wanyu/IteraTeR_v2 huggingface dataset:
Original:  First, we show that outcomes that satisfy an economically intuitive solution concept---trail stability---always exist and can be found in linear time.
Revised:  First, we show that outcomes that satisfy trail stability always exist and can be found in linear time.
Edit intent: clarity
Original:  Second, we consider a slightly stronger solution concept in which agents can simultaneously offer an upstream and a downstream contract.
Revised:  Here we consider a slightly stronger solution concept in which agents can simultaneously offer an upstream and a downstream contract.
Edit intent: fluency
Original:  It follows that the existence of stable outcomes---immune to deviations by arbitrary sets of agents---is also an NP-complete problem in trading networks (and in flow networks).
Revised:  It follows that the existence of stable outcomes--immune to deviations by arbitrary sets of agents---is also an NP-complete problem in trading networks (and in flow networks).
Edit intent: fluency
Original: We present a statistical mechanics model of deep feed forward neural networks (FFN) . Our energy-based approachnaturally explains several known results and heuristics, providing a solid theoretical framework and new instruments for a systematic development of FFN . We infer that FFN can be understood as performing three basic steps: encoding, representation validation and propagation.
Revised: We present a statistical mechanics model of deep feed forward neural networks (FFN) approach . We infer that FFN can be understood as performing three basic steps: encoding, representation validation and propagation.
Edit intent: clarity
Original:  We obtain a set of natural activations -- such as sigmoid , \tanh and ReLu -- together with a state-of-the-art one, recently obtained by Ramachandran et al.(arXiv:1710.05941) using an extensive search algorithm.
Revised:  We obtain a set of natural activations -- such as Sigmoid , \tanh and ReLu -- together with a state-of-the-art one, recently obtained by Ramachandran et al.(arXiv:1710.05941) using an extensive search algorithm.
Edit intent: fluency
Original:  We find that ESP allows for faster training and more consistent performances over a wide range of network architectures.
Revised:  We find that ESP allows for faster training and more consistent performances over a wider range of network architectures.
Edit intent: fluency
Original:  A simple dedicated data structure and a number of improvements in the insertion algorithm have permitted to accelerate  by a factor three reference implementations .
Revised:  A simple dedicated data structure and a number of improvements in the insertion algorithm have permitted to accelerate reference implementations by a factor three reference implementations .
Edit intent: fluency
Original:  A simple dedicated data structure and a number of improvements in the insertion algorithm have permitted to accelerate  by a factor three reference implementations .
Revised:  A simple dedicated data structure and a number of improvements in the insertion algorithm have permitted to accelerate by a factor three .
Edit intent: clarity
Original:  Our second contribution is a multi-threaded version of the Delaunay kernel  able to concurrently insert vertices.
Revised:  Our second contribution is a multi-threaded version of the Delaunay kernel that is able to concurrently insert vertices.
Edit intent: clarity
Original:  Moore curve coordinates are used to partition the point set, avoiding so heavy synchronization overheads.
Revised:  Moore curve coordinates are used to partition the point set, avoiding heavy synchronization overheads.
Edit intent: fluency
Original:  Conflicts are managed by modification of the partition with a simple rescaling of the space-filling curve.
Revised:  Conflicts are managed by modifying the partitions with a simple rescaling of the space-filling curve.
Edit intent: fluency
Original:  It is finally shown how this very efficient parallel Delaunay triangulation can be integrated in a Delaunay refinement mesh generator taking as input the  boundary of the domain to mesh.
Revised:  It is finally shown how this very efficient parallel Delaunay triangulation can be integrated in a Delaunay refinement mesh generator which takes as input the boundary of the domain to mesh.
Edit intent: coherence
Original:  It is finally shown how this very efficient parallel Delaunay triangulation can be integrated in a Delaunay refinement mesh generator taking as input the  boundary of the domain to mesh.
Revised:  It is finally shown how this very efficient parallel Delaunay triangulation can be integrated in a Delaunay refinement mesh generator taking as input the boundary of the volume to mesh.
Edit intent: clarity
Original:  Typically the target distribution takes the form of a product distribution over a dataset with a large number of entries.
Revised:  Often the target distribution takes the form of a product distribution over a dataset with a large number of entries.
Edit intent: fluency
Original:  This scheme is shown to bias computed averages to second order in the stepsize while giving exact results in the special case of sampling a Gaussian distribution with a normally distributed stochastic gradient 
Revised:  This scheme is shown to bias computed averages to second order in the stepsize while giving exact results in the special case of sampling a Gaussian distribution with a normally distributed stochastic gradient .
Edit intent: fluency
Original: With recent developments in remote sensing technologies, plot-level forest resources can be estimated using airborne laser scanning (ALS) data.
