wanyu/IteraTeR_v2 huggingface dataset:
Original:  For the former, we propose a novel partitioning scheme, y-plane partitioning, designed to ( ii ) provide consistent execution time when the layer output is large compared to the TEE secure memory;
Revised:  For the former, we propose a novel partitioning scheme, y-plane partitioning, designed to ( i ) provide consistent execution time when the layer output is large compared to the TEE secure memory;
Edit intent: fluency
Original:  The self-tuning variable-bandwidth kernel method [Berry \& Harlim,\emph{Appl.\ Comput.
Revised:  The self-tuning variable-bandwidth kernel method [Berry \emph{Appl.\ Comput.
Edit intent: clarity
Original:  This process becomes harder when the program is a trained machine learning model and even harder for  opaque deep learning models .
Revised:  This process becomes harder when the program is a trained machine learning model and even harder for an opaque deep learning models .
Edit intent: fluency
Original:  In particular, we categorize and discuss existing works along three main dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect human debuggers , and highlight open problems that could be future research directions.
Revised:  In particular, we categorize and discuss existing work along three dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect human debuggers , and highlight open problems that could be future research directions.
Edit intent: clarity
Original:  In particular, we categorize and discuss existing works along three main dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect human debuggers , and highlight open problems that could be future research directions.
Revised:  In particular, we categorize and discuss existing works along three main dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect the feedback providers , and highlight open problems that could be future research directions.
Edit intent: clarity
Original:  R.D. Kongskov, Y. Dong K. Knudsen, BIT 59, 2019  in the case of impulse and Gaussian noise.
Revised:  R.D. Kongskov, Y. Dong and 2019 in the case of impulse and Gaussian noise.
Edit intent: clarity
Original:  R.D. Kongskov, Y. Dong K. Knudsen, BIT 59, 2019  in the case of impulse and Gaussian noise.
Revised:  R.D. Kongskov, Y. Dong K. Knudsen, BIT 59, 2019 , in the case of impulse and Gaussian noise.
Edit intent: fluency
Original:  It can be also easily be computed in O(m%DIFDELCMD < \bar%%% \delta(G)) expected time, where%DIFDELCMD < \bar%%% \delta(G) is the average graph degeneracy and is related to the arboricity.
Revised:  It can be also easily be computed in O(m%DIFDELCMD < \bar%%% %DIFDELCMD < \bar%%% \delta(G) is the average graph degeneracy and is related to the arboricity.
Edit intent: clarity
Original:  We leave it as an open problem to deterministically compute triangle neighbors in O(m%DIFDELCMD < \bar%%% \delta(G) ) time and O(m+n) space.
Revised:  We leave it as an open problem to deterministically compute triangle neighbors in O(m%DIFDELCMD < \bar%%% ) time and O(m+n) space.
Edit intent: coherence
Original: We propose a family of mixed finite elements that are robust for the nearly incompressible strain gradient model .
Revised: We propose a family of mixed finite element that is robust for the nearly incompressible strain gradient model .
Edit intent: fluency
Original:  Optimal rate of convergence is proved that is robust in the incompressible limit.
Revised:  Optimal rate of convergence that is robust in the incompressible limit.
Edit intent: clarity
Original: While many NLP papers, tasks and pipelines assume raw, clean texts, many texts we encounter in the wild  are not so clean, with many of them being visually structured documents (VSDs) such as PDFs.
Revised: While many NLP pipelines assume raw, clean texts, many texts we encounter in the wild are not so clean, with many of them being visually structured documents (VSDs) such as PDFs.
Edit intent: clarity
Original:  Conventional preprocessing tools for VSDs mainly focused on word segmentation and coarse layout analysis, while fine-grained logical structure analysis (such as identifying paragraph boundaries and their hierarchies) of VSDs is underexplored.
Revised:  Conventional preprocessing tools for VSDs mainly focused on word segmentation and coarse layout analysis, whereas fine-grained logical structure analysis (such as identifying paragraph boundaries and their hierarchies) of VSDs is underexplored.
