wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Under the hood, we introduce MPI communication primitives like send and recv on layer boundaries for data exchange between model-partitions and allreduce for gradient exchange across model-replicas. Our proposed designs in HyPar-Flow offer up to 3.1x speedup over sequential training for ResNet-110 and up to 1.6x speedup over Horovod-based data-parallel training for ResNet-1001;
Revised:  Under the hood, we introduce MPI communication primitives like send and recv to exchange layer-data/partial-errors while allreduce is used to accumulate/average gradients across model replicas. Beyond the design and implementation of HyPar-Flow, we also provide comprehensive correctness and performance results on three state-of-the-art HPC systems including TACC Frontera (#5 on URL). For ResNet-1001, an ultra-deep model, HyPar-Flow offer up to 3.1x speedup over sequential training for ResNet-110 and up to 1.6x speedup over Horovod-based data-parallel training for ResNet-1001;
Edit intent: meaning-changed
Original:  Our proposed designs in HyPar-Flow offer up to 3.1x speedup over sequential training for ResNet-110 and up to 1.6x speedup over Horovod-based data-parallel training for ResNet-1001;
Revised:  Our proposed designs in HyPar-Flow provides: 1) Up to 1.6x speedup over Horovod-based data-parallel training for ResNet-1001;
Edit intent: clarity
Original:  Our proposed designs in HyPar-Flow offer up to 3.1x speedup over sequential training for ResNet-110 and up to 1.6x speedup over Horovod-based data-parallel training for ResNet-1001; a model that has 1, 001 layers and 30 million parameters. We provide an in-depth performance characterization of the HyPar-Flow framework on multiple HPC systems with diverse CPU architectures including Intel Xeon(s)and AMD EPYC. HyPar-Flow provides 110x speed up on 128 nodes of the Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .
Revised:  Our proposed designs in HyPar-Flow offer up to 3.1x speedup over sequential training for ResNet-110 and up to 1.6x speedup over Horovod-based data-parallel training, 2) 110x speed up on 128 nodes of the Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .
Edit intent: coherence
Original:  HyPar-Flow provides 110x speed up on 128 nodes of the Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .
Revised:  HyPar-Flow provides 110x speedup over single-node on 128 nodes of the Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .
Edit intent: meaning-changed
Original:  HyPar-Flow provides 110x speed up on 128 nodes of the Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .
Revised:  HyPar-Flow provides 110x speed up on 128 nodes of the Stampede2 nodes, and 3) 481x speedup over single-node on 512 Frontera nodes .
Edit intent: meaning-changed
Original: Assortment optimization is an important problem that arises in many practical applications such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products that maximize a seller's expected revenue.
Revised: Assortment optimization is an important problem that arises in many industries such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products that maximize a seller's expected revenue.
Edit intent: clarity
Original: Assortment optimization is an important problem that arises in many practical applications such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products that maximize a seller's expected revenue.
Revised: Assortment optimization is an important problem that arises in many practical applications such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products which maximize seller's expected revenue.
Edit intent: fluency
Original:  In particular, we handle dynamic preferences and the choice overload phenomenon using a Markovian comparison model that is a generalization of the Markovian substitution framework of Blanchet et al. The Markovian comparison framework allows us to implicitly model the search cost in the choice process and thereby, modeling both dynamic preferences as well as the choice overload phenomenon. We consider the assortment optimization problem for the special case of our generalized Markov chain model where the underlying Markov chain is rank-1 (this is a generalization of the Multinomial Logit model). We show that the assortment optimization problem under this model is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) for this problem .
Revised:  In particular, we handle dynamic preferences and the choice overload phenomenon using a Markovian comparison model that is a generalization of MNL with no-purchase attractions dependent on the assortment S and strictly increasing with the size of assortment S. While we show that the assortment optimization problem under this model is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) for this problem .
Edit intent: meaning-changed
Original:  Most existing models of interference posit that treatments assigned to alters only affect the ego 's response through a low-dimensional exposure mapping, which only depends on units within some known network radius around the ego.
