wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Using a simple individual-based model for vegetation dynamics, we show that environmental  variability --a well-recognized characteristic feature of semiarid environments-- promotes the emergence of power-law vegetation patches in a robust way (i.e. for a broad range of parameter values)  .
Revised:  Using a simple individual-based model for vegetation dynamics, we show that environmental variability --a well-recognized characteristic feature of semiarid environments-- promotes the emergence of power-law vegetation patches in a robust way (i.e. for a broad range of parameter values) the emergence of scale-free vegetation patches .
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Original:  The model also reveals that the emerging power-law exponents depend on the overall vegetation-cover density exhibiting the same trend as empirical data and, remarkably, the fitted exponents agree reasonably well with the ones measured in well-known empirical studies. We conclude that environmental variability plays a key role in the formation of such vegetation patterns.
Revised:  The model also reveals that the emerging power-law exponents depend on the overall vegetation-cover density , in agreement with empirical observations, suggesting that environmental variability plays a key role in the formation of such vegetation patterns.
Edit intent: clarity
Original:  This gap mainly stems from the four main dynamic factors in sEMG-based gesture recognition: gesture intensity, limb position, electrode shift  and transient changes in the signal .
Revised:  This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors in sEMG-based gesture recognition: gesture intensity, limb position, electrode shift and transient changes in the signal .
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Original:  This gap mainly stems from the four main dynamic factors in sEMG-based gesture recognition: gesture intensity, limb position, electrode shift  and transient changes in the signal . These factors are hard to include within an offline dataset as each of them exponentially augment the number of segments to be recorded.
Revised:  This gap mainly stems from the four main dynamic factors in sEMG-based gesture recognition: gesture intensity, limb position, electrode shift and transient changes in the signal ), as including their permutations drastically increases the amount of data to be recorded.
Edit intent: clarity
Original:  On the other hand , online datasets are biased towards the sEMG-based algorithms providing feedback to the participants, limiting the usability of such datasets as benchmarks. This paper proposes a virtual reality (VR) environment and a real-time experimental protocol from which the four main dynamic factors can more easily be studied.
Revised:  On the other hand , online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a virtual reality (VR) environment and a real-time experimental protocol from which the four main dynamic factors can more easily be studied.
Edit intent: clarity
Original:  This paper proposes a virtual reality (VR) environment and a real-time experimental protocol from which the four main dynamic factors can more easily be studied. During the online experiment, the gesture recognition feedback is provided through the leap motion camera, enabling the proposed dataset to be re-used to compare future sEMG-based algorithms.
Revised:  This paper proposes a virtual reality (VR) environment and a real-time experimental protocol from which the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be re-used to compare future sEMG-based algorithms.
Edit intent: clarity
Original:  Finally, TADANN, a new transfer learning-based algorithm, is proposed for long term gesture classification and significantly (p<0.05) outperforms  fine-tuning a network .
Revised:  The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p<0.05) outperforms fine-tuning a network .
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Original:  Finally, TADANN, a new transfer learning-based algorithm, is proposed for long term gesture classification and significantly (p<0.05) outperforms  fine-tuning a network .
Revised:  Finally, TADANN, a new transfer learning-based algorithm, is proposed for long term gesture classification and significantly (p<0.05) outperforms fine-tuning as the recalibration technique .
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Original:  Such codes provide multiple disjoint recovery groups for each data object, which makes it possible for multiple users to access the same object in a non-overlapping way. However in the presence of server-side performance variability, downloading an object using a recovery group takes longer than using a single server hosting the object. Therefore it is not immediately clear whether availability codes reduce latency to access hot data . Accordingly, the goal of this paper is to analyze, using a queuing theoretical approach, the download time in storage systems that employ availability codes .
Revised:  Such codes provide multiple disjoint recovery groups for each data object, thus providing flexibility in accessing data objects. This paper presents techniques for analyzing the download time in storage systems that employ availability codes .
Edit intent: clarity
Original:  For data access, we consider the widely adopted Fork-Join model with redundancy. In this model, each request arrival splits into multiple copies and completes as soon as any one of the copies finishes service. We first carry out the analysis under the low-traffic regime in which case the system consists of at most one download request at any time.
Revised:  For data access, we consider the widely adopted Fork-Join scheme for data access. With the Fork-Join access scheme, each data request is replicated (forked) to the systematic server containing the object and all of its recovery groups. For low-traffic regime in which case the system consists of at most one download request at any time.
