wanyu/IteraTeR_full_sent huggingface dataset:
Original:   ]  Gawrychowski, Landau, Sung, and Weimann [ICALP'18] devise a greedy algorithm that runs in time O(n^{1.5}\log n) based on micro-macro decomposition .
Revised:  WABI'19] Gawrychowski, Landau, Sung, and Weimann [ICALP'18] devise a greedy algorithm that runs in time O(n^{1.5}\log n) based on micro-macro decomposition .
Edit intent: meaning-changed
Original:   ]  Gawrychowski, Landau, Sung, and Weimann [ICALP'18] devise a greedy algorithm that runs in time O(n^{1.5}\log n) based on micro-macro decomposition .
Revised:  ] who employ the greedy algorithm by Gawrychowski, Landau, Sung, and Weimann [ICALP'18] devise a greedy algorithm that runs in time O(n^{1.5}\log n) based on micro-macro decomposition .
Edit intent: meaning-changed
Original:   ]  Gawrychowski, Landau, Sung, and Weimann [ICALP'18] devise a greedy algorithm that runs in time O(n^{1.5}\log n) based on micro-macro decomposition .
Revised:  ] Gawrychowski, Landau, Sung, and Weimann [ICALP'18] that runs in time O(n^{1.5}\log n) based on micro-macro decomposition .
Edit intent: clarity
Original:   ]  Gawrychowski, Landau, Sung, and Weimann [ICALP'18] devise a greedy algorithm that runs in time O(n^{1.5}\log n) based on micro-macro decomposition .
Revised:  ] Gawrychowski, Landau, Sung, and Weimann [ICALP'18] devise a greedy algorithm that runs in time O(n^{1.5}\log n) .
Edit intent: meaning-changed
Original: To evaluate if the effort to contain the epidemic spreading of the new Coronavirus disease 2019 is really working, we carried out an analysis of the time evolution of the infection in different Countries, by considering two well-known macroscopic growth laws, the Gompertz law  and the logistic law.
Revised: To evaluate the effectiveness of the containments on the epidemic spreading of the new Coronavirus disease 2019 is really working, we carried out an analysis of the time evolution of the infection in different Countries, by considering two well-known macroscopic growth laws, the Gompertz law and the logistic law.
Edit intent: clarity
Original: To evaluate if the effort to contain the epidemic spreading of the new Coronavirus disease 2019 is really working, we carried out an analysis of the time evolution of the infection in different Countries, by considering two well-known macroscopic growth laws, the Gompertz law  and the logistic law.
Revised: To evaluate if the effort to contain the epidemic spreading of the new Coronavirus disease 2019 , we carry on an analysis of the time evolution of the infection in different Countries, by considering two well-known macroscopic growth laws, the Gompertz law and the logistic law.
Edit intent: clarity
Original: To evaluate if the effort to contain the epidemic spreading of the new Coronavirus disease 2019 is really working, we carried out an analysis of the time evolution of the infection in different Countries, by considering two well-known macroscopic growth laws, the Gompertz law  and the logistic law.
Revised: To evaluate if the effort to contain the epidemic spreading of the new Coronavirus disease 2019 is really working, we carried out an analysis of the time evolution of the infection in different Countries, by considering well-known macroscopic growth laws, the Gompertz law and the logistic law.
Edit intent: clarity
Original: To evaluate if the effort to contain the epidemic spreading of the new Coronavirus disease 2019 is really working, we carried out an analysis of the time evolution of the infection in different Countries, by considering two well-known macroscopic growth laws, the Gompertz law  and the logistic law.
Revised: To evaluate if the effort to contain the epidemic spreading of the new Coronavirus disease 2019 is really working, we carried out an analysis of the time evolution of the infection in different Countries, by considering two well-known macroscopic growth laws, the Gompertz law , and the logistic law.
Edit intent: fluency
Original:   For South Korea and Italy, instead, the data of the first week of March on the new infections , will be crucial to understand if the saturation will be reached for lower or higher numbers of infected individuals .
Revised:  In Singapore a large growth rate, recently observed, suggests the start of a new strong spreading. For South Korea and Italy, instead, the data of the first week of March on the new infections , will be crucial to understand if the saturation will be reached for lower or higher numbers of infected individuals .
Edit intent: meaning-changed
Original:   For South Korea and Italy, instead, the data of the first week of March on the new infections , will be crucial to understand if the saturation will be reached for lower or higher numbers of infected individuals . This will show whether a strong containment policy is worth keeping .
Revised:  For South Korea and Italy, instead, the data of the first week of March on the new infections , will be crucial to understand if the saturation will be reached for lower or higher numbers of infected individuals .
