wanyu/IteraTeR_v2 huggingface dataset:
Original:  The  IER-kNN algorithm , which previously showed the highest FANN search performance, used the R-tree and pruned off unnecessary nodes based on the Euclidean coordinates of objects in road networks.
Revised:  The IER-kNN algorithm used the R-tree and pruned off unnecessary nodes based on the Euclidean coordinates of objects in road networks.
Edit intent: coherence
Original:  However, IER-kNN made many unnecessary accesses to index nodes since the Euclidean distance between objects is much different from the actual shortest-path distance between them.
Revised:  However, IER-kNN made many unnecessary accesses to index nodes since the Euclidean distances between objects are significantly different from the actual shortest-path distance between them.
Edit intent: clarity
Original:  However, IER-kNN made many unnecessary accesses to index nodes since the Euclidean distance between objects is much different from the actual shortest-path distance between them.
Revised:  However, IER-kNN made many unnecessary accesses to index nodes since the Euclidean distance between objects is much different from the actual shortest-path distances between them.
Edit intent: fluency
Original:  In contrast, our algorithm proposed in this study can greatly reduce unnecessary accesses to index nodes compared to IER-kNN since the M-tree is constructed based on the actual shortest-path distances between objects.
Revised:  In contrast, our algorithm proposed in this study can greatly reduce unnecessary accesses to index nodes compared with IER-kNN since the M-tree is constructed based on the actual shortest-path distances between objects.
Edit intent: fluency
Original:  To the best of our knowledge, our algorithm is the first exact FANN algorithm using the M-tree.
Revised:  To the best of our knowledge, our algorithm is the first exact FANN algorithm that uses the M-tree.
Edit intent: fluency
Original:  As a result of a series of experiments using various real road network datasets, our algorithm always showed a better performance than IER-kNN and was improved by up to 6.92 times.
Revised:  In conducting a series of experiments using various real road network datasets, our algorithm always showed a better performance than IER-kNN and was improved by up to 6.92 times.
Edit intent: clarity
Original:  As a result of a series of experiments using various real road network datasets, our algorithm always showed a better performance than IER-kNN and was improved by up to 6.92 times.
Revised:  As a result of a series of experiments using various real road network datasets, our algorithm consistently outperformed IER-kNN and was improved by up to 6.92 times.
Edit intent: clarity
Original:  As a result of a series of experiments using various real road network datasets, our algorithm always showed a better performance than IER-kNN and was improved by up to 6.92 times.
Revised:  As a result of a series of experiments using various real road network datasets, our algorithm always showed a better performance than IER-kNN by up to 6.92 times.
Edit intent: coherence
Original:  We treat the measurement outliers independently for independent observations leading to selective rejection of the corrupted observations during inference.
Revised:  We treat the measurement outliers independently for independent observations leading to selective rejection of the corrupted data during inference.
Edit intent: clarity
Original:  We then incorporate team fairness into policy optimization -- introducing Fairness through Equivariance (Fair-E) , a novel learning strategy that achieves provably fair reward distributions .
Revised:  We then incorporate team fairness into policy optimization -- introducing Fairness through Equivariance (Fair-E) and demonstrate its effectiveness empirically .
Edit intent: clarity
Original:  We then introduce Fairness through Equivariance Regularization (Fair-ER) as a soft-constraint version of Fair-E and show that Fair-ER reaches higher levels of utility than Fair-E and fairer outcomes than policies with no equivariance .
Revised:  We then introduce Fairness through Equivariance Regularization (Fair-ER) as a soft-constraint version of Fair-E and show that it reaches higher levels of utility than Fair-E and fairer outcomes than policies with no equivariance .
Edit intent: clarity
Original:  We then introduce Fairness through Equivariance Regularization (Fair-ER) as a soft-constraint version of Fair-E and show that Fair-ER reaches higher levels of utility than Fair-E and fairer outcomes than policies with no equivariance .
Revised:  We then introduce Fairness through Equivariance Regularization (Fair-ER) as a soft-constraint version of Fair-E and show that Fair-ER reaches higher levels of utility than Fair-E and fairer outcomes than non-equivariant policies .
