wanyu/IteraTeR_v2 huggingface dataset:
Original:  First, Sub-GMN  uses graph representation learning to map nodes to node-level embedding , and then combines metric learning and attention mechanisms to model the relationship between matched nodes in the data graph and query graph.
Revised:  First, Sub-GMN uses graph representation learning to map nodes to node-level embedding . It then combines metric learning and attention mechanisms to model the relationship between matched nodes in the data graph and query graph.
Edit intent: coherence
Original:  Compared with\\%DIF > and 3.2\\% higher than  the previous GNNs-based method for subgraph matching task,  Sub-GMN can obtain the node-to-node matching relationships and allow varying the input composed of query graph and data graph in the test phase, while previous GNNs-based methods for subgraph matching task can only match a fixed and unchanged subgraph and cannot output the node-to-node matching relationships.
Revised:  Compared with\\%DIF > and 3.2\\% higher than the previous GNNs-based methods for subgraph matching task, Sub-GMN can obtain the node-to-node matching relationships and allow varying the input composed of query graph and data graph in the test phase, while previous GNNs-based methods for subgraph matching task can only match a fixed and unchanged subgraph and cannot output the node-to-node matching relationships.
Edit intent: fluency
Original:  Compared with\\%DIF > and 3.2\\% higher than  the previous GNNs-based method for subgraph matching task,  Sub-GMN can obtain the node-to-node matching relationships and allow varying the input composed of query graph and data graph in the test phase, while previous GNNs-based methods for subgraph matching task can only match a fixed and unchanged subgraph and cannot output the node-to-node matching relationships.
Revised:  Compared with\\%DIF > and 3.2\\% higher than the previous GNNs-based method for subgraph matching task, Sub-GMN allows varying query and data graphes in the test phase, while previous GNNs-based methods for subgraph matching task can only match a fixed and unchanged subgraph and cannot output the node-to-node matching relationships.
Edit intent: clarity
Original:  Compared with\\%DIF > and 3.2\\% higher than  the previous GNNs-based method for subgraph matching task,  Sub-GMN can obtain the node-to-node matching relationships and allow varying the input composed of query graph and data graph in the test phase, while previous GNNs-based methods for subgraph matching task can only match a fixed and unchanged subgraph and cannot output the node-to-node matching relationships.
Revised:  Compared with\\%DIF > and 3.2\\% higher than the previous GNNs-based method for subgraph matching task, Sub-GMN can obtain the node-to-node matching relationships and allow varying the input composed of query graph and data graph in the test /application stage, while most previous GNNs-based methods for subgraph matching task can only match a fixed and unchanged subgraph and cannot output the node-to-node matching relationships.
Edit intent: clarity
Original:  The first contribution is that Sub-GMN is the first learning based methods for subgraph matching task and can output node-to-node matching relationships.
Revised:  The first contribution is that Sub-GMN is that it can output a list of node-to-node matching relationships.
Edit intent: clarity
Original:  CCCG 2019 , who proposed a 2-approximation algorithm in O(m^{25}n) time.
Revised:  , who proposed a 2-approximation algorithm in O(m^{25}n) time.
Edit intent: coherence
Original:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{12}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Revised:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{12}}\rfloor) k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Edit intent: fluency
Original:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{12}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Revised:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{12}}\rfloor k}n\log k) time for a given 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Edit intent: clarity
Original:  The RTSWE is a non-canonical Hamiltonian partial differential equation (PDE) with the associated conserved quantities, i.e., Hamiltonian (energy), mass, buoyancy, and the total vorticity.
Edit intent: coherence
Original:  Discretization of the RTSWE in space with centered finite differences leads to Hamiltonian system of ordinary differential equations (ODEs) with linear and quadratic terms.
Revised:  Discretization of the RTSWE in space with centered finite differences leads to Hamiltonian system of ordinary differential equations with linear and quadratic terms.
Edit intent: clarity
Original:  In the parametric case, for both methods, we make use of the parameter dependency at the level of the PDE without interpolating between the reduced operators.
