wanyu/IteraTeR_v2 huggingface dataset:
Original: - of the randomized greedy maximal matching algorithm, improving over the bound of Yoshida, Yamamoto and Ito STOC'09%DIFDELCMD < ]%%% . For any n-vertex graph of average degree d, this leads to the following sublinear-time algorithms for estimating the size of maximum matching and minimum vertex cover, all of which are provably time-optimal up to logarithmic factors: \bullet A multiplicative (2+\epsilon)-approximation in (n/\epsilon^2) time using adjacency list queries.
Revised: - of the randomized greedy maximal matching algorithm, improving over the bound of Yoshida, Yamamoto and Ito %DIFDELCMD < ]%%% . For any n-vertex graph of average degree d, this leads to the following sublinear-time algorithms for estimating the size of maximum matching and minimum vertex cover, all of which are provably time-optimal up to logarithmic factors: \bullet A multiplicative (2+\epsilon)-approximation in (n/\epsilon^2) time using adjacency list queries.
Edit intent: clarity
Original: - of the randomized greedy maximal matching algorithm, improving over the bound of Yoshida, Yamamoto and Ito STOC'09%DIFDELCMD < ]%%% . For any n-vertex graph of average degree d, this leads to the following sublinear-time algorithms for estimating the size of maximum matching and minimum vertex cover, all of which are provably time-optimal up to logarithmic factors: \bullet A multiplicative (2+\epsilon)-approximation in (n/\epsilon^2) time using adjacency list queries.
Revised: - of the randomized greedy maximal matching algorithm, improving over the bound of Yoshida, Yamamoto and Ito STOC'09%DIFDELCMD < ]%%% study the problem of estimating the size of maximum matching and minimum vertex cover in sublinear time. Denoting the number of vertices by n and the average degree in the graph by d, this leads to the following sublinear-time algorithms for estimating the size of maximum matching and minimum vertex cover, all of which are provably time-optimal up to logarithmic factors: \bullet A multiplicative (2+\epsilon)-approximation in (n/\epsilon^2) time using adjacency list queries.
Edit intent: clarity
Original: - of the randomized greedy maximal matching algorithm, improving over the bound of Yoshida, Yamamoto and Ito STOC'09%DIFDELCMD < ]%%% . For any n-vertex graph of average degree d, this leads to the following sublinear-time algorithms for estimating the size of maximum matching and minimum vertex cover, all of which are provably time-optimal up to logarithmic factors: \bullet A multiplicative (2+\epsilon)-approximation in (n/\epsilon^2) time using adjacency list queries.
Revised: - of the randomized greedy maximal matching algorithm, improving over the bound of Yoshida, Yamamoto and Ito STOC'09%DIFDELCMD < ]%%% . For any n-vertex graph of average degree d, we obtain the following results for both problems: * A multiplicative (2+\epsilon)-approximation in (n/\epsilon^2) time using adjacency list queries.
Edit intent: clarity
Original: - of the randomized greedy maximal matching algorithm, improving over the bound of Yoshida, Yamamoto and Ito STOC'09%DIFDELCMD < ]%%% . For any n-vertex graph of average degree d, this leads to the following sublinear-time algorithms for estimating the size of maximum matching and minimum vertex cover, all of which are provably time-optimal up to logarithmic factors: \bullet A multiplicative (2+\epsilon)-approximation in (n/\epsilon^2) time using adjacency list queries.
Revised: - of the randomized greedy maximal matching algorithm, improving over the bound of Yoshida, Yamamoto and Ito STOC'09%DIFDELCMD < ]%%% . For any n-vertex graph of average degree d, this leads to the following sublinear-time algorithms for estimating the size of maximum matching and minimum vertex cover, all of which are provably time-optimal up to logarithmic factors: \bullet A multiplicative (2+\epsilon)-approximation that takes (n/\epsilon^2) time using adjacency list queries.
