wanyu/IteraTeR_v2 huggingface dataset:
Original:  With our model, we can simultaneously classify a given sentence to categorical emotions as well as predict VAD scores .
Revised:  With our model, we can simultaneously classify the emotion categories and predict the VAD scores for a given sentence .
Edit intent: clarity
Original:  We use pre-trained BERT-Large and fine-tune on SemEval dataset (11 categorical emotions) and evaluate on EmoBank (VAD dimensional emotions), in order to show our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification task and significant positive correlations with  ground truth VAD scores.
Revised:  We use pre-trained BERT-Large and fine-tune on SemEval dataset (11 categorical emotions) and evaluate on EmoBank corpus with VAD scores. We show that our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification task and significant positive correlations with ground truth VAD scores.
Edit intent: clarity
Original:  We use pre-trained BERT-Large and fine-tune on SemEval dataset (11 categorical emotions) and evaluate on EmoBank (VAD dimensional emotions), in order to show our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification task and significant positive correlations with  ground truth VAD scores.
Revised:  We use pre-trained BERT-Large and fine-tune on SemEval dataset (11 categorical emotions) and evaluate on EmoBank (VAD dimensional emotions), in order to show our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification and shows significant positive correlations with ground truth VAD scores.
Edit intent: clarity
Original:  We use pre-trained BERT-Large and fine-tune on SemEval dataset (11 categorical emotions) and evaluate on EmoBank (VAD dimensional emotions), in order to show our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification task and significant positive correlations with  ground truth VAD scores.
Revised:  We use pre-trained BERT-Large and fine-tune on SemEval dataset (11 categorical emotions) and evaluate on EmoBank (VAD dimensional emotions), in order to show our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification task and significant positive correlations with the ground truth VAD scores.
Edit intent: fluency
Original:  Also, if one continues training our model with supervision of VAD labels , it outperforms state-of-the-art VAD regression models.
Revised:  Also, further training with supervision of VAD labels , it outperforms state-of-the-art VAD regression models.
Edit intent: clarity
Original:   Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and  practices to ensure comparability across systems with wildly differing architectures.
Revised:  Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and practices to ensure comparability across systems with wildly differing architectures.
Edit intent: clarity
Original:  In this paper, we present the method and design principles of the initial MLPerf Inference release.
Edit intent: coherence
Original:  The first call for submissions garnered more than 600  inference-performance measurements from 14 organizations, representing over 30 systems that show a range of capabilities  .
Revised:  The first call for submissions garnered more than 600 inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities .
Edit intent: fluency
Original:  Let V _target be a set of query terms and V_random a random selection of terms in C . Let X indicate the occurrence of V _target and V_random in C. Gextext learns a graph G(V,E) by correlation thresholding on the covariance matrix of X, where thresholds are estimated by the correlations with randomly selected terms.
Revised:  Let V be a set of query terms and V_random a random selection of terms in C . Let X indicate the occurrence of V _target and V_random in C. Gextext learns a graph G(V,E) by correlation thresholding on the covariance matrix of X, where thresholds are estimated by the correlations with randomly selected terms.
Edit intent: fluency
Original:  Let V _target be a set of query terms and V_random a random selection of terms in C . Let X indicate the occurrence of V _target and V_random in C. Gextext learns a graph G(V,E) by correlation thresholding on the covariance matrix of X, where thresholds are estimated by the correlations with randomly selected terms.
Revised:  Let V _target be a set of query terms and V_random a random selection of terms in C . Let X indicate the occurrence of V in C. Gextext learns a graph G(V,E) by correlation thresholding on the covariance matrix of X, where thresholds are estimated by the correlations with randomly selected terms.
