wanyu/IteraTeR_full_sent huggingface dataset:
Original:   Searching for the optimal mapping is challenging because of a massive space of possible data-layouts and loop transformations for the DNN layers. For example, there are over 10^19 valid mappingsfor a single convolution layer on average for mapping ResNet50 and MobileNetV2 on a representative DNN edge accelerator. This challenge gets exacerbated with new layer types (e.g., depth-wise and point-wise convolutions) and diverse hardware accelerator configurations.
Revised:  Searching for the optimal mapping is challenging because of the large space of mappings, and this challenge gets exacerbated with new layer types (e.g., depth-wise and point-wise convolutions) and diverse hardware accelerator configurations.
Edit intent: coherence
Original:  The motivation for this decomposition is to dramatically reduce the size of the search space , and to also prioritize the optimization of off-chip data movement, which is 2-3 orders of magnitude more compared to the on-chip data movement.
Revised:  The motivation for this decomposition is to reduce the size of the search space , and to also prioritize the optimization of off-chip data movement, which is 2-3 orders of magnitude more compared to the on-chip data movement.
Edit intent: clarity
Original:  The motivation for this decomposition is to dramatically reduce the size of the search space , and to also prioritize the optimization of off-chip data movement, which is 2-3 orders of magnitude more compared to the on-chip data movement.
Revised:  The motivation for this decomposition is to dramatically reduce the size of the search space dramatically and also to prioritize the optimization of off-chip data movement, which is 2-3 orders of magnitude more compared to the on-chip data movement.
Edit intent: meaning-changed
Original:  We introduce {\em Marvel}, which implements the above approach by leveraging two cost models to explore the two subspaces -- a classical distinct-block (DB) locality cost model for the off-chip subspace, and a state-of-the-art DNN accelerator behavioral cost model, MAESTRO, for the on-chip subspace.
Revised:  We implemented our approach in a tool called {\em Marvel}, which implements the above approach by leveraging two cost models to explore the two subspaces -- a classical distinct-block (DB) locality cost model for the off-chip subspace, and a state-of-the-art DNN accelerator behavioral cost model, MAESTRO, for the on-chip subspace.
Edit intent: clarity
Original:  We introduce {\em Marvel}, which implements the above approach by leveraging two cost models to explore the two subspaces -- a classical distinct-block (DB) locality cost model for the off-chip subspace, and a state-of-the-art DNN accelerator behavioral cost model, MAESTRO, for the on-chip subspace. Our approach also considers dimension permutation, a form of data-layouts, in the mapping space formulation along with the loop transformations .
Revised:  We introduce {\em Marvel}, and another major benefit of our approach is that it is applicable to any DNN operator conformable with the MDC notation .
Edit intent: coherence
Original: We develop an FPT algorithm and a kernel for the Weighted Edge Clique Partition (WECP) problem, where a graph with n vertices and integer edge weights is given together with an integer k, and the aim is to find k cliques, such that every edge appears in exactly as many cliques as its weight.
Revised: We develop an FPT algorithm and a bi-kernel for the Weighted Edge Clique Partition (WECP) problem, where a graph with n vertices and integer edge weights is given together with an integer k, and the aim is to find k cliques, such that every edge appears in exactly as many cliques as its weight.
Edit intent: meaning-changed
Original:  For WECP we develop a bi-kernel with 4^k vertices, and an algorithm with runtime 2^{ O (k^{3/2}w^{1/2}\log(k/w))}n^{O(1)}, where w is the maximum edge weight.
Revised:  For WECP we develop a bi-kernel with 4^k vertices, and an algorithm with runtime 2^{ \mathcal{O (k^{3/2}w^{1/2}\log(k/w))}n^{O(1)}, where w is the maximum edge weight.
Edit intent: meaning-changed
Original:  The latter in particular improves the runtime for ECP to~2^{ O (k^{3/2}\log k)}n^{O(1)} . We also show that our algorithm necessarily needs a runtime of 2^{\Theta(k^{3/2\log k)}n^{O(1)} to solve ECP} .
Revised:  The latter in particular improves the runtime for ECP to~2^{ \mathcal{O (k^{3/2}\log k)}n^{O(1)} . We also show that our algorithm necessarily needs a runtime of 2^{\Theta(k^{3/2\log k)}n^{O(1)} to solve ECP} .
