wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The probability of existence of accessible paths between two genotypes at a distance linear in L displays a sharp transition from 0 to 1 at a threshold value \beta^\ast of the fitness difference between the initial and final genotype.
Revised:  The probability of existence of accessible paths between two genotypes at a distance linear in L displays a sharp transition from 0 to 1 at a threshold \beta_c for the fitness difference between the initial and final genotype.
Edit intent: clarity
Original:  Our results generalize previous results for accessibility percolation on the biallelic hypercube, and compare favorably to published numerical results for multiallelic Hamming graphs . A key tool of our analysis is the concept of quasi-accessibility, which eliminates the need to account for the self-avoidance of accessible paths .
Revised:  Our results generalize previous results for accessibility percolation on the biallelic hypercube, and compare favorably to published numerical results for multiallelic Hamming graphs .
Edit intent: coherence
Original:  In the setting of language models, we show that a comparative analysis of model snapshots before and after an update can reveal a surprising amount of detailed information about the changes in the data used for training before and after the update .
Revised:  In the setting of language models, we show that a differential analysis of language model snapshots before and after an update can reveal a surprising amount of detailed information about the changes in the data used for training before and after the update .
Edit intent: clarity
Original:  In the setting of language models, we show that a comparative analysis of model snapshots before and after an update can reveal a surprising amount of detailed information about the changes in the data used for training before and after the update .
Revised:  In the setting of language models, we show that a comparative analysis of model snapshots before and after an update can reveal a surprising amount of detailed information about changes in the data used for training before and after the update .
Edit intent: fluency
Original:  In the setting of language models, we show that a comparative analysis of model snapshots before and after an update can reveal a surprising amount of detailed information about the changes in the data used for training before and after the update .
Revised:  In the setting of language models, we show that a comparative analysis of model snapshots before and after an update can reveal a surprising amount of detailed information about the changes in the training data. We propose two new metrics---differential score and differential rank---for analyzing the leakage due to updates of natural language models. We perform leakage analysis using these metrics across models trained on several different datasets using different methods and configurations .
Edit intent: meaning-changed
Original:  That is we define  problem, we provide reasonable justifications for the choice that we made during our designing of the training and test steps and we performed a statistical analysis of function names in a large real-world corpora of over 4 millions of functions  .
Revised:  That is we define the problem, we provide reasonable justifications for the choice that we made during our designing of the training and test steps and we performed a statistical analysis of function names in a large real-world corpora of over 4 millions of functions .
Edit intent: fluency
Original:  That is we define  problem, we provide reasonable justifications for the choice that we made during our designing of the training and test steps and we performed a statistical analysis of function names in a large real-world corpora of over 4 millions of functions  .
Revised:  That is we define problem, we provide reasonable justifications for the choices that we made during our designing of the training and test steps and we performed a statistical analysis of function names in a large real-world corpora of over 4 millions of functions .
Edit intent: fluency
Original:  That is we define  problem, we provide reasonable justifications for the choice that we made during our designing of the training and test steps and we performed a statistical analysis of function names in a large real-world corpora of over 4 millions of functions  .
Revised:  That is we define problem, we provide reasonable justifications for the choice that we made for the design of training and the tests. We performed an analysis on a large real-world corpora of over 4 millions of functions .
Edit intent: clarity
Original:  That is we define  problem, we provide reasonable justifications for the choice that we made during our designing of the training and test steps and we performed a statistical analysis of function names in a large real-world corpora of over 4 millions of functions  .
Revised:  That is we define problem, we provide reasonable justifications for the choice that we made during our designing of the training and test steps and we performed a statistical analysis of function names in a large real-world corpora constituted by nearly 9 millions of functions .
Edit intent: meaning-changed
Original:  That is we define  problem, we provide reasonable justifications for the choice that we made during our designing of the training and test steps and we performed a statistical analysis of function names in a large real-world corpora of over 4 millions of functions  .
Revised:  That is we define problem, we provide reasonable justifications for the choice that we made during our designing of the training and test steps and we performed a statistical analysis of function names in a large real-world corpora of over 4 millions of functions taken from more than 22k softwares .
Edit intent: meaning-changed
Original:  In such framework we test several baselines coming from the field of NLP .