Edit intent: fluency
Original:  We show that brain communication dynamics can be divided into three main communication regimes of information transfer: absent communication (no communication happening);
Revised:  We show that brain communication dynamics can be divided into three main 'communication regimes' of information transfer: absent communication (no communication happening);
Edit intent: fluency
Original:  We use PBS to categorize brain regions based on the way they broadcast information  Subcortical regions are mainly direct broadcasters to multiple receivers;
Revised:  We use PBS to categorize brain regions based on the way they broadcast information . Subcortical regions are mainly direct broadcasters to multiple receivers;
Edit intent: fluency
Original:  i) the transmitting nodes do not need an incentive  and ii) they do need an incentive and are paid by their corresponding receiving nodes by virtual tokens.
Revised:  i) the transmitting nodes do not need an incentive for transmission and ii) they do need an incentive and are paid by their corresponding receiving nodes by virtual tokens.
Edit intent: clarity
Original:  We discuss how the network is formed in a decentralized way, find the actions of the nodes in the game and show that  the proposed game reaches a Nash equilibrium .
Revised:  We discuss how the network is formed in a decentralized way, find the action of the nodes in the game and show that the proposed game reaches a Nash equilibrium .
Edit intent: fluency
Original:  We discuss how the network is formed in a decentralized way, find the actions of the nodes in the game and show that  the proposed game reaches a Nash equilibrium .
Revised:  We discuss how the network is formed in a decentralized way, find the actions of the nodes in the game and show that , despite being decentralized, the proposed game reaches a Nash equilibrium .
Edit intent: coherence
Original:  We discuss how the network is formed in a decentralized way, find the actions of the nodes in the game and show that  the proposed game reaches a Nash equilibrium .
Revised:  We discuss how the network is formed in a decentralized way, find the actions of the nodes in the game and show that the proposed game converges to a stable solution .
Edit intent: clarity
Original: We analysed a recently released dataset of scientific manuscripts that were either rejected or accepted from various conferences in artificial intelligence.
Revised: We analysed a dataset of scientific manuscripts that were either rejected or accepted from various conferences in artificial intelligence.
Edit intent: clarity
Original: We analysed a recently released dataset of scientific manuscripts that were either rejected or accepted from various conferences in artificial intelligence.
Revised: We analysed a recently released dataset of scientific manuscripts that were submitted to various conferences in artificial intelligence.
Edit intent: clarity
Original:  We used a combination of semantic, lexical and psycholinguistic analyses of the full text of the manuscripts to compare them based on the outcome of the peer review process.
Revised:  We performed a combination of semantic, lexical and psycholinguistic analyses of the full text of the manuscripts to compare them based on the outcome of the peer review process.
Edit intent: clarity
Original:  We used a combination of semantic, lexical and psycholinguistic analyses of the full text of the manuscripts to compare them based on the outcome of the peer review process.
Revised:  We used a combination of semantic, lexical and psycholinguistic analyses of the full text of the manuscripts and compared them with the outcome of the peer review process.
Edit intent: clarity
Original:  To evaluate the running times of our algorithms we develop mathematical tools for analyzing the asymptotic behavior of a family of two variables recurrence relations . }\gamma k k\gamma{k} \log p(\alpha k,k) = -\max_{1\leq j \leq N} M_j, where M_j depends only on \alpha, }\bar{\gamma}\bar{b}\bar{k} To this end, we show an equivalence between the recurrence and a random process.
Revised:  The running times of our algorithms we develop mathematical tools for analyzing the asymptotic behavior of a family of two variables recurrence relations . }\gamma k k\gamma{k} \log p(\alpha k,k) = -\max_{1\leq j \leq N} M_j, where M_j depends only on \alpha, }\bar{\gamma}\bar{b}\bar{k} To this end, we show an equivalence between the recurrence and a random process.
Edit intent: clarity
Original:  To evaluate the running times of our algorithms we develop mathematical tools for analyzing the asymptotic behavior of a family of two variables recurrence relations . }\gamma k k\gamma{k} \log p(\alpha k,k) = -\max_{1\leq j \leq N} M_j, where M_j depends only on \alpha, }\bar{\gamma}\bar{b}\bar{k} To this end, we show an equivalence between the recurrence and a random process.
