wanyu/IteraTeR_v2 huggingface dataset:
Original:  We thoroughly evaluate the resulting techniques in various real-life repetitiveness scenarios, and also show how they are combined to solve ranked conjunctive and disjunctive  queries under the simple tf-idf model of relevance  .
Revised:  Finally, we show how the tools we developed can be combined to solve ranked conjunctive and disjunctive queries under the simple tf-idf model of relevance .
Edit intent: clarity
Original:  Our analysis suggests competitive dominance as a plausible mechanism underlying community assembly patterns over  continental scales.
Revised:  Our analysis suggests competitive dominance as a plausible mechanism underlying community assembly patterns over these continental scales.
Edit intent: fluency
Original:  However, ecologists still know little about the scales and conditions at which biotic interactions matter and their interplay with the environment to structure species assemblages.
Revised:  However, ecologists still know little about the scales and conditions at which biotic interactions matter and how these interact with the environment to structure species assemblages.
Edit intent: clarity
Original:  Here we develop ecological theory to analyze plant distribution and trait data across Europe and find that plant height clustering is related to  evapotranspiration and gross primary productivity.
Revised:  Here we develop ecological theory to analyze plant distribution and trait data across Europe and find that plant height clustering is related to both evapotranspiration and gross primary productivity.
Edit intent: clarity
Original:  Under severe conditions, either climate is too harsh and overrides the effect of competition  or other interactions play a relevant role .
Revised:  Under severe conditions, either climate is too harsh and overrides the effect of competition , or other interactions play a relevant role .
Edit intent: fluency
Original:  Under severe conditions, either climate is too harsh and overrides the effect of competition  or other interactions play a relevant role .
Revised:  Under severe conditions, either climate is too harsh and overrides the effect of competition or other interactions become increasingly important .
Edit intent: clarity
Original: In this talk , we present a continuous time extension of the framework for modeling market microstructure , developed in our previous work .
Revised: In this work , we present a continuous time extension of the framework for modeling market microstructure , developed in our previous work .
Edit intent: clarity
Original:  These beliefs may change according to the information observed by the agents (e.g. represented by a relevant stochastic factor), implying a change in the shape of the LOB.
Revised:  The agents' beliefs may change according to the information observed by the agents (e.g. represented by a relevant stochastic factor), implying a change in the shape of the LOB.
Edit intent: clarity
Original:  If the relevant signal is a function of the LOBitself , then, our approach allows one to model the "indirect" market impact (as opposed to the "direct" impact that a market order makes on the LOB, by eliminating certain limit orders instantaneously), showing how any change to the LOB causes further changes to it .
Revised:  If the relevant signal is a function of the LOBitself , then, our approach allows one to model the "indirect" market impact (as opposed to the "direct" impact that a market order makes on the LOB, by eliminating certain limit orders ) .
Edit intent: clarity
Original:  On the mathematical side, we formulate the problem as a mixed control-stopping game , with a continuum of players .
Revised:  On the mathematical side, we formulate the proposed modeling framework as a continuum-player control-stopping game , with a continuum of players .
Edit intent: clarity
Original:  On the mathematical side, we formulate the problem as a mixed control-stopping game , with a continuum of players .
Revised:  On the mathematical side, we formulate the problem as a mixed control-stopping game .
Edit intent: clarity
Original:  We manage to split the equilibrium problem into two parts , and represent one of them through a two-dimensional system of Reflected Backward Stochastic Differential Equations , and the otherone with an infinite-dimensional fixed-point equation.
Revised:  We manage to split the equilibrium problem into two parts . The first one is described by a two-dimensional system of Reflected Backward Stochastic Differential Equations , and the otherone with an infinite-dimensional fixed-point equation.
Edit intent: coherence
Original:  We prove the existence of the solutions to both problems and show how they can be computed in a simple example .
Revised:  We prove the existence of their solutions in the paper .
Edit intent: clarity
Original:  Our data-driven method learns low-dimensional strain features originating from different aerodynamic environments using Proper Orthogonal Decomposition (POD) modes in the frequency domain, and leverages compressed sensing and sparse approximation to classify a set of strain frequency signatures using a dictionary of POD modes.
Revised:  Our data-driven method learns low-dimensional strain features originating from different aerodynamic environments using proper orthogonal decomposition (POD) modes in the frequency domain, and leverages compressed sensing and sparse approximation to classify a set of strain frequency signatures using a dictionary of POD modes.