Edit intent: clarity
Original:  Finally, we investigate the fairness-utility trade-off in multi-agent settings  .
Revised:  Finally, we present novel findings regarding the fairness-utility trade-off in multi-agent settings .
Edit intent: clarity
Original: In the era of digitalization, utilization of data-driven control approaches to minimize energy consumption of residential/commercial building is of far-reaching significance.
Revised: Data-driven control approaches for the minimization of energy consumption of residential/commercial building is of far-reaching significance.
Edit intent: clarity
Original: Problems of satisfiability and resiliency in workflows have been widely studied in the last decade.
Edit intent: coherence
Original:  A solution may not exist because of the restrictions imposed by the base authorization relation and constraints that form part of the input to APEP.
Edit intent: coherence
Original:  (Most constraints known to be of practical use satisfy these restrictions.)
Revised:  (Most constraints known to be of practical use satisfy both restrictions.)
Edit intent: clarity
Original:  HASI carefully injects noise into the model at inference time and used the model's response to differentiate adversarial inputs from benign ones.
Revised:  We show that by carefully injecting noise into the model at inference time and used the model's response to differentiate adversarial inputs from benign ones.
Edit intent: coherence
Original:  HASI carefully injects noise into the model at inference time and used the model's response to differentiate adversarial inputs from benign ones.
Revised:  HASI carefully injects noise into the model at inference time , we can differentiate adversarial inputs from benign ones.
Edit intent: coherence
Original:   We show an adversarial detection rate of average 87\% which exceeds the detection rate of the state-of-the-art approaches, with a much lower overhead.
Revised:  We show an adversarial detection rate of average 87\% which exceeds the detection rate of the state of the art approaches, with a much lower overhead.
Edit intent: fluency
Original: Safety is a key issue in human-robot interaction , but perceived safety has not been well-studied in comparison to physical safety .
Revised: Safety in human-robot interaction , but perceived safety has not been well-studied in comparison to physical safety .
Edit intent: clarity
Original:  The questionnaire results show a strong correlation between these factors and the perceived safety.
Revised:  The questionnaire results show a correlation between comfort, sense of control, trust, and perceived safety.
Edit intent: clarity
Original:  The effect of individual characteristics such as personality and gender on perceived safetywas also discussed.
Revised:  The effect of individual human characteristics ( such as personality and gender on perceived safetywas also discussed.
Edit intent: clarity
Original:  We propose a new strategy for discretizing the solution at irregular points on a nine point compact stencil such that the high order compactness is maintained throughout the whole computational domain.
Revised:  We propose a new strategy for discretizing the solution at irregular points on a nine point compact stencil such that the higher-order compactness is maintained throughout the whole computational domain.
Edit intent: fluency
Original: Recent work on speech self-supervised learning (speech SSL) demonstrated the benefits of scale in learning rich and transferable representations for Automatic Speech Recognition (ASR) with limited parallel data.
Revised: Self-supervised speech representation learning (speech SSL) demonstrated the benefits of scale in learning rich and transferable representations for Automatic Speech Recognition (ASR) with limited parallel data.
Edit intent: clarity
Original: Recent work on speech self-supervised learning (speech SSL) demonstrated the benefits of scale in learning rich and transferable representations for Automatic Speech Recognition (ASR) with limited parallel data.
Revised: Recent work on speech self-supervised learning (speech SSL) has demonstrated the benefit of scale in learning rich and transferable representations for Automatic Speech Recognition (ASR) with limited parallel data.
Edit intent: clarity
Original: Recent work on speech self-supervised learning (speech SSL) demonstrated the benefits of scale in learning rich and transferable representations for Automatic Speech Recognition (ASR) with limited parallel data.
Revised: Recent work on speech self-supervised learning (speech SSL) demonstrated the benefits of scale in learning rich representations for Automatic Speech Recognition (ASR) with limited parallel data.
Edit intent: clarity
Original:  It is then natural to investigate the existence of sparse and transferrable subnetworks in pre-trained speech SSL models that can achieve even better low-resource ASR performance .
Revised:  It is then natural to investigate the existence of sparse subnetworks in pre-trained speech SSL models that can achieve even better low-resource ASR performance .