Edit intent: coherence
Original: To evaluate the effectiveness of the containments on the epidemic spreading of the new Coronavirus disease 2019, we carry on an analysis of the time evolution of the infection in  different Countries, by considering well-known macroscopic growth laws, the Gompertz law, and the logistic law.
Revised: To evaluate the effectiveness of the containment on the epidemic spreading of the new Coronavirus disease 2019, we carry on an analysis of the time evolution of the infection in different Countries, by considering well-known macroscopic growth laws, the Gompertz law, and the logistic law.
Edit intent: fluency
Original: To evaluate the effectiveness of the containments on the epidemic spreading of the new Coronavirus disease 2019, we carry on an analysis of the time evolution of the infection in  different Countries, by considering well-known macroscopic growth laws, the Gompertz law, and the logistic law.
Revised: To evaluate the effectiveness of the containments on the epidemic spreading of the new Coronavirus disease 2019, we carry on an analysis of the time evolution of the infection in a selected number of different Countries, by considering well-known macroscopic growth laws, the Gompertz law, and the logistic law.
Edit intent: meaning-changed
Original:  Our experiments using a large scale Limit Order Book (LOB) consisting of more than 4 millions order events show that BiN-TABL outperforms TABL networks using other state-of-the-arts normalization schemes by a large margin.
Revised:  Our experiments using a large scale Limit Order Book (LOB) consisting of more than 4 million order events show that BiN-TABL outperforms TABL networks using other state-of-the-arts normalization schemes by a large margin.
Edit intent: fluency
Original:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for the AV to make the pedestrian feel safe after it is noticed ?
Revised:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for an AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for the AV to make the pedestrian feel safe after it is noticed ?
Edit intent: fluency
Original:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for the AV to make the pedestrian feel safe after it is noticed ?
Revised:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make pedestrians understand its driving intentions after it is noticed ? 2) What timing for the AV to make the pedestrian feel safe after it is noticed ?
Edit intent: fluency
Original:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for the AV to make the pedestrian feel safe after it is noticed ?
Revised:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after being noticed by them ? 2) What timing for the AV to make the pedestrian feel safe after it is noticed ?
Edit intent: clarity
Original:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for the AV to make the pedestrian feel safe after it is noticed ?
Revised:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for an AV to make the pedestrian feel safe after it is noticed ?
Edit intent: coherence
Original:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for the AV to make the pedestrian feel safe after it is noticed ?
Revised:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for the AV to make pedestrians feel safe after it is noticed ?
Edit intent: coherence
Original:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for the AV to make the pedestrian feel safe after it is noticed ?
Revised:  However, the following questions should be answered regarding how the AV sends the information to them: 1) What timing for the AV to make the pedestrian understand its driving intentions after it is noticed ? 2) What timing for the AV to make the pedestrian feel safe after being noticed by them ?
Edit intent: clarity
Original:  Initially designed to represent and navigate through scientific publications in different disciplines, CARTOLABE has evolved to become a generic system and accommodate various corpora, ranging from Wikipedia (4.5M entries) to the French National Debate (4.3M entries).
Revised:  Initially designed to represent and navigate through scientific publications in different disciplines, CARTOLABE has evolved to become a generic framework and accommodate various corpora, ranging from Wikipedia (4.5M entries) to the French National Debate (4.3M entries).
Edit intent: clarity
Original:  Recent research, however, shows that both large and small teams have their own merits. Small teams tend to expand the frontier of knowledge by creating disruptive research outcomes, while large teams are more apt to work in the established field and develop existing problems. Given different roles of big and small teams in research, the extent to which a country's scientific work is carried out by big/small teams is of great importance .
Revised:  Recent research, however, shows that both large and small sizes are essential in research, prompting us to analyze the extent to which a country's scientific work is carried out by big/small teams is of great importance .
Edit intent: clarity
Original:  However, the drop in small team output is much steeper in China. More importantly, as research teams in China shift from small to large size, the team diversity that is essential for innovative works does not increase as much as that in other countries.
Revised:  However, the drop in small team output is much steeper . As teams in China shift from small to large size, the team diversity that is essential for innovative works does not increase as much as that in other countries.
Edit intent: clarity
Original:  Papers by big teams tend to receive more citations, but this alone is insufficient to explain the dominance of big teams in China, because the citation boost is roughly the same in every country. However, using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small team works than the National Science Foundation of U.S. (NSF) does, implying that big teams are more preferred by grant agencies in China.
Revised:  Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small team works than the National Science Foundation of U.S. (NSF) does, implying that big teams are more preferred by grant agencies in China.
