wanyu/IteraTeR_full_sent huggingface dataset:
Original:  For many tasks such as repeated infrastructure inspection, item delivery or inventory transport, a route repeating capability rather than full navigation stack can be sufficient and offers potential practical advantages  . Previous teach and repeat research has achieved high performance in difficult conditions generally by using sophisticated, often expensive sensors, and has often had high computational requirements.
Revised:  For many tasks such as repeated infrastructure inspection, item delivery or inventory transport, a route repeating capability rather than full navigation stack can be sufficient and offers potential practical advantages over a full navigation stack . Previous teach and repeat research has achieved high performance in difficult conditions generally by using sophisticated, often expensive sensors, and has often had high computational requirements.
Edit intent: clarity
Original:  Our approach is more robust and requires significantly less compute than the state-of-the-art. It is also capable of intervention-free -- no parameter changes required --  cross-platform generalisation, learning to navigate a route on one robot and repeating that route on a different type of robot with different camera.
Revised:  Our approach is more robust and requires significantly less compute . We also -- no parameter changes required -- cross-platform generalisation, learning to navigate a route on one robot and repeating that route on a different type of robot with different camera.
Edit intent: clarity
Original:  It is also capable of intervention-free -- no parameter changes required --  cross-platform generalisation, learning to navigate a route on one robot and repeating that route on a different type of robot with different camera.
Revised:  It is also capable of intervention-free -- for the first time -- cross-platform generalisation, learning to navigate a route on one robot and repeating that route on a different type of robot with different camera.
Edit intent: meaning-changed
Original:  It is also capable of intervention-free -- no parameter changes required --  cross-platform generalisation, learning to navigate a route on one robot and repeating that route on a different type of robot with different camera.
Revised:  It is also capable of intervention-free -- no parameter changes required -- demonstrate versatile cross-platform generalisation, learning to navigate a route on one robot and repeating that route on a different type of robot with different camera.
Edit intent: meaning-changed
Original:  It is also capable of intervention-free -- no parameter changes required --  cross-platform generalisation, learning to navigate a route on one robot and repeating that route on a different type of robot with different camera.
Revised:  It is also capable of intervention-free -- no parameter changes required -- cross-platform teach and repeat without changing parameters, in which we learn to navigate a route on one robot and repeating that route on a different type of robot with different camera.
Edit intent: meaning-changed
Original:  It is also capable of intervention-free -- no parameter changes required --  cross-platform generalisation, learning to navigate a route on one robot and repeating that route on a different type of robot with different camera.
Revised:  It is also capable of intervention-free -- no parameter changes required -- cross-platform generalisation, learning to navigate a route with one robot and repeating that route on a different type of robot with different camera.
Edit intent: fluency
Original:  This preconditioner is compared in condition number and eigenvalue distribution, and in numerical experiments with others in the literature: block Jacobi, block Gauss-Seidel, and the optimized block Gauss-Seidel method of 10.4173/mic.2006.2.3.
Revised:  This preconditioner is compared in condition number and eigenvalue distribution, and in numerical experiments with others in the literature: block Jacobi, block Gauss-Seidel, and the optimized block Gauss-Seidel method of Staff, Mardal, and Nilssen
Edit intent: meaning-changed
Original: [{\em  ] Experiments are run with implicit Runge-Kutta stages up to s=7, and it is found that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Revised: [{\em Modeling, Identification and Control ] Experiments are run with implicit Runge-Kutta stages up to s=7, and it is found that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Edit intent: meaning-changed
Original: [{\em  ] Experiments are run with implicit Runge-Kutta stages up to s=7, and it is found that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Revised: [{\em , 27 (2006), pp. 109-123] Experiments are run with implicit Runge-Kutta stages up to s=7, and it is found that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Edit intent: meaning-changed
Original: [{\em  ] Experiments are run with implicit Runge-Kutta stages up to s=7, and it is found that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Revised: [{\em ]. Experiments are run with implicit Runge-Kutta stages up to s=7, and it is found that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Edit intent: fluency
Original: [{\em  ] Experiments are run with implicit Runge-Kutta stages up to s=7, and it is found that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Revised: [{\em ] Experiments are run on two test problems, a 2D heat equation and a model advection-diffusion problem, using implicit Runge-Kutta stages up to s=7, and it is found that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Edit intent: meaning-changed
Original: [{\em  ] Experiments are run with implicit Runge-Kutta stages up to s=7, and it is found that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Revised: [{\em ] Experiments are run with implicit Runge-Kutta methods with two to seven stages. We find that the new preconditioner outperforms the others, with the improvement becoming more pronounced as spatial discretization is refined and as temporal order is increased.