Revised:  In such framework we test several baselines coming from the field of Natural Language Processing (e.g., Seq2Seq networks and Transformer). Interestingly, our evaluation shows promising results beating the state-of-the-art and reaching good performance. We investigate the applicability of tine-tuning (i.e., taking a model already trained on a large generic corpora and retraining it for a specific task). Such technique is popular and well-known in the NLP field. Our results confirm that fine-tuning is effective even when neural networks are applied to binaries. We show that a model, pre-trained on the aforementioned corpora, when fine-tuned has higher performances on specific domains (such as predicting names in system utilites, malware, etc) .
Edit intent: meaning-changed
Original:  Based on the local context focus (LCF) mechanism, this paper firstly proposes a multi-task learning model for Chineseoriented aspect-based sentiment analysis, namely LCF-ATEPC.
Revised:  Based on the local context focus (LCF) mechanism, this paper firstly proposes a multi-task learning model for Chinese-oriented aspect-based sentiment analysis, namely LCF-ATEPC.
Edit intent: fluency
Original:  By integrating the domain-adapted BERT model, the LCF-ATEPC model achieved the state-ofthe-art performance of aspect term extraction and aspect polarity classification in four Chinese review datasets.
Revised:  By integrating the domain-adapted BERT model, the LCF-ATEPC model achieved the state-of-the-art performance of aspect term extraction and aspect polarity classification in four Chinese review datasets.
Edit intent: fluency
Original:  Besides, the experimental results on the most commonly used SemEval-2014 task4 Restaurant and Laptop datasets outperform the state-of-the-art performance on the ATE  subtask.
Revised:  Besides, the experimental results on the most commonly used SemEval-2014 task4 Restaurant and Laptop datasets outperform the state-of-the-art performance on the ATE and APC subtask.
Edit intent: meaning-changed
Original:  In this work, a new methodology for fast detection and segmentation of GBM on FLAIR images is presented. The methodology leverages topological data analysis, textural features and  interpretable machine learning algorithm , it was evaluated on a public available dataset .
Revised:  In this work, a new methodology for fast detection and segmentation of GBM on FLAIR images is proposed. The framework can be used both for an initial detection of GBM and in case for its segmentation. The novelty of the methodology is the combination of new topological features computed by topological data analysis, textural features and interpretable machine learning algorithm , it was evaluated on a public available dataset .
Edit intent: meaning-changed
Original:  The methodology leverages topological data analysis, textural features and  interpretable machine learning algorithm , it was evaluated on a public available dataset . The machine learning classifier uses only eight input numerical features and it reaches up to the 97\% of accuracy on the detection task and up to 95\% of accuracy on the segmentation task .
Revised:  The methodology leverages topological data analysis, textural features and interpretable machine learning algorithm , it was evaluated on a public available dataset and it reaches up to the 97\% of accuracy on the detection task and up to 95\% of accuracy on the segmentation task .
Edit intent: coherence
Original:  The machine learning classifier uses only eight input numerical features and it reaches up to the 97\% of accuracy on the detection task and up to 95\% of accuracy on the segmentation task . Tools from information theory were used for interpreting, in a human readable format, what are the main numerical characteristics of an image to be classified ill or healthy .
Revised:  The machine learning classifier uses only eight input numerical features and it reaches up to the 97\% of accuracy on the detection task and up to 95\% of accuracy on the segmentation task .
Edit intent: coherence
Original: Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumor , it tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumors . Usually, GBMs are detected by magnetic resonance images (MRI).
Revised: Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumour , it tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumors . Usually, GBMs are detected by magnetic resonance images (MRI).
Edit intent: fluency
Original: Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumor , it tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumors . Usually, GBMs are detected by magnetic resonance images (MRI).
Revised: Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumor , it tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumours . Usually, GBMs are detected by magnetic resonance images (MRI).
Edit intent: fluency
Original:  In this work, a new framework for radiomics analysis of GBM on FLAIR images is proposed. The framework can be used both for an initial detection of GBM and in case for its segmentation. The novelty of the methodology is the combination of new topological features computed by topological data analysis , textural features and of automatic interpretable machine learning algorithm.