Revised:  In the parametric case, we make use of the parameter dependency at the level of the PDE without interpolating between the reduced operators.
Edit intent: clarity
Original:  Both reduced models are able to accurately re-predict the training data and capture much of the overall system behavior in the prediction period.
Revised:  Both reduced models are able to accurately predict the test and training data and capture much of the overall system behavior in the prediction period.
Edit intent: clarity
Original:  Both reduced models are able to accurately re-predict the training data and capture much of the overall system behavior in the prediction period.
Revised:  Both reduced models are able to accurately re-predict the training data and capture system behavior in the prediction period.
Edit intent: clarity
Original:  Audio samples can be found under the following link: URL
Revised:  Audio samples can be found under URL
Edit intent: clarity
Original: Classical distributed estimation scenarios typically assume timely and reliable exchange of information over the multi-agent network.
Revised: Classical distributed estimation scenarios typically assume timely and reliable exchanges of information over the multi-agent network.
Edit intent: fluency
Original: Classical distributed estimation scenarios typically assume timely and reliable exchange of information over the multi-agent network.
Revised: Classical distributed estimation scenarios typically assume timely and reliable exchange of information over the sensor network.
Edit intent: clarity
Original:  This paper, in contrast, considers single time-scale distributed estimation of (potentially) unstable full-rank dynamical systems via a multi-agent network subject to transmission time-delays.
Revised:  This paper, in contrast, considers single time-scale distributed estimation via a sensor network subject to transmission time-delays.
Edit intent: clarity
Original:  The agents only share their a-priori estimates with their in-neighbors over time-delayed transmission links.
Revised:  The sensors only share their a-priori estimates with their in-neighbors over time-delayed transmission links.
Edit intent: clarity
Original:  The agents only share their a-priori estimates with their in-neighbors over time-delayed transmission links.
Revised:  The agents only share their a-priori estimates with their out-neighbors over (possibly) time-delayed transmission links.
Edit intent: clarity
Original:  Considering the most general case, the delays are assumed to be time-varying, arbitrary, unknown, but upper-bounded.
Revised:  The delays are assumed to be time-varying, arbitrary, unknown, but upper-bounded.
Edit intent: coherence
Original:  This may significantly reduce the communication/sensing loads on agents in large-scale, while making the (distributed) observability analysis more challenging .
Revised:  This may significantly reduce the communication/sensing loads on sensors .
Edit intent: clarity
Original:  Using the notions of augmented matrices and Kronecker product, the geometric convergence of the proposed estimator over strongly-connected networks is proved irrespective of the bound on the time-delay .
Revised:  Using the notions of augmented matrices and Kronecker product, the convergence of the proposed estimator over strongly-connected networks is proved irrespective of the bound on the time-delay .
Edit intent: clarity
Original: The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely wide neural nets trained under least squares loss by gradient descent (Jacot et al. , 2018).
Revised: The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely wide neural nets trained under least squares loss by gradient descent .
Edit intent: clarity
Original:  We show that in standard large-scale regression and classification tasks a linear regressor trained on our features outperforms trained NNs and Nystrom method with NTK kernels .
Revised:  We show that in standard large-scale regression and classification tasks a linear regressor trained on our features outperforms trained Neural Nets and Nystrom approximation of NTK kernel .
Edit intent: clarity
Original:  This formulation targets the minimization of power consumption by integrating base-station activation and many-to-many associations into the same mixed-integer nonlinear programming (MINLP) problem .
Revised:  This formulation targets the minimization of power consumption by integrating base-station activation and many-to-many associations while minimizing power consumption .
Edit intent: clarity
Original:  We give computational results in scenarios involving both macrocells and picocells . Some results align well with what is expected, but others are unexpected , pointing essentially to the need to better understand the role of macrocells in such networks .
Revised:  We give computational results in scenarios involving both macrocells and picocells often align with what is expected, but others are unexpected , pointing essentially to the need to better understand the role of macrocells in such networks .
