wanyu/IteraTeR_v2 huggingface dataset:
Original:  Specifically, multi-layered hierarchical networks, i.e., the orchestration among different aerial/space platforms, including Unmanned Aerial Vehicles (UAVs), High Altitude Platforms (  HAPs), and satellites co-operating at different altitudes, currently represents one the most attractive technological options to solve coverage and latency constraints associated with the NTN paradigm.
Revised:  Specifically, multi-layered hierarchical networks, i.e., the orchestration among different aerial/space platforms, including Unmanned Aerial Vehicles (UAVs), High Altitude Platforms ( HAPs), and satellites co-operating at different altitudes, currently represent one the most attractive technological options to solve coverage and latency constraints associated with the NTN paradigm.
Edit intent: fluency
Original:  In our work, we evaluate the performance of different multi-layered non-terrestrial configurations, and then provide guidelines on the optimal working point(s) for which it is possible to achieve a good compromise between improved system flexibility and network performance, with respect to a baseline standalone deployment.
Revised:  In this work, we evaluate the performance of different multi-layered non-terrestrial configurations, and then provide guidelines on the optimal working point(s) for which it is possible to achieve a good compromise between improved system flexibility and network performance, with respect to a baseline standalone deployment.
Edit intent: fluency
Original:  In our work, we evaluate the performance of different multi-layered non-terrestrial configurations, and then provide guidelines on the optimal working point(s) for which it is possible to achieve a good compromise between improved system flexibility and network performance, with respect to a baseline standalone deployment.
Revised:  In our work, we evaluate the performance of different multi-layered non-terrestrial configurations, and provide guidelines on the optimal working point(s) for which it is possible to achieve a good compromise between improved system flexibility and network performance, with respect to a baseline standalone deployment.
Edit intent: coherence
Original:  To the best of our knowledge, our approach is the first method to classify engagement based on a multimodal semi-supervised network .
Revised:  To the best of our knowledge, our approach is the first method to classify engagement based on a multimodal semi-supervised setup .
Edit intent: clarity
Original:  We take four popular data models, i.e., RELATION, GRAPH, KV, DOCUMENT, as the foundation of Multi-SQL design.
Revised:  Multi-SQL design.
Edit intent: clarity
Original:  Multi-SQL can also be expanded to suit more complicated scenarios as it is flexible to add other data models.
Revised:  Multi-SQL can also be expanded to suit more complicated scenarios as it is flexible to support other data models.
Edit intent: clarity
Original:  We study the conditions under which the TR encodes a near-optimal solution for MRMP---satisfying these conditions implies near optimality for a variety of popular planners, including dRRT*, and the discrete methods M* and CBS when applied to the continuous domain.
Revised:  We study the conditions under which the TR encodes a near-optimal solution for MRMP -- satisfying these conditions implies near optimality for a variety of popular planners, including dRRT*, and the discrete methods M* and CBS when applied to the continuous domain.
Edit intent: fluency
Original:  This significantly improves upon a previous asymptotic analysis, wherein the number of samples tends to infinity , and supports guaranteed high-quality solutions in practice , within bounded running time.
Revised:  This significantly improves upon a previous asymptotic analysis, wherein the number of samples tends to infinity , and supports guaranteed high-quality solutions in practice within finite time.
Edit intent: clarity
Original:  To this end, we first conduct a multi-faceted analysis showing that females do not reach top visibility (often referred to as the glass ceiling effect) via contrasting perspectives on an Instagram dataset: visibility vs. endorsement and network degree vs. interaction intensity, which are mainly discussed independently by the prior art.
Edit intent: coherence
Original: The central theme of this paper is to answer how gender differences and similarities can impact the information spreading process.
Revised: Online social networks have become a crucial medium to disseminate the latest political, commercial, and social information. Users with high visibility are often selected as seeds to spread information and affect their adoption in target groups. We study how gender differences and similarities can impact the information spreading process.
Edit intent: coherence
Original:  The analysis is based on two datasets: a large-scale Instagram data and a small-scale Facebook data.
Revised:  Using a large-scale Instagram data and a small-scale Facebook data.
Edit intent: clarity
Original:  The analysis is based on two datasets: a large-scale Instagram data and a small-scale Facebook data.
Revised:  The analysis is based on two datasets: a large-scale Instagram dataset and a small-scale Facebook data.
Edit intent: fluency
Original:  OAn extensive simulation comparison with target-agnostic algorithms shows that the proposed Disparity can disseminate information according to the disparity requirement while effectively maximizing the information spread .
Revised:  OAn extensive simulation comparison with target-agnostic algorithms show that Disparity Seeding meets the target percentage while effectively maximizing the information spread .