Edit intent: clarity
Original:  The (unbalanced) QR iteration used for Chebyshev rootfinding does not guarantee backward stability on the polynomial coefficients, unless the vector of coefficients satisfy %DIFDELCMD < \lVert %%% p%DIFDELCMD < \rVert %%%  \approx 1, an hypothesis which is almost never verified for polynomials approximating smooth functions.
Revised:  The (unbalanced) QR iteration used for Chebyshev rootfinding does not guarantee backward stability on the polynomial coefficients, unless the vector of coefficients satisfy %DIFDELCMD < \lVert %%% %DIFDELCMD < \rVert %%% \approx 1, an hypothesis which is almost never verified for polynomials approximating smooth functions.
Edit intent: clarity
Original:  The (unbalanced) QR iteration used for Chebyshev rootfinding does not guarantee backward stability on the polynomial coefficients, unless the vector of coefficients satisfy %DIFDELCMD < \lVert %%% p%DIFDELCMD < \rVert %%%  \approx 1, an hypothesis which is almost never verified for polynomials approximating smooth functions.
Revised:  The (unbalanced) QR iteration used for Chebyshev rootfinding does not guarantee backward stability on the polynomial coefficients, unless the vector of coefficients satisfy %DIFDELCMD < \lVert %%% p%DIFDELCMD < \rVert %%% \|p\| \approx 1, an hypothesis which is almost never verified for polynomials approximating smooth functions.
Edit intent: meaning-changed
Original:  Even though the presented method is mathematically equivalent to the latter algorithm, we show that exploiting the rank structure allows to guarantee a small backward error on the polynomial, up to an explicitly computable amplification factor \hat\gamma_1(p), which depends on the polynomial under consideration.
Revised:  Even though the presented method is mathematically equivalent to the unbalanced QR algorithm, we show that exploiting the rank structure allows to guarantee a small backward error on the polynomial, up to an explicitly computable amplification factor \hat\gamma_1(p), which depends on the polynomial under consideration.
Edit intent: meaning-changed
Original:  We show that this parameter is almost always of moderate size, making the method accurate on several numerical tests, in contrast with what happens in the unstructured unbalanced QR . We also discuss the connection between the size of this amplification factor and the existence of a good balancing. This provides some insight on why the accuracy of our method is often very close to balanced QR iteration .
Revised:  We show that this parameter is almost always of moderate size, making the method accurate on several numerical tests, in contrast with what happens in the unstructured unbalanced QR .
Edit intent: coherence
Original: End-to-end Speech-to-text Translation ( E2E- ST ), which directly translates source language speech to target language text, is widely useful in practice, but traditional cascaded approaches (ASR+MT) often suffer from error propagation in the pipeline.
Revised: End-to-end Speech-to-text Translation ( E2E-ST ), which directly translates source language speech to target language text, is widely useful in practice, but traditional cascaded approaches (ASR+MT) often suffer from error propagation in the pipeline.
Edit intent: fluency
Original:  This technique , termed Masked Acoustic Modeling (MAM), can also perform pre-training , for the first time, on any acoustic signals (including non-speech ones) without annotation.
Revised:  This technique , termed Masked Acoustic Modeling (MAM), not only provides an alternative solution to improving E2E-ST, but also can perform pre-training , for the first time, on any acoustic signals (including non-speech ones) without annotation.
Edit intent: meaning-changed
Original:  Compared with current state-of-the-art models on ST , our technique achieves  + 1.4 BLEUimprovement without using transcriptions , and + 1.2 BLEU using transcriptions.
Revised:  We conduct our experiments over 8 different translation directions. In the setting without using any transcriptions , our technique achieves + 1.4 BLEUimprovement without using transcriptions , and + 1.2 BLEU using transcriptions.
Edit intent: meaning-changed
Original:  Compared with current state-of-the-art models on ST , our technique achieves  + 1.4 BLEUimprovement without using transcriptions , and + 1.2 BLEU using transcriptions.
Revised:  Compared with current state-of-the-art models on ST , our technique achieves an average improvement of + 1.4 BLEUimprovement without using transcriptions , and + 1.2 BLEU using transcriptions.
Edit intent: meaning-changed
Original:  Compared with current state-of-the-art models on ST , our technique achieves  + 1.4 BLEUimprovement without using transcriptions , and + 1.2 BLEU using transcriptions.
Revised:  Compared with current state-of-the-art models on ST , our technique achieves + 1.1 BLEU , and + 1.2 BLEU using transcriptions.
Edit intent: meaning-changed
Original:  Compared with current state-of-the-art models on ST , our technique achieves  + 1.4 BLEUimprovement without using transcriptions , and + 1.2 BLEU using transcriptions. The pre-training  of MAM with arbitrary acoustic signals also boosts the downstream speech-related tasks .