Revised:  Most existing models of interference posit that treatments assigned to alters beyond a certain network distance from the ego have no effect on the ego 's response through a low-dimensional exposure mapping, which only depends on units within some known network radius around the ego.
Edit intent: meaning-changed
Original:  Most existing models of interference posit that treatments assigned to alters only affect the ego 's response through a low-dimensional exposure mapping, which only depends on units within some known network radius around the ego. We propose a substantially weaker  "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters far from the ego to have a small , but potentially nonzero, impact on the ego's response.
Revised:  Most existing models of interference posit that treatments assigned to alters only affect the ego 's response . However, many models of social interactions do not satisfy this assumption. This paper proposes a substantially weaker "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters far from the ego to have a small , but potentially nonzero, impact on the ego's response.
Edit intent: meaning-changed
Original:  We propose a substantially weaker  "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters far from the ego to have a small , but potentially nonzero, impact on the ego's response.
Revised:  We propose a substantially weaker model of "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters far from the ego to have a small , but potentially nonzero, impact on the ego's response.
Edit intent: clarity
Original:  We propose a substantially weaker  "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters far from the ego to have a small , but potentially nonzero, impact on the ego's response.
Revised:  We propose a substantially weaker "approximate neighborhood interference" (ANI) , under which treatments assigned to alters far from the ego to have a small , but potentially nonzero, impact on the ego's response.
Edit intent: clarity
Original:  We propose a substantially weaker  "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters far from the ego to have a small , but potentially nonzero, impact on the ego's response.
Revised:  We propose a substantially weaker "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters further from the ego to have a small , but potentially nonzero, impact on the ego's response.
Edit intent: clarity
Original:  We propose a substantially weaker  "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters far from the ego to have a small , but potentially nonzero, impact on the ego's response.
Revised:  We propose a substantially weaker "approximate neighborhood interference" (ANI) assumption, which allows treatments assigned to alters far from the ego have a smaller , but potentially nonzero, impact on the ego's response.
Edit intent: fluency
Original:  Despite its generality, inference in a single-network setting is still possible under ANI, as we prove that standard inverse-probability weighting estimators can consistently estimate treatment and spillover effects and are asymptotically normal .
Revised:  Despite its generality, inference in a single-network setting is still possible under ANI, standard inverse-probability weighting estimators can consistently estimate treatment and spillover effects and are asymptotically normal .
Edit intent: coherence
Original:  Despite its generality, inference in a single-network setting is still possible under ANI, as we prove that standard inverse-probability weighting estimators can consistently estimate treatment and spillover effects and are asymptotically normal .
Revised:  Despite its generality, inference in a single-network setting is still possible under ANI, as we prove that standard inverse-probability weighting estimators can consistently estimate useful exposure effects and are asymptotically normal .
Edit intent: clarity
Original:  Despite its generality, inference in a single-network setting is still possible under ANI, as we prove that standard inverse-probability weighting estimators can consistently estimate treatment and spillover effects and are asymptotically normal . For practical inference, we propose a new conservative variance estimatorbased on a network bootstrap and suggest a data-dependent bandwidth using the network diameter.
Revised:  Despite its generality, inference in a single-network setting is still possible under ANI, as we prove that standard inverse-probability weighting estimators can consistently estimate treatment and spillover effects and are asymptotically normal under asymptotics taking the network size large. For inference, we propose a new conservative variance estimatorbased on a network bootstrap and suggest a data-dependent bandwidth using the network diameter.
Edit intent: meaning-changed
Original:  For practical inference, we propose a new conservative variance estimatorbased on a network bootstrap and suggest a data-dependent bandwidth using the network diameter. Finally, we illustrate our results in a simulation study and empirical application .
Revised:  For practical inference, we consider a network HAC variance estimator. Under a finite population model, we show the estimator is biased but that the bias can be interpreted as the variance of unit-level exposure effects. This generalizes Neyman's well-known result on conservative variance estimation to settings with interference .
Edit intent: meaning-changed
Original:  To seek the fountainhead of heroin, we identified the most likely origins of drugs in Philadelphia, PA, Cuyahoga and Hamilton, OH, Jefferson, KY, Kanawha, WV, and Bedford, VA. Based on the facts, advised concentration includes the spread of Oxycodone, Hydrocodone, Heroin  and Buprenorphine.