Edit intent: fluency
Original:  To that end, we proposed to formulate the task as prediction of transition labels between text fragments that maps the fragments to a tree, and developed a feature-based machine learning system that fuses visual, textual and semantic cues.
Revised:  To that end, we proposed to formulate the task as prediction of "transition labels" between text fragments that maps the fragments to a tree, and developed a feature-based machine learning system that fuses visual, textual and semantic cues.
Edit intent: fluency
Original:  For example, our system obtained a paragraph boundary detection F1 score of 0.951 which is significantly better than a popular PDF-to-text tool with a F1 score of 0.739.
Revised:  For example, our system obtained a paragraph boundary detection F1 score of 0.953 which is significantly better than a popular PDF-to-text tool with a F1 score of 0.739.
Edit intent: fluency
Original:  For example, our system obtained a paragraph boundary detection F1 score of 0.951 which is significantly better than a popular PDF-to-text tool with a F1 score of 0.739.
Revised:  For example, our system obtained a paragraph boundary detection F1 score of 0.951 which is significantly better than a popular PDF-to-text tool with an F1 score of 0.739.
Edit intent: fluency
Original:  In previous work, it was shown that planar fronts of the bidomain Allen--Cahn equation can become unstable in contrast to the classical Allen--Cahn equation.
Revised:  In previous work, it was shown that planar fronts of the bidomain Allen-Cahn equation can become unstable in contrast to the classical Allen--Cahn equation.
Edit intent: fluency
Original:  In previous work, it was shown that planar fronts of the bidomain Allen--Cahn equation can become unstable in contrast to the classical Allen--Cahn equation.
Revised:  In previous work, it was shown that planar fronts of the bidomain Allen--Cahn equation can become unstable in contrast to the classical Allen-Cahn equation.
Edit intent: fluency
Original:  However, unlike the bidomain Allen--Cahn case, the destabilized pulse does not necessarily develop into a zigzag pulse.
Revised:  However, unlike the bidomain Allen-Cahn case, the destabilized pulse does not necessarily develop into a zigzag pulse.
Edit intent: fluency
Original:  With most recent interest being in analyzing emotions from the spoken voice, little experimentation has been performed to discover how emotions are recognized in the singing voice -- both in noiseless and noisy data (i.e., data that is either inaccurate, difficult to interpret, has corrupted/distorted/nonsense information like actual noise sounds in this case, or has a low ratio of usable/unusable information).
Revised:  While recent interest has focused on analyzing emotions from the spoken voice, little experimentation has been performed to discover how emotions are recognized in the singing voice -- both in noiseless and noisy data (i.e., data that is either inaccurate, difficult to interpret, has corrupted/distorted/nonsense information like actual noise sounds in this case, or has a low ratio of usable/unusable information).
Edit intent: clarity
Original:  \beta) of the Ising model in terms of the relation between the edge interaction ~ \beta and a parameter ~ \Delta which is an upper bound on the maximum degree of the input graph ~ G. Following recent trends in both statistical physics and algorithmic research, we allow the edge interaction ~ \beta to be any complex number.
Revised:  \beta) of the Ising model in terms of the relation between the edge interaction \beta and a parameter ~ \Delta which is an upper bound on the maximum degree of the input graph ~ G. Following recent trends in both statistical physics and algorithmic research, we allow the edge interaction ~ \beta to be any complex number.
Edit intent: fluency
Original:  \beta) of the Ising model in terms of the relation between the edge interaction ~ \beta and a parameter ~ \Delta which is an upper bound on the maximum degree of the input graph ~ G. Following recent trends in both statistical physics and algorithmic research, we allow the edge interaction ~ \beta to be any complex number.
Revised:  \beta) of the Ising model in terms of the relation between the edge interaction ~ \beta and a parameter \Delta which is an upper bound on the maximum degree of the input graph ~ G. Following recent trends in both statistical physics and algorithmic research, we allow the edge interaction ~ \beta to be any complex number.