Edit intent: clarity
Original:  This (nearly) matches an \Omega( +1) lower bound of Parnas and Ron%DIFDELCMD < [%%% TCS'07 which holds for any (O(1), \epsilon n)-approximation, and improves over the bounds of%DIFDELCMD < [%%% Yoshida et al.
Revised:  +1) lower bound of Parnas and Ron%DIFDELCMD < [%%% TCS'07 which holds for any (O(1), \epsilon n)-approximation, and improves over the bounds of%DIFDELCMD < [%%% Yoshida et al.
Edit intent: clarity
Original:  This (nearly) matches an \Omega( +1) lower bound of Parnas and Ron%DIFDELCMD < [%%% TCS'07 which holds for any (O(1), \epsilon n)-approximation, and improves over the bounds of%DIFDELCMD < [%%% Yoshida et al.
Revised:  This (nearly) matches an \Omega( %DIFDELCMD < [%%% TCS'07 which holds for any (O(1), \epsilon n)-approximation, and improves over the bounds of%DIFDELCMD < [%%% Yoshida et al.
Edit intent: clarity
Original:  This (nearly) matches an \Omega( +1) lower bound of Parnas and Ron%DIFDELCMD < [%%% TCS'07 which holds for any (O(1), \epsilon n)-approximation, and improves over the bounds of%DIFDELCMD < [%%% Yoshida et al.
Revised:  This (nearly) matches an \Omega( +1) lower bound of Parnas and Ron%DIFDELCMD < [%%% which holds for any (O(1), \epsilon n)-approximation, and improves over the bounds of%DIFDELCMD < [%%% Yoshida et al.
Edit intent: coherence
Original:  This (nearly) matches an \Omega( +1) lower bound of Parnas and Ron%DIFDELCMD < [%%% TCS'07 which holds for any (O(1), \epsilon n)-approximation, and improves over the bounds of%DIFDELCMD < [%%% Yoshida et al.
Revised:  This (nearly) matches an \Omega( +1) lower bound of Parnas and Ron%DIFDELCMD < [%%% TCS'07 %DIFDELCMD < [%%% Yoshida et al.
Edit intent: coherence
Original:  SODA'12 and%DIFDELCMD < [%%% Kapralov et al.
Revised:  SODA'12 %DIFDELCMD < [%%% Kapralov et al.
Edit intent: fluency
Original:  \bullet A (2, \epsilon n)-approximation in \tilde{O} (n/\epsilon ^3 ) time using adjacency matrix queries.
Revised:  \bullet A (2, \epsilon n)-approximation in \tilde{O} (n/\epsilon ^{3 ) time using adjacency matrix queries.
Edit intent: fluency
Original:  This (nearly) matches an \Omega(n) time lower bound in this model and improves over the%DIFDELCMD < \widetilde{O}%%% (n\sqrt{n)-time (2, \epsilon n)-approximate algorithm of }  [ Chen, Kannan, and Khanna ICALP'20 ] . It also turns out that any non-trivial multiplicative approximation in the adjacency matrix model requires \Omega(n^2) time, so the additive \epsilon n error is necessary too.
Revised:  %DIFDELCMD < \widetilde{O}%%% (n\sqrt{n)-time (2, \epsilon n)-approximate algorithm of } [ Chen, Kannan, and Khanna ICALP'20 ] . It also turns out that any non-trivial multiplicative approximation in the adjacency matrix model requires \Omega(n^2) time, so the additive \epsilon n error is necessary too.
Edit intent: coherence
Original:  This (nearly) matches an \Omega(n) time lower bound in this model and improves over the%DIFDELCMD < \widetilde{O}%%% (n\sqrt{n)-time (2, \epsilon n)-approximate algorithm of }  [ Chen, Kannan, and Khanna ICALP'20 ] . It also turns out that any non-trivial multiplicative approximation in the adjacency matrix model requires \Omega(n^2) time, so the additive \epsilon n error is necessary too.