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Original:  We first carry out the analysis under the low-traffic regime in which case the system consists of at most one download request at any time. In this setting, we compare the download time in  systems with availability, maximum distance separable (MDS), and replication codes.
Revised:  We first carry out the analysis under the low-traffic regime in which case the system consists of at most one request in the system, we compute the download time in systems with availability, maximum distance separable (MDS), and replication codes.
Edit intent: clarity
Original:  For this case  we present upper and lower bounds on the download time . These bounds yield insight on system performance with respect to varying popularities over the stored objects. We also derive an M/G/1 queue approximation for the system, and show with simulations that it performs well in estimating the actual system performance .
Revised:  For this case we present upper and lower bounds on the download time , and an M/G/1 queue approximation for the system, and show with simulations that it performs well in estimating the actual system performance .
Edit intent: clarity
Original:  We also derive an M/G/1 queue approximation for the system, and show with simulations that it performs well in estimating the actual system performance .
Revised:  We also derive an M/G/1 queue approximation for several special cases of interest. Via numerical simulations, we evaluate our bounds, and demonstrate that the M/G/1 queue approximation has a high degree of accuracy. We also compare availability codes with MDS and replication codes .
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Original: While there has been great interest in generating imperceptible adversarial examples in continuous data domain (e.g. image and audio)to explore the model vulnerabilities, generatingadversarial text in the discrete domain is still challenging .
Revised: Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Though raised great concerns, such adversarial attacks can be leveraged to estimate the robustness of NLP models. Compared with the adversarial example generation in continuous data domain (e.g. image and audio)to explore the model vulnerabilities, generatingadversarial text in the discrete domain is still challenging .
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Original: While there has been great interest in generating imperceptible adversarial examples in continuous data domain (e.g. image and audio)to explore the model vulnerabilities, generatingadversarial text in the discrete domain is still challenging .
Revised: While there has been great interest in generating imperceptible adversarial examples in continuous data domain (e.g. image and audio)to explore the model vulnerabilities, generating in the discrete domain is still challenging .
Edit intent: clarity
Original: While there has been great interest in generating imperceptible adversarial examples in continuous data domain (e.g. image and audio)to explore the model vulnerabilities, generatingadversarial text in the discrete domain is still challenging . The main contribution of this paper is to propose a general targeted attack framework AdvCodec for adversarial text generation which addresses the challenge of discrete input space and is easily adapted to general natural language processing (NLP )  tasks.
Revised: While there has been great interest in generating imperceptible adversarial examples in continuous data domain (e.g. image and audio)to explore the model vulnerabilities, generatingadversarial text , image ) tasks.
Edit intent: coherence
Original:  The main contribution of this paper is to propose a general targeted attack framework AdvCodec for adversarial text generation which addresses the challenge of discrete input space and is easily adapted to general natural language processing (NLP )  tasks.
Revised:  The main contribution of this paper is to propose a general targeted attack framework AdvCodec for adversarial text generation which addresses the challenge of discrete input space and is easily adapted to general natural language processing (NLP ) , generating adversarial text that preserves the original meaning is challenging since the text space is discrete and non-differentiable. To handle these challenges, we propose a target-controllable adversarial attack framework T3, which is applicable to a range of NLP tasks.
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Original:  A  tree-based decoder is then applied to ensure the grammar correctness of the generated text . It also enables the flexibility of making manipulations on different levels of text, such as sentence (AdvCodec(sent)) and word (AdvCodec(word)) levels .
Revised:  A tree-based decoder is then applied to ensure the grammar correctness of the generated text and manipulate it on either sentence (T3(Sent)) or word (T3(Word)) level .
Edit intent: clarity
Original:  In particular, our attack causes a BERT-based sentiment classifier accuracy to drop from 0.703 to 0.006, and a BERT-based QA model's F1 score to drop from 88.62 to 33.21 (with best targeted attack F1 score as 46.54). Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective  way to examine the robustness of existing NLP models. 
Revised:  Moreover, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective way to examine the robustness of existing NLP models. 
Edit intent: coherence
Original:  Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective  way to examine the robustness of existing NLP models. 
Revised:  Furthermore, we show that the white-box generated adversarial texts have high transferability which enables the black-box models, shedding light on an effective way to examine the robustness of existing NLP models. 