Revised:  Compared with current state-of-the-art models on ST , our technique achieves + 1.4 BLEUimprovement without using transcriptions , and + 2.3 BLEU with MAM pre-training of MAM with arbitrary acoustic signals also boosts the downstream speech-related tasks .
Edit intent: meaning-changed
Original:  The pre-training  of MAM with arbitrary acoustic signals also boosts the downstream speech-related tasks .
Revised:  The pre-training of MAM with arbitrary acoustic signals also has an average improvement with +1.6 BLEU for those languages. Compared with ASR multi-task learning solution, which replies on transcription during training, our pre-trained MAM model, which does not use transcription, achieves similar accuracy .
Edit intent: meaning-changed
Original:   To derive the proposed representation, Varadhan's formula is exploited to approximate the non-linear Eikonal partial differential equation (PDE) of the Euclidean distance field~(EDF ) by the logarithm of the screen Poisson equation that is linear .
Revised:  Our key contribution is the realisation that the regularised Eikonal equation can be simply solved by applying the logarithmic transformation to a GPIS formulation to recover the accurate Euclidean distance field (EDF) and, at the same time, the implicit surface. To derive the proposed representation, Varadhan's formula is exploited to approximate the non-linear Eikonal partial differential equation (PDE) of the Euclidean distance field~(EDF ) by the logarithm of the screen Poisson equation that is linear .
Edit intent: meaning-changed
Original:   To derive the proposed representation, Varadhan's formula is exploited to approximate the non-linear Eikonal partial differential equation (PDE) of the Euclidean distance field~(EDF ) by the logarithm of the screen Poisson equation that is linear .
Revised:  To derive the proposed representation, Varadhan's formula is exploited to approximate the non-linear Eikonal partial differential equation (PDE) of the EDF by the logarithm of the screen Poisson equation that is linear .
Edit intent: clarity
Original:   To derive the proposed representation, Varadhan's formula is exploited to approximate the non-linear Eikonal partial differential equation (PDE) of the Euclidean distance field~(EDF ) by the logarithm of the screen Poisson equation that is linear .
Revised:  To derive the proposed representation, Varadhan's formula is exploited to approximate the non-linear Eikonal partial differential equation (PDE) of the Euclidean distance field~(EDF ) by the logarithm of a linear PDE .
Edit intent: clarity
Original:  We show that members of the Matern covariance family directly satisfy this linear PDE . Thus, our key contribution is the realisation that the regularised Eikonal equation can be simply solved by applying the logarithmic function to a GPIS formulation to recover the accurate EDF and, at the same time, the implicit surface .
Revised:  We show that members of the Matern covariance family directly satisfy this linear PDE .
Edit intent: coherence
Original: Recent results in end-to-end ASR have demonstrated the efficacy of simple pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Classification (CTC) and Sequence-to-Sequence (seq2seq) losses.
Revised: Recent results in end-to-end automatic speech recognition have demonstrated the efficacy of simple pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Classification (CTC) and Sequence-to-Sequence (seq2seq) losses.
Edit intent: clarity
Original: Recent results in end-to-end ASR have demonstrated the efficacy of simple pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Classification (CTC) and Sequence-to-Sequence (seq2seq) losses.
Revised: Recent results in end-to-end ASR have demonstrated the efficacy of pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Classification (CTC) and Sequence-to-Sequence (seq2seq) losses.
Edit intent: clarity
Original:  Iterative Pseudo-Labeling (IPL), which continuously trains a single model using pseudo-labels iteratively re-generated as the model learns, has been shown to further increase performance in ASR.
Revised:  Iterative Pseudo-Labeling (IPL), which continuously trains a single model using pseudo-labels iteratively re-generated as the model learns, has been shown to further improve performance in ASR.
Edit intent: clarity
Original:  We call this approach Language-Model-Free IPL (slimIPL) and we give a resultant training setup for CTC and seq2seq models. At inference, our experiments show that decoding with a strong language model is more beneficial with slimIPL than IPL, asIPL exhibits some language model over-fitting issues. Compared to prior work on semi-supervised and unsupervised approaches, slimIPL not only simplifies the training process, but also achieves competitive and state-of-the-art results on LibriSpeech test sets in both standard and low-resource settings .
Revised:  We call this approach Language-Model-Free IPL (slimIPL) and we give a resultant training setup for low-resource settings .
Edit intent: coherence
Original:  Compared to prior work on semi-supervised and unsupervised approaches, slimIPL not only simplifies the training process, but also achieves competitive and state-of-the-art results on LibriSpeech test sets in both standard and low-resource settings .