Revised:  This (nearly) matches an \Omega(n) time lower bound in this model and improves over the%DIFDELCMD < \widetilde{O}%%% )-time (2, \epsilon n)-approximate algorithm of } [ Chen, Kannan, and Khanna ICALP'20 ] . It also turns out that any non-trivial multiplicative approximation in the adjacency matrix model requires \Omega(n^2) time, so the additive \epsilon n error is necessary too.
Edit intent: coherence
Original:  We tested this hypothesis by training the model on an augmented version of ImageNet where the biases of natural images were either removed or reversed.
Revised:  We tested this hypothesis by training the model on augmented versions of ImageNet where the biases of natural images were either removed or reversed.
Edit intent: fluency
Original:  All source code and stimuli are publicly available  URL
Revised:  All source code and stimuli are publicly available at URL
Edit intent: clarity
Original: In this paper, we initiate the use of spectral analysis for assessing locking phenomena in finite element formulations.
Revised: In this paper, we take a fresh look at using spectral analysis for assessing locking phenomena in finite element formulations.
Edit intent: clarity
Original:  We propose to ``measure'' locking by comparing the difference between eigenvalue and mode error curves computed on coarse meshes with ``asymptotic'' error curves computed on ``overkill'' meshes, both plotted with respect to the normalized mode number.
Revised:  We propose to "measure" locking by comparing the difference between eigenvalue and mode error curves computed on coarse meshes with ``asymptotic'' error curves computed on ``overkill'' meshes, both plotted with respect to the normalized mode number.
Edit intent: fluency
Original:  We propose to ``measure'' locking by comparing the difference between eigenvalue and mode error curves computed on coarse meshes with ``asymptotic'' error curves computed on ``overkill'' meshes, both plotted with respect to the normalized mode number.
Revised:  We propose to ``measure'' locking by comparing the difference between eigenvalue and mode error curves computed on coarse meshes with "asymptotic" error curves computed on ``overkill'' meshes, both plotted with respect to the normalized mode number.
Edit intent: fluency
Original:  We propose to ``measure'' locking by comparing the difference between eigenvalue and mode error curves computed on coarse meshes with ``asymptotic'' error curves computed on ``overkill'' meshes, both plotted with respect to the normalized mode number.
Revised:  We propose to ``measure'' locking by comparing the difference between eigenvalue and mode error curves computed on coarse meshes with ``asymptotic'' error curves computed on "overkill" meshes, both plotted with respect to the normalized mode number.
Edit intent: fluency
Original:  We then define a simple relation on the set of non-trivial finite-order elements of the group , and prove that the group is plain if and only if this relation is transitive on a bounded set.
Revised:  We then define a simple relation on the set of non-trivial finite-order elements of the group is transitive on a bounded set.
Edit intent: clarity
Original: High performance trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) is a fast growing research area due to the increase in popularity and demand .
Revised: The demand for accurate and fast trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) is a fast growing research area due to the increase in popularity and demand .
Edit intent: clarity
Original: High performance trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) is a fast growing research area due to the increase in popularity and demand .
Revised: High performance trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) have grown recently due to advances in UAV avionics technology and application domains .
Edit intent: clarity
Original:  In many applications, the multirotor UAV dynamics would change in-flight resulting in performance degradation, or even instability, such that the control system is required to adapt its parameters to the new dynamics .
Revised:  In many applications, the multirotor UAV is required to adapt its parameters to the new dynamics .
Edit intent: clarity
Original:  In this paper, we developed a real-time identification approach based on  Deep Neural Networks (DNNs) and the Modified Relay Feedback Test ( MRFT) to optimally tune PID controllers suitable for aggressive trajectory tracking.
Revised:  In this paper, we propose a systematic controller tuning approach based on Deep Neural Networks (DNNs) and the Modified Relay Feedback Test ( MRFT) to optimally tune PID controllers suitable for aggressive trajectory tracking.
Edit intent: clarity
Original:  In this paper, we developed a real-time identification approach based on  Deep Neural Networks (DNNs) and the Modified Relay Feedback Test ( MRFT) to optimally tune PID controllers suitable for aggressive trajectory tracking.
