four-corner based transport unit side detection.
Edit intent: clarity
Original:  We demonstrate the proposed method by applying it to basketball teams, showing how past teams are analyzed and how new lineups are examined . Because the method can be adapted to individual application cases by extending the network structure, it can be a general method by which practical systems can be built.
Revised:  We demonstrate the proposed method by applying it to basketball teams, and compare with a benchmark system for outcome prediction and lineup reconstruction tasks . Because the method can be adapted to individual application cases by extending the network structure, it can be a general method by which practical systems can be built.
Edit intent: meaning-changed
Original: In spite of the considerable interest, a thorough statistical analysis of foreign exchange markets was hampered by limited access to data. This changed, and nowadays such data analyses are possible down to the level of ticks and over long time scales. We analyze price response functions in the  foreign exchange market for different years and different time scales.
Revised: We carry out a detailed large-scale data analysis of price response functions in the foreign exchange market for different years and different time scales.
Edit intent: clarity
Original: We propose a neural model of multi-spin interacting system simulating neurons that interact each other through the surroundings . In such open system approach, we consider the neurons coupled with their surroundingsincluding the axons connecting between neurons and the glial cells which distribute around the neurons. The surrounding environment is physically modeled as a collection of all kinds of vibrational modes .
Revised: We propose a neural model of multi-spin interacting system simulating neurons that interact each other through the surroundings of neuronal cell bodies. We physically model the neuronal cell surroundings, include the dendrites, the axons and the synapses as well as the surrounding glial cells, as a collection of all kinds of vibrational modes .
Edit intent: clarity
Original:  Through these substantially different approaches, we robustly obtain that PM_{2.5} levels are the main predictor of R_0 in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition.
Revised:  Through these substantially different approaches, we robustly obtain that PM_{2.5} is a major predictor of R_0 in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition.
Edit intent: clarity