Edit intent: fluency
Original:  Our data-driven method learns low-dimensional strain features originating from different aerodynamic environments using Proper Orthogonal Decomposition (POD) modes in the frequency domain, and leverages compressed sensing and sparse approximation to classify a set of strain frequency signatures using a dictionary of POD modes.
Revised:  Our data-driven method learns low-dimensional strain features originating from different aerodynamic environments using Proper Orthogonal Decomposition (POD) modes in the frequency domain, and leverages sparse approximation to classify a set of strain frequency signatures using a dictionary of POD modes.
Edit intent: clarity
Original:  A sensor placement algorithm identifies the frequency samples that best separate the different aerodynamic environments in rank-reduced POD feature space.
Revised:  A measurement selection algorithm identifies frequencies that best discriminate the different aerodynamic environments in rank-reduced POD feature space.
Edit intent: clarity
Original:  Linear optimization can compute steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude.
Revised:  Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude.
Edit intent: clarity
Original:  ME models currently have 70,000 constraints and variables and will grow larger , so that exact simplex solvers are not practical .
Revised:  ME models currently have 70,000 constraints and variables and will grow larger .
Edit intent: coherence
Original:  Here we ?rst show that a number of issues in vision may be addressed through an analytic expression of the Jacobian of these linear-nonlinear transforms.
Revised:  Here we first show that a number of issues in vision may be addressed through an analytic expression of the Jacobian of these linear-nonlinear transforms.
Edit intent: fluency
Original:  We show here that data on the molecular basis of synaptic plasticity, specifically on the role of CaMKII in its activated form, support a substantially more efficient Hamiltonian sampling of network configurations .
Revised:  We show that the experimental data on the molecular basis of synaptic plasticity, specifically on the role of CaMKII in its activated form, support a substantially more efficient Hamiltonian sampling of network configurations .
Edit intent: clarity
Original:  We show here that data on the molecular basis of synaptic plasticity, specifically on the role of CaMKII in its activated form, support a substantially more efficient Hamiltonian sampling of network configurations .
Revised:  We show here that data on the specific role of CaMKII in its activated form, support a substantially more efficient Hamiltonian sampling of network configurations .
Edit intent: clarity
Original: A growing trend for information technology is to not just react to changes, but  as much as possible anticipate them .
Revised: A growing trend for information technology is to not just react to changes, but as much as possible .
Edit intent: clarity
Original:  This paradigm made modern solutions  such as recommendation systems  a ubiquitous presence in today's digital transactions.
Revised:  This paradigm made modern solutions , such as recommendation systems a ubiquitous presence in today's digital transactions.
Edit intent: fluency
Original:  This paradigm made modern solutions  such as recommendation systems  a ubiquitous presence in today's digital transactions.
Revised:  This paradigm made modern solutions such as recommendation systems , a ubiquitous presence in today's digital transactions.
Edit intent: fluency
Original:  Quantum-dot Cellular Automata (QCA) has been widely advocated in nanotechnology as a response to the physical limits associated with complementary metal oxide semiconductor (CMOS) technology in atomic scales.
Revised: New technologies such as Quantum-dot Cellular Automata (QCA) has been widely advocated in nanotechnology as a response to the physical limits associated with complementary metal oxide semiconductor (CMOS) technology in atomic scales.
Edit intent: clarity
Original:  Some of its peculiar features are its smaller size, higher speed, higher switching frequency, lower power consumption, and higher scale integration.
Revised:  Some of their peculiar features are its smaller size, higher speed, higher switching frequency, lower power consumption, and higher scale integration.
Edit intent: fluency
Original:  Some of its peculiar features are its smaller size, higher speed, higher switching frequency, lower power consumption, and higher scale integration.
Revised:  Some of its peculiar features are their smaller size, higher speed, higher switching frequency, lower power consumption, and higher scale integration.
Edit intent: fluency
Original:  In this technology , the majority and NOT gates are employed for the production of the functions as these two gates together make a universal set of Boolean primitives in QCA technology .
Revised:  In these technologies , the majority and NOT gates are employed for the production of the functions as these two gates together make a universal set of Boolean primitives in QCA technology .
Edit intent: fluency
Original:  In this technology , the majority and NOT gates are employed for the production of the functions as these two gates together make a universal set of Boolean primitives in QCA technology .
Revised:  In this technology , the majority and NOT gates are employed for the production of the functions as this set of gates makes a universal set of Boolean primitives in QCA technology .
Edit intent: clarity
Original:  In this technology , the majority and NOT gates are employed for the production of the functions as these two gates together make a universal set of Boolean primitives in QCA technology .
