wanyu/IteraTeR_v2 huggingface dataset:
Original: We study the behaviour of WiFi and 5G cellular networks as they exploit the recently unlocked 6 GHz spectrum for unlicensed access , while conforming to the constraints imposed by the incumbent users.
Revised: We study the interaction of WiFi and 5G cellular networks as they exploit the recently unlocked 6 GHz spectrum for unlicensed access , while conforming to the constraints imposed by the incumbent users.
Edit intent: clarity
Original: We study the behaviour of WiFi and 5G cellular networks as they exploit the recently unlocked 6 GHz spectrum for unlicensed access , while conforming to the constraints imposed by the incumbent users.
Revised: We study the behaviour of WiFi and 5G cellular networks as they exploit the recently unlocked 6-GHz spectrum for unlicensed access , while conforming to the constraints imposed by the incumbent users.
Edit intent: fluency
Original: We study the behaviour of WiFi and 5G cellular networks as they exploit the recently unlocked 6 GHz spectrum for unlicensed access , while conforming to the constraints imposed by the incumbent users.
Revised: We study the behaviour of WiFi and 5G cellular networks as they exploit the recently unlocked 6 GHz spectrum for unlicensed access while conforming to the constraints imposed by the incumbent users.
Edit intent: fluency
Original:  We use tools from stochastic geometry to derive the theoretical performance metrics for users of each radio access technology , which helps us in capturing the aggregate behaviour of the network in a snapshot .
Revised:  We derive the theoretical performance metrics for users of each radio access technology , which helps us in capturing the aggregate behaviour of the network in a snapshot .
Edit intent: clarity
Original:  We use tools from stochastic geometry to derive the theoretical performance metrics for users of each radio access technology , which helps us in capturing the aggregate behaviour of the network in a snapshot .
Revised:  We use tools from stochastic geometry to derive the theoretical performance metrics for users of each radio access technology using stochastic geometry, thereby capturing the aggregate behaviour of the network in a snapshot .
Edit intent: coherence
Original:  We use tools from stochastic geometry to derive the theoretical performance metrics for users of each radio access technology , which helps us in capturing the aggregate behaviour of the network in a snapshot .
Revised:  We use tools from stochastic geometry to derive the theoretical performance metrics for users of each radio access technology , which helps us in capturing the aggregate behaviour of the network .
Edit intent: clarity
Original:  We propose a framework where the portions of cellular and WiFi networks are grouped together to form entities .
Revised:  We propose a framework where the portions of cellular and WiFi networks are grouped to form entities .
Edit intent: clarity
Original:  These entities interact to satisfy their Quality of Service demands by playing a non-cooperative game.
Revised:  These entities interact to satisfy their QoS demands by playing a non-cooperative game.
Edit intent: clarity
Original:  The action of an entity corresponds to the fraction of its network elements (WiFi access point and cellular base stations) operating in the 6 GHz band.
Revised:  The action of an entity corresponds to the fraction of its network elements operating in the 6 GHz band.
Edit intent: clarity
Original:  The action of an entity corresponds to the fraction of its network elements (WiFi access point and cellular base stations) operating in the 6 GHz band.
Revised:  The action of an entity corresponds to the fraction of its network elements (WiFi access point and cellular base stations) operating in the 6-GHz band.
Edit intent: fluency
Original:  Due to the decentralized nature of the entities , we find the Nash equilibrium using distributed Best Response Algorithm (D-BRA), where each entity takes actions without any centralized scheduling.
Revised:  Due to the decentralized nature of the game , we find the Nash equilibrium using distributed Best Response Algorithm (D-BRA), where each entity takes actions without any centralized scheduling.
Edit intent: clarity
Original:  Due to the decentralized nature of the entities , we find the Nash equilibrium using distributed Best Response Algorithm (D-BRA), where each entity takes actions without any centralized scheduling.
Revised:  Due to the decentralized nature of the entities , we find the solution using distributed Best Response Algorithm (D-BRA), where each entity takes actions without any centralized scheduling.
Edit intent: clarity
Original:  The results demonstrate how the system parameters affect the performance of a network at equilibrium and highlight the throughput gains of the networks as a result of using the 6 GHz bands, which offer considerably larger bandwidths.