Edit intent: clarity
Original:  By now, we have quite fast algorithms---usually sublogarithimic-time and often \text{poly (\log\log n)-time, or even faster---for a number of fundamental graph problems in the massively parallel computation (MPC) model.
Revised:  By now, we have quite fast algorithms---usually sublogarithmic-time and often poly (\log\log n)-time, or even faster---for a number of fundamental graph problems in the massively parallel computation (MPC) model.
Edit intent: fluency
Original:  Contributing to this line of work on MPC graph algorithms, we present \text{poly (\log k) \in \text{poly (\log\log n) round MPC algorithms for computing O(k^{1+{o(1)}})-spanners in the strongly sublinear regime of local memory.
Revised:  Contributing to this line of work on MPC graph algorithms, we present poly (\log k) \in \text{poly (\log\log n) round MPC algorithms for computing O(k^{1+{o(1)}})-spanners in the strongly sublinear regime of local memory.
Edit intent: fluency
Original:  Contributing to this line of work on MPC graph algorithms, we present \text{poly (\log k) \in \text{poly (\log\log n) round MPC algorithms for computing O(k^{1+{o(1)}})-spanners in the strongly sublinear regime of local memory.
Revised:  Contributing to this line of work on MPC graph algorithms, we present \text{poly (\log k) \in poly (\log\log n) round MPC algorithms for computing O(k^{1+{o(1)}})-spanners in the strongly sublinear regime of local memory.
Edit intent: fluency
Original:  One important consequence, by letting k = \log n, is a O(\log^2\log n)-round algorithm for O(\log^{1+o(1)} n) approximation of all pairs shortest path (APSP) in the near-linear regime of local memory.
Revised:  To the best of our knowledge, these are the first sublogarithmic-time MPC algorithms for spanner construction. As primary applications of our spanners, we get two important implications, as follows: -For the MPC setting, we get an O(\log^2\log n)-round algorithm for O(\log^{1+o(1)} n) approximation of all pairs shortest path (APSP) in the near-linear regime of local memory.
Edit intent: meaning-changed
Original:  One important consequence, by letting k = \log n, is a O(\log^2\log n)-round algorithm for O(\log^{1+o(1)} n) approximation of all pairs shortest path (APSP) in the near-linear regime of local memory.
Revised:  One important consequence, by letting k = \log n, is a O(\log^2\log n)-round algorithm for O(\log^{1+o(1)} n) approximation of all pairs shortest paths (APSP) in the near-linear regime of local memory.
Edit intent: fluency
Original:  To the best of our knowledge, these are the first sublogarithmic-time MPC algorithms for computing spanners and distance approximation .
Revised:  To the best of our knowledge, these are the first sublogarithmic-time MPC algorithm for distance approximations. -Our result above also extends to the Congested Clique model of distributed computing, with the same round complexity and approximation guarantee. This gives the first sub-logarithmic algorithm for approximating APSP in weighted graphs in the Congested Clique model .
Edit intent: meaning-changed
Original: Class selectivity, typically defined as how different a neuron's responses are across different classes of stimuli or data samples, is a common metric used to interpret the function of individual neurons in biological and artificial neural networks .
Revised: The properties of individual neurons are often analyzed in order to understand the biological and artificial neural networks in which they're embedded. Class selectivity-typically defined as how different a neuron's responses are across different classes of stimuli or data samples, is a common metric used to interpret the function of individual neurons in biological and artificial neural networks .
Edit intent: meaning-changed
Original:  Using this regularizer , we were able to reduce mean class selectivity across units in convolutional neural networks by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (\sim2\%) change in test accuracy.
Revised:  Using this regularizer to reduce class selectivity across units in convolutional neural networks by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (\sim2\%) change in test accuracy.
Edit intent: clarity
Original:  Using this regularizer , we were able to reduce mean class selectivity across units in convolutional neural networks by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (\sim2\%) change in test accuracy.
Revised:  Using this regularizer , we were able to reduce mean class selectivity across units in convolutional neural networks increased test accuracy by over 2\% in ResNet18 trained on Tiny ImageNet. In ResNet20 trained on CIFAR10 we could reduce class selectivity by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (\sim2\%) change in test accuracy.
Edit intent: meaning-changed
Original:  Using this regularizer , we were able to reduce mean class selectivity across units in convolutional neural networks by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (\sim2\%) change in test accuracy.
Revised:  Using this regularizer , we were able to reduce mean class selectivity across units in convolutional neural networks by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (\sim2\%) drop in test accuracy.