Revised:  In this technology , the majority and NOT gates are employed for the production of the functions as these two gates together make a universal set of Boolean primitives in these technologies .
Edit intent: clarity
Original:  Also, for the MCNC benchmark circuits, there is an average reduction of 10.5\% in the number of levels as well as 16.8\% and 33.5\% in the number of majority and NOT gates, as compared to the best available method respectively.
Revised:  Also, for the MCNC benchmark circuits, there is an average reduction of 10.5\% in the number of levels as well as 16.8\% and 33.5\% in the number of majority and inverter gates, as compared to the best available method respectively.
Edit intent: clarity
Original: Quantum consciousness stems from dynamical flows of the hydrogen ions within the brain liquid.
Revised: Emergence of quantum consciousness stems from dynamical flows of the hydrogen ions within the brain liquid.
Edit intent: clarity
Original: Quantum consciousness stems from dynamical flows of the hydrogen ions within the brain liquid.
Revised: Quantum consciousness stems from dynamical flows of hydrogen ions in the brain liquid.
Edit intent: clarity
Original: Emergence of quantum consciousness stems from dynamical flows of hydrogen ions in the brain liquid.
Revised: The emergence of quantum consciousness stems from dynamical flows of hydrogen ions in the brain liquid.
Edit intent: fluency
Original: Emergence of quantum consciousness stems from dynamical flows of hydrogen ions in the brain liquid.
Revised: Emergence of quantum consciousness stems from dynamic flows of hydrogen ions in the brain liquid.
Edit intent: clarity
Original: Emergence of quantum consciousness stems from dynamical flows of hydrogen ions in the brain liquid.
Revised: Emergence of quantum consciousness stems from dynamical flows of hydrogen ions in brain liquid.
Edit intent: fluency
Original:  Transition from unordered flows to superconducting macroscopic ones is achieved with the advent of hexagonal packing of water molecules, of so-called the fourth phase of water.
Revised:  This liquid contains vast areas of the fourth phase of water with hexagonal packing of water molecules, of so-called the fourth phase of water.
Edit intent: clarity
Original:  Transition from unordered flows to superconducting macroscopic ones is achieved with the advent of hexagonal packing of water molecules, of so-called the fourth phase of water.
Revised:  Transition from unordered flows to superconducting macroscopic ones is achieved with the advent of hexagonal packing of water molecules, of water.
Edit intent: clarity
Original:  Long-living hydrogen ion motion on such a hexagonal lattice arises as the eightfold path of the ions supported by the Grotthuss mechanism.
Revised:  The hydrogen ion motion on such a hexagonal lattice arises as the eightfold path of the ions supported by the Grotthuss mechanism.
Edit intent: clarity
Original:  Long-living hydrogen ion motion on such a hexagonal lattice arises as the eightfold path of the ions supported by the Grotthuss mechanism.
Revised:  Long-living hydrogen ion motion on such hexagonal lattices shows as the hopping of the ions supported by the Grotthuss mechanism.
Edit intent: clarity
Original:  In average, the flows are described by the modified Navier-Stokes equation  with the continuity equation .
Revised:  In average, the flows are described by the modified Navier-Stokes equation . It, along with the continuity equation .
Edit intent: coherence
Original:  They together lead to the nonlinear Schrodinger equation describing quantum effects of these flows, such as  interference on gap junctions.
Revised:  They together lead to the nonlinear Schrodinger equation , which describes the quantum effects of these flows, such as interference on gap junctions.
Edit intent: coherence
Original: Strictly Chordality-k graphs ( SCk graphs) are graphs which are either cycle free or every induced cycle is exactly k, for some fixed k, k \geq 3. Note that k = 3 and k = 4 are precisely the Chordal graphs and Chordal Bipartite graphs, respectively.
Revised: Strictly Chordality-k graphs ( SC_k graphs) are graphs which are either cycle free or every induced cycle is exactly k, for some fixed k, k \geq 3. Note that k = 3 and k = 4 are precisely the Chordal graphs and Chordal Bipartite graphs, respectively.
Edit intent: clarity
Original:  Monthly client IPv4 address counts showed constant growth for years prior, but since 2014,  IPv4 count has stagnated while IPv6 counts have grown.
Revised:  Monthly client IPv4 address counts showed constant growth for years prior, but since 2014, the IPv4 count has stagnated while IPv6 counts have grown.