Edit intent: clarity
Original:  We give computational results in scenarios involving both macrocells and picocells . Some results align well with what is expected, but others are unexpected , pointing essentially to the need to better understand the role of macrocells in such networks .
Revised:  We give computational results in scenarios involving both macrocells and picocells . Some results align well with what is expected, but sometimes are unexpected and essentially point to the need to better understand the role of macrocells in such networks .
Edit intent: coherence
Original:  We give computational results in scenarios involving both macrocells and picocells . Some results align well with what is expected, but others are unexpected , pointing essentially to the need to better understand the role of macrocells in such networks .
Revised:  We give computational results in scenarios involving both macrocells and picocells . Some results align well with what is expected, but others are unexpected , pointing essentially to the need to better understand the role of macrocells in helping provide capacity while remaining energetically advantageous .
Edit intent: clarity
Original:  Here, we develop a novel agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal preventive testing strategiesto curb this spread .
Revised:  Here, we develop a novel agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal prevention strategies .
Edit intent: clarity
Original:  The model is microscopically calibrated to high-resolution data from actual nursing homes in Austria, including the detailed networks of social contacts of their residents and information on past outbreaks.
Revised:  The model is microscopically calibrated to high-resolution data from nursing homes in Austria, including the detailed networks of social contacts of their residents and information on past outbreaks.
Edit intent: clarity
Original: Due to its high lethality amongst the elderly, nursing homes are in the eye of the COVID-19 storm .
Revised: Due to its high lethality amongst the elderly, the safety of nursing homes has been of central importance during the COVID-19 storm .
Edit intent: clarity
Original:  Here, we develop a novel agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal prevention strategies.
Revised:  Here, we develop a detailed agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal prevention strategies.
Edit intent: clarity
Original:  The model is microscopically calibrated to high-resolution data from nursing homes in Austria, including the detailed networks of social contacts of their residents and information on past outbreaks.
Revised:  The model is microscopically calibrated to high-resolution data from nursing homes in Austria, including detailed social contact networks and information on past outbreaks.
Edit intent: clarity
Original:  Our approach can produce a distribution over  future trajectories for all agents under consideration , or focus upon the trajectory of an ego-agent.
Revised:  Our approach can produce a distribution over the future trajectories for all agents under consideration , or focus upon the trajectory of an ego-agent.
Edit intent: fluency
Original: Individuals infected with SARS-CoV-2, the virus that causes COVID-19, may shed the virus in stool before developing symptoms, suggesting that measurements of SARS-CoV-2 concentrations in wastewater could be a "leading indicator" of COVID-19 disease activity .
Revised: Individuals infected with SARS-CoV-2, the virus that causes COVID-19, may shed the virus in stool before developing symptoms, suggesting that measurements of SARS-CoV-2 concentrations in wastewater could be a "leading indicator" of COVID-19 prevalence .
Edit intent: clarity
Original: We consider discrete Schr\"odinger operators with aperiodic potentials given by a Sturmian word, which is a natural generalisation of the Fibonacci-Hamiltonian .
Revised: We consider discrete Schr\"odinger operators with aperiodic potentials given by a Sturmian word, which is a natural generalisation of the Fibonacci Hamiltonian .
Edit intent: fluency
Original: We revisit the two-player planar target-defense game posed in 1%DIFDELCMD < ]%%% , a special class of pursuit-evasion games in which the pursuer (or defender) strives to defend a stationary target area from the evader (or intruder)who desires to reach it, if possible, or approach it as close as possible.
Revised: We revisit the two-player planar target-defense game 1%DIFDELCMD < ]%%% , a special class of pursuit-evasion games in which the pursuer (or defender) strives to defend a stationary target area from the evader (or intruder)who desires to reach it, if possible, or approach it as close as possible.
Edit intent: clarity
Original: We revisit the two-player planar target-defense game posed in 1%DIFDELCMD < ]%%% , a special class of pursuit-evasion games in which the pursuer (or defender) strives to defend a stationary target area from the evader (or intruder)who desires to reach it, if possible, or approach it as close as possible.