Revised:  To evaluate the running times of our algorithms are derived from an asymptotic analysis of a wide class of two-variable recurrence relations of the form: p(b,k) = 1\leq j \leq N i=1^{r_j }\gamma k k\gamma{k} \log p(\alpha k,k) = -\max_{1\leq j \leq N} M_j, where M_j depends only on \alpha, }\bar{\gamma}\bar{b}\bar{k} To this end, we show an equivalence between the recurrence and a random process.
Edit intent: clarity
Original:  We analyze this process via an adaptation of Sanov's theorem to our setting , which facilitates the use of method of types and information theory for the first time in the analysis of branching algorithms .
Revised:  We analyze this process via an adaptation of Sanov's theorem to our setting .
Edit intent: coherence
Original:  We believe our novel analysis of recurrence relations may be of independent interest and is one of the main contributions of this paper.
Revised:  We believe our novel analysis of recurrence relations which is of independent interest and is one of the main contributions of this paper.
Edit intent: clarity
Original:  We believe our novel analysis of recurrence relations may be of independent interest and is one of the main contributions of this paper.
Revised:  We believe our novel analysis of recurrence relations may be of independent interest is a main contribution of this paper.
Edit intent: coherence
Original: We consider the optimal multi-agent persistent monitoring problem defined for a team of agents traversing on a set of nodes (targets) interconnected according to a fixed graph topology.
Revised: We consider the optimal multi-agent persistent monitoring problem defined for a team of agents on a set of nodes (targets) interconnected according to a fixed graph topology.
Edit intent: clarity
Original:  The underlying objective is to minimize a measure of mean overall node state uncertainty evaluated over a finite interval.
Revised:  The objective is to minimize a measure of mean overall node state uncertainty evaluated over a finite interval.
Edit intent: clarity
Original:  In literature, for this problem, a class of distributed threshold-based parametric controllers has been proposed where agent  transitions from one node to the next are controlled via enforcing thresholds on the respective node uncertainties.
Revised:  In literature, for this problem, a class of distributed threshold-based parametric controllers has been proposed where agent transitions from one node to the next are controlled by enforcing thresholds on the respective node uncertainties.
Edit intent: fluency
Original:  Under such a policy, the behavior of the agent-target system is a hybrid dynamic system, which enables the use of Infinitesimal Perturbation Analysis (IPA) to find the optimal threshold parameters in an on-line manner using gradient descent .
Revised:  Under such a threshold policy, on-line gradient-based techniques (such as the Infinitesimal Perturbation Analysis (IPA) to find the optimal threshold parameters in an on-line manner using gradient descent .
Edit intent: clarity
Original:  Under such a policy, the behavior of the agent-target system is a hybrid dynamic system, which enables the use of Infinitesimal Perturbation Analysis (IPA) to find the optimal threshold parameters in an on-line manner using gradient descent .
Revised:  Under such a policy, the behavior of the agent-target system is a hybrid dynamic system, which enables the use of Infinitesimal Perturbation Analysis (IPA) ) are then used to determine optimal threshold values .
Edit intent: clarity
Original:  To overcome this initialization challenge, we extensively analyze the asymptotic steady-state behavior of the agent-target hybrid system under periodic agent trajectories.
Revised:  To overcome this initialization challenge, in this paper, the asymptotic steady-state behavior of the agent-target hybrid system under periodic agent trajectories.
Edit intent: clarity
Original:  Then, based on the obtained theoretical results, we propose a computationally efficient offline greedy approach to generate a reasonably well-performing initial thresholds.
Revised:  Then, based on the obtained theoretical results, a computationally efficient offline greedy approach to generate a reasonably well-performing initial thresholds.
Edit intent: clarity
Original:  Then, based on the obtained theoretical results, we propose a computationally efficient offline greedy approach to generate a reasonably well-performing initial thresholds.
Revised:  Then, based on the obtained theoretical results, we propose a computationally efficient off-line greedy technique is developed to systematically generate initial thresholds.