Edit intent: clarity
Original:  Applying these results , we compute the probability distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort (assumed to be constant in time) , and estimate that the SARS-CoV-2 variant of concern B.1.1.7, detected in September 2020 , first appeared in the United Kingdom early August 2020.
Revised:  Applying these results , we compute the distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort (assumed to be constant in time) , and estimate that the SARS-CoV-2 variant of concern B.1.1.7, detected in September 2020 , first appeared in the United Kingdom early August 2020.
Edit intent: clarity
Original:  Applying these results , we compute the probability distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort (assumed to be constant in time) , and estimate that the SARS-CoV-2 variant of concern B.1.1.7, detected in September 2020 , first appeared in the United Kingdom early August 2020.
Revised:  Applying these results , we compute the probability distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort , and estimate that the SARS-CoV-2 variant of concern B.1.1.7, detected in September 2020 , first appeared in the United Kingdom early August 2020.
Edit intent: clarity
Original:  Applying these results , we compute the probability distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort (assumed to be constant in time) , and estimate that the SARS-CoV-2 variant of concern B.1.1.7, detected in September 2020 , first appeared in the United Kingdom early August 2020.
Revised:  Applying these results , we compute the probability distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort (assumed to be constant in time) , and estimate that the SARS-CoV-2 variant of concern B.1.1.7, detected in September 2020 first appeared in the United Kingdom early August 2020.
Edit intent: fluency
Original:  We estimate a minimal testing frequency to detect clusters before they exceed a certain threshold size, and we compute the detection rate of infected individuals during a single mass testing effort.
Revised:  We also compute a minimal testing frequency to detect clusters before they exceed a certain threshold size, and we compute the detection rate of infected individuals during a single mass testing effort.
Edit intent: clarity
Original:  These results improve our theoretical understanding of early epidemics and are useful for the study and control of local infectious disease clusters.
Revised:  These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.
Edit intent: clarity
Original:  Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates and sub optimal query plans, or over-complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.
Revised:  Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates and sub optimal query plans, or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.
Edit intent: fluency
Original:  It utilizes both independent and conditional factorization to adaptively model different levels of attributes correlations, and thus subsumes all existing CardEst models and dovetails their advantages.
Revised:  It utilizes both independent and conditional factorization to adaptively model different levels of attributes correlations, and thus dovetails their advantages.
Edit intent: clarity
Original:  FLAT supports efficient online probability computation in near liner time on the underlying FSPN model, and provides effective offline model construction  .
Revised:  FLAT supports efficient online probability computation in near liner time on the underlying FSPN model, provides effective offline model construction .
Edit intent: coherence
Original:  It can estimate cardinality for both single table queries and multi-table join queries.
Revised:  It can estimate cardinality for both single table queries and multi table join queries.
Edit intent: fluency
Original:  Extensive experimental study demonstrates the superiority of FLAT over existing CardEst methods on well-known benchmarks: FLAT achieves 1 to 5 orders of magnitude better accuracy, 1 to 3 orders of magnitude faster probability computation speed (around 0.2ms) and 1 to 2 orders of magnitude lower storage cost (only tens of KB) .
Revised:  Extensive experimental study demonstrates the superiority of FLAT over existing CardEst methods on well known benchmarks: FLAT achieves 1 to 5 orders of magnitude better accuracy, 1 to 3 orders of magnitude faster probability computation speed (around 0.2ms) and 1 to 2 orders of magnitude lower storage cost (only tens of KB) .
Edit intent: fluency
Original:  Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates and sub optimal query plans , or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.
Revised:  Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates , or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.
Edit intent: clarity
Original:  FLAT supports efficient online probability computation in near liner time on the underlying FSPN model, provides effective offline model construction and enables incremental model updating .
Revised:  FLAT supports efficient online probability computation in near liner time on the underlying FSPN model, provides effective offline model construction and enables incremental model updates .
Edit intent: fluency
Original:  We also integrate FLAT as a counterpart of the CardEst module in Postgres , which improves the end to end  query execution time by 12.9\% on benchmark workload .
Revised:  We also integrate FLAT into Postgres to perform an end to end query execution time by 12.9\% on benchmark workload .