Revised: With recent developments in remote sensing technologies, plot-level forest resources can be predicted utilizing airborne laser scanning (ALS) data.
Edit intent: clarity
Original:  From the ALS data, we extract patches of vegetation at a number of height layers using thresholded canopy height models.
Revised:  From the ALS data, we extract patches of vegetation at a number of height levels, which are extracted from the ALS data using thresholded canopy height models.
Edit intent: clarity
Original: With recent developments in remote sensing technologies, plot-level forest resources can be predicted utilizing airborne laser scanning (ALS) data .
Revised: With recent developments in remote sensing technologies, plot-level forest resources can be predicted utilizing airborne laser scanning (ALS) .
Edit intent: clarity
Original:  The prediction is most often assisted by ALS feature variables which summarize the vertical distribution of the pulse returns.
Revised:  The prediction is often assisted by ALS feature variables which summarize the vertical distribution of the pulse returns.
Edit intent: clarity
Original:  We propose to use spatial summary characteristics, most importantly the Euler number  and the empty-space function, to capture the structure of the patches and the empty spacearound them.
Revised:  We propose new features based on the Euler number and the empty-space function, to capture the structure of the patches and the empty spacearound them.
Edit intent: clarity
Original:  We illustrate usefulness of the proposed spatial features for predicting different forest biodiversity indices that summarize the spatial structure of trees, species diversity, or their breast height diameter distribution.
Revised:  We illustrate usefulness of the proposed spatial features for predicting different forest variables that summarize the spatial structure of trees, species diversity, or their breast height diameter distribution.
Edit intent: clarity
Original:  We illustrate usefulness of the proposed spatial features for predicting different forest biodiversity indices that summarize the spatial structure of trees, species diversity, or their breast height diameter distribution.
Revised:  We illustrate usefulness of the proposed spatial features for predicting different forest biodiversity indices that summarize the spatial structure of forests or their breast height diameter distribution.
Edit intent: clarity
Original:  An biological image classifier is proposed to utilize this fixed point.
Revised:  A biological image classifier is proposed to utilize this fixed point.
Edit intent: fluency
Original: In neural network, synaptic strength could be seen as probability to transmit impulse, and function could exist from transmission probability to synaptic strength.
Revised: Synaptic strength can be seen as probability to transmit impulse, and function could exist from transmission probability to synaptic strength.
Edit intent: coherence
Original: In neural network, synaptic strength could be seen as probability to transmit impulse, and function could exist from transmission probability to synaptic strength.
Revised: In neural network, synaptic strength could be seen as probability to propagate impulse, and function could exist from transmission probability to synaptic strength.
Edit intent: clarity
Original: In neural network, synaptic strength could be seen as probability to transmit impulse, and function could exist from transmission probability to synaptic strength.
Revised: In neural network, synaptic strength could be seen as probability to transmit impulse, and function could exist from propagation activity to synaptic strength.
Edit intent: clarity
Original:  If the function satisfies constraint such as continuity and monotonicity, neural network  would always go to one unique fixed point.
Revised:  If the function satisfies constraints such as continuity and monotonicity, neural network would always go to one unique fixed point.
Edit intent: fluency
Original:  A biological image classifier is proposed to utilize this fixed point .
Revised:  A biological image classifier is proposed to utilize this mapping .
Edit intent: clarity
Original:  We take inspiration from neuroscience, specifically the Hippocampal-Entorhinal system (containing place and grid cells), known to be important for generalisation.
Revised:  We take inspiration from neuroscience, specifically the hippocampal-entorhinal system known to be important for generalisation.
Edit intent: clarity
Original:  We experimentally support model assumptions, showing a preserved relationship between grid and place cells across environments.
Revised:  We experimentally support model assumptions, showing a preserved relationship between entorhinal grid and hippocampal place cells across environments.
Edit intent: clarity
Original:  Since in applications it may be difficult to estimate a related function space, we make use of dimension incremental construction methods for sparse frequency sets.
Revised:  Since in applications it may be difficult to choose a related function space, we make use of dimension incremental construction methods for sparse frequency sets.
Edit intent: clarity
Original:  TCN does multi-modal classification based on a few available labels by urging the%DIFDELCMD < {\em %DIFDELCMD < %%% interpretations of different modalities to resemble each other.
Revised:  %DIFDELCMD < {\em %DIFDELCMD < %%% interpretations of different modalities to resemble each other.
Edit intent: coherence
Original:  TCN does multi-modal classification based on a few available labels by urging the%DIFDELCMD < {\em %DIFDELCMD < %%% interpretations of different modalities to resemble each other.
Revised:  TCN does multi-modal classification based on a few available labels by urging the%DIFDELCMD < {\em %DIFDELCMD < %%% of different modalities to resemble each other.