Edit intent: clarity
Original:  It is then natural to investigate the existence of sparse and transferrable subnetworks in pre-trained speech SSL models that can achieve even better low-resource ASR performance .
Revised:  It is then natural to investigate the existence of sparse and transferrable subnetworks in pre-trained speech SSL models that achieve even better low-resource ASR performance .
Edit intent: clarity
Original:  It is then natural to investigate the existence of sparse and transferrable subnetworks in pre-trained speech SSL models that can achieve even better low-resource ASR performance .
Revised:  It is then natural to investigate the existence of sparse and transferrable subnetworks in pre-trained speech SSL models that can achieve even better low-resource ASR results .
Edit intent: clarity
Original:  Moreover, contrary to what LTH predicts, the discovered subnetworks yield minimal performance gain compared to the original dense network.
Revised:  Moreover, we show that the discovered subnetworks yield minimal performance gain compared to the original dense network.
Edit intent: coherence
Original:  In this work, we propose Prune-Adjust- Re-Prune (PARP), which discovers and finetunes subnetworks for much better ASR performance, while only requiring a single downstream  finetuning run.
Revised:  We present Prune-Adjust-Re-Prune (PARP), which discovers and finetunes subnetworks for much better ASR performance, while only requiring a single downstream finetuning run.
Edit intent: clarity
Original:  In this work, we propose Prune-Adjust- Re-Prune (PARP), which discovers and finetunes subnetworks for much better ASR performance, while only requiring a single downstream  finetuning run.
Revised:  In this work, we propose Prune-Adjust- Re-Prune (PARP), which discovers and finetunes subnetworks for much better performance, while only requiring a single downstream finetuning run.
Edit intent: clarity
Original:  In this work, we propose Prune-Adjust- Re-Prune (PARP), which discovers and finetunes subnetworks for much better ASR performance, while only requiring a single downstream  finetuning run.
Revised:  In this work, we propose Prune-Adjust- Re-Prune (PARP), which discovers and finetunes subnetworks for much better ASR performance, while only requiring a single downstream ASR finetuning run.
Edit intent: fluency
Original:  PARP is inspired by our surprising observation that subnetworks pruned for pre-training tasks only needed to be slightly adjusted to achieve a sizeable performance boost in downstream ASR tasks.
Revised:  PARP is inspired by our surprising observation that subnetworks pruned for pre-training tasks need merely a slight adjustment to achieve a sizeable performance boost in downstream ASR tasks.
Edit intent: clarity
Original:  Extensive experiments on low-resource English and multi-lingual ASR show (1) sparse subnetworks exist in  pre-trained speech SSL, and (2) the computational advantage and performance gain of PARP over baseline pruning methods.
Revised:  Extensive experiments on low-resource ASR verify (1) sparse subnetworks exist in pre-trained speech SSL, and (2) the computational advantage and performance gain of PARP over baseline pruning methods.
Edit intent: clarity
Original:  On the 10min Librispeech split without LM decoding, PARP discovers subnetworks from wav2vec 2.0 with an absolute 10.9\%/12.6\% WER decrease compared to the full model.
Revised:  In particular, on the 10min Librispeech split without LM decoding, PARP discovers subnetworks from wav2vec 2.0 with an absolute 10.9\%/12.6\% WER decrease compared to the full model.
Edit intent: coherence
Original:  We demonstrate PARP mitigates performance degradation in cross-lingual mask transfer, and investigate the possibility of discovering a single subnetwork for 10 spoken languages in one run .
Revised:  We further demonstrate the effectiveness of PARP via: cross-lingual mask transfer, and investigate the possibility of discovering a single subnetwork for 10 spoken languages in one run .
Edit intent: clarity
Original: Recovery of support of a sparse vector from simple measurements is a widely studied problem, considered under the frameworks of compressed sensing, 1-bit compressed sensing, and more general single index models.
Revised: Recovery of support of a sparse vector from simple measurements is a widely-studied problem, considered under the frameworks of compressed sensing, 1-bit compressed sensing, and more general single index models.
Edit intent: fluency
Original:  Both of these problems were also extensively studied recently.