Edit intent: meaning-changed
Original:  The latter in particular improves the runtime for ECP to~2^{ O (k^{3/2}\log k)}n^{O(1)} . We also show that our algorithm necessarily needs a runtime of 2^{\Theta(k^{3/2\log k)}n^{O(1)} to solve ECP} .
Revised:  The latter in particular improves the runtime for ECP to~2^{ O (k^{3/2}\log k)}n^{O(1)} \log k)}n^{O(1)} to solve ECP} .
Edit intent: meaning-changed
Original: One of the obstacles of abstractive summarization is the presence of various potentially correct predictions. Widely used objective functions for supervised learning, such as cross-entropy loss, cannot handle alternative answers effectively. Rather, they act as a training noise .
Revised: By harnessing pre-trained language models, summarization models had rapid progress recently. However, the models are mainly assessed by automatic evaluation metrics such as ROUGE. Although ROUGE is known for having a positive correlation with human evaluation scores, it has been criticized for its vulnerability and the gap between actual qualities .
Edit intent: meaning-changed
Original:  In this paper, we propose Semantic Similarity strategy that can consider semantic meanings of generated summaries while training. Our training objective includes maximizing semantic similarity score which is calculated by an additional layer that estimates semantic similarity between generated summary and reference summary. By leveraging pre-trained language models, our model achieves a new state-of-the-art performance, ROUGE-L score of 41.5 on CNN/DM dataset.
Revised:  In this paper, we compare the generated summaries from recent LM, BART, and the reference summaries from a benchmark dataset, CNN/DM dataset.
Edit intent: meaning-changed
Original:  By leveraging pre-trained language models, our model achieves a new state-of-the-art performance, ROUGE-L score of 41.5 on CNN/DM dataset. To support automatic evaluation, we also conducted human evaluation and received higher scores relative to both baseline and reference summaries .
Revised:  By leveraging pre-trained language models, our model achieves a new state-of-the-art performance, ROUGE-L score of 41.5 on CNN/DM , using a crowd-sourced human evaluation metric. Interestingly, model-generated summaries receive higher scores relative to both baseline and reference summaries .
Edit intent: clarity
Original:  To support automatic evaluation, we also conducted human evaluation and received higher scores relative to both baseline and reference summaries .
Revised:  To support automatic evaluation, we also conducted human evaluation and received higher scores relative to reference summaries. Stemming from our experimental results, we first argue the intrinsic characteristics of the CNN/DM dataset, the progress of pre-trained language models, and their ability to generalize on the training data. Finally, we share our insights into the model-generated summaries and presents our thought on learning methods for abstractive summarization .
Edit intent: meaning-changed
Original: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Revised: Motivated by }}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Edit intent: coherence
Original: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Revised: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% %DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Edit intent: fluency
Original: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Revised: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% }%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Edit intent: coherence
Original: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Revised: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% } is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Edit intent: coherence
Original: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Revised: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% _{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Edit intent: clarity
Original: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% ^{V.
Revised: Motivated by team formation applications , we study discrete optimization problems of the form \max_{S\in\mathcal{S}}%DIFDELCMD < \left(%%% f(S)-w(S)%DIFDELCMD < \right)%%% , where f:2^{V}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative monotone submodular function, w:2^{V}}%DIFDELCMD < \to%%% \mathbb{R_{+} is a non-negative linear function , and S}%DIFDELCMD < \subseteq2%%% .
Edit intent: coherence
Original:  Our algorithms use a very simple scaling approach: we pick an absolute constant c}%DIFDELCMD < \geq1 %%% and optimize the function f(S)-c\cdot w(S) using a black-box application of standard algorithms , such as the classical Greedy algorithm and the single-threshold Greedy algorithm.
