wanyu/IteraTeR_v2 huggingface dataset:
Original:  A large class of neuro-symbolic approaches is based on First-Order Logic to represent prior knowledge, relaxed to a differentiable form using fuzzy logic.
Revised:  A large class of neural-symbolic approaches is based on First-Order Logic to represent prior knowledge, relaxed to a differentiable form using fuzzy logic.
Edit intent: fluency
Original:  This paper shows that the loss function expressing these neuro-symbolic learning tasks can be unambiguously determined given the selection of a t-norm generator.
Revised:  This paper shows that the loss function expressing these neural-symbolic learning tasks can be unambiguously determined given the selection of a t-norm generator.
Edit intent: fluency
Original:  However, the proposed learning formulation extends the advantages of the cross-entropy loss to the general knowledge that can be represented by a neuro-symbolic method.
Revised:  However, the proposed learning formulation extends the advantages of the cross-entropy loss to the general knowledge that can be represented by a neural-symbolic method.
Edit intent: coherence
Original: We investigate a nonlinear version of coevolving voter models, in which both node states and network structure update as a coupled stochastic dynamical process.
Revised: We investigate a nonlinear version of coevolving voter models, in which node states and network structure update as a coupled stochastic dynamical process.
Edit intent: clarity
Original:  We compare our nonlinear coevolving model to several existing linear models, and we find in our model that initial network topology can play a larger role in the dynamics , whereas the choice of rewiring mechanism plays a smaller role.
Revised:  We compare our nonlinear coevolving model to several existing linear models, and we find in our model that initial network topology plays a larger role in the dynamics , whereas the choice of rewiring mechanism plays a smaller role.
Edit intent: clarity
Original:  We compare our nonlinear coevolving model to several existing linear models, and we find in our model that initial network topology can play a larger role in the dynamics , whereas the choice of rewiring mechanism plays a smaller role.
Revised:  We compare our nonlinear coevolving model to several existing linear models, and we find in our model that initial network topology can play a larger role in the dynamics and the choice of rewiring mechanism plays a smaller role.
Edit intent: coherence
Original:  A particularly interesting feature of our model is that, under certain conditions, the opinion state that is initially held by a minority of  nodes can effectively spread to almost every node in a network if the minority nodes views themselves as the majority.
Revised:  A particularly interesting feature of our model is that, under certain conditions, the opinion state that is held initially by a minority of nodes can effectively spread to almost every node in a network if the minority nodes views themselves as the majority.
Edit intent: fluency
Original:  A particularly interesting feature of our model is that, under certain conditions, the opinion state that is initially held by a minority of  nodes can effectively spread to almost every node in a network if the minority nodes views themselves as the majority.
Revised:  A particularly interesting feature of our model is that, under certain conditions, the opinion state that is initially held by a minority of the nodes can effectively spread to almost every node in a network if the minority nodes views themselves as the majority.
Edit intent: fluency
Original:  A particularly interesting feature of our model is that, under certain conditions, the opinion state that is initially held by a minority of  nodes can effectively spread to almost every node in a network if the minority nodes views themselves as the majority.
Revised:  A particularly interesting feature of our model is that, under certain conditions, the opinion state that is initially held by a minority of nodes can effectively spread to almost every node in a network if the minority nodes view themselves as the majority.
Edit intent: fluency
Original:  SDPC also provides protection against privacy leakage.
Revised:  Moreover, SDPC naming scheme provides protection against privacy leakage.
Edit intent: clarity
Original: The digitization of archaeological artefacts has become an essential part of cultural heritage research be it for purposes of preservation or restoration  . Statues, in particular, have been at the center of many projects.
Revised: The digitization of archaeological artefacts has become an essential part of cultural heritage research for preservation or restoration . Statues, in particular, have been at the center of many projects.
Edit intent: coherence
Original:  Our method performs a Forward And bacKward Iterative Registration (FAKIR) which proceeds joint by joint, needing only a few iterations to converge.
Revised:  Our approach performs a Forward And bacKward Iterative Registration (FAKIR) which proceeds joint by joint, needing only a few iterations to converge.
Edit intent: clarity
Original:  By combining FAKIR with our skinning method we are able to detect the pose and the elementary anatomy of a sculpture and modify it, paving the way for pose-independent style comparison and statue restoration by combination of parts belonging to statues with different poses .
