wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Light detection and ranging (LiDAR) can measure the three dimensional position of reflective material by sending laser pulses through the canopy. We examined the influence of three forest stand attributes: species, crown closure , and age  on the vertical distribution of aerial LiDAR returns of forested stands.
Revised:  Light detection and ranging (LiDAR) provides information on the vertical structure of forest stands enabling detailed and extensive ecosystem study. The vertical structure is often summarized by scalar features and data-reduction techniques that limit the interpretation of results. Instead, we quantified the influence of three forest stand attributes: species, crown closure , and age on the vertical distribution of aerial LiDAR returns of forested stands.
Edit intent: meaning-changed
Original:  We studied over five thousand regular, even-aged stands in Quebec (Canada) with five dominant species: balsam fir (Abies balsamea (L.) Mill.), paper birch (Betula papyrifera Marsh), black spruce (Picea mariana (Mill.) BSP), white spruce (Picea glauca Moench) and aspen (Populus tremuloides Michx.).
Revised:  We studied 5,428 regular, even-aged stands in Quebec (Canada) with five dominant species: balsam fir (Abies balsamea (L.) Mill.), paper birch (Betula papyrifera Marsh), black spruce (Picea mariana (Mill.) BSP), white spruce (Picea glauca Moench) and aspen (Populus tremuloides Michx.).
Edit intent: meaning-changed
Original:  Experiment results showthat our method can achieve the same level of performance asthose RGB-based benchmarks on real world action recognition,and also achieve the state of the art (SOTA) result on gesturerecognition .
Revised:  Experiment results show that our method can achieve the same level of performance asthose RGB-based benchmarks on real world action recognition,and also achieve the state of the art (SOTA) result on gesturerecognition .
Edit intent: fluency
Original:  Experiment results showthat our method can achieve the same level of performance asthose RGB-based benchmarks on real world action recognition,and also achieve the state of the art (SOTA) result on gesturerecognition .
Revised:  Experiment results showthat our method can achieve the same level of performance as those RGB-based benchmarks on real world action recognition,and also achieve the state of the art (SOTA) result on gesturerecognition .
Edit intent: fluency
Original:  Experiment results showthat our method can achieve the same level of performance asthose RGB-based benchmarks on real world action recognition,and also achieve the state of the art (SOTA) result on gesturerecognition .
Revised:  Experiment results showthat our method can achieve the same level of performance asthose RGB-based benchmarks on real world action recognition,and also achieve the state of the art (SOTA) result on gesture recognition .
Edit intent: fluency
Original:  In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of %DIFDELCMD < \zion %%%  platform, namely %DIFDELCMD < \zionex%%%  . We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40\times speedup in terms of time to solution over previous systems.
Revised:  In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of %DIFDELCMD < \zion %%% Zion platform, namely %DIFDELCMD < \zionex%%% . We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40\times speedup in terms of time to solution over previous systems.
Edit intent: meaning-changed
Original:  In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of %DIFDELCMD < \zion %%%  platform, namely %DIFDELCMD < \zionex%%%  . We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40\times speedup in terms of time to solution over previous systems.
Revised:  In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of %DIFDELCMD < \zion %%% platform, namely %DIFDELCMD < \zionex%%% ZionEX . We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40\times speedup in terms of time to solution over previous systems.
Edit intent: meaning-changed
Original:  In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of %DIFDELCMD < \zion %%%  platform, namely %DIFDELCMD < \zionex%%%  . We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40\times speedup in terms of time to solution over previous systems.
Revised:  In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of %DIFDELCMD < \zion %%% platform, namely %DIFDELCMD < \zionex%%% . We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40\times speedup in terms of time to solution over previous systems.
Edit intent: fluency
Original:  In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of %DIFDELCMD < \zion %%%  platform, namely %DIFDELCMD < \zionex%%%  . We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40\times speedup in terms of time to solution over previous systems.
Revised:  In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of %DIFDELCMD < \zion %%% platform, namely %DIFDELCMD < \zionex%%% . We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems.
Edit intent: clarity
Original:  We achieve this by (i) designing the %DIFDELCMD < \zionex %%%  platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers;
Revised:  We achieve this by (i) designing the %DIFDELCMD < \zionex %%% ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers;
Edit intent: meaning-changed
Original:  By using the Dempster combination rule and confidence function based on the entire time series fused at each time pointand future time points, and the preliminary forecast value obtainedthrough the time relationship, the accurate forecast value can be restored.