Edit intent: clarity
Original:  To understand the mechanisms of TCN , we formulate the%DIFDELCMD < {\em %%% similarity of the interpretations as the negative relative Jensen-Shannon divergence, and show that a consensus state beneficial for classification desires a stable but not perfect similarity between the interpretations.
Revised:  To understand the mechanisms of TCN , we formulate the%DIFDELCMD < {\em %%% of the interpretations as the negative relative Jensen-Shannon divergence, and show that a consensus state beneficial for classification desires a stable but not perfect similarity between the interpretations.
Edit intent: fluency
Original:  To understand the mechanisms of TCN , we formulate the%DIFDELCMD < {\em %%% similarity of the interpretations as the negative relative Jensen-Shannon divergence, and show that a consensus state beneficial for classification desires a stable but not perfect similarity between the interpretations.
Revised:  To understand the mechanisms of TCN , we formulate the%DIFDELCMD < {\em %%% similarity of the interpretations as the negative relative Jensen-Shannon divergences. We show that a consensus state beneficial for classification desires a stable but not perfect similarity between the interpretations.
Edit intent: coherence
Original:  To understand the mechanisms of TCN , we formulate the%DIFDELCMD < {\em %%% similarity of the interpretations as the negative relative Jensen-Shannon divergence, and show that a consensus state beneficial for classification desires a stable but not perfect similarity between the interpretations.
Revised:  To understand the mechanisms of TCN , we formulate the%DIFDELCMD < {\em %%% similarity of the interpretations as the negative relative Jensen-Shannon divergence, and show that a consensus state beneficial for classification desires a stable but imperfect similarity between the interpretations.
Edit intent: clarity
Original:  We show the performances of TCN are better than best benchmark algorithms given only 20 and 80 labeled samples on  Bank Marketing and the DementiaBank dataset respectively, and align with their performances given more labeled samples .
Revised:  We show the performances of TCN are better than best benchmark algorithms given 20 and 80 labeled samples on Bank Marketing and the DementiaBank dataset respectively, and align with their performances given more labeled samples .
Edit intent: clarity
Original:  We show the performances of TCN are better than best benchmark algorithms given only 20 and 80 labeled samples on  Bank Marketing and the DementiaBank dataset respectively, and align with their performances given more labeled samples .
Revised:  We show the performances of TCN are better than best benchmark algorithms given only 20 and 80 labeled samples on the Bank Marketing and the DementiaBank dataset respectively, and align with their performances given more labeled samples .
Edit intent: fluency
Original:  We show the performances of TCN are better than best benchmark algorithms given only 20 and 80 labeled samples on  Bank Marketing and the DementiaBank dataset respectively, and align with their performances given more labeled samples .
Revised:  We show the performances of TCN are better than best benchmark algorithms given only 20 and 80 labeled samples on Bank Marketing and the DementiaBank datasets .
Edit intent: clarity
Original: We study zero-sum games, a variant of the classical combinatorial Subtraction games (studied for example in the monumental work `Winning Ways' , by Berlekamp, Conway and Guy), called Cumulative Subtraction (CS).
Revised: We study zero-sum games, a variant of the classical combinatorial Subtraction games (studied for example in the monumental work "Winning Ways" , by Berlekamp, Conway and Guy), called Cumulative Subtraction (CS).
Edit intent: fluency
Original:  We show how to find good solutions even in the presence of conflicting prior information, by formulating a " constraint-based regularization " of the objective.
Revised:  We show how to find good solutions even in the presence of conflicting prior information, by formulating a constraint-based regularization " of the objective.
Edit intent: fluency
Original:  We show how to find good solutions even in the presence of conflicting prior information, by formulating a " constraint-based regularization " of the objective.
Revised:  We show how to find good solutions even in the presence of conflicting prior information, by formulating a " constraint-based regularization of the objective.
Edit intent: fluency
Original:  Finally, we explore a variation of this objective for dissimilarity information [Cohen-Addad et al., 2018] and improve upon current techniques  .
Revised:  We further explore a variation of this objective for dissimilarity information [Cohen-Addad et al., 2018] and improve upon current techniques .
Edit intent: clarity
Original:  We show that problems associated with sequential learning of different tasks also occur in this setting.
Edit intent: coherence
Original:  The performance of PRINS Euclidean distance, dot product and histogram implementation  exceeds the attainable performance of a reference architecture by up to four orders of magnitude, depending on the dataset size.
Revised:  The performance of PRINS Euclidean distance, dot product and histogram implementation , exceeds the attainable performance of a reference architecture by up to four orders of magnitude, depending on the dataset size.