Edit intent: fluency
Original:  \beta) of the Ising model in terms of the relation between the edge interaction ~ \beta and a parameter ~ \Delta which is an upper bound on the maximum degree of the input graph ~ G. Following recent trends in both statistical physics and algorithmic research, we allow the edge interaction ~ \beta to be any complex number.
Revised:  \beta) of the Ising model in terms of the relation between the edge interaction ~ \beta and a parameter ~ \Delta which is an upper bound on the maximum degree of the input graph G. Following recent trends in both statistical physics and algorithmic research, we allow the edge interaction ~ \beta to be any complex number.
Edit intent: fluency
Original:  \beta) of the Ising model in terms of the relation between the edge interaction ~ \beta and a parameter ~ \Delta which is an upper bound on the maximum degree of the input graph ~ G. Following recent trends in both statistical physics and algorithmic research, we allow the edge interaction ~ \beta to be any complex number.
Revised:  \beta) of the Ising model in terms of the relation between the edge interaction ~ \beta and a parameter ~ \Delta which is an upper bound on the maximum degree of the input graph ~ G. Following recent trends in both statistical physics and algorithmic research, we allow the edge interaction \beta to be any complex number.
Edit intent: fluency
Original:  The core of the proof is showing that there are no inputs~G that make the partition function ~ 0 when \beta is in this range.
Revised:  The core of the proof is showing that there are no inputs~G that make the partition function 0 when \beta is in this range.
Edit intent: fluency
Original:  \beta) when  is an algebraic number such that \beta }\not  \cup \{i, -i\} and } \lvert \beta - 1\rvert / \lvert \beta + 1 \rvert > 1 / \Delta - 1. These are the first results to show intractability of approximating Z_{Ising}(-, \beta) on bounded degree graphs with complex ~ \beta.
Revised:  \beta) when is an algebraic number such that \beta }\not \cup \{i, -i\} and } \lvert \beta - 1\rvert / \lvert \beta + 1 \rvert > 1 / \Delta - 1. These are the first results to show intractability of approximating Z_{Ising}(-, \beta) on bounded degree graphs with complex \beta.
Edit intent: clarity
Original:  Existing works heavily rely on the tracking of detected objects but difficult to recall revisited objects precisely.
Revised:  Existing works heavily rely on the tracking of detected objects but have difficulty to recall revisited objects precisely.
Edit intent: clarity
Original:  To be robust to the number of key-points detected, we propose a feature sparse encoding and object dense encoding method to ensure that each key-point can only affect a small part of the object descriptors, leading it  robust to viewpoint changes, scaling, occlusion, and even object deformation.
Revised:  To be robust to the number of key-points detected, we propose a feature sparse encoding and object dense encoding method to ensure that each key-point can only affect a small part of the object descriptors, leading it to be robust to viewpoint changes, scaling, occlusion, and even object deformation.
Edit intent: fluency
Original:  In the experiments, we show that it achieves superior performance for object identification than the state-of-the art algorithm and is able to provide reliable semantic relocalization.
Revised:  In the experiments, we show that it achieves superior performance for object identification than the state-of-the art algorithms and is able to provide reliable semantic relocalization.
Edit intent: fluency
Original:  It is a plug-and-play module and we expect that it will play an important role in the robotic applications.
Revised:  It is a plug-and-play module and we expect that it will play an important role in various applications.
Edit intent: clarity
Original: Recent years have witnessed an increasing interest in improving the perception performance of LiDARs on autonomous vehicles.
Revised: The past few years have witnessed an increasing interest in improving the perception performance of LiDARs on autonomous vehicles.
Edit intent: clarity
Original:  While most of the existing works focus on developing novel model architecturesto process point cloud data , we study the problem from an optimal sensing perspective.
Revised:  While most of the existing works focus on developing new deep learning algorithms or model architectures , we study the problem from an optimal sensing perspective.