Revised:  The results demonstrate how the system parameters affect the performance of a network at equilibrium and highlight the throughput gains as a result of using the 6 GHz bands, which offer considerably larger bandwidths.
Edit intent: clarity
Original:  To study the relative contribution of \beta-cell mass to \beta-cell function in T2D, we make use of a comprehensive electrophysiological model from human \beta-cell clusters.
Revised:  To study the relative contribution of \beta-cell mass to \beta-cell function in T2D, we make use of a comprehensive electrophysiological model of human \beta-cell clusters.
Edit intent: fluency
Original:  The model demonstrates  good correspondence between suppression of synchronizing electrical activity and pulsatile insulin release, and published experimental measurements.
Revised:  The model demonstrates a good correspondence between suppression of synchronizing electrical activity and pulsatile insulin release, and published experimental measurements.
Edit intent: fluency
Original:  The model demonstrates  good correspondence between suppression of synchronizing electrical activity and pulsatile insulin release, and published experimental measurements.
Revised:  The model demonstrates good correspondence between suppression of synchronizing electrical activity and published experimental measurements.
Edit intent: clarity
Original:  Sensing catheter interaction inside the artery is advantageous to minimize such complications and enhances navigation safety.
Revised:  Intravascular sensing of tool interaction with the arteries is advantageous to minimize such complications and enhances navigation safety.
Edit intent: clarity
Original:  Sensing catheter interaction inside the artery is advantageous to minimize such complications and enhances navigation safety.
Revised:  Sensing catheter interaction inside the artery is advantageous to minimize such complications and enhance navigation safety.
Edit intent: fluency
Original:  We present monitoring of the intraluminal catheter interaction with the arterial wall using an image-based estimation approach within vascular robotic navigation.
Revised:  We present monitoring of the intraluminal tool interaction with the arterial wall using an image-based estimation approach within vascular robotic navigation.
Edit intent: clarity
Original:  The proposed image-based method employs continuous finite element simulation of the catheter motion using imaging data to estimate multi-point forces along catheter-vessel interaction.
Revised:  The proposed image-based method employs continuous finite element simulation of the tool using imaging data to estimate multi-point forces along catheter-vessel interaction.
Edit intent: clarity
Original:  We implemented imaging algorithms to detect and track contacts and compute catheter pose measurements.
Revised:  We implemented imaging algorithms to detect and track contacts , and compute pose measurements.
Edit intent: fluency
Original:  Results suggest that high-risk intraluminal forces may happen even in low insertion forces .
Revised:  Results suggest that high risk intraluminal forces may happen even in low insertion forces .
Edit intent: fluency
Original:  Results suggest that high-risk intraluminal forces may happen even in low insertion forces .
Revised:  Results suggest that high-risk intraluminal forces may happen even with low insertion force .
Edit intent: clarity
Original: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise \mbox{ kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
Revised: We study {\em online} active learning of homogeneous halfspaces in R^d with adversarial noise \mbox{ kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
Edit intent: clarity
Original: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise \mbox{ kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
Revised: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: fluency
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( d \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: fluency
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, 1{\epsilon})\big) and sample complexity bound of O\big( \frac{s{\epsilon} \cdot polylog(d)} \frac{d{\epsilon} } \big).
Edit intent: coherence
Original: \tilde{O}\big( {\epsilon}) }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{ \cite{kearns1992toward }\hspace{0pt} }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised: \tilde{O}\big( {\epsilon}) }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big). As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{ \cite{kearns1992toward }\hspace{0pt} } where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: fluency
Original: \tilde{O}\big( {\epsilon}) }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{ \cite{kearns1992toward }\hspace{0pt} }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised: \tilde{O}\big( {\epsilon}) }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary , we show that under the more challenging agnostic model \mbox{ \cite{kearns1992toward }\hspace{0pt} } where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: clarity
Original: \tilde{O}\big( {\epsilon}) }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{ \cite{kearns1992toward }\hspace{0pt} }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised: \tilde{O}\big( {\epsilon}) }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{ \cite{kearns1992toward }\hspace{0pt} } agnostic model where no assumption is made on the noise rate , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: fluency
Original: \tilde{O}\big( {\epsilon}) }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{ \cite{kearns1992toward }\hspace{0pt} }  where no assumption is made on the noise rate  , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Revised: \tilde{O}\big( {\epsilon}) }\big) \tilde{O}\big({\epsilon} \cdot polylog(d) }\big) As an immediate corollary of our main result , we show that under the more challenging agnostic model \mbox{ \cite{kearns1992toward }\hspace{0pt} } where no assumption is made on the noise rate \nu , our active learner achieves an error rate of O(OPT) + \epsilon with the same running time and label and sample complexity, where OPT is the best possible error rate achievable by any homogeneous s-sparse halfspace.