Edit intent: clarity
Original:  In almost all the considered cases, it was observed that the initial thresholds provided by the proposing greedy technique is optimal (still local) and performs better than the locally optimal threshold policies given by the IPA techniques (with randomly chosen initial threshold) .
Revised:  Extensive numerical results show that the initial thresholds provided by the proposing greedy technique is optimal (still local) and performs better than the locally optimal threshold policies given by the IPA techniques (with randomly chosen initial threshold) .
Edit intent: clarity
Original:  In almost all the considered cases, it was observed that the initial thresholds provided by the proposing greedy technique is optimal (still local) and performs better than the locally optimal threshold policies given by the IPA techniques (with randomly chosen initial threshold) .
Revised:  In almost all the considered cases, it was observed that the initial thresholds provided by the proposing greedy technique is optimal (still local) and performs better than the locally optimal solutions known to date .
Edit intent: clarity
Original: We consider the optimal multi-agent persistent monitoring problem defined for a team of agents on a set of nodes (targets) interconnected according to a fixed graph topology.
Revised: This paper considers the optimal multi-agent persistent monitoring problem defined for a team of agents on a set of nodes (targets) interconnected according to a fixed graph topology.
Edit intent: clarity
Original: We consider the optimal multi-agent persistent monitoring problem defined for a team of agents on a set of nodes (targets) interconnected according to a fixed graph topology.
Revised: We consider the optimal multi-agent persistent monitoring problem defined for a team of agents on a set of nodes (targets) interconnected according to a fixed network topology.
Edit intent: clarity
Original:  The objective is to minimize a measure of mean overall node state uncertainty evaluated over a finite time interval.
Revised:  The objective is to minimize a measure of overall node state uncertainty evaluated over a finite time interval.
Edit intent: clarity
Original:  In prior work, a class of distributed threshold-based parametric controllers has been proposed where agent dwell times at nodes and transitions from one node to the next are controlled by enforcing thresholds on the respective node uncertainties .
Revised:  A class of distributed threshold-based parametric controllers has been proposed where agent dwell times at nodes and transitions from one node to the next are controlled by enforcing thresholds on the respective node uncertainties .
Edit intent: clarity
Original:  In prior work, a class of distributed threshold-based parametric controllers has been proposed where agent dwell times at nodes and transitions from one node to the next are controlled by enforcing thresholds on the respective node uncertainties .
Revised:  In prior work, a class of distributed threshold-based parametric controllers has been proposed in prior work to control agent dwell times at nodes and transitions from one node to the next are controlled by enforcing thresholds on the respective node uncertainties .
Edit intent: clarity
Original:  In prior work, a class of distributed threshold-based parametric controllers has been proposed where agent dwell times at nodes and transitions from one node to the next are controlled by enforcing thresholds on the respective node uncertainties .
Revised:  In prior work, a class of distributed threshold-based parametric controllers has been proposed where agent dwell times at nodes and next-node destinations by enforcing thresholds on the respective node uncertainties .
Edit intent: clarity
Original:  In prior work, a class of distributed threshold-based parametric controllers has been proposed where agent dwell times at nodes and transitions from one node to the next are controlled by enforcing thresholds on the respective node uncertainties .
Revised:  In prior work, a class of distributed threshold-based parametric controllers has been proposed where agent dwell times at nodes and transitions from one node to the next are controlled by enforcing thresholds on the respective node states .
Edit intent: clarity
Original:  Under such a threshold policy, on-line gradient-based techniques (such as the Infinitesimal Perturbation Analysis (IPA)) are then used to determine optimal threshold values.
Revised:  Under such a Threshold Control Policy (TCP), an on-line gradient-based techniques (such as the Infinitesimal Perturbation Analysis (IPA)) are then used to determine optimal threshold values.
Edit intent: clarity
Original:  Under such a threshold policy, on-line gradient-based techniques (such as the Infinitesimal Perturbation Analysis (IPA)) are then used to determine optimal threshold values.
Revised:  Under such a threshold policy, on-line gradient technique was used to determine optimal threshold values.
