wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show that the interaction between the two oppositely-directed speech jetscan increase or decrease infection probabilities, which peak for a height difference between the speakers of about 10cm (30cm) when the axial separationbetween them is 2ft (6ft). These results can help improve the existing public health guidelines for COVID mitigation by introducing a lateral shift between people during short conversations. Moreover, simulations like these can provide useful inputs to the existing epidemiological models for improving their reliability and accuracy .
Revised:  We show that the interaction between the two speech jets, than two faces at the same level! For small axial separation, speech jets show large oscillations and reach the other person intermittently. We suggest a range of lateral separations between two people to minimize transmission risk. A realistic estimate of the infection probability is provided by including exposure through eyes and mouth, in addition to the more common method of using inhaled virions alone. We expect that our results will provide useful inputs to the existing epidemiological models for improving their reliability and accuracy .
Edit intent: meaning-changed
Original: Lottery Ticket Hypothesis  raises keen attention to identifying sparse trainable subnetworks  or winning tickets, at the initialization (or early stage) of training, which can be trained in isolation to achieve similar or even better performance compared to the full models.
Revised: Lottery Ticket Hypothesis (LTH) raises keen attention to identifying sparse trainable subnetworks or winning tickets, at the initialization (or early stage) of training, which can be trained in isolation to achieve similar or even better performance compared to the full models.
Edit intent: meaning-changed
Original: Lottery Ticket Hypothesis  raises keen attention to identifying sparse trainable subnetworks  or winning tickets, at the initialization (or early stage) of training, which can be trained in isolation to achieve similar or even better performance compared to the full models.
Revised: Lottery Ticket Hypothesis raises keen attention to identifying sparse trainable subnetworks , or winning tickets, at the initialization (or early stage) of training, which can be trained in isolation to achieve similar or even better performance compared to the full models.
Edit intent: fluency
Original: Lottery Ticket Hypothesis  raises keen attention to identifying sparse trainable subnetworks  or winning tickets, at the initialization (or early stage) of training, which can be trained in isolation to achieve similar or even better performance compared to the full models.
Revised: Lottery Ticket Hypothesis raises keen attention to identifying sparse trainable subnetworks or winning tickets, of training, which can be trained in isolation to achieve similar or even better performance compared to the full models.
Edit intent: clarity
Original:  Based on these results, we articulate the Elastic Lottery Ticket Hypothesis (E-LTH): by mindfully replicating (or dropping) and re-ordering layers for one network, its corresponding winning ticket could be stretched (or squeezed) into a subnetwork for another deeper (or shallower) network from the same family, whose performance is nearly as competitive as the latter's winning ticket directly found by IMP.
Revised:  Based on these results, we articulate the Elastic Lottery Ticket Hypothesis (E-LTH): by mindfully replicating (or dropping) and re-ordering layers for one network, its corresponding winning ticket could be stretched (or squeezed) into a subnetwork for another deeper (or shallower) network from the same family, whose performance is nearly the same competitive as the latter's winning ticket directly found by IMP.
Edit intent: fluency
Original:  We have also thoroughly compared E-LTH with pruning-at-initialization and dynamic sparse training methods, and discuss the generalizability of E-LTH to different model families, layer types, and even across datasets.
Revised:  We have also thoroughly compared E-LTH with pruning-at-initialization and dynamic sparse training methods, and discuss the generalizability of E-LTH to different model families, layer types, or across datasets.
Edit intent: coherence
Original:  This is the source of multiple inefficiencies: To engage in costly signaling , a bidder who bids first and has a high valuation will generally over-deposit in equilibrium, i.e.  deposit more than he will bid.
Revised:  This is the source of multiple inefficiencies: To engage in costly signalling , a bidder who bids first and has a high valuation will generally over-deposit in equilibrium, i.e. deposit more than he will bid.
Edit intent: fluency
Original:  This is the source of multiple inefficiencies: To engage in costly signaling , a bidder who bids first and has a high valuation will generally over-deposit in equilibrium, i.e.  deposit more than he will bid.
Revised:  This is the source of multiple inefficiencies: To engage in costly signaling , a bidder who bids first and has a high valuation will generally over-deposit in equilibrium, i.e. ~ deposit more than he will bid.