Revised:  Compared to prior work on semi-supervised and unsupervised approaches, slimIPL not only simplifies the training process, but also achieves competitive and state-of-the-art results on LibriSpeech test sets in both standard and low-resource settings with CTC-based models. slimIPL features a dynamic cache for pseudo-labels which reduces sensitivity to changes in relabeling hyperparameters and results in improves training stability. slimIPL is also highly-efficient and requires 3.5-4x fewer computational resources to converge than other state-of-the-art semi/self-supervised approaches. With only 10 hours of labeled audio, slimIPL is competitive with self-supervised approaches, and is state-of-the-art with 100 hours of labeled audio without the use of a language model both at test time and during pseudo-label generation .
Edit intent: meaning-changed
Original:  Supported by the FIS, a hierarchical planner plan exploration motions in three steps, which find efficient global coverage paths, refine a local set of viewpoints and generate minimum-time trajectories in sequence.
Revised:  Supported by the FIS, a hierarchical planner plans exploration motions in three steps, which find efficient global coverage paths, refine a local set of viewpoints and generate minimum-time trajectories in sequence.
Edit intent: fluency
Original: Affinity Maturation (AM) is the process through which our immune system is able to develop potent antibodies against new pathogens it encounters, and is at the base of the efficacy of vaccines.
Revised: Affinity Maturation (AM) is the process through which the immune system is able to develop potent antibodies against new pathogens it encounters, and is at the base of the efficacy of vaccines.
Edit intent: clarity
Original:  At his core AM is analogous to a Darwinian evolutionary process, where B-cells mutate and are selected on the base of their affinity for an Antigen (Ag), and Ag availability tunes the selective pressure.
Revised:  At its core AM is analogous to a Darwinian evolutionary process, where B-cells mutate and are selected on the base of their affinity for an Antigen (Ag), and Ag availability tunes the selective pressure.
Edit intent: fluency
Original:  We then extend our results to the full population, both in the absence and presence of competition for T-cell help, and quantify the population survival probability as a function of Ag concentration and population initial size .
Revised:  We then extend our results to the full population, both in the absence and presence of competition for T-cell help, and quantify the population survival probability as a function of Ag concentration and initial population size. Our study suggests the population bottleneck phenomenology might represent a limit case in the space of biologically plausible maturation scenarios, whose characterization could help guide the process of vaccine development .
Edit intent: meaning-changed
Original: YouTube has revolutionized the way people discover and consume videos, becoming one of the primary news sources for Internet users. Since content on YouTube is generated by its users, the platform is particularly vulnerable to misinformative and conspiratorial videos. Even worse, the role played by YouTube's recommendation algorithm in unwittingly promoting questionable content is not well understood, and could potentially make the problem even worse.
Revised: The role played by YouTube's recommendation algorithm in unwittingly promoting questionable content is not well understood, and could potentially make the problem even worse.
Edit intent: coherence
Original:  Even worse, the role played by YouTube's recommendation algorithm in unwittingly promoting questionable content is not well understood, and could potentially make the problem even worse. This can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, e.g., during the COVID-19 pandemic.
Revised:  Even worse, the role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, e.g., during the COVID-19 pandemic.
Edit intent: meaning-changed
Original:  We collect 6.6K videos related to COVID-19, the flat earth theory, the anti-vaccination , and anti-mask movements ; using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant .
Revised:  We collect 6.6K videos related to COVID-19, the flat earth theory, the anti-vaccination , and anti-mask movements . Using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant .
Edit intent: fluency
Original:  using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant . We then train a deep learning classifier to detect pseudoscientific videos with an accuracy of 76.1\%.
Revised:  using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant and train a deep learning classifier to detect pseudoscientific videos with an accuracy of 76.1\%.
Edit intent: clarity
Original:  We then train a deep learning classifier to detect pseudoscientific videos with an accuracy of 76.1\%. Next, we quantify user exposure to this content on various parts of the platform (i.e., a user's homepage, recommended videos while watching a specific video, or search results) and how this exposure changes based on the user's watch history.
Revised:  We then train a deep learning classifier to detect pseudoscientific videos with an accuracy of 0.79. We quantify user exposure to this content on various parts of the platform (i.e., a user's homepage, recommended videos while watching a specific video, or search results) and how this exposure changes based on the user's watch history.
Edit intent: meaning-changed
Original:  We find that YouTube 's recommendation algorithm is more aggressive in suggesting pseudoscientific content when users are searching for specific topics , while these recommendations are less common on a user's homepage or when actively watching pseudoscientific videos.
Revised:  We find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth, anti-vaccination) than for emerging ones (like COVID-19). At the same time, these recommendations are less common on a user's homepage or when actively watching pseudoscientific videos.
Edit intent: meaning-changed