Revised: We revisit the two-player planar target-defense game posed in %DIFDELCMD < ]%%% , a special class of pursuit-evasion games in which the pursuer (or defender) strives to defend a stationary target area from the evader (or intruder)who desires to reach it, if possible, or approach it as close as possible.
Edit intent: clarity
Original: We revisit the two-player planar target-defense game posed in 1%DIFDELCMD < ]%%% , a special class of pursuit-evasion games in which the pursuer (or defender) strives to defend a stationary target area from the evader (or intruder)who desires to reach it, if possible, or approach it as close as possible.
Revised: We revisit the two-player planar target-defense game posed in 1%DIFDELCMD < ]%%% , a special class of pursuit-evasion games in which the pursuer (or defender) attempts to guard a target set from an attack by an evader (or intruder)who desires to reach it, if possible, or approach it as close as possible.
Edit intent: clarity
Original: A key question concerning collective decisions is whether a collective decision-making system can settle on the best available option when some members learn socially instead of evaluating the options on their own.
Revised: A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn socially instead of evaluating the options on their own.
Edit intent: clarity
Original: A key question concerning collective decisions is whether a collective decision-making system can settle on the best available option when some members learn socially instead of evaluating the options on their own.
Revised: A key question concerning collective decisions is whether a collective decision-making system can settle on the best available option when some members learn from others instead of evaluating the options on their own.
Edit intent: clarity
Original: In this paper, we introduce the problem of unitarization.
Revised: We introduce the problem of unitarization.
Edit intent: clarity
Original:  But ancilla qubits must always be returned to 0 for ANY input, and we are only guaranteed the input circuits returns ancilla qubits to 0 on the all 0 input.
Revised:  But ancilla qubits must always be returned to 0 for any input. The input circuits may use ancilla qubits, but we are only guaranteed the input circuits returns ancilla qubits to 0 on the all 0 input.
Edit intent: clarity
Original:  But ancilla qubits must always be returned to 0 for ANY input, and we are only guaranteed the input circuits returns ancilla qubits to 0 on the all 0 input.
Revised:  But ancilla qubits must always be returned to 0 for ANY input, and we are only guaranteed the they return ancilla qubits to 0 on the all 0 input.
Edit intent: clarity
Original:  The unitarization problem seems hard if the output states are neither orthogonal to or in the span of the computational basis vectors that need to map to them.
Revised:  The unitarization problem seems hard if the output states are neither orthogonal to or in the span of the computational basis states that need to map to them.
Edit intent: clarity
Original:  Take any k input unit vectors, (%DIFDELCMD < \lvert %%%  \psi_i \rangle)_{i\in [k]}  on polynomial in n qubits prepared by quantum oracles, (V_i)_{i \in [k]} , (that we can control call and control invert).
Revised:  Take any k input %DIFDELCMD < \lvert %%% \psi_i \rangle)_{i\in [k]} on polynomial in n qubits prepared by quantum oracles, (V_i)_{i \in [k]} , (that we can control call and control invert).
Edit intent: clarity
Original:  Take any k input unit vectors, (%DIFDELCMD < \lvert %%%  \psi_i \rangle)_{i\in [k]}  on polynomial in n qubits prepared by quantum oracles, (V_i)_{i \in [k]} , (that we can control call and control invert).
Revised:  Take any k input unit vectors, (%DIFDELCMD < \lvert %%% \psi_i \rangle)_{i\in [k]} , on polynomial in n qubits prepared by quantum oracles, (V_i)_{i \in [k]} , (that we can control call and control invert).
Edit intent: fluency
Original:  Take any k input unit vectors, (%DIFDELCMD < \lvert %%%  \psi_i \rangle)_{i\in [k]}  on polynomial in n qubits prepared by quantum oracles, (V_i)_{i \in [k]} , (that we can control call and control invert).
