wanyu/IteraTeR_v2 huggingface dataset:
Original:  Meanwhile, intrinsically, a local intrinsic-preserved loss is introduced to preserve the geodesic priors while avoiding the heavy computations.
Revised:  Meanwhile, intrinsically, a local intrinsic-preserved loss is introduced to preserve the geodesic priors while avoiding heavy computations.
Edit intent: fluency
Original:  In this respect, biological matter can play a primary role because it is a material with interesting and innovative features which has found several applications in technology, from highly sensitive sensors for medical treatments to devices for energy harvesting.
Revised:  In this respect, biological matter can play a primary role because it is a material with interesting and innovative features that have found several applications in technology, from highly sensitive sensors for medical treatments to devices for energy harvesting.
Edit intent: coherence
Original:  All findings are in qualitative agreement with the known role of pH in biochemical activities of Reaction Center and similar proteins, therefore supporting a proof-of-concept for the development of new electron devices based on biomaterials 
Revised:  All findings are in qualitative agreement with the known role of pH in biochemical activities of Reaction Center and similar proteins, therefore supporting a proof-of-concept for the development of new electron devices based on biomaterials .
Edit intent: fluency
Original:  We scrutinize these assumptions and propose the advanced adversarial classification game, which incorporates all relevant parameters of an adversary and a defender in adversarial classification . Especially, we take into account economic factors on both sides and the fact that all so far proposed countermeasures against adversarial examples reduce accuracy on benign samples.
Revised:  We scrutinize these assumptions and propose the advanced adversarial classification game, which incorporates all relevant parameters of an adversary and a defender . Especially, we take into account economic factors on both sides and the fact that all so far proposed countermeasures against adversarial examples reduce accuracy on benign samples.
Edit intent: coherence
Original:  With the renaissance of deep learning, automatic diagnostic systems for computed tomography (CT) have achieved many successful applications.
Revised: . With the renaissance of deep learning, automatic diagnostic systems for computed tomography (CT) have achieved many successful applications.
Edit intent: fluency
Original:   Recent studies have shown that self-supervised learning is an effective approach for learning representations, but most of them rely on the empirical design of transformations and pretext tasks.
Revised:  Introduction Recent studies have shown that self-supervised learning is an effective approach for learning representations, but most of them rely on the empirical design of transformations and pretext tasks.
Edit intent: fluency
Original:   Recent studies have shown that self-supervised learning is an effective approach for learning representations, but most of them rely on the empirical design of transformations and pretext tasks.
Revised:  . Recent studies have shown that self-supervised learning is an effective approach for learning representations, but most of them rely on the empirical design of transformations and pretext tasks.
Edit intent: fluency
Original:   To avoid the subjectivity associated with these methods, we propose the MVCNet, a novel unsupervised three dimensional (3D) representation learning method working in a transformation-free manner.
Revised:  Methods To avoid the subjectivity associated with these methods, we propose the MVCNet, a novel unsupervised three dimensional (3D) representation learning method working in a transformation-free manner.
Edit intent: fluency
Original:   To avoid the subjectivity associated with these methods, we propose the MVCNet, a novel unsupervised three dimensional (3D) representation learning method working in a transformation-free manner.
Revised:  . To avoid the subjectivity associated with these methods, we propose the MVCNet, a novel unsupervised three dimensional (3D) representation learning method working in a transformation-free manner.
Edit intent: fluency
Original:   Experimental results show that MVCNet achieves state-of-the-art accuracies on the LIDC-IDRI (89.55 \%), LNDb (77.69\%) and \\%DIF > ), LNDb (77.69\\%) and TianChi (79.96 \%) datasets for unsupervised representation learning.
Revised:  Results Experimental results show that MVCNet achieves state-of-the-art accuracies on the LIDC-IDRI (89.55 \%), LNDb (77.69\%) and \\%DIF > ), LNDb (77.69\\%) and TianChi (79.96 \%) datasets for unsupervised representation learning.
Edit intent: fluency
Original:   Experimental results show that MVCNet achieves state-of-the-art accuracies on the LIDC-IDRI (89.55 \%), LNDb (77.69\%) and \\%DIF > ), LNDb (77.69\\%) and TianChi (79.96 \%) datasets for unsupervised representation learning.
Revised:  . Experimental results show that MVCNet achieves state-of-the-art accuracies on the LIDC-IDRI (89.55 \%), LNDb (77.69\%) and \\%DIF > ), LNDb (77.69\\%) and TianChi (79.96 \%) datasets for unsupervised representation learning.