Revised:  In this paper, we developed a real-time identification approach based on Deep Neural Networks with the Modified Relay Feedback Test ( MRFT) to optimally tune PID controllers suitable for aggressive trajectory tracking.
Edit intent: coherence
Original:  We prove the effectiveness and applicability of our developed approach through a set of experiments  where accurate trajectory tracking is maintained despite significant changes to the UAV aerodynamic characteristics and the application of external wind .
Revised:  We prove the effectiveness and applicability of our developed approach through a set of experiments , where accurate trajectory tracking is maintained despite significant changes to the UAV aerodynamic characteristics and the application of external wind .
Edit intent: fluency
Original:  We demonstrate low discrepancy between simulation and experimental results which proves the potential of using the suggested approach for planning and fault detection tasks.
Revised:  We demonstrate low discrepancy between simulation and experimental results which is beneficial for potential use of the proposed approach for real-time model-based planning and fault detection tasks.
Edit intent: clarity
Original:  The achieved tracking results on figure-eight trajectory is on par with the state-of-the-art.
Revised:  The achieved tracking results on figure-eight trajectory is on par with state-of-the-art.
Edit intent: fluency
Original:  But most of them were subject-dependent studies which were not able to generalize on the subject-independent tasks due to the inter-subject variability in EEG .
Revised:  But most of them were subject-dependent studies which were not able to generalize on the subject-independent tasks due to the inter-subject variability present in EEG data .
Edit intent: clarity
Original:  First, an unsupervised Long Short-Term Memory (LSTM) with channel-attention autoencoder is proposed for getting a correlated lower dimensional latent space representation of the EEG data for each subject .
Revised:  First, an unsupervised Long Short-Term Memory (LSTM) with channel-attention autoencoder is proposed for getting a correlated lower dimensional latent space representation of the EEG data of each individual .
Edit intent: clarity
Original:  Secondly, a convolutional neural network (CNN) with attention framework , which takes the first component as input, is presented for performing the task of subject-independent emotion recognition  .
Revised:  Secondly, a convolutional neural network (CNN) with attention framework is presented for performing the task of subject-independent emotion recognition .
Edit intent: clarity
Original:  With the attention mechanism, the proposed approach could highlight the channel of interest as well as the temporal localization of the EEG signal, which contributes to the emotion under consideration as validated by the results.
Revised:  With the attention mechanism, the proposed approach could highlight the significant time-segments of the EEG signal, which contributes to the emotion under consideration as validated by the results.
Edit intent: clarity
Original:  The proposed approach has been validated using various widely employed datasets for EEG signals including DEAP dataset, SEED dataset and CHB-MIT dataset.
Revised:  The proposed approach has been validated using publicly available datasets for EEG signals including DEAP dataset, SEED dataset and CHB-MIT dataset.
Edit intent: clarity
Original:  The proposed approach has been validated using various widely employed datasets for EEG signals including DEAP dataset, SEED dataset and CHB-MIT dataset.
Revised:  The proposed approach has been validated using various widely employed datasets for EEG signals such as DEAP dataset, SEED dataset and CHB-MIT dataset.
Edit intent: fluency
Original:  With  proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset , 76.7\% for positive-negative classification in SEED dataset is obtainedand further for the CHB-MIT dataset  average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification is obtained.
Revised:  With the proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset , 76.7\% for positive-negative classification in SEED dataset is obtainedand further for the CHB-MIT dataset average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification is obtained.
Edit intent: fluency
Original:  With  proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset , 76.7\% for positive-negative classification in SEED dataset is obtainedand further for the CHB-MIT dataset  average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification is obtained.
Revised:  With proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset and 76.7\% for positive-negative classification in SEED dataset is obtainedand further for the CHB-MIT dataset average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification is obtained.
Edit intent: coherence
Original:  With  proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset , 76.7\% for positive-negative classification in SEED dataset is obtainedand further for the CHB-MIT dataset  average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification is obtained.