Revised:  Take any k input unit vectors, (%DIFDELCMD < \lvert %%% \psi_i \rangle)_{i\in [k]} on polynomial in n qubits prepared by quantum oracles, (V_i)_{i \in [k]} (that we can control call and control invert).
Edit intent: fluency
Original:  Then there is a quantum circuit with polynomial size in n and access to the oracles (V_i)_{i \in [k]} that with at least 1 - \epsilon probability, computes at most k circuits with size polynomial in n and oracle access to (V_i)_{i \in [k]} that \epsilon approximately computes an \epsilon approximate orthonormal basis for ( %DIFDELCMD < \lvert %%%  \psi_i \rangle)_{i\in [k]}.
Revised:  Then there is a quantum circuit with polynomial size in n with access to the oracles (V_i)_{i \in [k]} that with at least 1 - \epsilon probability, computes at most k circuits with size polynomial in n and oracle access to (V_i)_{i \in [k]} that \epsilon approximately computes an \epsilon approximate orthonormal basis for ( %DIFDELCMD < \lvert %%% \psi_i \rangle)_{i\in [k]}.
Edit intent: fluency
Original:  Then there is a quantum circuit with polynomial size in n and access to the oracles (V_i)_{i \in [k]} that with at least 1 - \epsilon probability, computes at most k circuits with size polynomial in n and oracle access to (V_i)_{i \in [k]} that \epsilon approximately computes an \epsilon approximate orthonormal basis for ( %DIFDELCMD < \lvert %%%  \psi_i \rangle)_{i\in [k]}.
Revised:  Then there is a quantum circuit with polynomial size in n and access to the oracles (V_i)_{i \in [k]} that with at least 1 - \epsilon probability, computes at most k circuits with size polynomial in n and oracle access to (V_i)_{i \in [k]} that \epsilon approximately computes an \epsilon approximate orthonormal basis for ( %DIFDELCMD < \lvert %%% | \psi_i \rangle)_{i\in [k]}.
Edit intent: clarity
Original:  It seems  to be used as a black box, while never mentioning how this model was derived as a special case of the seminal Kermack-McKendrick theory from 1927.
Revised:  It seems often to be used as a black box, while never mentioning how this model was derived as a special case of the seminal Kermack-McKendrick theory from 1927.
Edit intent: clarity
Original:  It seems  to be used as a black box, while never mentioning how this model was derived as a special case of the seminal Kermack-McKendrick theory from 1927.
Revised:  It seems to be used as a black box, not taking into account that this model was derived as a special case of the seminal Kermack-McKendrick theory from 1927.
Edit intent: clarity
Original:  We compare the adapted K-McK-model to the standard SIR model and observe big differences if the contact rates are changing .
Revised:  We compare the adapted K-McK-model to the standard SIR model and observe condiderable differences if the contact rates are changing .
Edit intent: clarity
Original:  We compare the adapted K-McK-model to the standard SIR model and observe big differences if the contact rates are changing .
Revised:  We compare the adapted K-McK-model to the standard SIR model and observe big differences if the contact rates are not constant .
Edit intent: clarity
Original:  In this work, our goal is instead to fill the gap between affordance discovery and affordance-based policy learning by integrating the two objectives in an end-to-end imitation learning framework based on deep neural networks.
Revised:  In this work, our goal is instead to fill the gap between affordance discovery and affordance-aware policy learning in an end-to-end imitation learning framework based on deep neural networks.
Edit intent: clarity
Original:  Therefore, with an end-to-end neural network, we propose to learn affordance cues as visual attention that serves as a useful indicating signal of how a demonstrator accomplishes tasks , instead of explicitly modeling affordances .
Revised:  Therefore, with an end-to-end neural network, we propose to learn affordance cues as visual attention that serves as a useful indicating signal of how a demonstrator accomplishes tasks .
Edit intent: clarity
Original:  Our experimental results demonstrate that our model with the coupled triplet loss achieves the highest grasping success rate in a simulated robot environment .
Revised:  Our experimental results demonstrate that our model with the coupled triplet loss achieves the highest grasping success rate .
Edit intent: clarity