Edit intent: fluency
Original:   Experimental results show that MVCNet achieves state-of-the-art accuracies on the LIDC-IDRI (89.55 \%), LNDb (77.69\%) and \\%DIF > ), LNDb (77.69\\%) and TianChi (79.96 \%) datasets for unsupervised representation learning.
Revised:  Experimental results show that MVCNet achieves state-of-the-art accuracies on the LIDC-IDRI (89.55 \\%DIF > ), LNDb (77.69\\%) and TianChi (79.96 \%) datasets for unsupervised representation learning.
Edit intent: fluency
Original:   Experimental results show that MVCNet achieves state-of-the-art accuracies on the LIDC-IDRI (89.55 \%), LNDb (77.69\%) and \\%DIF > ), LNDb (77.69\\%) and TianChi (79.96 \%) datasets for unsupervised representation learning.
Revised:  Experimental results show that MVCNet achieves state-of-the-art accuracies on the LIDC-IDRI (89.55 \%), LNDb (77.69\%) and \\%DIF > ), LNDb (77.69\\%) and TianChi (79.96 
Edit intent: clarity
Original:  When fine-tuned on 10 \% of the labeled data, the accuracies are comparable to the \\%DIF > of the labeled data, the accuracies are comparable to  supervised learning model (89.46 \% vs. 85.03\%, 73.85\% vs. 73.44\%, \\%DIF > vs. 85.03\\%, 73.85\\% vs. 73.44\\%, 83.56 \% vs. 83.34\% on the three datasets, respectively), indicating the \\%DIF > vs. 83.34\\% on the three datasets, respectively).
Revised:  When fine-tuned on 10 \\%DIF > of the labeled data, the accuracies are comparable to supervised learning model (89.46 \% vs. 85.03\%, 73.85\% vs. 73.44\%, \\%DIF > vs. 85.03\\%, 73.85\\% vs. 73.44\\%, 83.56 \% vs. 83.34\% on the three datasets, respectively), indicating the \\%DIF > vs. 83.34\\% on the three datasets, respectively).
Edit intent: fluency
Original:  When fine-tuned on 10 \% of the labeled data, the accuracies are comparable to the \\%DIF > of the labeled data, the accuracies are comparable to  supervised learning model (89.46 \% vs. 85.03\%, 73.85\% vs. 73.44\%, \\%DIF > vs. 85.03\\%, 73.85\\% vs. 73.44\\%, 83.56 \% vs. 83.34\% on the three datasets, respectively), indicating the \\%DIF > vs. 83.34\\% on the three datasets, respectively).
Revised:  When fine-tuned on 10 \% of the labeled data, the accuracies are comparable to the \\%DIF > of the labeled data, the accuracies are comparable to the supervised learning model (89.46 \% vs. 85.03\%, 73.85\% vs. 73.44\%, \\%DIF > vs. 85.03\\%, 73.85\\% vs. 73.44\\%, 83.56 \% vs. 83.34\% on the three datasets, respectively), indicating the \\%DIF > vs. 83.34\\% on the three datasets, respectively).
Edit intent: fluency
Original:  When fine-tuned on 10 \% of the labeled data, the accuracies are comparable to the \\%DIF > of the labeled data, the accuracies are comparable to  supervised learning model (89.46 \% vs. 85.03\%, 73.85\% vs. 73.44\%, \\%DIF > vs. 85.03\\%, 73.85\\% vs. 73.44\\%, 83.56 \% vs. 83.34\% on the three datasets, respectively), indicating the \\%DIF > vs. 83.34\\% on the three datasets, respectively).
Revised:  When fine-tuned on 10 \% of the labeled data, the accuracies are comparable to the \\%DIF > of the labeled data, the accuracies are comparable to supervised learning model (89.46 \\%DIF > vs. 85.03\\%, 73.85\\% vs. 73.44\\%, 83.56 \% vs. 83.34\% on the three datasets, respectively), indicating the \\%DIF > vs. 83.34\\% on the three datasets, respectively).
Edit intent: fluency
Original:  When fine-tuned on 10 \% of the labeled data, the accuracies are comparable to the \\%DIF > of the labeled data, the accuracies are comparable to  supervised learning model (89.46 \% vs. 85.03\%, 73.85\% vs. 73.44\%, \\%DIF > vs. 85.03\\%, 73.85\\% vs. 73.44\\%, 83.56 \% vs. 83.34\% on the three datasets, respectively), indicating the \\%DIF > vs. 83.34\\% on the three datasets, respectively).
