wanyu/IteraTeR_v2 huggingface dataset:
Original:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), which utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Revised:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), whichL utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Edit intent: clarity
Original:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), which utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Revised:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), whichearning for utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Edit intent: clarity
Original:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), which utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Revised:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), whichS utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Edit intent: clarity
Original:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), which utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Revised:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), whichequential utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Edit intent: fluency
Original:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), which utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Revised:  We propose a novel model called Contrastive Pre-training for Sequential Recommendation (CP4Rec), whichRec utilizes the contrastive pre-training framework to extract meaningful user patterns and further encode the user representation effectively.
Edit intent: clarity
Original:  We particularly use hyperelastic materials of the St. \, Venant-Kirchhoff and compressible Neo-Hookean types.
Revised:  We particularly use hyperelastic materials of the St. Venant-Kirchhoff and compressible Neo-Hookean types.
Edit intent: fluency
Original:  Further, we model our new NAS problem using the supernet strategy  which models the architecture search problem as a one-shot training process of a single supernet.
Revised:  Further, we model our new NAS problem using the supernet strategy , which models the architecture search problem as a one-shot training process of a single supernet.
Edit intent: fluency
Original:  Empirical results show that our algorithm excels in discovering better SPD network design , and providing models that are more than 3 times lighter than searched by state-of-the-art NAS algorithms.
Revised:  Empirical results show that our algorithm excels in discovering better SPD network design and providing models that are more than 3 times lighter than searched by state-of-the-art NAS algorithms.
Edit intent: fluency
Original:  Further, we model our new NAS problem using the supernet strategy, which models the architecture search problem as a one-shot training process of a single supernet.
Revised:  Further, we model our new NAS problem with a one-shot training process of a single supernet.
Edit intent: clarity
Original:  Statistical evaluation of our method on drone, action, and emotion recognition tasks mostly provides better results than the state-of-the-art SPD networks and  NAS algorithms.
Revised:  Statistical evaluation of our method on drone, action, and emotion recognition tasks mostly provides better results than the state-of-the-art SPD networks and traditional NAS algorithms.
Edit intent: clarity
Original:  Empirical results show that our algorithm excels in discovering better  SPD network design and providing models that are more than 3 times lighter than searched by  state-of-the-art NAS algorithms.
Revised:  Empirical results show that our algorithm excels in discovering better SPD network design and provides models that are more than 3 times lighter than searched by state-of-the-art NAS algorithms.
Edit intent: fluency
Original:  Empirical results show that our algorithm excels in discovering better  SPD network design and providing models that are more than 3 times lighter than searched by  state-of-the-art NAS algorithms.
Revised:  Empirical results show that our algorithm excels in discovering better SPD network design and providing models that are more than three times lighter than searched by state-of-the-art NAS algorithms.
Edit intent: fluency
Original:  Empirical results show that our algorithm excels in discovering better  SPD network design and providing models that are more than 3 times lighter than searched by  state-of-the-art NAS algorithms.
Revised:  Empirical results show that our algorithm excels in discovering better SPD network design and providing models that are more than 3 times lighter than searched by the state-of-the-art NAS algorithms.
Edit intent: fluency
Original:  Given the sophistication of current models of population dynamics , one of the most difficult obstacles lies in making analytical deductions, either qualitative or quantitative, about the long-term outcomes of an evolutionary process .
Revised:  Given the sophistication of current models of population dynamics , one of the most difficult obstacles lies in making analytical deductions, either qualitative or quantitative, about the long-term outcomes of evolution .
Edit intent: clarity
Original:  The "structure coefficient theorem" is a well-known approach to this problem for mutation-selection processes under weak selection, but a general method of evaluating the terms it comprises is still lacking.
Revised:  The "structure coefficient theorem" is a well-known approach to this problem for mutation-selection processes under weak selection, but a general method of evaluating the terms it comprises is lacking.
Edit intent: clarity
Original:  Here  we provide such a method for populations of fixed (but arbitrary) size and structure, using easily interpretable measures of population heterogeneity .
Revised:  Here , we provide such a method for populations of fixed (but arbitrary) size and structure, using easily interpretable measures of population heterogeneity .
Edit intent: fluency
Original:  Here  we provide such a method for populations of fixed (but arbitrary) size and structure, using easily interpretable measures of population heterogeneity .
Revised:  Here we provide such a method for populations of fixed (but arbitrary) size and structure, using easily interpretable demographic measures .
Edit intent: clarity
Original:  We apply the method to the case of social goods produced and distributed among individuals in spatially-heterogeneous populations .
Revised:  We apply the method to study social goods produced and distributed among individuals in spatially-heterogeneous populations .
Edit intent: clarity
Original:  We show that the outcome of selection varies dramatically depending on how goods are produced and distributed, on the details of population structure, and on the rate of mutation .