Revised:  In the present investigation, we intend to demonstrate by means of numerical experiments that topological features combined with textural features can be enrolled for GBM analysis and morphological characterization on FLAIR. To this extent, we have performed three numerical experiments. In the first experiment, Topological Data Analysis (TDA) of a simplified 2D tumour growth mathematical model had allowed to understand the bio-chemical conditions that facilitate tumour growth: the higher the concentration of chemical nutrients the more virulent the process. In the second experiment topological data analysis was used for evaluating GBM temporal progression on FLAIR recorded within 90 days following treatment (e.g., chemo-radiation therapy - CRT) completion and at progression. The experiment had confirmed that persistent entropy is a viable statistics for monitoring GBM evolution during the follow-up period. In the third experiment we had developed a novel methodology based on topological and textural features and of automatic interpretable machine learning algorithm.
Edit intent: meaning-changed
Original:  The novelty of the methodology is the combination of new topological features computed by topological data analysis , textural features and of automatic interpretable machine learning algorithm. The framework was evaluated on a public available dataset and it reaches up to the 97\% of accuracy on the detection task and up to 95\% of accuracy on the segmentation task .
Revised:  The novelty of the methodology is the combination of new topological features computed by topological data analysis , textural features and of automatic interpretable machine learning for automatic GBM classification on FLAIR. The algorithm reached a classification accuracy up to the 97\% of accuracy on the detection task and up to 95\% of accuracy on the segmentation task .
Edit intent: meaning-changed
Original:  In particular, the probability distribution of patch sizes can be fitted by a power law, i.e. vegetation patches are approximately scale free  . Different explanatory mechanisms, such as plant-plant interactions and plant-water feedback loops have been proposed to rationalize the emergence of such scale-free patterns, yet a full understanding has not been reached.
Revised:  In particular, the probability distribution of patch sizes can be fitted by a power law, i.e. vegetation patches are approximately scale free up to some maximum size . Different explanatory mechanisms, such as plant-plant interactions and plant-water feedback loops have been proposed to rationalize the emergence of such scale-free patterns, yet a full understanding has not been reached.
Edit intent: meaning-changed
Original:  Using a simple model for vegetation dynamics, we show that environmental temporal variability --a well-recognized feature of semiarid environments-- promotes in a robust way (i.e. for a broad range of parameter values) the emergence of scale-free vegetation patches .
Revised:  Using a simple model for vegetation dynamics, we show that environmental temporal variability -- a well-recognized feature of semiarid environments-- promotes in a robust way (i.e. for a broad range of parameter values) the emergence of scale-free vegetation patches .
Edit intent: fluency
Original:  Using a simple model for vegetation dynamics, we show that environmental temporal variability --a well-recognized feature of semiarid environments-- promotes in a robust way (i.e. for a broad range of parameter values) the emergence of scale-free vegetation patches .
Revised:  Using a simple model for vegetation dynamics, we show that environmental temporal variability --a well-recognized feature of semiarid environments -- promotes in a robust way (i.e. for a broad range of parameter values) the emergence of scale-free vegetation patches .
Edit intent: fluency
Original:  Using a simple model for vegetation dynamics, we show that environmental temporal variability --a well-recognized feature of semiarid environments-- promotes in a robust way (i.e. for a broad range of parameter values) the emergence of scale-free vegetation patches .
Revised:  Using a simple model for vegetation dynamics, we show that environmental temporal variability --a well-recognized feature of semiarid environments-- promotes in a robust way (i.e. for a wide range of parameter values) the emergence of scale-free vegetation patches .
Edit intent: fluency
Original:  Using a simple model for vegetation dynamics, we show that environmental temporal variability --a well-recognized feature of semiarid environments-- promotes in a robust way (i.e. for a broad range of parameter values) the emergence of scale-free vegetation patches .
Revised:  Using a simple model for vegetation dynamics, we show that environmental temporal variability --a well-recognized feature of semiarid environments-- promotes in a robust way (i.e. for a broad range of parameter values) the emergence of vegetation patches with broadly distributed cluster sizes .
Edit intent: meaning-changed
Original:  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 scale-free vegetation patterns.
Revised:  The model also reveals that the 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 scale-free vegetation patterns.
Edit intent: clarity
Original:  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 scale-free vegetation patterns.