Revised:  When fine-tuned on 10 \% of the labeled data, the accuracies are comparable to the \\%DIF > of the labeled data, the accuracies are comparable to supervised learning model (89.46 \% vs. 85.03\%, 73.85\% vs. 73.44\%, \\%DIF > vs. 85.03\\%, 73.85\\% vs. 73.44\\%, 83.56 \\%DIF > vs. 83.34\\% on the three datasets, respectively).
Edit intent: clarity
Original:  Conclusion.  superiority of MVCNet in learning representations with limited annotations.
Revised:  Conclusion. Results indicate the superiority of MVCNet in learning representations with limited annotations.
Edit intent: clarity
Original: Code is released at: URL \emph{ 
Revised: Code is released at: URL \emph{ .
Edit intent: fluency
Original:  In this work, we investigate the support that KAT provides for reasoning about incorrectness , instead, as embodied by Ohearn's recently proposed incorrectness logic.
Revised:  In this work, we investigate the support that KAT provides for reasoning about incorrectness , instead, as embodied by Ohearn's recently proposed incorrectness logic.
Edit intent: fluency
Original:  To address this issue, we study Kleene algebra with Top and Tests (TopKAT), an extension of KAT with a top element.
Revised:  To address this issue, we study Kleene Algebra with Top and Tests (TopKAT), an extension of KAT with a top element.
Edit intent: fluency
Original: We generalize K\"ahler information manifolds of complex-valued signal processing filters by introducing weighted Hardy spaces and generic composite functions of  transfer functions.
Revised: We generalize K\"ahler information manifolds of complex-valued signal processing filters by introducing weighted Hardy spaces and composite functions of transfer functions.
Edit intent: clarity
Original:  Additionally, the K\"ahler potential of the linear system geometry corresponds to the square of the weighted Hardy norms for composite functions of its transfer function .
Revised:  Additionally, the K\"ahler potential of the linear system geometry corresponds to the square of the weighted Hardy norms of its composite transfer functions .
Edit intent: fluency
Original:  By using the properties of K\"ahler manifolds, it is possible to compute various geometric objects on the manifolds from arbitrary weight vectors  in much simpler ways.
Revised:  By using the properties of K\"ahler manifold, geometric objects on the manifolds from arbitrary weight vectors in much simpler ways.
Edit intent: clarity
Original:  By using the properties of K\"ahler manifolds, it is possible to compute various geometric objects on the manifolds from arbitrary weight vectors  in much simpler ways.
Revised:  By using the properties of K\"ahler manifolds, it is possible to compute various geometric objects on the manifolds from arbitrary weight vectors are computed in much simpler ways.
Edit intent: clarity
Original:  Additionally , K\"ahler information manifolds of signal filters in weighted Hardy spaces can generate various information manifolds such as K\"ahlerian information geometries from the unweighted complex cepstrum or the unweighted power cepstrum, the geometry of the weighted stationarity filters, and mutual information geometry under the unified framework.
Revised:  Moreover , K\"ahler information manifolds of signal filters in weighted Hardy spaces can generate various information manifolds such as K\"ahlerian information geometries from the unweighted complex cepstrum or the unweighted power cepstrum, the geometry of the weighted stationarity filters, and mutual information geometry under the unified framework.
Edit intent: fluency
Original:  We also cover several examples from time series models of which metric tensor, Levi-Civita connection, and K\"ahler potentials are represented with polylogarithm of poles and zeros from the transfer functions when the weight vectors are in terms of polynomials .
Revised:  We also cover several examples from time series models of which metric tensor, Levi-Civita connection, and K\"ahler potentials are represented with polylogarithm of poles and zeros from the transfer functions with the weight vectors are in terms of polynomials .
Edit intent: clarity
Original:  We also cover several examples from time series models of which metric tensor, Levi-Civita connection, and K\"ahler potentials are represented with polylogarithm of poles and zeros from the transfer functions when the weight vectors are in terms of polynomials .
Revised:  We also cover several examples from time series models of which metric tensor, Levi-Civita connection, and K\"ahler potentials are represented with polylogarithm of poles and zeros from the transfer functions when the weight vectors are given as a family of exponential forms .
