wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our results suggest that, first, for the cases when ordinary MCA depicts differences between groups, cMCA can further identify characteristics that divide the target group ;
Revised:  Our results suggest that, first, cMCA can identify substantively important dimensions and divisions among (sub)groups that are overlooked by traditional methods ;
Edit intent: clarity
Original:  second, for the caseswhen MCA does not show clear differences , cMCA can successfully identify meaningful directions and subgroups, which traditional methods overlook .
Revised:  second, for certain cases , cMCA can successfully identify meaningful directions and subgroups, which traditional methods overlook .
Edit intent: clarity
Original:  The current GCN models overwhelmingly assume that  node feature information is complete.
Revised:  The current GCN models overwhelmingly assume that the node feature information is complete.
Edit intent: fluency
Original:  We conduct experiments on the node label classification task and demonstrate that our approach significantly outperforms the best imputation based methods by up to 99.43\%, 102.96\%, 6.97\%, 35.36\% in four benchmark graphs when a large portion of features are missing.
Revised:  We demonstrate through extensive experiments that our approach significantly outperforms the best imputation based methods by up to 99.43\%, 102.96\%, 6.97\%, 35.36\% in four benchmark graphs when a large portion of features are missing.
Edit intent: clarity
Original:  At each level, numerical relaxation is used to stabilize the iterative process , and computing the initial guess by interpolating from the previous level accelerates the convergence .
Revised:  At each level, numerical relaxation is used to stabilize the iterative process .
Edit intent: coherence
Original:  A second-order discretization scheme is derived for higher order convergence.
Revised:  A second-order discretization scheme is derived for higher-order convergence.
Edit intent: fluency
Original:  The architecture leverages ordinary SMTP \mbox{ smtp for message delivery and Tor \mbox{ tor for peer-to-peer communication.
Revised:  The architecture leverages ordinary SMTP for message delivery and Tor \mbox{ tor for peer-to-peer communication.
Edit intent: clarity
Original:  The architecture leverages ordinary SMTP \mbox{ smtp for message delivery and Tor \mbox{ tor for peer-to-peer communication.
Revised:  The architecture leverages ordinary SMTP \mbox{ smtp for message delivery and Tor for peer-to-peer communication.
Edit intent: fluency
Original:  We show how the system can interoperate with existing email systems and paradigms, allowing users to hold messages that others can retrieve via IMAP \mbox{ imap or to operate as a relay between system participants and external email users.
Revised:  We show how the system can interoperate with existing email systems and paradigms, allowing users to hold messages that others can retrieve via IMAP or to operate as a relay between system participants and external email users.
Edit intent: coherence
Original:  We introduce the Liverpool 360 degree Street View (LIV360SV) dataset for evaluating our workflow.
Revised:  We introduce the Liverpool 360 Street View (LIV360SV) dataset for evaluating our workflow.
Edit intent: clarity
Original:  The dataset contains 26,645 , 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th -- 18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405) (e.g., cars and broadband).
Revised:  The dataset contains 26,645 , 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th - 18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405) (e.g., cars and broadband).
Edit intent: fluency
Original:  We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas , and those frequented by students and children carrying excess weight .
Revised:  We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas and those frequented by students and children carrying excess weight .
Edit intent: fluency
Original:  We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas , and those frequented by students and children carrying excess weight .
Revised:  We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas , and those frequented by students .
Edit intent: clarity
Original: For a (finite) partially ordered set (poset) P, we call a dominating set D in the comparability graph Comp(P) of P, an order-sensitive dominating set in P if either x\in D or else a<x<b in P for some a,b\in D for every element x in P which is neither maximal nor minimal, and denote by os(P), the least size of an order-sensitive dominating set of P.
Revised: For a (finite) partially ordered set (poset) P, we call a dominating set D in the comparability graph of P, an order-sensitive dominating set in P if either x\in D or else a<x<b in P for some a,b\in D for every element x in P which is neither maximal nor minimal, and denote by os(P), the least size of an order-sensitive dominating set of P.
Edit intent: clarity
Original:  The bottom-line is a simple policy prescription for institutions: distribute the total tests over several batches instead of using them all at once  .
Revised:  The bottom-line is a simple policy prescription for institutions: distribute your total tests over several batches instead of using them all at once .
Edit intent: style
Original: Data sparsity and cold-start issues emerge as two major bottlenecks for matrix completion in the context of user-item interaction matrix .
Revised: Recommendation models can effectively estimate underlying user interests and predict one's future behaviors by factorizing an observed user-item interaction matrix .
Edit intent: clarity
Original:  Moreover, extensive experiments on real-world datasets demonstrate that our model outperforms several state-of-the-art methods by achieving significant improvements on MAE and AUC for warm-start, few-shot (sparsity) and zero-shot (cold-start) recommendation .
Revised:  Moreover, extensive experiments on real-world datasets demonstrate that our model achieves promising results for recommendation on few-shot (sparsity) and zero-shot (cold-start) recommendation .
Edit intent: clarity
Original:  We address this shortcoming by grounding the motion planning problem in differential geometry.