Edit intent: clarity
Original:  Through comparisons  with human performance in a new analogy experiment with 1, 329 participants, as well as in four classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children.
Revised:  Through comparisons of model predictions with human performance in a new analogy experiment with 1, 329 participants, as well as in four classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children.
Edit intent: clarity
Original:  Through comparisons  with human performance in a new analogy experiment with 1, 329 participants, as well as in four classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children.
Revised:  Through comparisons with human performance in a novel mapping task requiring integration of multiple relations, as well as in four classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children.
Edit intent: meaning-changed
Original:  Through comparisons  with human performance in a new analogy experiment with 1, 329 participants, as well as in four classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children.
Revised:  Through comparisons with human performance in a new analogy experiment with 1, 329 participants, as well as in several classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children.
Edit intent: clarity
Original:  The key insight is that rich semantic representations of individual concepts and relations , coupled with a generic prior favoring isomorphic mappings, yield human-like analogical mapping .
Revised:  We also show the potential for extending the model to deal with analog retrieval. Our approach demonstrates that human-like analogical mapping can emerge from comparison mechanisms applied to rich semantic representations of individual concepts and relations , coupled with a generic prior favoring isomorphic mappings, yield human-like analogical mapping .
Edit intent: meaning-changed
Original: There are now several comprehensive web applications, stand-alone computer programs and computer algebra functions that, given a floating point number such as 6.518670730718491, can return concise nonfloat constants such as \, 3 %DIFDELCMD < \arctan2%%%  + %DIFDELCMD < \ln9%%%  + 1 \, that closely approximate the float.
Revised: There are now several comprehensive web applications, stand-alone computer programs and computer algebra functions that, given a floating point number such as 6.518670730718491, can return concise nonfloat constants such as 3 %DIFDELCMD < \arctan2%%% + %DIFDELCMD < \ln9%%% + 1 \, that closely approximate the float.
Edit intent: fluency
Original: There are now several comprehensive web applications, stand-alone computer programs and computer algebra functions that, given a floating point number such as 6.518670730718491, can return concise nonfloat constants such as \, 3 %DIFDELCMD < \arctan2%%%  + %DIFDELCMD < \ln9%%%  + 1 \, that closely approximate the float.
Revised: There are now several comprehensive web applications, stand-alone computer programs and computer algebra functions that, given a floating point number such as 6.518670730718491, can return concise nonfloat constants such as \, 3 %DIFDELCMD < \arctan2%%% arctan 2 + %DIFDELCMD < \ln9%%% + 1 \, that closely approximate the float.
Edit intent: meaning-changed
Original: There are now several comprehensive web applications, stand-alone computer programs and computer algebra functions that, given a floating point number such as 6.518670730718491, can return concise nonfloat constants such as \, 3 %DIFDELCMD < \arctan2%%%  + %DIFDELCMD < \ln9%%%  + 1 \, that closely approximate the float.
Revised: There are now several comprehensive web applications, stand-alone computer programs and computer algebra functions that, given a floating point number such as 6.518670730718491, can return concise nonfloat constants such as \, 3 %DIFDELCMD < \arctan2%%% + %DIFDELCMD < \ln9%%% ln 9 + 1 \, that closely approximate the float.
Edit intent: clarity
Original: There are now several comprehensive web applications, stand-alone computer programs and computer algebra functions that, given a floating point number such as 6.518670730718491, can return concise nonfloat constants such as \, 3 %DIFDELCMD < \arctan2%%%  + %DIFDELCMD < \ln9%%%  + 1 \, that closely approximate the float.
Revised: There are now several comprehensive web applications, stand-alone computer programs and computer algebra functions that, given a floating point number such as 6.518670730718491, can return concise nonfloat constants such as \, 3 %DIFDELCMD < \arctan2%%% + %DIFDELCMD < \ln9%%% + 1 , that closely approximate the float.
Edit intent: fluency
Original:  To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image analysis . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalized prediction scores with an L2 penalty.
Revised:  To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalized prediction scores with an L2 penalty.
Edit intent: clarity
Original:  To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image analysis . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalized prediction scores with an L2 penalty.