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Original:  Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective  way to examine the robustness of existing NLP models. 
Revised:  Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box attacks in practice. Our work sheds light on an effective way to examine the robustness of existing NLP models. 
Edit intent: clarity
Original:  Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective  way to examine the robustness of existing NLP models. 
Revised:  Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective and general way to examine the robustness of existing NLP models. 
Edit intent: meaning-changed
Original:  Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective  way to examine the robustness of existing NLP models. 
Revised:  Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective way to examine the robustness of NLP models. 
Edit intent: clarity
Original:  Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective  way to examine the robustness of existing NLP models. 
Revised:  Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective way to examine the robustness of existing NLP models. Our code is publicly available at URL
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Original:  Here, we presented a new 3D small molecule fingerprint, the three-dimensional force fields fingerprint (TF3P) , learned by deep capsular network whose training is in no need of labeled dataset for specific predictive tasks.
Revised:  Here, we unprecedentedly introduce deep learning into 3D small molecule fingerprint, the three-dimensional force fields fingerprint (TF3P) , learned by deep capsular network whose training is in no need of labeled dataset for specific predictive tasks.
Edit intent: clarity
Original:  Here, we presented a new 3D small molecule fingerprint, the three-dimensional force fields fingerprint (TF3P) , learned by deep capsular network whose training is in no need of labeled dataset for specific predictive tasks.
Revised:  Here, we presented a new 3D small molecule fingerprints, presenting a new one we termed as the three-dimensional force fields fingerprint (TF3P) , learned by deep capsular network whose training is in no need of labeled dataset for specific predictive tasks.
Edit intent: meaning-changed
Original:  Here, we presented a new 3D small molecule fingerprint, the three-dimensional force fields fingerprint (TF3P) , learned by deep capsular network whose training is in no need of labeled dataset for specific predictive tasks.
Revised:  Here, we presented a new 3D small molecule fingerprint, the three-dimensional force fields fingerprint (TF3P) . TF3P is learned by a deep capsular network whose training is in no need of labeled dataset for specific predictive tasks.
Edit intent: coherence
Original:  Here, we presented a new 3D small molecule fingerprint, the three-dimensional force fields fingerprint (TF3P) , learned by deep capsular network whose training is in no need of labeled dataset for specific predictive tasks.
Revised:  Here, we presented a new 3D small molecule fingerprint, the three-dimensional force fields fingerprint (TF3P) , learned by deep capsular network whose training is in no need of labeled datasets for specific predictive tasks.
Edit intent: fluency
Original:  TF3P can encode the 3D force fields information of molecules and demonstrates its stronger ability to capture 3D structural changes,  recognize molecules alike in 3D but not in 2D, and recognize similar targets inaccessible by other fingerprints, including the solely existing 3D fingerprint E3FP, based on only ligands similarity.
Revised:  TF3P can encode the 3D force fields information of molecules and demonstrates the stronger ability to capture 3D structural changes, recognize molecules alike in 3D but not in 2D, and recognize similar targets inaccessible by other fingerprints, including the solely existing 3D fingerprint E3FP, based on only ligands similarity.
Edit intent: clarity
Original:  TF3P can encode the 3D force fields information of molecules and demonstrates its stronger ability to capture 3D structural changes,  recognize molecules alike in 3D but not in 2D, and recognize similar targets inaccessible by other fingerprints, including the solely existing 3D fingerprint E3FP, based on only ligands similarity.
Revised:  TF3P can encode the 3D force fields information of molecules and demonstrates its stronger ability to capture 3D structural changes, to recognize molecules alike in 3D but not in 2D, and recognize similar targets inaccessible by other fingerprints, including the solely existing 3D fingerprint E3FP, based on only ligands similarity.
Edit intent: coherence
Original:  TF3P can encode the 3D force fields information of molecules and demonstrates its stronger ability to capture 3D structural changes,  recognize molecules alike in 3D but not in 2D, and recognize similar targets inaccessible by other fingerprints, including the solely existing 3D fingerprint E3FP, based on only ligands similarity.
Revised:  TF3P can encode the 3D force fields information of molecules and demonstrates its stronger ability to capture 3D structural changes, recognize molecules alike in 3D but not in 2D, and to identify similar targets inaccessible by other fingerprints, including the solely existing 3D fingerprint E3FP, based on only ligands similarity.