Revised:  By combining FAKIR with our skinning method we are able to detect the pose and elementary anatomy of a sculpture and modify it, paving the way for pose-independent style comparison and statue restoration by combination of parts belonging to statues with different poses .
Edit intent: fluency
Original: Facial image manipulation has achieved great progresses in recent years.
Revised: Facial image manipulation has achieved great progress in recent years.
Edit intent: fluency
Original:  MaskGAN has two main components: 1) Dense Mapping Network , and 2) Editing Behavior Simulated Training  . Specifically, Dense mapping network learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results.
Revised:  MaskGAN has two main components: 1) Dense Mapping Network (DMN) and 2) Editing Behavior Simulated Training . Specifically, Dense mapping network learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results.
Edit intent: clarity
Original:  MaskGAN has two main components: 1) Dense Mapping Network , and 2) Editing Behavior Simulated Training  . Specifically, Dense mapping network learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results.
Revised:  MaskGAN has two main components: 1) Dense Mapping Network , and 2) Editing Behavior Simulated Training . Specifically, DMN learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results.
Edit intent: clarity
Original:  Editing behavior simulated training models the user editing behavior on the source mask, making the overall framework more robust to various manipulated inputs.
Revised:  EBST models the user editing behavior on the source mask, making the overall framework more robust to various manipulated inputs.
Edit intent: clarity
Original:  The code, models  and dataset are available at URL
Revised:  The code, models , and dataset are available at URL
Edit intent: fluency
Original: We present an overlay design\Pe  called Sparse Robust Addressable Network (Spartan) that can tolerate heavy adversarial churn.
Revised: A Peer-to-Peer (P2P) network is a dynamic collection of nodes that connect with each other via virtual overlay links built upon an underlying network (usually, the Internet). P2P networks are highly dynamic and can experience very heavy churn, i.e., a large number of nodes join/leave the network continuously. Thus, building and maintaining a stable overlay network is an important problem that has been studied extensively for two decades. In this paper, we present our\Pe called Sparse Robust Addressable Network (Spartan) that can tolerate heavy adversarial churn.
Edit intent: coherence
Original:  We show that Spartan can be built efficiently in a fully distributed manner within O(\log n) rounds.
Revised:  We show that Spartan can be quickly and efficiently built in a fully distributed manner within O(\log n) rounds.
Edit intent: clarity
Original:  We show that Spartan can be built efficiently in a fully distributed manner within O(\log n) rounds.
Revised:  We show that Spartan can be built efficiently in a fully distributed fashion within O(\log n) rounds.
Edit intent: fluency
Original:  When the number of nodes in the network lies in [n, fn] for any fixed f\ge 1 the adversary can remove up to \epsilon n nodes and add up to \epsilon n nodes (for some small but fixed \epsilon > 0)  within {\em any} period of P rounds for some P \in O(\log \log n).
Revised:  When the number of nodes in the network lies in [n, fn] for any fixed f\ge 1. Up to \epsilon n nodes and add up to \epsilon n nodes (for some small but fixed \epsilon > 0) within {\em any} period of P rounds for some P \in O(\log \log n).
Edit intent: clarity
Original:  When the number of nodes in the network lies in [n, fn] for any fixed f\ge 1 the adversary can remove up to \epsilon n nodes and add up to \epsilon n nodes (for some small but fixed \epsilon > 0)  within {\em any} period of P rounds for some P \in O(\log \log n).
Revised:  When the number of nodes in the network lies in [n, fn] for any fixed f\ge 1 the adversary can remove up to \epsilon n nodes and add up to \epsilon n nodes (for some small but fixed \epsilon > 0) can be adversarially added/deleted within {\em any} period of P rounds for some P \in O(\log \log n).
Edit intent: clarity
Original:  Moreover, the adversary can add or remove nodes from the network at will and without any forewarning.
Edit intent: coherence
Original: Gene regulatory networks (GRNs) are complex systems in which many genes mutually regulate their expressions for changing the cell state adaptively to the environmental conditions.
Revised: Gene regulatory networks (GRNs) are complex systems in which many genes regulate mutually to adapt the cell state adaptively to the environmental conditions.