Revised:  This paper proposes a similar confidence function for the time point in the time series. The Dempster combination rule and confidence function based on the entire time series fused at each time pointand future time points, and the preliminary forecast value obtainedthrough the time relationship, the accurate forecast value can be restored.
Edit intent: meaning-changed
Original:  By using the Dempster combination rule and confidence function based on the entire time series fused at each time pointand future time points, and the preliminary forecast value obtainedthrough the time relationship, the accurate forecast value can be restored. This article will introduce the prediction method of evidence theory .
Revised:  By using the Dempster combination rule can be used to fuse the growth rate of the last time point, and finally a relatively accurate forecast data can be obtained. Stock price forecasting is a concern of economics. The stock price data is large in volume, and more accurate forecasts are required at the same time. The classic methods of time series, such as ARIMA, cannot balance forecasting efficiency and forecasting accuracy at the same time. In this paper, the fusion method of evidence theory .
Edit intent: meaning-changed
Original:  Specifically, we utilize a k-nearest neighbor graph (kNNG) based on the similarity of node attributes to replace the GNN's computation graph defined by the neighborhood-based aggregation of each node.
Revised:  Specifically, we start by constructing a k-nearest neighbor graph (kNNG) based on the similarity of node attributes to replace the GNN's computation graph defined by the neighborhood-based aggregation of each node.
Edit intent: clarity
Original:  By evaluating the performance of various downstream machine learning (ML) tasks, we empirically demonstrate that Edgeless-GNN consistently outperforms state-of-the-art methods of inductive network embedding  .
Revised:  By evaluating the performance of various downstream machine learning (ML) tasks, we empirically demonstrate that Edgeless-GNN consistently outperforms state-of-the-art methods of inductive network embedding . Moreover, our findings corroborate the effectiveness of Edgeless-GNN in judiciously combining the replaced computation graph with our newly designed loss .
Edit intent: meaning-changed
Original: We show that the T-SVD factorization of a third order real tensor  by going through Discrete Fourier Transform (DFT), standard matrix SVD and inverse DFT , decomposes that third order tensor to the T-product of two orthogonal tensors and an s-diagonal tensor .
Revised: A third order real tensor by going through Discrete Fourier Transform (DFT), standard matrix SVD and inverse DFT , decomposes that third order tensor to the T-product of two orthogonal tensors and an s-diagonal tensor .
Edit intent: clarity
Original: We show that the T-SVD factorization of a third order real tensor  by going through Discrete Fourier Transform (DFT), standard matrix SVD and inverse DFT , decomposes that third order tensor to the T-product of two orthogonal tensors and an s-diagonal tensor .
Revised: We show that the T-SVD factorization of a third order real tensor is mapped to a special f-diagonal tensor by going through Discrete Fourier Transform (DFT), standard matrix SVD and inverse DFT , decomposes that third order tensor to the T-product of two orthogonal tensors and an s-diagonal tensor .
Edit intent: meaning-changed
Original: We show that the T-SVD factorization of a third order real tensor  by going through Discrete Fourier Transform (DFT), standard matrix SVD and inverse DFT , decomposes that third order tensor to the T-product of two orthogonal tensors and an s-diagonal tensor . Here, an s-diagonal tensor is a special f-diagonal tensor.
Revised: We show that the T-SVD factorization of a third order real tensor by going through Discrete Fourier Transform (DFT), standard matrix SVD and inverse DFT . We call such an f-diagonal tensor an s-diagonal tensor is a special f-diagonal tensor.
Edit intent: clarity
Original:  Here, an s-diagonal tensor is a special f-diagonal tensor. We call such a T-SVD factorization a standard T-SVD (ST-SVD) factorization. We show that an f-diagonal tensor is an s-diagonal tensor if and only if ST-TVD factorization diagonalizes it to itself . Four meaningful necessary conditions for s-diagonal tensors are presented.
Revised:  Here, an s-diagonal tensor is a special f-diagonal tensor is an s-diagonal tensor if and only if ST-TVD factorization diagonalizes it to itself . Four meaningful necessary conditions for s-diagonal tensors are presented.