Revised:  To seek the fountainhead of heroin, we identified the most likely origins of drugs in Philadelphia, PA, Cuyahoga and Hamilton, OH, Jefferson, KY, Kanawha, WV, and Bedford, VA. Based on the facts, advised concentration includes the spread of Oxycodone, Hydrocodone, Heroin , and Buprenorphine.
Edit intent: fluency
Original:  According to the propagation curve predicted by our model, the transmission of KY state is still in its early stage, while that of VA, WV is in the middle point, and OH, PA in its latter ones.
Revised:  According to the propagation curve predicted by our model, the transfer of KY state is still in its early stage, while that of VA, WV is in the middle point, and OH, PA in its latter ones.
Edit intent: fluency
Original:  For methodology, with  Principal component analysis technique, 22 variables in socioeconomic data related to the continuous use of opioid drugs was filtered, where the 'Relationship' Part deserves  highlight.
Revised:  For methodology, with the Principal component analysis technique, 22 variables in socioeconomic data related to the continuous use of opioid drugs was filtered, where the 'Relationship' Part deserves highlight.
Edit intent: fluency
Original:  For methodology, with  Principal component analysis technique, 22 variables in socioeconomic data related to the continuous use of opioid drugs was filtered, where the 'Relationship' Part deserves  highlight.
Revised:  For methodology, with Principal component analysis technique, 22 variables in socio-economic data related to the continuous use of opioid drugs was filtered, where the 'Relationship' Part deserves highlight.
Edit intent: fluency
Original:  For methodology, with  Principal component analysis technique, 22 variables in socioeconomic data related to the continuous use of opioid drugs was filtered, where the 'Relationship' Part deserves  highlight.
Revised:  For methodology, with Principal component analysis technique, 22 variables in socioeconomic data related to the continuous use of Opioid drugs was filtered, where the 'Relationship' Part deserves highlight.
Edit intent: fluency
Original:  For methodology, with  Principal component analysis technique, 22 variables in socioeconomic data related to the continuous use of opioid drugs was filtered, where the 'Relationship' Part deserves  highlight.
Revised:  For methodology, with Principal component analysis technique, 22 variables in socioeconomic data related to the continuous use of opioid drugs was filtered, where the 'Relationship' Part deserves a highlight.
Edit intent: fluency
Original:  A typical approach is to represent the data elements at each time step in a compact one-dimensional form or via a one-dimensional ordering . Such 1D representations can then be placed in temporal order along a time line.
Revised:  A typical approach is to represent the data elements at each time step in a compact one-dimensional form or via a one-dimensional ordering ; visualizations of such orderings can then be placed in temporal order along a time line.
Edit intent: clarity
Original:  There are two main criteria to assess the quality of the resulting visual summary: spatial quality -- how well does the 1D representation capture the structure of the data at each time step, and stability -- how coherent are the 1D representations over consecutive time steps or temporal ranges?
Revised:  There are two main criteria to assess the quality of the resulting visual summary: spatial quality -- how well does the ordering capture the structure of the data at each time step, and stability -- how coherent are the 1D representations over consecutive time steps or temporal ranges?
Edit intent: clarity
Original:  There are two main criteria to assess the quality of the resulting visual summary: spatial quality -- how well does the 1D representation capture the structure of the data at each time step, and stability -- how coherent are the 1D representations over consecutive time steps or temporal ranges?
Revised:  There are two main criteria to assess the quality of the resulting visual summary: spatial quality -- how well does the 1D representation capture the structure of the data at each time step, and stability -- how coherent are the orderings over consecutive time steps or temporal ranges?
Edit intent: clarity
Original:  We focus on techniques that create such visual summaries for entities moving in 2D. Previous work has considered only the creation of 1D orderings, using spatial subdivisions and clustering techniques. In contrast, we propose to use actual dimensionality-reduction techniques to compute stable and spatially informative 1D representations. These more general 1D representations provide the user with additional visual cues describing the spatial distribution of the data, and naturally imply also a 1D ordering. To make dimensionality-reduction techniques suitable for visual summaries, we introduce stable variants of Principle Component Analysis, Sammon mapping, and t-SNE. Our Stable Principal Component method is explicitly parametrized for stability, allowing a trade-off between the spatial quality and stability.