Revised:  We show that the outcome of selection varies dramatically depending on the nature of the social good, the spatial topology, and frequency with which mutations arise .
Edit intent: clarity
Original:  They are traditionally aimed at generating meaningful information on large data-sets, and today, they are  used for engineering more effective features for complex AI models.
Revised:  They are traditionally aimed at generating meaningful information on large data-sets, and today, they are being used for engineering more effective features for complex AI models.
Edit intent: clarity
Original:  Reductions are usually carried out on top of data abstractions such as tables/ arrays and are combined with other operations such as grouping of values.
Revised:  Aggregations are usually carried out on top of data abstractions such as tables/ arrays and are combined with other operations such as grouping of values.
Edit intent: clarity
Original:  We study the relationship between the systems-level costs of different representations of network traffic to the ultimate target performance metric -- e. g., accuracy -- of the models trained from these representations .
Revised:  We study the relationship between the systems-level costs of different representations of network traffic .
Edit intent: clarity
Original:   We demonstrate the benefit of exploring a range of representations of network traffic and present Network Microscope , a proof-of-concept reference implementation that both monitors network traffic at high speed and transforms the traffic in real time to produce a variety of representations for input to machine learning models.
Revised:  We demonstrate the benefit of exploring a range of representations of network traffic and present Network Microscope , a proof-of-concept reference implementation that both monitors network traffic at high speed and transforms the traffic in real time to produce a variety of feature representations for machine learning models.
Edit intent: clarity
Original:  Systems like Network Microscope can ultimately help network operators better explore the design space of data representation for learning, balancing systems costs related to feature extraction and model training against  resulting model performance.
Revised:  Systems like Network Microscope can ultimately help network operators better explore the design space of data representation for learning, balancing systems costs related to feature extraction and model training against the resulting model performance.
Edit intent: fluency
Original:  furthermore, the features that the model relies on will ultimately determine where (and even whether ) the model can be deployed in practice.
Revised:  furthermore, the features that the model relies on , and the representation of those features, ultimately determine model accuracy, as well as where and whether the model can be deployed in practice.
Edit intent: clarity
Original:  We highlight these two dimensions for a practical network management task , video streaming quality inference , and show that the appropriate operating point for these two dimensions depends on the deployment scenario .
Revised:  We highlight these two dimensions for two practical network management task , video streaming quality inference , and show that the appropriate operating point for these two dimensions depends on the deployment scenario .
Edit intent: fluency
Original:  We highlight these two dimensions for a practical network management task , video streaming quality inference , and show that the appropriate operating point for these two dimensions depends on the deployment scenario .
Revised:  We highlight these two dimensions for a practical network management tasks , video streaming quality inference , and show that the appropriate operating point for these two dimensions depends on the deployment scenario .
Edit intent: fluency
Original:  We highlight these two dimensions for a practical network management task , video streaming quality inference , and show that the appropriate operating point for these two dimensions depends on the deployment scenario .
Revised:  We highlight these two dimensions for a practical network management task , video streaming quality inference , and show that the appropriate operating point .
Edit intent: clarity
Original:  a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models . Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance .
Revised:  a proof-of-concept implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models . Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance .
Edit intent: clarity
Original:  a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models . Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance .
Revised:  a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms traffic in real time to produce a variety of feature representations for machine learning models . Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance .
Edit intent: clarity
Original:  a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models . Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance .
Revised:  a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning . Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance .
Edit intent: clarity
Original:  a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models . Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance .
Revised:  a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models . Traffic Refinery both highlights this design space and makes it possible to explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance .
Edit intent: clarity
Original:  a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models . Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance .
Revised:  a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models . Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against model accuracy .
Edit intent: clarity
Original: This chapter offers a detailed look, both qualitative and quantitative, at districting with respect to recent voting patterns in one state: Pennsylvania.
Revised: This preprint offers a detailed look, both qualitative and quantitative, at districting with respect to recent voting patterns in one state: Pennsylvania.
Edit intent: clarity
Original:  The authors investigate how much the partisan playing field is tilted by political geography.
Revised:  We investigate how much the partisan playing field is tilted by political geography.
Edit intent: clarity
Original:  In particular they closely examine the role of scale.
Revised:  In particular we closely examine the role of scale.
Edit intent: fluency
Original:  They find that partisan-neutral maps rarely give seats proportional to votes, and that making the district size smaller tends to make it even harder to find a proportional map.
Revised:  We find that partisan-neutral maps rarely give seats proportional to votes, and that making the district size smaller tends to make it even harder to find a proportional map.
Edit intent: fluency
Original:  This is a preprint of a chapter of Political Geometry, a forthcoming book looking at redistricting from the point of view of various scientific disciplines.