Edit intent: fluency
Original:  First, we give a quantitative construction of the ballistic orbits of ABC and Kolmogorov flows, namely those with maximal large time asymptotic speeds in a coordinate direction.
Revised:  We give a quantitative construction of the ballistic orbits of ABC and Kolmogorov flows, namely those with maximal large time asymptotic speeds in a coordinate direction.
Edit intent: coherence
Original:  To study the tightness of the estimates, we compute  front speeds of G-equation based on a semi-Lagrangian (SL) scheme with Strang splitting and weighted essentially non-oscillatory (WENO) interpolation.
Revised:  To study the tightness of the estimates, we compute the front speeds of G-equation based on a semi-Lagrangian (SL) scheme with Strang splitting and weighted essentially non-oscillatory (WENO) interpolation.
Edit intent: fluency
Original:  The SL scheme is stable when the ratio of time step and spatial grid size (in the propagation direction) is smaller than a positive constant independent of the flow intensity.
Revised:  Time step size is chosen so that the Courant number grows sublinearly with the flow intensity.
Edit intent: clarity
Original:  Furthermore, we apply locality-sensitive hashing (LSH) to design a novel approximate SCAN algorithm and prove guarantees for its clustering quality .
Revised:  Furthermore, we apply locality-sensitive hashing (LSH) to design a novel approximate SCAN algorithm and prove guarantees for its clustering behavior .
Edit intent: clarity
Original:  We present an experimental evaluation of our parallel algorithms on large real-world graphs.
Revised:  We present an experimental evaluation of our algorithms on large real-world graphs.
Edit intent: clarity
Original:  Moreover, our experiments show that applying LSH results in much faster index construction on denser graphs without large sacrifices in clustering quality.
Revised:  Moreover, our experiments show that applying LSH results in faster index construction on denser graphs without large sacrifices in clustering quality.
Edit intent: clarity
Original:  Moreover, our experiments show that applying LSH results in much faster index construction on denser graphs without large sacrifices in clustering quality.
Revised:  Moreover, our experiments show that applying LSH results in much faster index construction while maintaining good clustering quality.
Edit intent: clarity
Original:  Nonetheless, efforts in providing visual publication summaries have been few and fart apart, primarily focusing on the biomedical domain.
Revised:  Nonetheless, efforts in providing visual publication summaries have been few and far apart, primarily focusing on the biomedical domain.
Edit intent: fluency
Original:  The resulting linear algebraic systems are shown to be symmetric and therefore can be very efficiently solved by means of a standard matrix-free conjugate gradient algorithm.
Revised:  The resulting linear algebraic systems are shown to be symmetric and therefore can be solved by means of a standard matrix-free conjugate gradient algorithm.
Edit intent: clarity
Original:  The resulting linear algebraic systems are shown to be symmetric and therefore can be very efficiently solved by means of a standard matrix-free conjugate gradient algorithm.
Revised:  The resulting linear algebraic systems are shown to be symmetric and therefore can be very efficiently solved by means of an efficient standard matrix-free conjugate gradient algorithm.
Edit intent: clarity
Original:  Classically, the convergence to the ergodic averages for integrable functions can in general, be arbitrarily slow.
Revised:  Classically, the convergence of the ergodic averages for integrable functions can in general, be arbitrarily slow.
Edit intent: fluency
Original:  Classically, the convergence to the ergodic averages for integrable functions can in general, be arbitrarily slow.
Revised:  Classically, the convergence to the ergodic averages for integrable functions can be arbitrarily slow.
Edit intent: clarity