Revised:  To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image analysis . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty.
Edit intent: fluency
Original:   To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty .
Revised:  All neural networks intended for clinical practice need to be robust to variation in data caused by differences in imaging equipment, sample preparation and patient populations. To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty .
Edit intent: meaning-changed
Original:   To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty .
Revised:  To address these challenges, we evaluate the utility of spectral decoupling as an implicit bias mitigation method . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty .
Edit intent: clarity
Original:   To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty . Simulation experiments show that spectral decoupling allows training neural networks on datasets with strong spurious correlations.
Revised:  To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image classification . Spectral decoupling encourages the neural network to learn more features by simply regularising the networks' unnormalised prediction scores with an L2 penalty , thus having no added computational costs. We show that spectral decoupling allows training neural networks on datasets with strong spurious correlations.
Edit intent: coherence
Original:  To validate our findings, we train networks with and without spectral decoupling to detect prostate cancer on haematoxylin and eosin stained whole slide images. The networks are then evaluated with data scanned in the same medical centre with two different scanners, and data from a different centre .
Revised:  To validate our findings, we train networks with and without spectral decoupling to detect prostate cancer tissue slides and COVID-19 in chest radiographs .
Edit intent: meaning-changed
Original:  In this paper  we show how OLIVAW successfully met this challenge .
Revised:  In this paper we show how the AlphaGo Zero's paradigm can be successfully applied to the popular game of Othello using only commodity hardware and free cloud services. While being simpler than Chess or Go, Othello maintains a considerable search space and difficulty in evaluating board positions. To achieve this result, OLIVAW implements some improvements inspired by recent works to accelerate the standard AlphaGo Zero learning process. The main modification implies doubling the positions collected per game during the training phase, by including also positions not played but largely explored by the agent. We tested the strength of OLIVAW in three different ways: by pitting it against Edax, the strongest open-source Othello engine, by playing anonymous games on the web platform OthelloQuest, and finally in two in-person matches against top-notch human players: a national champion and a former world champion .
Edit intent: meaning-changed
Original:  These results show which implementation details are co-adapted and co-evolved with algorithms, and which are transferable across algorithms: as examples, we identified that tanh  policy and network sizes are highly adapted to algorithmic types, while layer normalization and ELU are critical for MPO's performances but also transfer to noticeable gains in SAC.
Revised:  These results show which implementation details are co-adapted and co-evolved with algorithms, and which are transferable across algorithms: as examples, we identified that tanh Gaussian policy and network sizes are highly adapted to algorithmic types, while layer normalization and ELU are critical for MPO's performances but also transfer to noticeable gains in SAC.
Edit intent: meaning-changed
Original:  We make a conjecture that the sum of squares of the largest s singular valuesof a third order tensor is greater than or equal to the sum of squares of any s entries of that third order tensor. Kilmer and Martin showed that an Eckart-Young theorem holds for the tensor tubal rank of third order tensors. We show that our conjecture is true if and only if another Eckart-Young theorem holds for the T-rank of third order tensors .
Revised:  Some properties of singular values, T-rank and best T-rank of third order tensors .
Edit intent: meaning-changed
Original:  Despite  state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, to reach SOTA performance they usually need a long warmup schedule with hundreds or thousands epochs  and are thus still quite costlyfor training .
Revised:  Despite that state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, to reach SOTA performance they usually need a long warmup schedule with hundreds or thousands epochs and are thus still quite costlyfor training .
Edit intent: fluency
Original:  Despite  state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, to reach SOTA performance they usually need a long warmup schedule with hundreds or thousands epochs  and are thus still quite costlyfor training .
Revised:  Despite state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, they usually use a long warmup schedule with hundreds or thousands epochs and are thus still quite costlyfor training .
Edit intent: clarity
Original:  Despite  state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, to reach SOTA performance they usually need a long warmup schedule with hundreds or thousands epochs  and are thus still quite costlyfor training .
Revised:  Despite state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, to reach SOTA performance they usually need a long warmup schedule with hundreds or thousands epochs to reach SOTA performance and are thus still quite costlyfor training .