Revised:  The model also reveals that the emerging power-law exponents fitting the tails of the probability distributions depend on the overall vegetation-cover density, in agreement with empirical observations , suggesting that environmental variability plays a key role in the formation of scale-free vegetation patterns.
Edit intent: meaning-changed
Original:  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 scale-free 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 . This supports the idea that environmental variability plays a key role in the formation of scale-free vegetation patterns.
Edit intent: coherence
Original:  In particular, based on existing ultrasonic friction reduction models, we consider how the mechanical properties (density and Young's modulus) and thickness of commonly-used glass formulations affect TPaD performance, namely the relation between  friction reduction ability and  real power consumption.
Revised:  In particular, based on existing ultrasonic friction reduction models, we consider how the mechanical properties (density and Young's modulus) and thickness of commonly-used glass formulations affect TPaD performance, namely the relation between its friction reduction ability and real power consumption.
Edit intent: clarity
Original:  In particular, based on existing ultrasonic friction reduction models, we consider how the mechanical properties (density and Young's modulus) and thickness of commonly-used glass formulations affect TPaD performance, namely the relation between  friction reduction ability and  real power consumption.
Revised:  In particular, based on existing ultrasonic friction reduction models, we consider how the mechanical properties (density and Young's modulus) and thickness of commonly-used glass formulations affect TPaD performance, namely the relation between friction reduction ability and its real power consumption.
Edit intent: clarity
Original:  In addition, most of embedding-based EA methods either fuse different features on representation-level and generate unified entity embedding for alignment, which potentially causes information loss, or aggregate features on outcome-level with hand-tuned weights, which is not practical with increasing numbers of features. To tackle these deficiencies , we propose a collective embedding-based EA frameworkwith adaptive feature fusion mechanism .
Revised:  To fill this gap , we propose a collective embedding-based EA frameworkwith adaptive feature fusion mechanism .
Edit intent: clarity
Original:  These features are then integrated at outcome-level, with dynamically assigned weights generated by our carefully devised adaptive feature fusion strategy. Eventually, in order to make collective EA decisions, we formulate EA as the classical stable matching problem between entities to be aligned, with preference lists constructed using fused feature matrix.
Revised:  In order to make collective EA decisions, we formulate EA as the classical stable matching problem between entities to be aligned, with preference lists constructed using fused feature matrix.
Edit intent: coherence
Original:  Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority . We also perform ablation study to gain insights into framework modules .
Revised:  Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority .
Edit intent: coherence
Original:  The aim of this  paper is to highlight the major works that have been undertaken in the space of document-level machine translation before and after the neural revolution  so that researchers can recognise where we started from and which direction we are heading in.
Revised:  The aim of this survey paper is to highlight the major works that have been undertaken in the space of document-level machine translation before and after the neural revolution so that researchers can recognise where we started from and which direction we are heading in.
Edit intent: meaning-changed
Original:  The aim of this  paper is to highlight the major works that have been undertaken in the space of document-level machine translation before and after the neural revolution  so that researchers can recognise where we started from and which direction we are heading in.
Revised:  The aim of this paper is to highlight the major works that have been undertaken in the space of document-level machine translation after the neural revolution so that researchers can recognise where we started from and which direction we are heading in.
Edit intent: coherence
Original:  The aim of this  paper is to highlight the major works that have been undertaken in the space of document-level machine translation before and after the neural revolution  so that researchers can recognise where we started from and which direction we are heading in.
Revised:  The aim of this paper is to highlight the major works that have been undertaken in the space of document-level machine translation before and after the neural revolution , so that researchers can recognise where we started from and which direction we are heading in.
Edit intent: fluency
Original:  The aim of this  paper is to highlight the major works that have been undertaken in the space of document-level machine translation before and after the neural revolution  so that researchers can recognise where we started from and which direction we are heading in. When talking about the literature in statistical machine translation (SMT), we focus on works which have tried to improve the translation of specific discourse phenomena, while in neural machine translation (NMT), we focus on works which use the wider context explicitly .
Revised:  The aim of this paper is to highlight the major works that have been undertaken in the space of document-level machine translation before and after the neural revolution so that researchers can recognise the current state and future directions of this field. We provide URLanisation of the literature based on novelties in modelling and architectures as well as training and decoding strategies .
Edit intent: meaning-changed