Edit intent: clarity
Original:  These results indicate that class selectivity in individual units is neither neither sufficient nor strictly necessary for DNN performance, and more generally encourage caution when focusing on the properties of single units as representative of the mechanisms by which DNNs function.
Revised:  These results indicate that class selectivity in individual units is neither sufficient nor strictly necessary for DNN performance, and more generally encourage caution when focusing on the properties of single units as representative of the mechanisms by which DNNs function.
Edit intent: fluency
Original:  These results indicate that class selectivity in individual units is neither neither sufficient nor strictly necessary for DNN performance, and more generally encourage caution when focusing on the properties of single units as representative of the mechanisms by which DNNs function.
Revised:  These results indicate that class selectivity in individual units is neither neither sufficient nor strictly necessary , and can even impair DNN performance. They also encourage caution when focusing on the properties of single units as representative of the mechanisms by which DNNs function.
Edit intent: meaning-changed
Original:  In ResNet20 trained on CIFAR10 we could reduce class selectivity by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (\sim2\%) drop in test accuracy.
Revised:  For ResNet20 trained on CIFAR10 we could reduce class selectivity by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (\sim2\%) drop in test accuracy.
Edit intent: fluency
Original:  The anomalous diffusion of particles in the biofilm is described by a fractional subdiffusion-absorption equation  .
Revised:  The idea of this model is: We deduce details of the diffusion process in a medium in which direct experimental study is difficult, based on probing diffusion in external regions. Since a biofilm has a gel-like consistency, we suppose that subdiffusion of particles in the biofilm is described by a fractional subdiffusion-absorption equation .
Edit intent: meaning-changed
Original:  Both types of these pigments are introduced focusing on their exceptional properties like energy harvesting, ultra-low energy consumption during transmissions, picoseconds delays,  ability for signal conversions  and bio-compatibility.
Revised:  Both types of these pigments are introduced focusing on their exceptional properties like energy harvesting, ultra-low energy consumption during transmissions, picoseconds delays, an ability for signal conversions and bio-compatibility.
Edit intent: fluency
Original:  Both types of these pigments are introduced focusing on their exceptional properties like energy harvesting, ultra-low energy consumption during transmissions, picoseconds delays,  ability for signal conversions  and bio-compatibility.
Revised:  Both types of these pigments are introduced focusing on their exceptional properties like energy harvesting, ultra-low energy consumption during transmissions, picoseconds delays, ability for signal conversions , and bio-compatibility.
Edit intent: fluency
Original:  Treating carotenoid molecules as transmitters and chlorophylls as receivers, a throughput of about 1 Gbit/s is achievable with  bit error rate below 10-3 , average transmission delays about 20 ps, and energy consumption c.a. 2.0x10^-18 J per bit.
Revised:  Treating carotenoid molecules as transmitters , chlorophylls as receivers, a throughput of about 1 Gbit/s is achievable with bit error rate below 10-3 , average transmission delays about 20 ps, and energy consumption c.a. 2.0x10^-18 J per bit.
Edit intent: fluency
Original:  Treating carotenoid molecules as transmitters and chlorophylls as receivers, a throughput of about 1 Gbit/s is achievable with  bit error rate below 10-3 , average transmission delays about 20 ps, and energy consumption c.a. 2.0x10^-18 J per bit.
Revised:  Treating carotenoid molecules as transmitters and chlorophylls as receivers, and the energy transfer between them as a way to encode information, a throughput up to 1 Gbit/s is achievable with bit error rate below 10-3 , average transmission delays about 20 ps, and energy consumption c.a. 2.0x10^-18 J per bit.
Edit intent: meaning-changed
Original:  Treating carotenoid molecules as transmitters and chlorophylls as receivers, a throughput of about 1 Gbit/s is achievable with  bit error rate below 10-3 , average transmission delays about 20 ps, and energy consumption c.a. 2.0x10^-18 J per bit.
Revised:  Treating carotenoid molecules as transmitters and chlorophylls as receivers, a throughput of about 1 Gbit/s is achievable with a bit error rate below 10-3 , average transmission delays about 20 ps, and energy consumption c.a. 2.0x10^-18 J per bit.
Edit intent: fluency
Original:  Treating carotenoid molecules as transmitters and chlorophylls as receivers, a throughput of about 1 Gbit/s is achievable with  bit error rate below 10-3 , average transmission delays about 20 ps, and energy consumption c.a. 2.0x10^-18 J per bit.
Revised:  Treating carotenoid molecules as transmitters and chlorophylls as receivers, a throughput of about 1 Gbit/s is achievable with bit error rate below 10^-3 , average transmission delays about 20 ps, and energy consumption c.a. 2.0x10^-18 J per bit.
Edit intent: fluency