Revised:  We address this shortcoming by grounding the solution in differential geometry.
Edit intent: clarity
Original: Machine-aided programming tools such as  type predictors and code summarizers are increasingly learning-based.
Revised: Machine-aided programming tools such as automated type predictors and code summarizers are increasingly learning-based.
Edit intent: clarity
Original: Machine-aided programming tools such as  type predictors and code summarizers are increasingly learning-based.
Revised: Machine-aided programming tools such as type predictors and autocomplete are increasingly learning-based.
Edit intent: clarity
Original:  However, most code representation learning approaches rely on supervised learning with task-specific annotated datasets.
Revised:  However, current approaches predominantly rely on supervised learning with task-specific annotated datasets.
Edit intent: clarity
Original:  However, most code representation learning approaches rely on supervised learning with task-specific annotated datasets.
Revised:  However, most code representation learning approaches rely on supervised learning with task-specific datasets.
Edit intent: clarity
Original:  Our approach uses no human-provided labels, relying only on the raw text of programs.
Revised:  Our approach uses no human-provided labels, only the raw text of programs.
Edit intent: clarity
Original:  We train a neural model to identify variants of an anchor program within a large batch of  negatives.
Revised:  We train a neural network to identify variants of an anchor program within a large batch of negatives.
Edit intent: clarity
Original:  We train a neural model to identify variants of an anchor program within a large batch of  negatives.
Revised:  We train a neural model to identify variants of anchor programs within a large batch of negatives.
Edit intent: fluency
Original:  To solve this task, the network must extract program features representing the functionality, not form, of the program.
Revised:  To solve this task, the network must extract features representing the functionality, not form, of the program.
Edit intent: fluency
Original:  We pre-train models over 1.8 m unannotated JavaScript methods mined from GitHub  . ContraCode pre-training improves code summarization accuracy by 7.9\% over supervised approaches and 4.8\%over RoBERTa pre-training.
Revised:  We pre-train ContraCode with 1.8 m unannotated JavaScript methods mined from GitHub . ContraCode pre-training improves code summarization accuracy by 7.9\% over supervised approaches and 4.8\%over RoBERTa pre-training.
Edit intent: clarity
Original:  We pre-train models over 1.8 m unannotated JavaScript methods mined from GitHub  . ContraCode pre-training improves code summarization accuracy by 7.9\% over supervised approaches and 4.8\%over RoBERTa pre-training.
Revised:  We pre-train models over 1.8 M unannotated JavaScript methods mined from GitHub . ContraCode pre-training improves code summarization accuracy by 7.9\% over supervised approaches and 4.8\%over RoBERTa pre-training.
Edit intent: fluency
Original:  We then train a neural network to identify variants of anchor programs within a large batch of non-equivalent negatives.
Revised:  We then train a neural network to identify functionally similar variants of a program among many non-equivalent negatives.
Edit intent: clarity
Original:  This work intended for presentation at the Maple Conference 2020.
Revised:  This work was presented at the Maple Conference 2020.
Edit intent: clarity
Original:  We also link this dynamic inversion to Gauss-Newton optimization, suggesting a biologically-plausible approximation to second-order learning .
Revised:  Finally, we discuss potential links between dynamic inversion and second-order learning .
Edit intent: clarity
Original:  We also link this dynamic inversion to Gauss-Newton optimization, suggesting a biologically-plausible approximation to second-order learning .
Revised:  We also link this dynamic inversion to Gauss-Newton optimization, suggesting a biologically-plausible approximation to second-order optimization .
Edit intent: clarity
Original:  In particular, four observation functions of different forms and various levels of complexity are examined in connection with the classic Susceptible-Infectious-Recovered (SIR)
Revised:  In particular, four observation functions of different forms in connection with the classic Susceptible-Infectious-Recovered (SIR)
Edit intent: clarity
Original:  However, typical self-attention models, like Transformer, lack prior knowledge to distinguish the importance of different tokens , which has been proved to play a critical role in information retrieval tasks.
Revised:  However, typical self-attention models, like Transformer, lack prior knowledge to distinguish the importance of different words , which has been proved to play a critical role in information retrieval tasks.
Edit intent: clarity
Original:  By leveraging BM25 as prior weights , BISON learns weighted attention scores jointly with query matrix Q and key matrix K.
Revised:  By leveraging BM25 , GLOW successfully learns weighted attention scores jointly with query matrix Q and key matrix K.
Edit intent: clarity
Original:  We demonstrate BISON is more efficient to capture the topical and semantic representation both in query and document .
Revised:  We demonstrate BISON is more efficient to capture the topical and semantic representation both in queries and documents .
Edit intent: fluency
Original:  Intrinsic evaluation and experiments conducted on public data sets reveal BISON to be a general framework for document ranking task.
Revised:  Intrinsic evaluation and experiments conducted on public data sets reveal BISON to be a general framework for document retrieve task.
Edit intent: clarity
Original:  Numerical case studies show that our cotranscriptional RNA folding model can predict the formation of metastable conformations that are favored in actual biological systems.