Edit intent: fluency
Original:  Despite  state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, to reach SOTA performance they usually need a long warmup schedule with hundreds or thousands epochs  and are thus still quite costlyfor training .
Revised:  Despite state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, to reach SOTA performance they usually need a long warmup schedule with hundreds or thousands epochs and are thus still costly .
Edit intent: clarity
Original:  In this paper, we discover that the weight initialization adopted by prior works, such as Xavier or orthogonal initialization, which was originally designed for standard network training, results in very loose certified bounds at initialization thus a longer warmup schedule must be used. We also find that IBP based training leads to a significant imbalance in ReLU activation states , which can hamper model performance.
Revised:  In this paper, we discover that the weight initialization adopted by prior works, such as Xavier or orthogonal initialization, which was originally designed for standard network training, results in very loose certified bounds at initialization , and the imbalance in ReLU activation states , which can hamper model performance.
Edit intent: coherence
Original:  We also find that IBP based training leads to a significant imbalance in ReLU activation states , which can hamper model performance. Based on our findings, we derive a new IBP initialization as well as principled regularizers during the warmup stage to stabilize certified bounds during initialization and warmup stage, which can significantly reduce the warmup schedule and improve the balance of ReLU activation states .
Revised:  We also find that IBP based training leads to a significant imbalance in ReLU activation states . These two issues make certified training difficult and unstable, and thereby long warmup schedules were needed in prior works. To mitigate these issues and conduct certified training with shorter warmup, we propose three improvements: 1) We derive a new IBP initialization as well as principled regularizers during the warmup stage to stabilize certified bounds during initialization and warmup stage, which can significantly reduce the warmup schedule and improve the balance of ReLU activation states .
Edit intent: meaning-changed
Original:  Based on our findings, we derive a new IBP initialization as well as principled regularizers during the warmup stage to stabilize certified bounds during initialization and warmup stage, which can significantly reduce the warmup schedule and improve the balance of ReLU activation states .
Revised:  Based on our findings, we derive a new weight initialization method for IBP training; 2) We propose to fully add Batch Normalization (BN) to each layer in the model, since we find BN can reduce the imbalance in ReLU activation states .
Edit intent: meaning-changed
Original:  Based on our findings, we derive a new IBP initialization as well as principled regularizers during the warmup stage to stabilize certified bounds during initialization and warmup stage, which can significantly reduce the warmup schedule and improve the balance of ReLU activation states . Additionally, we find that batch normalization (BN) is a crucial architectural element to build best-performing networks for certified training, because it helps stabilize bound variance and balance ReLU activation states.
Revised:  Based on our findings, we derive a new IBP initialization as well as principled regularizers during the warmup stage to stabilize certified bounds during initialization and warmup stage, which can significantly reduce the warmup schedule and improve the balance of ReLU activation states ; 3) We also design regularization to explicitly tighten certified bounds and balance ReLU activation states.
Edit intent: coherence
Original:  With our proposed initialization, regularizers and architectural changes combined, we are able to obtain 65.03\% verified error on CIFAR-10 (\epsilon=\frac{8}{255}) and 82.13 \% verified error on TinyImageNet (\epsilon=\frac{1}{255}) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with a few hundreds or thousands epochs  .
Revised:  In our experiments, we are able to obtain 65.03\% verified error on CIFAR-10 (\epsilon=\frac{8}{255}) and 82.13 \% verified error on TinyImageNet (\epsilon=\frac{1}{255}) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with a few hundreds or thousands epochs .
Edit intent: clarity
Original:  With our proposed initialization, regularizers and architectural changes combined, we are able to obtain 65.03\% verified error on CIFAR-10 (\epsilon=\frac{8}{255}) and 82.13 \% verified error on TinyImageNet (\epsilon=\frac{1}{255}) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with a few hundreds or thousands epochs  .
Revised:  With our proposed initialization, regularizers and architectural changes combined, we are able to obtain 65.03\% verified error on CIFAR-10 (\epsilon=\frac{8}{255}) and 82.36 \% verified error on TinyImageNet (\epsilon=\frac{1}{255}) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with a few hundreds or thousands epochs .
Edit intent: meaning-changed