Edit intent: fluency
Original: GIRAF is an in-storage  architecture and algorithm framework based on Resistive Content Addressable Memory (RCAM). GIRAF functions simultaneously as a storage and a massively parallel associative processor.
Revised: Near-data in-storage architecture and algorithm framework based on Resistive Content Addressable Memory (RCAM). GIRAF functions simultaneously as a storage and a massively parallel associative processor.
Edit intent: clarity
Original: GIRAF is an in-storage  architecture and algorithm framework based on Resistive Content Addressable Memory (RCAM). GIRAF functions simultaneously as a storage and a massively parallel associative processor.
Revised: GIRAF is an in-storage architecture based on Resistive Content Addressable Memory (RCAM). GIRAF functions simultaneously as a storage and a massively parallel associative processor.
Edit intent: clarity
Original:  We show that GIRAF outperforms a reference computer architecture with a bandwidth-limited external storage access on a variety of data intensive workloads .
Revised:  We show that GIRAF outperforms a reference computer architecture with a bandwidth-limited external storage .
Edit intent: clarity
Original:  The performance of GIRAF Euclidean distance, dot product and histogram implementation , exceeds the attainable performance of a reference architecture by up to four orders of magnitude, depending on the dataset size.
Revised:  The performance of GIRAF Euclidean distance, dot product and histogram implementation exceeds the attainable performance of a reference architecture by up to four orders of magnitude, depending on the dataset size.
Edit intent: fluency
Original: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{R c . We prove an isometry theorem in this derived setting,  expressing the convolution distance  as a matching distance , mirroring the situation of persistence with one parameter.
Revised: Persistent homology has been recently studied with the tools of sheaf theory in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{R c . We prove an isometry theorem in this derived setting, expressing the convolution distance as a matching distance , mirroring the situation of persistence with one parameter.
Edit intent: clarity
Original: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{R c . We prove an isometry theorem in this derived setting,  expressing the convolution distance  as a matching distance , mirroring the situation of persistence with one parameter.
Revised: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{R c . We prove the isometry theorem in this derived setting, expressing the convolution distance as a matching distance , mirroring the situation of persistence with one parameter.
Edit intent: fluency
Original: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{R c . We prove an isometry theorem in this derived setting,  expressing the convolution distance  as a matching distance , mirroring the situation of persistence with one parameter.
Revised: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{R c . We prove an isometry theorem in this derived setting, thus expressing the convolution distance as a matching distance , mirroring the situation of persistence with one parameter.
Edit intent: coherence
Original: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{R c . We prove an isometry theorem in this derived setting,  expressing the convolution distance  as a matching distance , mirroring the situation of persistence with one parameter.
Revised: Following Curry and Kashiwara Schapira seminal works on adapting persistence theory ideas to sheaves on a real vector space in the derived setting , we extensively study their convolution distance on the category of constructible sheavesover the real line D^b_{R c . We prove an isometry theorem in this derived setting, expressing the convolution distance of sheaves as a matching distance , mirroring the situation of persistence with one parameter.
Edit intent: clarity
Original:  We also explicitly compute all morphisms in D^b_{R c}( k _R), which enables us to compute distances between indecomposable objects.
Revised:  To achieve so, we explicitly compute all morphisms in D^b_{R c}( k _R), which enables us to compute distances between indecomposable objects.
Edit intent: clarity
Original:  We also explicitly compute all morphisms in D^b_{R c}( k _R), which enables us to compute distances between indecomposable objects.
Revised:  We also explicitly compute all morphisms in D^b_{R c}( k _R), which enables us to compute distances between indecomposable objects.
Edit intent: fluency
Original: We present a near-optimal algorithm for properly learning convex polytopes in the realizable PAC setting from data with a margin.
Revised: We present an improved algorithm for properly learning convex polytopes in the realizable PAC setting from data with a margin.
Edit intent: clarity
Original:  Our first contribution is to identify distinct generalizations of the notion of%DIFDELCMD < {\em %%% margin from hyperplanes to polytopes and to understand how they relate geometrically;
Revised:  Our %DIFDELCMD < {\em %%% margin from hyperplanes to polytopes and to understand how they relate geometrically;
Edit intent: clarity
Original:  Our first contribution is to identify distinct generalizations of the notion of%DIFDELCMD < {\em %%% margin from hyperplanes to polytopes and to understand how they relate geometrically;
Revised:  Our first contribution is to identify distinct generalizations of the notion of%DIFDELCMD < {\em %%% from hyperplanes to polytopes and to understand how they relate geometrically;
Edit intent: clarity
Original: We present an improved algorithm for properly learning convex polytopes {\em   in the realizable PAC setting from data with a margin.
Revised: We present an improved algorithm for {\em in the realizable PAC setting from data with a margin.
Edit intent: clarity