Edit intent: clarity
Original:  Furthermore, the proposed conversion for language prior probabilities enables BERT to receive an extra 3\% relative WERR, and the combination of BERT, GPT and GPT-2 results in further improvements.
Revised:  Furthermore, the proposed conversion for language prior probabilities enables BERT to obtain an extra 3\% relative WERR, and the combination of BERT, GPT and GPT-2 results in further improvements.
Edit intent: fluency
Original: Existing salient object detection (SOD) models mainly rely on CNN-based U-shaped structures with skip connections to combine the global contexts and local spatial details that are crucial for locating salient objects and refining object details, respectively.
Revised: Existing salient object detection (SOD) methods mainly rely on CNN-based U-shaped structures with skip connections to combine the global contexts and local spatial details that are crucial for locating salient objects and refining object details, respectively.
Edit intent: clarity
Original:  However, directly applying the transformer to SOD is obviously suboptimal because the transformer lacks the ability to learn local spatial representations.
Revised:  However, directly applying the transformer to SOD is suboptimal because the transformer lacks the ability to learn local spatial representations.
Edit intent: coherence
Original:  We propose a transformer-based Asymmetric Bilateral U-Net ( AbiU-Net ).
Revised:  We propose a transformer-based Asymmetric Bilateral U-Net ( ABiU-Net ).
Edit intent: fluency
Original:  This work proposes batch-insensitive losses as a way to alleviate the non-IID negatives issue for federated movie recommendation .
Revised:  This work proposes batch-insensitive losses as a way to alleviate the non-IID negatives issue for federated movie recommendations .
Edit intent: fluency
Original:  We  open-source our code framework to accelerate further research and applications of federated deep retrieval models.
Revised:  We also open-source our code framework to accelerate further research and applications of federated deep retrieval models.
Edit intent: coherence
Original:   We provide a a thorough modeling of the available option data we have on each ETF (  the October 15, 2021 with 63 to expiration) based on the Generalized Gamma Distribution and compared it to the option pricing and RND modeling obtained directly from a well-calibrated Heston's (1993) SV model (both theoretically and empirically, using Monte-Carlo simulations of the spot's price).
Revised:  We provide a thorough modeling of the available option data we have on each ETF ( the October 15, 2021 with 63 to expiration) based on the Generalized Gamma Distribution and compared it to the option pricing and RND modeling obtained directly from a well-calibrated Heston's (1993) SV model (both theoretically and empirically, using Monte-Carlo simulations of the spot's price).
Edit intent: fluency
Original:   We provide a a thorough modeling of the available option data we have on each ETF (  the October 15, 2021 with 63 to expiration) based on the Generalized Gamma Distribution and compared it to the option pricing and RND modeling obtained directly from a well-calibrated Heston's (1993) SV model (both theoretically and empirically, using Monte-Carlo simulations of the spot's price).
Revised:  We provide a a thorough modeling of the available option data we have on each ETF ( with the October 15, 2021 with 63 to expiration) based on the Generalized Gamma Distribution and compared it to the option pricing and RND modeling obtained directly from a well-calibrated Heston's (1993) SV model (both theoretically and empirically, using Monte-Carlo simulations of the spot's price).
Edit intent: fluency
Original:   We provide a a thorough modeling of the available option data we have on each ETF (  the October 15, 2021 with 63 to expiration) based on the Generalized Gamma Distribution and compared it to the option pricing and RND modeling obtained directly from a well-calibrated Heston's (1993) SV model (both theoretically and empirically, using Monte-Carlo simulations of the spot's price).
Revised:  We provide a a thorough modeling of the available option data we have on each ETF ( the October 15, 2021 expiration) based on the Generalized Gamma Distribution and compared it to the option pricing and RND modeling obtained directly from a well-calibrated Heston's (1993) SV model (both theoretically and empirically, using Monte-Carlo simulations of the spot's price).
Edit intent: clarity
Original:   We provide a a thorough modeling of the available option data we have on each ETF (  the October 15, 2021 with 63 to expiration) based on the Generalized Gamma Distribution and compared it to the option pricing and RND modeling obtained directly from a well-calibrated Heston's (1993) SV model (both theoretically and empirically, using Monte-Carlo simulations of the spot's price).
Revised:  We provide a a thorough modeling of the available option data we have on each ETF ( the October 15, 2021 with 63 to expiration) based on the GG distribution and compared it to the option pricing and RND modeling obtained directly from a well-calibrated Heston's (1993) SV model (both theoretically and empirically, using Monte-Carlo simulations of the spot's price).