Revised:  In this paper we introduce a new Stable Principal Component method is explicitly parametrized for stability, allowing a trade-off between the spatial quality and stability.
Edit intent: coherence
Original:  To make dimensionality-reduction techniques suitable for visual summaries, we introduce stable variants of Principle Component Analysis, Sammon mapping, and t-SNE. Our Stable Principal Component method is explicitly parametrized for stability, allowing a trade-off between the spatial quality and stability.
Revised:  To make dimensionality-reduction techniques suitable for visual summaries, we introduce stable variants of Principle Component Analysis, Sammon mapping, and t-SNE. Our Stable Principal Component (SPC) method to compute such orderings, which is explicitly parameterized for stability, allowing a trade-off between the spatial quality and stability.
Edit intent: meaning-changed
Original:  We conduct  computational experiments that quantitatively compare the 1D orderings produced by our stable dimensionality-reduction methods to various state-of-the-art approaches using a set of well-established quality metrics that capture spatial quality and stability.
Revised:  We conduct extensive computational experiments that quantitatively compare the 1D orderings produced by our stable dimensionality-reduction methods to various state-of-the-art approaches using a set of well-established quality metrics that capture spatial quality and stability.
Edit intent: clarity
Original:  We conduct  computational experiments that quantitatively compare the 1D orderings produced by our stable dimensionality-reduction methods to various state-of-the-art approaches using a set of well-established quality metrics that capture spatial quality and stability.
Revised:  We conduct computational experiments that quantitatively compare the orderings produced by our stable dimensionality-reduction methods to various state-of-the-art approaches using a set of well-established quality metrics that capture spatial quality and stability.
Edit intent: meaning-changed
Original:  We conduct  computational experiments that quantitatively compare the 1D orderings produced by our stable dimensionality-reduction methods to various state-of-the-art approaches using a set of well-established quality metrics that capture spatial quality and stability.
Revised:  We conduct computational experiments that quantitatively compare the 1D orderings produced by ours and other stable dimensionality-reduction methods to various state-of-the-art approaches using a set of well-established quality metrics that capture spatial quality and stability.
Edit intent: meaning-changed
Original:  Indeed, they provide a viewpoint for the most classical time-scales of interest. They namely encompass the time-scale of the life of a single individual, the longer one on which a population can be characterized , the ecological one and at least four nested ones on which selection occurs.
Revised:  Indeed, they provide a viewpoint for the most classical time-scales . Namely, they encompass the timescale typical of the life-expectancy of a single individual, the longer one on which a population can be characterized , the ecological one and at least four nested ones on which selection occurs.
Edit intent: clarity
Original:  A common  response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Revised:  A common policy response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Edit intent: clarity
Original:  A common  response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Revised:  A common response is that consumers should be given control over which firms access their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Edit intent: clarity
Original:  A common  response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Revised:  A common response is that consumers should have control over their data and how . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Edit intent: clarity
Original:  A common  response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Revised:  A common response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms learn about a consumer's preferences based on the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Edit intent: clarity
Original:  A common  response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Revised:  A common response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen and the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Edit intent: fluency
Original:  A common  response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear.
Revised:  A common response is that consumers should have control over their data and the ability to choose howfirms access it . Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the equilibrium implications of consumer control are unclear.
Edit intent: clarity
Original:  First, consumers can use disclosure to amplify competition between firms . Second, consumers can share information that induces a seller---even a monopolist---to make price concessions. Furthermore, whether consumer control improves consumer surplus depends on both the technology of disclosure and the competitivenessof the marketplace.
Revised:  First, consumers can use disclosure to amplify competition between firms . Second, consumers can disclose information to induce even a monopolist to lower prices. Whether consumer control improves consumer surplus depends on both the technology of disclosure and the competitivenessof the marketplace.
Edit intent: clarity