Edit intent: clarity
Original: Gene regulatory networks (GRNs) are complex systems in which many genes mutually regulate their expressions for changing the cell state adaptively to the environmental conditions.
Revised: Gene regulatory networks (GRNs) are complex systems in which many genes mutually regulate their expressions for changing the cell state to environmental conditions.
Edit intent: clarity
Original:  Besides the functions , the GRNs utilized by living systems possess several kinds of robustness.
Revised:  In addition to function , the GRNs utilized by living systems possess several kinds of robustness.
Edit intent: fluency
Original:  Besides the functions , the GRNs utilized by living systems possess several kinds of robustness.
Revised:  Besides the functions , the GRNs possess several kinds of robustness.
Edit intent: clarity
Original:  Here, the robustness means that the GRNs do not lose their functions when exposed to mutation or noises.
Revised:  This robustness means that the GRNs do not lose their functions when exposed to mutation or noises.
Edit intent: coherence
Original:  Here, the robustness means that the GRNs do not lose their functions when exposed to mutation or noises.
Revised:  Here, the robustness means that systems do not lose their functions when exposed to mutation or noises.
Edit intent: clarity
Original:  Here, the robustness means that the GRNs do not lose their functions when exposed to mutation or noises.
Revised:  Here, the robustness means that the GRNs do not lose their functionality when exposed to mutation or noises.
Edit intent: fluency
Original:  Both the adaptive response and the robustness have been acquired through the evolution.
Revised:  Both the adaptive response and the robustness have been acquired through evolution.
Edit intent: fluency
Original:  We consider a toy model of GRNs having one input gene and one output gene.
Revised:  We consider a toy model of GRNs with one input gene and one output gene.
Edit intent: fluency
Original:  The difference in the expression levels between the input states "on" and "off" is taken as the fitness.
Revised:  The difference in the expression levels between the input states "on" and "off" , was considered as fitness.
Edit intent: clarity
Original:  Thus the more sensitively a GRN responds to the input , the fitter it is.
Revised:  Thus , the determination of the fitness of a GRN was based on how sensitively it responded to the input , the fitter it is.
Edit intent: clarity
Original:  Thus they exhibit bistability , which necessarily emerges as the fitness becomes high.
Edit intent: coherence
Original:  (2) They are robust against noises thanks to the bistability.
Revised:  (2) These highly-fit GRNs were robust against noise because of their bistability. In other words, noise robustness is a byproduct of high fitness.
Edit intent: clarity
Original:  (3) Many GRNs in the fittest ensemble are robust against mutation.
Revised:  (3) GRNs that were robust against mutations were not extremely rare among the highly-fit GRNs. This implies that mutational robustness is readily acquired through the evolutionary process.
Edit intent: clarity
Original:  These properties are universal irrespective of the evolutionary pathway, because we did not perform evolutionary simulations .
Revised:  These properties are universal irrespective of the evolutionary pathway, because the results do not rely on evolutionary simulation .
Edit intent: clarity
Original:  It has been studied for nearly half a century and has  rich history of bridging techniques from data structures, graph algorithms, and direct methods in scientific computing.
Revised:  It has been studied for nearly half a century and has a rich history of bridging techniques from data structures, graph algorithms, and direct methods in scientific computing.
Edit intent: fluency
Original:  It has been studied for nearly half a century and has  rich history of bridging techniques from data structures, graph algorithms, and direct methods in scientific computing.
Revised:  It has been studied for nearly half a century and has rich history of bridging techniques from data structures, graph algorithms, and scientific computing.
Edit intent: clarity
Original:  We present a simple but novel combinatorial algorithm for computing  minimum degree elimination orderings in O(nm) time that relies on a careful amortized analysis  .
Revised:  In this paper, we present a simple but novel combinatorial algorithm for computing minimum degree elimination orderings in O(nm) time that relies on a careful amortized analysis .
Edit intent: clarity
Original:  We present a simple but novel combinatorial algorithm for computing  minimum degree elimination orderings in O(nm) time that relies on a careful amortized analysis  .
Revised:  We present a simple but novel combinatorial algorithm for computing minimum degree elimination ordering in O(nm) time that relies on a careful amortized analysis .
Edit intent: fluency
Original:  Furthermore, we show that there cannot exist an algorithm for this problem that runs in O(nm^{1-\varepsilon}) time, for any \varepsilon > 0, assuming the strong exponential time hypothesis  .