Edit intent: coherence
Original:  However, due to the non-convexity of the problem, this approach leads to often poor local optima highly dependent on the initial thresholds used.
Revised:  However, due to the non-convexity of the problem, this approach often leads to a poor local optima highly dependent on the initial thresholds used.
Edit intent: fluency
Original:  Extensive numerical results show that the initial thresholds provided by this greedy technique are almost immediately (locally) optimal or quickly lead to optimal values.
Revised:  Extensive numerical results show that such initial thresholds are almost immediately (locally) optimal or quickly lead to optimal values.
Edit intent: clarity
Original:  We consider a random subspace embedding which is either drawn at the beginning and then fixed , or, refreshed at each iteration.
Revised:  We consider a random subspace embedding which is either drawn at the beginning of the algorithm and fixed throughout , or, refreshed at each iteration.
Edit intent: clarity
Original:  We provide an exact finite-time analysis of the refreshed embeddings method for a broad class of random matrices , an exact asymptotic analysis of the fixed embedding method with a Gaussian matrix, and a non-asymptotic analysis of the fixed embedding method for Gaussian and SRHT matrices , with and without momentum acceleration.
Revised:  For a broad class of random matrices, we provide an exact finite-time analysis of the refreshed embeddings method for a broad class of random matrices , an exact asymptotic analysis of the fixed embedding method with a Gaussian matrix, and a non-asymptotic analysis of the fixed embedding method for Gaussian and SRHT matrices , with and without momentum acceleration.
Edit intent: fluency
Original:  We provide an exact finite-time analysis of the refreshed embeddings method for a broad class of random matrices , an exact asymptotic analysis of the fixed embedding method with a Gaussian matrix, and a non-asymptotic analysis of the fixed embedding method for Gaussian and SRHT matrices , with and without momentum acceleration.
Revised:  We provide an exact finite-time analysis of the refreshed embeddings method , an exact asymptotic analysis of the fixed embedding method with a Gaussian matrix, and a non-asymptotic analysis of the fixed embedding method for Gaussian and SRHT matrices , with and without momentum acceleration.
Edit intent: clarity
Original:  We provide an exact finite-time analysis of the refreshed embeddings method for a broad class of random matrices , an exact asymptotic analysis of the fixed embedding method with a Gaussian matrix, and a non-asymptotic analysis of the fixed embedding method for Gaussian and SRHT matrices , with and without momentum acceleration.
Revised:  We provide an exact finite-time analysis of the refreshed embeddings method for a broad class of random matrices , an exact asymptotic analysis of the fixed embedding method , as well as a non-asymptotic analysis of the fixed embedding method for Gaussian and SRHT matrices , with and without momentum acceleration.
Edit intent: coherence
Original:  We provide an exact finite-time analysis of the refreshed embeddings method for a broad class of random matrices , an exact asymptotic analysis of the fixed embedding method with a Gaussian matrix, and a non-asymptotic analysis of the fixed embedding method for Gaussian and SRHT matrices , with and without momentum acceleration.
Revised:  We provide an exact finite-time analysis of the refreshed embeddings method for a broad class of random matrices , an exact asymptotic analysis of the fixed embedding method with a Gaussian matrix, and a non-asymptotic analysis , with and without momentum acceleration.
Edit intent: clarity
Original:  Surprisingly, we show that, for Gaussian matrices, the refreshed sketching method with no momentum yields the same asymptotic rate of convergence as the fixed embedding method accelerated with momentum.
Revised:  Surprisingly, we show that, for Gaussian matrices, the refreshed sketching method with no momentum yields the same convergence rate as the fixed embedding method accelerated with momentum.
Edit intent: clarity
Original:  Surprisingly, we show that, for Gaussian matrices, the refreshed sketching method with no momentum yields the same asymptotic rate of convergence as the fixed embedding method accelerated with momentum.
Revised:  Surprisingly, we show that, for Gaussian matrices, the refreshed sketching method with no momentum yields the same asymptotic rate of convergence as the fixed embedding method with momentum.
Edit intent: clarity