Edit intent: clarity
Original:  To this end, together with a fast evaluation function based on ray tracing within the perception region of a LiDAR configuration, we propose an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing.
Revised:  To this end, we introduce an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing.
Edit intent: coherence
Original:  To this end, together with a fast evaluation function based on ray tracing within the perception region of a LiDAR configuration, we propose an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing.
Revised:  To this end, together with a fast evaluation function based on ray tracing within the perception region of a LiDAR configuration, we propose an easy-to-compute information-theoretic surrogate metric to quantitatively and fast evaluate LiDAR placement for maximal sensing.
Edit intent: clarity
Original:  We show a correlation between our surrogate function and common object detection performance metrics.
Revised:  We show a correlation between our surrogate metric and object detection performance metrics.
Edit intent: clarity
Original:  Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10\% ~ 20\% on the state-of-the-art perception algorithms .
Revised:  Our results show that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10\% ~ 20\% on the state-of-the-art perception algorithms .
Edit intent: clarity
Original:  Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10\% ~ 20\% on the state-of-the-art perception algorithms .
Revised:  Our results confirm that sensor placement is non-negligible in 3D point cloud-based object detection and could lead to a variation of performance by 10\% ~ 20\% on the state-of-the-art perception algorithms .
Edit intent: clarity
Original:  We believe that this is one of the first studies to use LiDAR placement to improve the performance of perception .
Revised:  We believe that this is one of the first studies to quantitatively investigate the influence of LiDAR placement on perception performance .
Edit intent: clarity
Original:  Despite recent advances in multi-agent reinforcement learning (MARL) on traffic control, little research has focused on bus fleet control due to the tricky asynchronous characteristic -- control action only happens when a bus arrives at a bus stop and thus agents do not act simultaneously.
Revised:  Despite recent advances in multi-agent reinforcement learning (MARL) on traffic control, little research has focused on bus fleet control due to the tricky asynchronous characteristic -- control actions only happen when a bus arrives at a bus stop and thus agents do not act simultaneously.
Edit intent: fluency
Original: What exactly does "stack safety" mean?
Revised: What does "stack safety" mean?
Edit intent: clarity
Original:  But these mechanisms typically lack precise specifications, relying instead on informal descriptions and examples of  bad behaviors that they prevent.
Revised:  But these mechanisms typically lack precise specifications, relying instead on informal descriptions and examples of the bad behaviors that they prevent.
Edit intent: fluency
Original:  We propose a  formal characterization of stack safety , formulated with concepts from language-based security: a combination of an integrity property ("the private state in each caller's stack frame is held invariant by the callee"),  a confidentiality property ("the callee's behavior is insensitive to the caller's private state"), and a well-bracketedness property("each callee returns control to its immediate caller") .
Revised:  We propose a formal characterization of stack safety based on concepts from language-based security: a combination of an integrity property ("the private state in each caller's stack frame is held invariant by the callee"), a confidentiality property ("the callee's behavior is insensitive to the caller's private state"), and a well-bracketedness property("each callee returns control to its immediate caller") .
Edit intent: clarity
Original:  We propose a  formal characterization of stack safety , formulated with concepts from language-based security: a combination of an integrity property ("the private state in each caller's stack frame is held invariant by the callee"),  a confidentiality property ("the callee's behavior is insensitive to the caller's private state"), and a well-bracketedness property("each callee returns control to its immediate caller") .
Revised:  We propose a formal characterization of stack safety , formulated with concepts from language-based security: a combination of an integrity property ("the private state in each caller's stack frame is held invariant by the callee"), and a confidentiality property ("the callee's behavior is insensitive to the caller's private state"), and a well-bracketedness property("each callee returns control to its immediate caller") .
Edit intent: coherence
Original:  We use these properties to validate the stack-safety " micro-policies " proposed by Roessler and DeHon [2018].
Revised:  We use these properties to validate the stack safety micro-policies " proposed by Roessler and DeHon [2018].