Edit intent: clarity
Original:  Both these bounds are tight  and imply an O(\log W)  and O(\log \log W)-competitive ratio respectively when the page weights lie between 1 and W. Our model and results also simultaneously generalize the paging with predictions(Lykouris and Vassilvitskii, 2018) and the interleaved paging (Barve et al., 2000;
Revised:  Both these bounds are tight , and imply an O(\log W) and O(\log \log W)-competitive ratio respectively when the page weights lie between 1 and W. Our model and results also simultaneously generalize the paging with predictions(Lykouris and Vassilvitskii, 2018) and the interleaved paging (Barve et al., 2000;
Edit intent: fluency
Original:  Both these bounds are tight  and imply an O(\log W)  and O(\log \log W)-competitive ratio respectively when the page weights lie between 1 and W. Our model and results also simultaneously generalize the paging with predictions(Lykouris and Vassilvitskii, 2018) and the interleaved paging (Barve et al., 2000;
Revised:  Both these bounds are tight and imply an O(\log W) - and O(\log \log W)-competitive ratio respectively when the page weights lie between 1 and W. Our model and results also simultaneously generalize the paging with predictions(Lykouris and Vassilvitskii, 2018) and the interleaved paging (Barve et al., 2000;
Edit intent: fluency
Original:  Both these bounds are tight  and imply an O(\log W)  and O(\log \log W)-competitive ratio respectively when the page weights lie between 1 and W. Our model and results also simultaneously generalize the paging with predictions(Lykouris and Vassilvitskii, 2018) and the interleaved paging (Barve et al., 2000;
Revised:  Both these bounds are tight and imply an O(\log W) and O(\log \log W)-competitive ratio , respectively, when the page weights lie between 1 and W. Our model and results also simultaneously generalize the paging with predictions(Lykouris and Vassilvitskii, 2018) and the interleaved paging (Barve et al., 2000;
Edit intent: fluency
Original:  For offline unweighted paging, we also provide an alternate proof of the optimality of Belady's classic Farthest in Future algorithm, which may be of independent interest.
Revised:  For offline unweighted paging, we also provide an alternate proof of the optimality of Belady's algorithm, which may be of independent interest.
Edit intent: clarity
Original:  The present study focuses on the relationship between the semantic interference effect in a picture-word naming task (longer picture-naming latencies in trials with semantically related than unrelated distractor words) and the change in the magnitude of this effect in the tail of the response time distribution.
Revised:  The present study focuses on the relationship between the semantic interference effect in a picture-word naming task (longer picture-naming latencies in trials with semantically related than unrelated distractor words) and the change in the effect in the tail of the response time distribution.
Edit intent: clarity
Original: Auction theory historically focused on the question of designing the best way to sell a single item to potential buyers, with the concurrent objectives of maximizing the revenue generated or the welfare created.
Revised: Online auctions are one of the most fundamental facets of the modern economy and power an industry generating hundreds of billions of dollars a year in revenue. Auction theory has historically focused on the question of designing the best way to sell a single item to potential buyers, with the concurrent objectives of maximizing the revenue generated or the welfare created.
Edit intent: clarity
Original: Auction theory historically focused on the question of designing the best way to sell a single item to potential buyers, with the concurrent objectives of maximizing the revenue generated or the welfare created.
Revised: Auction theory historically focused on the question of designing the best way to sell a single item to potential buyers, with the concurrent objectives of maximizing revenue generated or the welfare created.
Edit intent: fluency
Original: Auction theory historically focused on the question of designing the best way to sell a single item to potential buyers, with the concurrent objectives of maximizing the revenue generated or the welfare created.
Revised: Auction theory historically focused on the question of designing the best way to sell a single item to potential buyers, with the concurrent objectives of maximizing the revenue generated or welfare created.
Edit intent: fluency
Original:  All those assumptions are no longer satisfied in new markets such as online advertisement : similar items are sold repeatedly, agents are agnostic and try to manipulate each-other.
Revised:  All those assumptions are no longer satisfied in new markets such as online advertising : similar items are sold repeatedly, agents are agnostic and try to manipulate each-other.
Edit intent: fluency
Original:  All those assumptions are no longer satisfied in new markets such as online advertisement : similar items are sold repeatedly, agents are agnostic and try to manipulate each-other.
Revised:  All those assumptions are no longer satisfied in new markets such as online advertisement : similar items are sold repeatedly, and agents are unaware of each other or might try to manipulate each-other.
Edit intent: clarity
Original:  On the other hand, statistical learning theory now provides tools to supplement those missing assumptions in the long run , as agents are able to learn from their environment to improve their strategies.
Revised:  On the other hand, statistical learning theory now provides tools to supplement those missing pieces of information given enough data , as agents are able to learn from their environment to improve their strategies.
Edit intent: clarity
Original:  On the other hand, statistical learning theory now provides tools to supplement those missing assumptions in the long run , as agents are able to learn from their environment to improve their strategies.
Revised:  On the other hand, statistical learning theory now provides tools to supplement those missing assumptions in the long run , as agents can learn from their environment to improve their strategies.
Edit intent: clarity
Original:  This survey covers the recent advances of learning in repeated auctions, starting from the traditional economical study of optimal one-shot auctions with a Bayesian prior.