Edit intent: clarity
Original:  The inadequacy of the Black-Scholes modeling in such instances with negatively skewed distribution is further illustrated by its impact on the hedging factor, delta, and the immediate implications to the retail trader  .
Revised:  The inadequacy of the Black-Scholes modeling in such instances which involve negatively skewed distribution is further illustrated by its impact on the hedging factor, delta, and the immediate implications to the retail trader .
Edit intent: clarity
Original:  However, these acceleration methods have not been applied to the basic RRT. Moreover, while a number of path optimisers can be used to accelerate the convergence rate, a comparison of their effectiveness is lacking.
Revised:  However, informed sampling and its combination with path optimisation have not been applied to the basic RRT. Moreover, while a number of path optimisers can be used to accelerate the convergence rate, a comparison of their effectiveness is lacking.
Edit intent: clarity
Original:  In this paper , we investigate the use of informed sampling and path optimisation to accelerate planners based on both the basic RRT and the RRT*, resulting in a family of algorithms known as optimised informed RRTs.
Revised:  This paper investigates the use of informed sampling and path optimisation to accelerate planners based on both the basic RRT and the RRT*, resulting in a family of algorithms known as optimised informed RRTs.
Edit intent: fluency
Original:  We report that only a few pangolin samples were found to contain coronavirus reads, and even then in low abundance, while other  viruses were present in much higher abundance.
Revised:  We report that only a few pangolin samples were found to contain coronavirus reads, and even then in low abundance, while other viruses were present in higher abundance.
Edit intent: clarity
Original:  This makes it unlikely that the pangolins in question had a coronavirus infection while alive.
Edit intent: coherence
Original:  By generating a large volume of feints, IDoS attacks deplete the cognition resources of human operators to prevent humans from identifying the real attacks hidden among feints.
Revised:  By generating a large volume of feints, IDoS attacks deplete the cognitive resources of human operators to prevent humans from identifying the real attacks hidden among feints.
Edit intent: fluency
Original:  To this end, we model the feint and real attacks ' sequential arrivals with category labels as a semi-Markov process .
Revised:  To this end, we use the semi-Markov process to model the sequential arrivals of feints and real attacks ' sequential arrivals with category labels as a semi-Markov process .
Edit intent: clarity
Original:  To this end, we model the feint and real attacks ' sequential arrivals with category labels as a semi-Markov process .
Revised:  To this end, we model the feint and real attacks with category labels as a semi-Markov process .
Edit intent: clarity
Original:  Furthermore, we characterize the fundamental limits of the minimum severity level under all AM strategies and the maximum length of the inspection period to reduce the IDoS risks .
Revised:  Furthermore, the results show that the minimum risk is achieved with a proper level of intentional inattention to alerts, which we refer to as the law of rational risk-reduction inattention .
Edit intent: coherence
Original:  Furthermore, we also propose a simple recurrent neural network (RNN), as an instance of deep learning-based sequential model, that we apply to our generated dataset.
Revised:  Furthermore, we also propose a recurrent neural network (RNN), as an instance of deep learning-based sequential model, that we apply to our generated dataset.
Edit intent: clarity
Original:  And in fact, by amortizing selectively , the needed supporting invariants can be simpler, making the invariant inference task more feasible and predictable.
Revised:  And in fact, by amortizing selectively , the needed supporting invariants can be simpler, making the invariant inference task more feasible and predictable.
Edit intent: fluency
Original: Users interacting with a UI-embedded machine or system are typically obliged to perform their actions in a pre-determined order, to successfully achieve certain functional goals.
Revised: Users interacting with a system through UI are typically obliged to perform their actions in a pre-determined order, to successfully achieve certain functional goals.
Edit intent: clarity
Original:  In order to improve the security with the awareness of unexpected user behaviors, a system can be redesigned to a more robust one by changing the order of actions in its specification.
Revised:  To improve the security with the awareness of unexpected user behaviors, a system can be redesigned to a more robust one by changing the order of actions in its specification.
Edit intent: coherence
Original:  In this paper, we propose an efficient algorithm to automatically produce specification revisions tackling with attack scenarios caused by the weakened user obligations.
Revised:  In this paper, we propose an efficient algorithm to automatically produce specification revisions tackling the attack scenarios caused by the weakened user obligations.
Edit intent: fluency