Revised:  Furthermore, we show there cannot exist an algorithm for this problem that runs in O(nm^{1-\varepsilon}) time, for any \varepsilon > 0, assuming the strong exponential time hypothesis .
Edit intent: fluency
Original:  If the basis also has product form, then the algorithms in 4 have computational complexity linear in d and m.
Revised:  If the basis also has product form, then 4 have computational complexity linear in d and m.
Edit intent: clarity
Original:  If the basis also has product form, then the algorithms in 4 have computational complexity linear in d and m.
Revised:  If the basis also has product form, then the algorithms in these algorithms have computational complexity linear in d and m.
Edit intent: clarity
Original:  In this paper we show that, when \Omega is an irregular domain such that the analytic form of an L^2(\Omega)-orthonormal basis in not available, stable and quasi-optimally weighted least-squares estimators can still be constructed from V_n, again with m of the order n \log n, but using a suitable surrogate basis of V_n orthonormal in a discrete sense.
Revised:  In this paper we show that, when \Omega is an irregular domain such that the analytic form of an L^2(\Omega)-orthonormal basis is not available, stable and quasi-optimally weighted least-squares estimators can still be constructed from V_n, again with m of the order n \log n, but using a suitable surrogate basis of V_n orthonormal in a discrete sense.
Edit intent: fluency
Original:  This paper proposes an innovative ``sequential waterfilling'' strategy for assigning Spreading Factors (SF) to end-devices .
Revised:  This paper proposes an innovative "sequential waterfilling" strategy for assigning Spreading Factors (SF) to end-devices .
Edit intent: fluency
Original:  Our design relies on three complementary approaches: i) equalize the Time-on-Air of the packets transmitted by the system's end-devices in each spreading factor's group;
Revised:  Our design relies on three complementary approaches: i) equalize the Time-on-Air of the packets transmitted by the system's end-devices in each spreading factor's group;
Edit intent: fluency
Original:  Our design relies on three complementary approaches: i) equalize the Time-on-Air of the packets transmitted by the system's end-devices in each spreading factor's group;
Revised:  Our design relies on three complementary approaches: i) equalize the Time-on-Air of the packets transmitted by the system's EDs in each spreading factor's group;
Edit intent: clarity
Original:  Extensive experiments on a dataset of 3,186,000 mobile users and real-world trajectories of 1701 km reveal that cellSim provides high accuracy (precision 62.4\% and recall of 89.8\%).
Revised:  Extensive experiments on a large-scale dataset and real-world trajectories of 1701 km reveal that cellSim provides high accuracy (precision 62.4\% and recall of 89.8\%).
Edit intent: clarity
Original: We present sql4ml , a system for expressing supervised machine learning (ML) models in SQL and automatically training them in TensorFlow.
Revised: We present sql4ml , a system for expressing supervised machine learning (ML) models in SQL and automatically training them in TensorFlow.
Edit intent: fluency
Original:  With sql4ml the user can express both feature engineering and ML algorithms in SQL, while the system translates this code to an appropriate representation for training inside a machine learning framework.
Revised:  With sql4ml the user can express both feature engineering and ML algorithms in SQL, while the system translates this code to an appropriate representation for training inside a machine learning framework.
Edit intent: fluency
Original: The maximum density subgraph problem, introduced in the 80s by Picard and Queyranne as well as Goldberg, is a classic problem in combinatorial optimization with a wide range of applications.
Revised: The densest subgraph problem, introduced in the 80s by Picard and Queyranne as well as Goldberg, is a classic problem in combinatorial optimization with a wide range of applications.
Edit intent: clarity
Original:  Let D denote the density of the maximum density subgraph of G. We give the following results : Given a value D \leq D and 0 < \epsilon < 1, we show that a subgraph of density at least (1-\epsilon)D can be identified deterministically in O((\log n) / \epsilon) rounds in the LOCAL model.
Revised:  Let D denote the density of the maximum density subgraph of G. Our main results are as follows. Given a value D \leq D and 0 < \epsilon < 1, we show that a subgraph of density at least (1-\epsilon)D can be identified deterministically in O((\log n) / \epsilon) rounds in the LOCAL model.