Revised:  This survey covers recent advances in learning in repeated auctions, starting from the traditional economical study of optimal one-shot auctions with a Bayesian prior.
Edit intent: clarity
Original:  This survey covers the recent advances of learning in repeated auctions, starting from the traditional economical study of optimal one-shot auctions with a Bayesian prior.
Revised:  This survey covers the recent advances of learning in repeated auctions, starting from the traditional economic study of optimal one-shot auctions with a Bayesian prior.
Edit intent: fluency
Original:  We will then focus on the question of learning these mechanism from a dataset of the past valuesof bidders .
Revised:  We then focus on the question of learning these mechanism from a dataset of the past valuesof bidders .
Edit intent: clarity
Original:  We will then focus on the question of learning these mechanism from a dataset of the past valuesof bidders .
Revised:  We will then focus on the question of learning optimal mechanisms from a dataset of the past valuesof bidders .
Edit intent: clarity
Original:  We will then focus on the question of learning these mechanism from a dataset of the past valuesof bidders .
Revised:  We will then focus on the question of learning these mechanism from a dataset of bidders' past values .
Edit intent: fluency
Original:  We will also investigate online variants where gathering those data has a cost to be integrated ("earning while learning").
Revised:  We will also investigate online variants where gathering data has a cost to be integrated ("earning while learning").
Edit intent: clarity
Original:  In a second step , we will further assume that bidders are also adaptive to the mechanism as they interact repeatedly with the same seller.
Revised:  Later in the survey , we will further assume that bidders are also adaptive to the mechanism as they interact repeatedly with the same seller.
Edit intent: clarity
Original:  We will show how strategic agents can actually manipulate repeated auctions, at their own advantage.
Revised:  We will show how strategic agents can actually manipulate repeated auctions, to their own advantage.
Edit intent: clarity
Original:  At the end of this stand-alone survey (reminders of the different techniques used are provided), we will describe new interesting direction of research on repeated auctions .
Revised:  All the questions discussed in this survey are grounded in real-world applications and many of the ideas and algorithms we describe are used every day to power the Internet economy .
Edit intent: coherence
Original: State-of-the-art deep classifiers are intriguingly vulnerable to universal adversarial perturbations: single disturbances of small magnitude that lead to misclassification of most inputs .
Revised: State-of-the-art deep classifiers are intriguingly vulnerable to universal adversarial perturbations: single disturbances of small magnitude that lead to misclassification of most in-puts .
Edit intent: fluency
Original:   For tensor completion, we show that w.h.p. the semidefinite program  introduced by Potechin \& Steurer (2017)  can exactly recover an (n\times n\times n)-tensor T with m random asymmetric components from only n^{3/2}m \, polylog(n) randomly observed entries.
Revised:  For tensor completion, we show that w.h.p. the semidefinite program , introduced by Potechin \& Steurer (2017) can exactly recover an (n\times n\times n)-tensor T with m random asymmetric components from only n^{3/2}m \, polylog(n) randomly observed entries.
Edit intent: fluency
Original:   For tensor completion, we show that w.h.p. the semidefinite program  introduced by Potechin \& Steurer (2017)  can exactly recover an (n\times n\times n)-tensor T with m random asymmetric components from only n^{3/2}m \, polylog(n) randomly observed entries.
Revised:  For tensor completion, we show that w.h.p. the semidefinite program introduced by Potechin Steurer (2017) can exactly recover an (n\times n\times n)-tensor T with m random asymmetric components from only n^{3/2}m \, polylog(n) randomly observed entries.
Edit intent: clarity
Original:   For tensor completion, we show that w.h.p. the semidefinite program  introduced by Potechin \& Steurer (2017)  can exactly recover an (n\times n\times n)-tensor T with m random asymmetric components from only n^{3/2}m \, polylog(n) randomly observed entries.
Revised:  For tensor completion, we show that w.h.p. the semidefinite program introduced by Potechin \& Steurer (2017) can exactly recover an (n\times n\times n)-tensor T with m random asymmetric components from only n^{3/2}m polylog(n) randomly observed entries.
Edit intent: fluency
Original:  However, building a DST module that is scalable and effective is still a challenging issue  .
Revised:  However, building a DST module that is highly effective is still a challenging issue .
Edit intent: clarity
Original:  It can jointly model the relations between intents, slots, and slot values;
Revised:  It can jointly model intents, slots, and slot values;
Edit intent: clarity
Original:  it can utilize rich language representations of utterances and schemas  ;
Revised:  it can leverage the rich representations of utterances and schemas ;
Edit intent: clarity
Original:  it can effectively deal with categorical slots, non-categorical slots, and unseen schemas.
Revised:  it can effectively deal with categorical and non-categorical slots, and unseen schemas.
Edit intent: clarity