Edit intent: coherence
Original:  We show that an f-diagonal tensor is an s-diagonal tensor if and only if ST-TVD factorization diagonalizes it to itself . Four meaningful necessary conditions for s-diagonal tensors are presented.
Revised:  We show that an f-diagonal tensor is an s-diagonal tensor if and only if it is mapped to itself in the above process. The third order tensor space is partitioned to orthogonal equivalence classes. Each orthogonal equivalence class has a unique s-diagonal tensor. Two s-diagonal tensors are equal if they are orthogonally equivalent. Third order tensors in an orthogonal equivalence class have the same tensor tubal rank and T-singular values . Four meaningful necessary conditions for s-diagonal tensors are presented.
Edit intent: meaning-changed
Original: The world is waiting for a vaccine to mitigate the spread of SARS-CoV-2 . However, once it becomes available, there will not be enough to vaccinate everybody at once .
Revised: The world is waiting for a vaccine to mitigate the spread of SARS-CoV-2 . However, due to limited production capacities and logistical challenges it will take months/years until herd immunity is achieved .
Edit intent: meaning-changed
Original:  Setting: Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data from the Secondary Uses Service and the Emergency Care Data Set . Participants: 839,278 laboratory-confirmed COVID-19 patients, of whom 36,233 had been hospitalised within 14 days, tested between 23rd November 2020 and 31st January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Revised:  Setting: Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data . Participants: 839,278 laboratory-confirmed COVID-19 patients, of whom 36,233 had been hospitalised within 14 days, tested between 23rd November 2020 and 31st January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF).
Edit intent: clarity
Original:  Results: In total, 27,710 of 592,409 SGTF patients (4.7\%) and 8,523 of 246,869 non-SGTF patients (3.5\%) had been hospitalised within 1-14 days.
Revised:  Results: 27,710 of 592,409 SGTF patients (4.7\%) and 8,523 of 246,869 non-SGTF patients (3.5\%) had been hospitalised within 1-14 days.
Edit intent: clarity
Original:  The effect was modified by age ( p <0.001), with HRs of 0.93-1.21 for SGTF compared to non-SGTF patients below age 20 years, 1.29 in those aged 20-29, and 1.45-1.65 in age groups 30 years or older.
Revised:  The effect was modified by age ( P <0.001), with HRs of 0.93-1.21 for SGTF compared to non-SGTF patients below age 20 years, 1.29 in those aged 20-29, and 1.45-1.65 in age groups 30 years or older.
Edit intent: fluency
Original: We study the classical online bipartite matching problem: One side of the graph is known and vertices of the other side arrive online. It is well known that when the graph is edge-weighted , and vertices arrive in an adversarial order, no online algorithm has a nontrivial competitive-ratio.
Revised: We study the greedy-based online algorithm for edge-weighted , and vertices arrive in an adversarial order, no online algorithm has a nontrivial competitive-ratio.
Edit intent: meaning-changed
Original:  It is well known that when the graph is edge-weighted , and vertices arrive in an adversarial order, no online algorithm has a nontrivial competitive-ratio. To bypass this hurdle we modify the rules such that the adversary still picks the graph but has to reveal a random part (say half) of it to the player. The remaining part is given to the player in an adversarial order. This models practical scenarios in which the online algorithm has some history to learn from.
Revised:  It is well known that when the graph is edge-weighted matching with (one-sided) vertex arrivals in bipartite graphs, and edge arrivals in general graphs. This algorithm was first studied more than a decade ago by Korula and P\'al for the bipartite case in the random-order model. While the weighted bipartite matching problem is solved in the random-order model, this is not the case in recent and exciting online models in which the online algorithm has some history to learn from.
Edit intent: meaning-changed
Original:  This models practical scenarios in which the online algorithm has some history to learn from. This way of modeling a history was formalized recently by the authors (SODA 20) and was called the AOS model (for Adversarial Online with a Sample). It allows developing online algorithms for the secretary problem that compete even when the secretaries arrive in an adversarial order . Here we use the same model to attack the much more challenging matching problem. We analyze a natural algorithmic framework that decides how to match an arriving vertex v by applying an offline matching algorithm to v and the sample.
Revised:  This models practical scenarios in which the online player is provided with a sample, and the sample.