Edit intent: clarity
Original:  Let D denote the density of the maximum density subgraph of G. We give the following results : Given a value D \leq D and 0 < \epsilon < 1, we show that a subgraph of density at least (1-\epsilon)D can be identified deterministically in O((\log n) / \epsilon) rounds in the LOCAL model.
Revised:  Let D denote the density of the maximum density subgraph of G. We give the following results : Given a value D \leq D and 0 < \epsilon < 1, we show that a subgraph with density at least (1-\epsilon)D can be identified deterministically in O((\log n) / \epsilon) rounds in the LOCAL model.
Edit intent: fluency
Original:  Using randomization , we show that such  subgraph can be identified in O((\log^3 n) / \epsilon^3) rounds in theCONGEST model with high probability.
Revised:  Using randomization , we show that such a subgraph can be identified in O((\log^3 n) / \epsilon^3) rounds in theCONGEST model with high probability.
Edit intent: fluency
Original:  Using randomization , we show that such  subgraph can be identified in O((\log^3 n) / \epsilon^3) rounds in theCONGEST model with high probability.
Revised:  Using randomization , we show that such subgraph can be identified in O((\log^3 n) / \epsilon^3) rounds CONGEST model with high probability.
Edit intent: fluency
Original:  Using randomization , we show that such  subgraph can be identified in O((\log^3 n) / \epsilon^3) rounds in theCONGEST model with high probability.
Revised:  Using randomization , we show that such subgraph can be identified in O((\log^3 n) / \epsilon^3) rounds in the model with high probability.
Edit intent: clarity
Original:  Using randomization , we show that such  subgraph can be identified in O((\log^3 n) / \epsilon^3) rounds in theCONGEST model with high probability.
Revised:  Using randomization , we show that such subgraph can be identified in O((\log^3 n) / \epsilon^3) rounds in theCONGEST with high probability.
Edit intent: clarity
Original:  We also give a \Omega(1 /\epsilon  )-round lower bound which shows that our result for theLOCAL model is tight up to a O( \log n) factor.
Revised:  We also give a \Omega(1 /\epsilon )-round LOCAL model is tight up to a O( \log n) factor.
Edit intent: clarity
Original:  Moreover, our result can be extended to solve the directed version of the problem introduced by Kannan and Vinay \mbox{ KV99 ]  Given an integer \tilde{D} \geq D and \Omega(1/\tilde{D}) < \epsilon < 1/4, we give an O(\tilde{O} \log^2 n \cdot (\log^{2.
Revised:  Moreover, our result can be extended to solve the directed version of the problem introduced by Kannan and Vinay \mbox{ KV99 ] Given an integer \tilde{D} \geq D and \Omega(1/\tilde{D}) < \epsilon < 1/4, we give an O(\tilde{O}(( \log^2 n \cdot (\log^{2.
Edit intent: fluency
Original: 71 \Delta} ) /\epsilon^2)-round deterministic algorithm in the \textsf{CONGEST model that computes an orientation where the outdegree of every vertex is upper bounded by (1+\epsilon)\tilde{D}.
Revised: 71 \Delta} ) /\epsilon^2)-round algorithm in the \textsf{CONGEST model that computes an orientation where the outdegree of every vertex is upper bounded by (1+\epsilon)\tilde{D}.
Edit intent: clarity
Original: 71 \Delta} ) /\epsilon^2)-round deterministic algorithm in the \textsf{CONGEST model that computes an orientation where the outdegree of every vertex is upper bounded by (1+\epsilon)\tilde{D}.
Revised: 71 \Delta} ) /\epsilon^2)-round deterministic algorithm in the \textsf{CONGEST model that computes an orientation where the outdegree of every vertex is upper bounded by (1+\epsilon)\tilde{D}.
Edit intent: fluency
Original:  Previously, the best deterministic algorithm for this problem is by Harris \mbox{ \cite{Harris19 }\hspace{0pt} that runs }  ]  in \tilde{O}((\log^6 n)/ \epsilon^4) rounds in the%DIFDELCMD < \local %%% \tilde{O} model.
Revised:  Previously, the best deterministic algorithm }\hspace{0pt} that runs } ] in \tilde{O}((\log^6 n)/ \epsilon^4) rounds in the%DIFDELCMD < \local %%% \tilde{O} model.
Edit intent: coherence