Revised:  Both of these problems have also received attention recently.
Edit intent: clarity
Original: Artificial Neural Networks (ANNs) are being deployed on an increasing number of safety-critical applications, including autonomous cars and medical diagnosis.
Revised: Artificial Neural Networks (ANNs) are being deployed for an increasing number of safety-critical applications, including autonomous cars and medical diagnosis.
Edit intent: fluency
Original:   Here, we develop and evaluate a  symbolic verification framework using incremental model checking ( IMC ) and satisfiability modulo theories (SMT) to check for vulnerabilities in ANNs.
Revised:  Here, we develop and evaluate a novel symbolic verification framework using incremental model checking ( IMC ) and satisfiability modulo theories (SMT) to check for vulnerabilities in ANNs.
Edit intent: clarity
Original:   Here, we develop and evaluate a  symbolic verification framework using incremental model checking ( IMC ) and satisfiability modulo theories (SMT) to check for vulnerabilities in ANNs.
Revised:  Here, we develop and evaluate a symbolic verification framework using incremental model checking ( SMC ) and satisfiability modulo theories (SMT) to check for vulnerabilities in ANNs.
Edit intent: fluency
Original:  With this  , we can provide  guarantees on the safe behavior of ANNs implemented both in floating-point and fixed-point (quantized) arithmetic.
Revised:  With this verification framework , we can provide guarantees on the safe behavior of ANNs implemented both in floating-point and fixed-point (quantized) arithmetic.
Edit intent: clarity
Original:  With this  , we can provide  guarantees on the safe behavior of ANNs implemented both in floating-point and fixed-point (quantized) arithmetic.
Revised:  With this , we can provide guarantees on the safe behavior of ANNs implemented both in floating- and fixed-point (quantized) arithmetic.
Edit intent: fluency
Original:  For small- to medium-sized ANN, our approach completes most of its verification runs in minutes.
Revised:  Furthermore, for small- to medium-sized ANN, our approach completes most of its verification runs in minutes.
Edit intent: coherence
Original:  Moreover, in contrast to most state-of-the-art methods, our approach is not restricted to specific choices of activation functions or non-quantized representations  .
Revised:  Moreover, in contrast to most state-of-the-art methods, our approach is not restricted to specific choices regarding activation functions and non-quantized representations .
Edit intent: clarity
Original: We study the graphical generalization of the  2-choice balls-into-bins process, where rather than choosing any two random bins, the bins correspond to vertices of an underlying graph, and only the bins connected by an edgecan be chosen.
Revised: The graphical balls-into-bins process is a generalization of the 2-choice balls-into-bins process, where rather than choosing any two random bins, the bins correspond to vertices of an underlying graph, and only the bins connected by an edgecan be chosen.
Edit intent: clarity
Original: We study the graphical generalization of the  2-choice balls-into-bins process, where rather than choosing any two random bins, the bins correspond to vertices of an underlying graph, and only the bins connected by an edgecan be chosen.
Revised: We study the graphical generalization of the 2-choice balls-into-bins process, where the bins correspond to vertices of an underlying graph, and only the bins connected by an edgecan be chosen.
Edit intent: clarity
Original:  For any k(n) edge-connected , d(n)-regular graph on n vertices  and any number of balls, we give an allocation strategy which guarantees thatthe maximum gap between the bin loads is O((d/k) \log^4  n \log \log n), with high probability.
Revised:  For any k(n) -edge-connected , d(n)-regular graph on n vertices and any number of balls, we give an allocation strategy which guarantees thatthe maximum gap between the bin loads is O((d/k) \log^4 n \log \log n), with high probability.
Edit intent: fluency
Original:  For any k(n) edge-connected , d(n)-regular graph on n vertices  and any number of balls, we give an allocation strategy which guarantees thatthe maximum gap between the bin loads is O((d/k) \log^4  n \log \log n), with high probability.
Revised:  For any k(n) edge-connected , d(n)-regular graph on n vertices , and any number of balls, we give an allocation strategy which guarantees thatthe maximum gap between the bin loads is O((d/k) \log^4 n \log \log n), with high probability.