Revised:  This preprint was prepared as a chapter in the forthcoming edited volume Political Geometry, a forthcoming book looking at redistricting from the point of view of various scientific disciplines.
Edit intent: clarity
Original:  In this paper, we show how to construct ensembles of spiking neural networks that both produce state-of-the-art results, and achieve this with less than 50\% of the parameters of the original models.
Revised:  In this paper, we show how to construct ensembles of spiking neural networks producing state-of-the-art results, and achieve this with less than 50\% of the parameters of the original models.
Edit intent: clarity
Original:  In this paper, we show how to construct ensembles of spiking neural networks that both produce state-of-the-art results, and achieve this with less than 50\% of the parameters of the original models.
Revised:  In this paper, we show how to construct ensembles of spiking neural networks that both produce state-of-the-art results, and achieve this with less than 50\% of the parameters of published reference models.
Edit intent: clarity
Original:  This is a fundamental problem for industry 4.0, where robots may have to maximize the battery autonomy and communication efficiency.
Revised:  This is a fundamental problem for industry 4.0, where robots may have to maximize their battery autonomy and communication efficiency.
Edit intent: fluency
Original:  The algorithm takes into account the obstacles' positions and a radio map to avoid collisions and satisfy the QoS constraint.
Revised:  The algorithm takes into account the obstacles' positions and a radio map and provides solutions that avoid collisions and satisfy the QoS constraint.
Edit intent: clarity
Original:  We show that the algorithm converges to a solution satisfying the Karush-Kuhn-Tucker (KKT) conditions.
Revised:  Moreover, we prove that the algorithm converges to a solution satisfying the Karush-Kuhn-Tucker (KKT) conditions.
Edit intent: coherence
Original:  We present a solution to the optimization problem that first exploits mm-wave channel characteristics to decouple beamforming and trajectory optimizations.
Revised:  We present a solution to the optimization problem that exploits mm-wave channel characteristics to decouple beamforming and trajectory optimizations.
Edit intent: coherence
Original:  Our model and results are heavily inspired by the work of Kleinberg and Kleinberg in "Delegated Search Approximates Efficient Search ". Building on their work, we show that there exists a connection between delegated stochastic probing and generalized prophet inequalities, which provides us with constant-factor deterministic mechanisms for a large class of delegated stochastic probing problems.
Revised:  Our model and results are heavily inspired by the work of Kleinberg and Kleinberg in "Delegated Search Approximates Efficient Search ." Building on their work, we show that there exists a connection between delegated stochastic probing and generalized prophet inequalities, which provides us with constant-factor deterministic mechanisms for a large class of delegated stochastic probing problems.
Edit intent: fluency
Original:  Though the GSVD is ubiquitous, powerful, and exible , it has very few implementations.
Revised:  Though the GSVD is ubiquitous, powerful, and flexible , it has very few implementations.
Edit intent: clarity
Original:  The theoretical background relies on the Lazard's structural theorem for lexicographic Groebner bases in two variables.
Revised:  The theoretical background relies on Lazard's structural theorem for lexicographic Groebner bases in two variables.
Edit intent: fluency
Original:  An implementation is realized in Magma , and extensive benchmarks are provided that show clearly the benefits, sometimes huge , of this approach compared to the computation of Groebner bases .
Revised:  An implementation is realized in Magma . Benchmarks show clearly the benefits, sometimes huge , of this approach compared to the computation of Groebner bases .
Edit intent: clarity
Original:  An implementation is realized in Magma , and extensive benchmarks are provided that show clearly the benefits, sometimes huge , of this approach compared to the computation of Groebner bases .
Revised:  An implementation is realized in Magma , and extensive benchmarks are provided that show clearly the benefits, sometimes huge , of this approach compared to the Groebner bases approach .
Edit intent: clarity
Original:  The most important in studying random walk on networks is to measure a structural parameter called mean first-passage time , denote by \mathcal{F} . As known, the commonly-utilized methods for determining%DIFDELCMD < \mathcal{F} %%%  are mainly based on Laplacian spectra on networks under consideration.
Revised:  The most important in studying random walk on networks is to measure a structural parameter called mean first-passage time \mathcal{F} . As known, the commonly-utilized methods for determining%DIFDELCMD < \mathcal{F} %%% are mainly based on Laplacian spectra on networks under consideration.
Edit intent: coherence
Original:  The most important in studying random walk on networks is to measure a structural parameter called mean first-passage time , denote by \mathcal{F} . As known, the commonly-utilized methods for determining%DIFDELCMD < \mathcal{F} %%%  are mainly based on Laplacian spectra on networks under consideration.
Revised:  The most important in studying random walk on networks is to measure a structural parameter called mean first-passage time , denote by \mathcal{F} %DIFDELCMD < \mathcal{F} %%% are mainly based on Laplacian spectra on networks under consideration.
Edit intent: clarity