Revised:  With proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset , 76.7\% for positive-negative classification in SEED dataset are obtained. Further for the CHB-MIT dataset average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification is obtained.
Edit intent: fluency
Original:  With  proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset , 76.7\% for positive-negative classification in SEED dataset is obtainedand further for the CHB-MIT dataset  average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification is obtained.
Revised:  With proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset , 76.7\% for positive-negative classification in SEED dataset is obtainedand further for the CHB-MIT dataset , average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification is obtained.
Edit intent: fluency
Original:  With  proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset , 76.7\% for positive-negative classification in SEED dataset is obtainedand further for the CHB-MIT dataset  average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification is obtained.
Revised:  With proposed methodology, average subject independent accuracies of 65.9\%, 69.5\% for valence and arousal classification in the DEAP dataset , 76.7\% for positive-negative classification in SEED dataset is obtainedand further for the CHB-MIT dataset average subject independent accuracies of 69.1\%, 67.6\%, 72.3\% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification are obtained.
Edit intent: fluency
Original: The introduction of the mm-Wave spectrum into 5G NR promises to bring about unprecedented data throughput to future mobile wireless networks , but comes with several challenges.
Revised: The introduction of the mm-Wave spectrum into 5G NR promises to bring about unprecedented data throughput to future mobile wireless networks but comes with several challenges.
Edit intent: fluency
Original:  Network densification has been proposed as a viable solution to increase RAN resilience and newly introduced IAB is considered as a key enabling technology with compelling cost-reducing opportunities for such dense deployments.
Revised:  Network densification has been proposed as a viable solution to increase RAN resilience , and the newly introduced IAB is considered as a key enabling technology with compelling cost-reducing opportunities for such dense deployments.
Edit intent: fluency
Original:  Network densification has been proposed as a viable solution to increase RAN resilience and newly introduced IAB is considered as a key enabling technology with compelling cost-reducing opportunities for such dense deployments.
Revised:  Network densification has been proposed as a viable solution to increase RAN resilience and newly introduced IAB is considered a key enabling technology with compelling cost-reducing opportunities for such dense deployments.
Edit intent: fluency
Original:  Reconfigurable Intelligent Surfaces ( RISes ) have recently gained extreme popularity as they can create Smart Radio Environments by EM wave manipulation .
Revised:  Reconfigurable Intelligent Surfaces ( RIS ) have recently gained extreme popularity as they can create Smart Radio Environments by EM wave manipulation .
Edit intent: clarity
Original:  With the scope of filling this gap, we propose a new mm-Wave IAB planning tool where RISes can be installed alongside base stations to maximize the network resilience against blockages due to nomadic obstacles and human self-blocking . Numerical results show how adding RISes to IAB deployments can provide high levels of blockage resistance , while they also significantly reduce the overall network planning cost.
Revised:  With the scope of filling this gap, we propose a new mm-Wave IAB planning tool where RISes can be installed alongside base stations to maximize the network resilience against blockages due to nomadic obstacles and human self-blocking . Numerical results show how adding RISs to IAB deployments can provide high levels of blockage resistance , while they also significantly reduce the overall network planning cost.
Edit intent: fluency
Original:  With the scope of filling this gap, we propose a new mm-Wave IAB planning tool where RISes can be installed alongside base stations to maximize the network resilience against blockages due to nomadic obstacles and human self-blocking . Numerical results show how adding RISes to IAB deployments can provide high levels of blockage resistance , while they also significantly reduce the overall network planning cost.
Revised:  With the scope of filling this gap, we propose a new mm-Wave IAB planning tool where RISes can be installed alongside base stations to maximize the network resilience against blockages due to nomadic obstacles and human self-blocking . Numerical results show how adding RISes to IAB deployments can provide high blockage resistance levels while significantly reducing the overall network planning cost.
Edit intent: coherence
Original:  Finally, we found some evidence that young age and deliberative thinking are associated with this tendency.
Revised:  Finally, we found some evidence that deliberative thinking and young age are associated with this tendency.
Edit intent: fluency