Edit intent: fluency
Original:  TF3P can encode the 3D force fields information of molecules and demonstrates its stronger ability to capture 3D structural changes,  recognize molecules alike in 3D but not in 2D, and recognize similar targets inaccessible by other fingerprints, including the solely existing 3D fingerprint E3FP, based on only ligands similarity.
Revised:  TF3P can encode the 3D force fields information of molecules and demonstrates its stronger ability to capture 3D structural changes, recognize molecules alike in 3D but not in 2D, and recognize similar targets inaccessible by other 2D or 3D fingerprint E3FP, based on only ligands similarity.
Edit intent: clarity
Original:  TF3P can encode the 3D force fields information of molecules and demonstrates its stronger ability to capture 3D structural changes,  recognize molecules alike in 3D but not in 2D, and recognize similar targets inaccessible by other fingerprints, including the solely existing 3D fingerprint E3FP, based on only ligands similarity.
Revised:  TF3P can encode the 3D force fields information of molecules and demonstrates its stronger ability to capture 3D structural changes, recognize molecules alike in 3D but not in 2D, and recognize similar targets inaccessible by other fingerprints, including the solely existing 3D fingerprints based on only ligands similarity.
Edit intent: clarity
Original:  This paper focuses on this competition in a hypothetical scenario--"if both AV and PT operators are profit-oriented ," and uses an ABM to quantitatively evaluate the system performance in this competition from the perspectives of four stakeholders--AV operator, PT operator, passengers, and public authority.
Revised:  This study examines the competitive perspective where both AV and PT operators are profit-oriented ," and uses an ABM to quantitatively evaluate the system performance in this competition from the perspectives of four stakeholders--AV operator, PT operator, passengers, and public authority.
Edit intent: clarity
Original:  This paper focuses on this competition in a hypothetical scenario--"if both AV and PT operators are profit-oriented ," and uses an ABM to quantitatively evaluate the system performance in this competition from the perspectives of four stakeholders--AV operator, PT operator, passengers, and public authority.
Revised:  This paper focuses on this competition in a hypothetical scenario--"if both AV and PT operators are profit-oriented with dynamic adjustable supply strategies under five regulatory structures regarding whether the AV operator is allowed to change the fleet size and whether the PT operator is allowed to adjust headway. Four out of the five scenarios are constrained competition while the other one focuses on unconstrained competition to find the Nash Equilibrium. We evaluate the competition process as well as the system performance from the standpoints of four stakeholders -- the AV operator, the PT operator, passengers, and public authority.
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Original:  This paper focuses on this competition in a hypothetical scenario--"if both AV and PT operators are profit-oriented ," and uses an ABM to quantitatively evaluate the system performance in this competition from the perspectives of four stakeholders--AV operator, PT operator, passengers, and public authority. In our model, AV operator updates its supply by changing fleet sizes while PT by adjusting headways, and both use heuristic approaches to update supply in order to increase profits.
Revised:  This paper focuses on this competition in a hypothetical scenario--"if both AV and PT operators are profit-oriented ," and uses an ABM to quantitatively evaluate the system performance in this competition from the perspectives of four stakeholders--AV operator, PT operator, passengers, and the transport authority. We also examine the impact of PT subsidies on the competition results including both demand-based and supply-based subsidies. A heuristic algorithm is proposed to update supply in order to increase profits.
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Original:  In our model, AV operator updates its supply by changing fleet sizes while PT by adjusting headways, and both use heuristic approaches to update supply in order to increase profits. We implement the model in the first-mile scenario in Tampines .
Revised:  In our model, AV operator updates its supply by changing fleet sizes while PT by adjusting headways, and both use heuristic approaches to update supply strategies for AV and PT based on the operators' historical actions and profits. An agent-based simulation model is implemented in the first-mile scenario in Tampines .
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Original:  We implement the model in the first-mile scenario in Tampines . In four regulation scenarios--two by two combinations regarding whether AV and PT are allowed to change supplies--we find that since AV can release the bus operator from low-demand routes, the competition can lead to higher profits of both, and higher system efficiency , simultaneously, rather than a one-sided loss-gain result.
Revised:  We implement the model in the first-mile scenario in Tampines , Singapore. We find that the competition can result in higher profits and higher system efficiency , simultaneously, rather than a one-sided loss-gain result.
Edit intent: coherence