Revised:  Our algorithms use a very simple scaling approach: we pick an absolute constant c}%DIFDELCMD < \geq1 %%% online crowdsourcing platforms as well as applications that span human-resource management in industrial and URLanizations researchers have studied extensively the team-formation problem. This problem has been primarily formalized as an optimization problem where the goal is to optimize some metric of the team performance, subject to the constraint that the team members should cover all the skills required by the task. In this paper, we generalize this problem formulation and set as our objective to optimize a function that maximizes the coverage of skills minus the cost of the corresponding team. This formulation appears as a more natural one, particularly in cases where one needs to strike a balance between the coverage achieved and the cost being paid. To the best of our knowledge we are the first to formalize the team-formation problem in this manner. From the algorithmic perspective, we demonstrate that by using simple variants of the standard greedy algorithm (used for submodular optimization) we can design algorithms that have provable approximation guarantees, are extremely efficient and work very well in practice. Our experiments with real data from online crowdsourcing platforms demonstrate the efficiency and the efficacy of our methods. Finally, we believe that our problem formulation and algorithms , such as the classical Greedy algorithm and the single-threshold Greedy algorithm.
Edit intent: meaning-changed
Original:  Our algorithms use a very simple scaling approach: we pick an absolute constant c}%DIFDELCMD < \geq1 %%% and optimize the function f(S)-c\cdot w(S) using a black-box application of standard algorithms , such as the classical Greedy algorithm and the single-threshold Greedy algorithm. These algorithms are based on recent works that use (time varying) scaling combined with classical algorithms such as the discrete and continuous Greedy algorithms (Feldman, WADS'19; Harshawet al. , ICML'19) .
Revised:  Our algorithms use a very simple scaling approach: we pick an absolute constant c}%DIFDELCMD < \geq1 %%% and optimize the function f(S)-c\cdot w(S) using a black-box application of standard algorithms et al. , ICML'19) .
Edit intent: meaning-changed
Original:  We show algorithms that require fewer rounds to update a solution to problems such as Minimum Spanning Forest  and Maximal Matching than would be required by their static counterparts to compute it from scratch.
Revised:  We show algorithms that require fewer rounds to update a solution to problems such as Minimum Spanning Forest , 2-Edge Connected Components, and Maximal Matching than would be required by their static counterparts to compute it from scratch.
Edit intent: meaning-changed
Original:  More precisely, we provide O(1) round algorithms that can process a batch of updated of size O (S) for the Minimum Spanning Forest problem and a batch of updates of size O (S^{1-\varepsilon}) for the Maximal Matchingproblem, where S is the limit on the local memory of a single machine.
Revised:  Our algorithms can process batches of updates of size \Theta (S) for the Minimum Spanning Forest problem and a batch of updates of size O (S^{1-\varepsilon}) for the Maximal Matchingproblem, where S is the limit on the local memory of a single machine.
Edit intent: meaning-changed
Original:  More precisely, we provide O(1) round algorithms that can process a batch of updated of size O (S) for the Minimum Spanning Forest problem and a batch of updates of size O (S^{1-\varepsilon}) for the Maximal Matchingproblem, where S is the limit on the local memory of a single machine.
Revised:  More precisely, we provide O(1) round algorithms that can process a batch of updated of size O (S) , for Minimum Spanning Forest problem and a batch of updates of size O (S^{1-\varepsilon}) for the Maximal Matchingproblem, where S is the limit on the local memory of a single machine.
Edit intent: fluency
Original:  More precisely, we provide O(1) round algorithms that can process a batch of updated of size O (S) for the Minimum Spanning Forest problem and a batch of updates of size O (S^{1-\varepsilon}) for the Maximal Matchingproblem, where S is the limit on the local memory of a single machine.
Revised:  More precisely, we provide O(1) round algorithms that can process a batch of updated of size O (S) for the Minimum Spanning Forest and 2-Edge Connected Components, and \Theta (S^{1-\varepsilon}) for the Maximal Matchingproblem, where S is the limit on the local memory of a single machine.
Edit intent: meaning-changed
Original:  More precisely, we provide O(1) round algorithms that can process a batch of updated of size O (S) for the Minimum Spanning Forest problem and a batch of updates of size O (S^{1-\varepsilon}) for the Maximal Matchingproblem, where S is the limit on the local memory of a single machine.
Revised:  More precisely, we provide O(1) round algorithms that can process a batch of updated of size O (S) for the Minimum Spanning Forest problem and a batch of updates of size O (S^{1-\varepsilon}) , for Maximal Matching, in O(1) rounds, where S is the limit on the local memory of a single machine.