Edit intent: fluency
Original:  For any k(n) edge-connected , d(n)-regular graph on n vertices  and any number of balls, we give an allocation strategy which guarantees thatthe maximum gap between the bin loads is O((d/k) \log^4  n \log \log n), with high probability.
Revised:  For any k(n) edge-connected , d(n)-regular graph on n vertices and any number of balls, we give an allocation strategy that, with high probability, ensures a gap of O((d/k) \log^4 n \log \log n), with high probability.
Edit intent: clarity
Original:  The construction is based on defining certain orthogonal flows on cut-based R\"{a is simple to implement and takes only O(\log(n)) time per allocation , and can be viewed as a  global version of the greedy strategy that compares average load on  sets of vertices, rather than on individual vertices  .
Revised:  The construction is based on defining certain orthogonal flows on cut-based R\"{a is simple to implement and requires only O(\log(n)) time per allocation , and can be viewed as a global version of the greedy strategy that compares average load on sets of vertices, rather than on individual vertices .
Edit intent: clarity
Original:  The construction is based on defining certain orthogonal flows on cut-based R\"{a is simple to implement and takes only O(\log(n)) time per allocation , and can be viewed as a  global version of the greedy strategy that compares average load on  sets of vertices, rather than on individual vertices  .
Revised:  The construction is based on defining certain orthogonal flows on cut-based R\"{a is simple to implement and takes only O(\log(n)) time per allocation . It can be viewed as a global version of the greedy strategy that compares average load on sets of vertices, rather than on individual vertices .
Edit intent: coherence
Original:  The construction is based on defining certain orthogonal flows on cut-based R\"{a is simple to implement and takes only O(\log(n)) time per allocation , and can be viewed as a  global version of the greedy strategy that compares average load on  sets of vertices, rather than on individual vertices  .
Revised:  The construction is based on defining certain orthogonal flows on cut-based R\"{a is simple to implement and takes only O(\log(n)) time per allocation , and can be viewed as a more global version of the greedy strategy that compares average load on sets of vertices, rather than on individual vertices .
Edit intent: clarity
Original:  The construction is based on defining certain orthogonal flows on cut-based R\"{a is simple to implement and takes only O(\log(n)) time per allocation , and can be viewed as a  global version of the greedy strategy that compares average load on  sets of vertices, rather than on individual vertices  .
Revised:  The construction is based on defining certain orthogonal flows on cut-based R\"{a is simple to implement and takes only O(\log(n)) time per allocation , and can be viewed as a global version of the greedy strategy that compares average load on certain fixed sets of vertices, rather than on individual vertices .
Edit intent: clarity
Original:  However, this set is too large to handle in many games .
Revised:  While this is acceptable in games like poker where the common-knowledge closure is relatively small, many practical games have more complex information structure, which renders the common-knowledge closure impractically large to enumerate or even reasonably approximate .
Edit intent: clarity
Original:  Even without the safety-guaranteeing additions, experiments on medium-sized games show that our approach always reduced exploitability  even when applied at every infoset, and a depth-limited version of it led to--to our knowledge--the first strong AI for the massive challenge problem dark chess.
Revised:  Even without the safety-guaranteeing additions, experiments on medium-sized games show that our approach always reduced exploitability even when applied at every infoset, and a depth-limited version of it led to -- to our knowledge -- the first strong AI for the massive challenge problem dark chess.
Edit intent: fluency
Original:  Even without the safety-guaranteeing additions, experiments on medium-sized games show that our approach always reduced exploitability  even when applied at every infoset, and a depth-limited version of it led to--to our knowledge--the first strong AI for the massive challenge problem dark chess.
Revised:  Even without the safety-guaranteeing additions, experiments on medium-sized games show that our approach always reduced exploitability even when applied at every infoset, and a depth-limited version of it led to--to our knowledge--the first strong AI for the challenge problem dark chess.
Edit intent: clarity
Original: Functional Encryption (FE) expands traditional public-key encryption in two different ways: it supports fine-grained access control and it allows to learn a function of the encrypted data.
Revised: Functional Encryption (FE) expands traditional public-key encryption in two different ways: it supports fine-grained access control and allows learning a function of the encrypted data.
Edit intent: fluency