Edit intent: clarity
Original:  We analyze a natural algorithmic framework that decides how to match an arriving vertex v by applying an offline matching algorithm to v and the sample. We get roughly 1/4 of the maximum weight by applying the offline greedy matching algorithm to the sample and v. Our analysis ties the performance of this algorithm to the performance of the offline greedy matching on the online part and we also prove that it is tight . Surprisingly, when replacing greedy with an optimal algorithm for maximum matching, no constant competitive-ratio can be guaranteed when the size of the sample is comparable to the size of the online part.
Revised:  We analyze a natural algorithmic framework that decides how to match an arriving vertex v by applying an offline matching algorithm to v and the arrival order is adversarial. The greedy-based algorithm is arguably the most natural and practical algorithm to be applied in these models. Despite its simplicity and appeal, and despite being studied in multiple works, the greedy-based algorithm was not fully understood in any of the studied online models, and its actual performance remained an open question for more than a decade. We provide a thorough analysis of the greedy-based algorithm in several online models. For vertex arrivals in bipartite graphs, we characterize the exact competitive-ratio of this algorithm to the performance of the offline greedy matching on the online part and we also prove that it is tight . Surprisingly, when replacing greedy with an optimal algorithm for maximum matching, no constant competitive-ratio can be guaranteed when the size of the sample is comparable to the size of the online part.
Edit intent: meaning-changed
Original:  We get roughly 1/4 of the maximum weight by applying the offline greedy matching algorithm to the sample and v. Our analysis ties the performance of this algorithm to the performance of the offline greedy matching on the online part and we also prove that it is tight . Surprisingly, when replacing greedy with an optimal algorithm for maximum matching, no constant competitive-ratio can be guaranteed when the size of the sample is comparable to the size of the online part. However, when the sample is quadratic in the size of the online part, we do get a competitive-ratio of 1/e .
Revised:  We get roughly 1/4 of the maximum weight by applying the offline greedy matching algorithm to the sample and v. Our analysis ties the performance of this algorithm in the random-order model, for any arrival order of the vertices subsequent to the sampling phase (adversarial and random orders in particular). We use it to derive tight analysis in the recent adversarial-order model with a sample (AOS model) for any sample size, providing the first result in this model beyond the simple secretary problem. Then, we generalize and strengthen the black box method of converting results in the random-order model to single-sample prophet inequalities, and use it to derive the state-of-the-art single-sample prophet inequality for the problem. Finally, we use our new techniques to analyze the greedy-based algorithm for edge arrivals in general graphs and derive results in all the mentioned online models. In this case as well, we improve upon the state-of-the-art single-sample prophet inequality .
Edit intent: meaning-changed
Original:  Training on randomly sampled 1,093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30,022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset.abstract 
Revised:  Training on randomly sampled 1,093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30,022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset.abstract \%) of low resource data. Using English as a hypothetical low resource language to translate from Spanish, we obtain a +24.7 BLEU increase over a multilingual baseline, and a +10.2 BLEU increase over our asymmetric baseline in the Bible dataset. Thirdly, we also use a real severely low resource Mayan language, Eastern Pokomchi. Finally, we add an order-preserving lexiconized component to translate named entities accurately. We build a massive lexicon table for 2,939 Bible named entities in 124 source languages, and include many that occur once and covers more than 66 severely low resource languages. Training on randomly sampled 1,093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30,022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset.
Edit intent: meaning-changed
Original:  Using English as a hypothetical low resource language to translate from Spanish, we obtain a +24.7 BLEU increase over a multilingual baseline, and a +10.2 BLEU increase over our asymmetric baseline in the Bible dataset. Thirdly, we also use a real severely low resource Mayan language, Eastern Pokomchi. Finally, we add an order-preserving lexiconized component to translate named entities accurately. We build a massive lexicon table for 2,939 Bible named entities in 124 source languages, and include many that occur once and covers more than 66 severely low resource languages.
Revised:  To translate named entities correctly, we build a massive lexicon table for 2,939 Bible named entities in 124 source languages, and include many that occur once and covers more than 66 severely low resource languages.
Edit intent: clarity
Original:  Training on randomly sampled 1, 093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30, 022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset  .
Revised:  Moreover, we also build a novel method of combining translations from different source languages into one. Using English as a hypothetical low resource language, we get a +23.9 BLEU increase over a multilingual baseline, and a +10.3 BLEU increase over our asymmetric baseline in the Bible dataset. We get a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset .
Edit intent: meaning-changed