Edit intent: fluency
Original:  We use these properties to validate the stack-safety " micro-policies " proposed by Roessler and DeHon [2018].
Revised:  We use these properties to validate the stack-safety " micro-policies proposed by Roessler and DeHon [2018].
Edit intent: fluency
Original:  Specifically, we check (with property-based random testing) that Roessler and Dehon's "eager" micro-policy, which catches violations as earlyas possible , enforces a simple "stepwise" variant of our properties and correctly detects several broken variants , and that (a repaired version of) their more performant "lazy" micro-policy corresponds to a slightly weaker and more extensional "observational " variant of our properties .
Revised:  Specifically, we check (with property-based random testing) that their "eager" micro-policy, which catches violations as earlyas possible , enforces a simple "stepwise" variant of our properties and correctly detects several broken variants , and that (a repaired version of) their more performant "lazy" micro-policy corresponds to a slightly weaker and more extensional "observational " variant of our properties .
Edit intent: clarity
Original:  Specifically, we check (with property-based random testing) that Roessler and Dehon's "eager" micro-policy, which catches violations as earlyas possible , enforces a simple "stepwise" variant of our properties and correctly detects several broken variants , and that (a repaired version of) their more performant "lazy" micro-policy corresponds to a slightly weaker and more extensional "observational " variant of our properties .
Revised:  Specifically, we check (with property-based random testing) that Roessler and Dehon's "eager" micro-policy, which catches violations early , enforces a simple "stepwise" variant of our properties and correctly detects several broken variants , and that (a repaired version of) their more performant "lazy" micro-policy corresponds to a slightly weaker and more extensional "observational " variant of our properties .
Edit intent: clarity
Original:  Specifically, we check (with property-based random testing) that Roessler and Dehon's "eager" micro-policy, which catches violations as earlyas possible , enforces a simple "stepwise" variant of our properties and correctly detects several broken variants , and that (a repaired version of) their more performant "lazy" micro-policy corresponds to a slightly weaker and more extensional "observational " variant of our properties .
Revised:  Specifically, we check (with property-based random testing) that Roessler and Dehon's "eager" micro-policy, which catches violations as earlyas possible , enforces a simple "stepwise" variant of our properties , and that (a repaired version of) their more performant "lazy" micro-policy corresponds to a slightly weaker and more extensional "observational " variant of our properties .
Edit intent: coherence
Original:  Specifically, we check (with property-based random testing) that Roessler and Dehon's "eager" micro-policy, which catches violations as earlyas possible , enforces a simple "stepwise" variant of our properties and correctly detects several broken variants , and that (a repaired version of) their more performant "lazy" micro-policy corresponds to a slightly weaker and more extensional "observational " variant of our properties .
Revised:  Specifically, we check (with property-based random testing) that Roessler and Dehon's "eager" micro-policy, which catches violations as earlyas possible , enforces a simple "stepwise" variant of our properties and correctly detects several broken variants , and that (a repaired version of) their more performant "lazy" micro-policy enforces a slightly weaker and more extensional "observational " variant of our properties .
Edit intent: clarity
Original:  What happens if this question is modified in a way that we get a possibly infinite family of graphs as an input, and the question if is there exists one graph satisfying \Phi  ?
Revised:  What happens if this question is modified in a way that we get a possibly infinite family of graphs as an input, and the question is if there is a graph satisfying \Phi ?
Edit intent: clarity
Original:  What happens if this question is modified in a way that we get a possibly infinite family of graphs as an input, and the question if is there exists one graph satisfying \Phi  ?
Revised:  What happens if this question is modified in a way that we get a possibly infinite family of graphs as an input, and the question if is there exists one graph satisfying \Phi in the family ?
Edit intent: clarity
Original:  Every L_\alpha defines in a natural way a single finite graph F _\alpha where some edges and vertices are marked.
Revised:  Every L_\alpha defines in a natural way a single finite graph F where some edges and vertices are marked.
Edit intent: fluency