Edit intent: clarity
Original:  More precisely, we provide O(1) round algorithms that can process a batch of updated of size O (S) for the Minimum Spanning Forest problem and a batch of updates of size O (S^{1-\varepsilon}) for the Maximal Matchingproblem, where S is the limit on the local memory of a single machine.
Revised:  More precisely, we provide O(1) round algorithms that can process a batch of updated of size O (S) for the Minimum Spanning Forest problem and a batch of updates of size O (S^{1-\varepsilon}) for the Maximal Matchingproblem, where S is the local memory of a single machine.
Edit intent: clarity
Original: A unit disk graph G on a given set of points P in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A  subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, the topological shortest path between p,q in G' has at most k edges.
Revised: A unit disk graph G on a given set P of points in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, the topological shortest path between p,q in G' has at most k edges.
Edit intent: fluency
Original: A unit disk graph G on a given set of points P in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A  subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, the topological shortest path between p,q in G' has at most k edges.
Revised: A unit disk graph G on a given set of points P in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A spanning subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, the topological shortest path between p,q in G' has at most k edges.
Edit intent: meaning-changed
Original: A unit disk graph G on a given set of points P in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A  subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, the topological shortest path between p,q in G' has at most k edges.
Revised: A unit disk graph G on a given set of points P in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, the topological shortest path between p,q in G' has at most k edges.
Edit intent: fluency
Original: A unit disk graph G on a given set of points P in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A  subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, the topological shortest path between p,q in G' has at most k edges.
Revised: A unit disk graph G on a given set of points P in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, there is a path between p,q in G' has at most k edges.
Edit intent: meaning-changed
Original: A unit disk graph G on a given set of points P in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A  subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, the topological shortest path between p,q in G' has at most k edges.
Revised: A unit disk graph G on a given set of points P in the plane is a geometric graph where an edge exists between two points p,q \in P if and only if |pq| \leq 1. A subgraph G' of G is a k-hop spanner if and only if for every edge pq\in G, the topological shortest path between p,q in G' with at most k edges.
Edit intent: fluency
Original:  This is the first nontrivial construction of 2-hop spanners. (IV) For every sufficiently large  n, there exists a set P of n points on a circle, such that every plane hop spanner on P has hop stretch factor at least 4. Previously, no lower bound greater than 2 was known. (V) For every  point set on a circle, there exists a plane  4-hop spanner.
Revised:  This is the first nontrivial construction of 2-hop spanners. (IV) For every sufficiently large positive integer n, there exists a set P of n points on a circle, such that every plane hop spanner on P has hop stretch factor at least 4. Previously, no lower bound greater than 2 was known. (V) For every point set on a circle, there exists a plane 4-hop spanner.
Edit intent: meaning-changed
Original:  This is the first nontrivial construction of 2-hop spanners. (IV) For every sufficiently large  n, there exists a set P of n points on a circle, such that every plane hop spanner on P has hop stretch factor at least 4. Previously, no lower bound greater than 2 was known. (V) For every  point set on a circle, there exists a plane  4-hop spanner.
Revised:  This is the first nontrivial construction of 2-hop spanners. (IV) For every sufficiently large n, there exists a set P of n points on a circle, such that every plane hop spanner on P has hop stretch factor at least 4. Previously, no lower bound greater than 2 was known. (V) For every finite point set on a circle, there exists a plane 4-hop spanner.
Edit intent: meaning-changed
Original:  This is the first nontrivial construction of 2-hop spanners. (IV) For every sufficiently large  n, there exists a set P of n points on a circle, such that every plane hop spanner on P has hop stretch factor at least 4. Previously, no lower bound greater than 2 was known. (V) For every  point set on a circle, there exists a plane  4-hop spanner.
Revised:  This is the first nontrivial construction of 2-hop spanners. (IV) For every sufficiently large n, there exists a set P of n points on a circle, such that every plane hop spanner on P has hop stretch factor at least 4. Previously, no lower bound greater than 2 was known. (V) For every point set on a circle, there exists a plane (i.e., crossing-free) 4-hop spanner.
Edit intent: meaning-changed