Revised:  Numerical case studies show that our cotranscriptional RNA folding model can predict the formation of conformations that are favored in actual biological systems.
Edit intent: clarity
Original:  While this problem is similar to the classical statistical problem of isotonic regression, we assume that the observational data arise from optimisation problems with partially controllable one-sided errors, and this setting alters several characteristics of the problem and opens natural algorithmic possibilities.
Revised:  While this problem is similar to the classical statistical problem of isotonic regression, the optimisation setting alters several characteristics of the problem and opens natural algorithmic possibilities.
Edit intent: coherence
Original:  We establish sufficient conditions for convergence of the reconstruction to the ground truth, and apply the method both to synthetic test cases and to a real-world example of uncertainty quantification for aerodynamic design.
Revised:  We establish sufficient conditions for convergence of the reconstruction to the ground truth, and apply the method to synthetic test cases and to a real-world example of uncertainty quantification for aerodynamic design.
Edit intent: clarity
Original:  We establish sufficient conditions for convergence of the reconstruction to the ground truth, and apply the method both to synthetic test cases and to a real-world example of uncertainty quantification for aerodynamic design.
Revised:  We establish sufficient conditions for convergence of the reconstruction to the ground truth, and apply the method both to synthetic test cases and a real-world example of uncertainty quantification for aerodynamic design.
Edit intent: fluency
Original:  Findings confirm the origin of inconsistencies at design phase and provide new insights how to tune self-healing mechanisms at design phase.
Revised:  Findings confirm the origin of inconsistencies at design phase and provide new insights how to tune self-healing at design phase.
Edit intent: clarity
Original:  Nevertheless, fault-tolerance alone without eventually correcting persistent faults makes systems underperformingas well .
Revised:  Nevertheless, fault-tolerance alone without eventually correcting persistent faults can make systems underperforming .
Edit intent: coherence
Original:  This paper introduces a novel and general-purpose modeling of fault scenarios .
Revised:  This paper contributes a novel and general-purpose modeling of fault scenarios .
Edit intent: clarity
Original:   The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network  aggregation using real-world data from a Smart Grid pilot project.
Revised:  The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network data aggregation using real-world data from a Smart Grid pilot project.
Edit intent: fluency
Original:  Findings confirm the origin of inconsistencies at design phase and provide new insights how to tune self-healing at design phase.
Revised:  Findings confirm the origin of inconsistencies at design phase.
Edit intent: coherence
Original:  At the mitigation stage of the epidemics in which several countries are now, the use of mobile apps that are able to trace contacts is of utmost importance in order to control new infected cases and contain further propagation.
Revised:  The use of mobile apps that are able to trace contacts is of utmost importance in order to control new infected cases and contain further propagation.
Edit intent: coherence
Original: We show how to define source-code transformations for forward- and reverse-mode Automatic Differentiation on a standard higher-order functional language.
Revised: We show how to define forward- and reverse-mode Automatic Differentiation on a standard higher-order functional language.
Edit intent: clarity
Original: Generalizing earlier works of Delbaen & Haezendonck [5] as well as  [18] and [16] for given compound mixed renewal process S under a probability measure P, we characterize all those probability measures Q on the domain of P such that Q and P are progressively equivalent and S remains a compound mixed renewal process under Q with improved properties.
Revised: Generalizing earlier works of Delbaen & Haezendonck [5] as well as of [18] and [16] for given compound mixed renewal process S under a probability measure P, we characterize all those probability measures Q on the domain of P such that Q and P are progressively equivalent and S remains a compound mixed renewal process under Q with improved properties.
Edit intent: clarity
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on I and that the output is the output node value x_o(I)
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostasis occurs frequently in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on I and that the output is the output node value x_o(I)
Edit intent: clarity
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on I and that the output is the output node value x_o(I)
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks and in other networks of interacting elements where mathematical models are based on differential equations associated to the network. These networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on I and that the output is the output node value x_o(I)
Edit intent: clarity
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on I and that the output is the output node value x_o(I)
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a distinguished input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on I and that the output is the output node value x_o(I)
Edit intent: fluency
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on I and that the output is the output node value x_o(I)
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota , a different distinguished output node o . We assume that only the input node depends explicitly on I and that the output is the output node value x_o(I)
Edit intent: fluency
Original:  . We then study infinitesimal homeostasis : points I_0 where dx_o{dI_0. Stability implies that there is a stable equilibrium X(I) for each I near I_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Revised:  is defined by a stable equilibrium X_0 at \mathcal{I_0. Stability implies that there is a stable equilibrium X(I) for each I near I_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Edit intent: clarity
Original:  . We then study infinitesimal homeostasis : points I_0 where dx_o{dI_0. Stability implies that there is a stable equilibrium X(I) for each I near I_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Revised:  . We then study infinitesimal homeostasis : points I_0 where dx_o{dI_0. Stability implies that there is a stable equilibrium X(I) for each I near I_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0. We show that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Edit intent: coherence