Edit intent: clarity
Original: In recent year, parallel implementations have been used to speed up the training of deep neural networks (DNN)
Revised: Parallelization framework has become a necessity to speed up the training of deep neural networks (DNN)
Edit intent: clarity
Original:  . Typically, the parameters of the local models are periodically communicated and averaged to get a global model until the training curve converges (denoted as MA-DNN)
Revised:  . Typically, the parameters of local models are periodically communicated and averaged to get a global model until the training curve converges (denoted as MA-DNN)
Edit intent: fluency
Original:  . Typically, the parameters of the local models are periodically communicated and averaged to get a global model until the training curve converges (denoted as MA-DNN)
Revised:  . Typically, the parameters of the local models are periodically communicated and averaged to obtain a global model until the training curve converges (denoted as MA-DNN)
Edit intent: fluency
Original:  . However, since DNN is a highly non-convex model, the global model obtained by averaging parameters does not have guarantee on its performance improvement over the local models and might even be worse than the average performance of the local models, which leads to the slow-down of convergence and the decrease of the final performance .
Revised:  . However, since DNN is a highly non-convex model, averaging parameters cannot ensure that such global model can perform better than those local models .
Edit intent: clarity
Original:  To tackle this problem, we propose a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e.,  the outputs of the local models are averaged instead of the parameters.
Revised:  To tackle this problem, we introduce a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e., the outputs of the local models are averaged instead of the parameters.
Edit intent: clarity
Original:  To tackle this problem, we propose a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e.,  the outputs of the local models are averaged instead of the parameters.
Revised:  To tackle this problem, we propose a new parallel training Ensemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e., the outputs of the local models are averaged instead of the parameters.
Edit intent: clarity
Original:  To tackle this problem, we propose a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e.,  the outputs of the local models are averaged instead of the parameters.
Revised:  To tackle this problem, we propose a new parallel training method called ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e., the outputs of the local models are averaged instead of the parameters.
Edit intent: coherence
Original:  To tackle this problem, we propose a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e.,  the outputs of the local models are averaged instead of the parameters.
Revised:  To tackle this problem, we propose a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN . In this framework , we propose to aggregate the local models by ensemble, i.e., the outputs of the local models are averaged instead of the parameters.
Edit intent: clarity
Original:  To tackle this problem, we propose a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e.,  the outputs of the local models are averaged instead of the parameters.
Revised:  To tackle this problem, we propose a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e., averaging the outputs of the local models are averaged instead of the parameters.
Edit intent: fluency
Original:  To tackle this problem, we propose a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e.,  the outputs of the local models are averaged instead of the parameters.
Revised:  To tackle this problem, we propose a new parallel training method calledEnsemble-Compression ( denoted as EC-DNN ). Specifically , we propose to aggregate the local models by ensemble, i.e., the outputs of local models instead of the parameters.
Edit intent: clarity
Original:  Considering that the widely used loss functions are convex to the output of the model , the performance of the global model obtained in this way is guaranteed to be at least as good as the average performance of local models.
Revised:  As most of prevalent loss functions are convex to the output of the model , the performance of the global model obtained in this way is guaranteed to be at least as good as the average performance of local models.
Edit intent: clarity
Original:  Considering that the widely used loss functions are convex to the output of the model , the performance of the global model obtained in this way is guaranteed to be at least as good as the average performance of local models.
Revised:  Considering that the widely used loss functions are convex to the output of the model , the performance of ensemble-based global model is guaranteed to be at least as good as the average performance of local models.
Edit intent: clarity
Original:  However, the size of the global model will increase after each ensemble and may explode after multiple rounds of ensembles .
Revised:  However, a big challenge lies in the explosion of model size since each round of ensemble can give rise to multiple times size increment .
Edit intent: clarity
Original:  Thus, we conduct model compression after each ensemble, to ensure the size of the global model to be the same as the local models.
Revised:  Thus, we carry out model compression after each ensemble, to ensure the size of the global model to be the same as the local models.
Edit intent: clarity
Original:  The experimental results demonstrate that our proposed EC-DNN can stably achieve better performance than MA-DNN  .
Revised:  The experimental results demonstrate that our proposed EC-DNN over MA-DNN .
Edit intent: clarity
Original:  The results show both accurate label assignment to topics as well as improved topic generation than those obtained using various labeling approaches of Latent Dirichlet allocation (LDA) when compared by pointwise mutual information (PMI) assessment .
Revised:  The results show both accurate label assignment to topics as well as improved topic generation than those obtained using various labeling approaches based off Latent Dirichlet allocation (LDA) when compared by pointwise mutual information (PMI) assessment .
Edit intent: clarity
Original:  The results show both accurate label assignment to topics as well as improved topic generation than those obtained using various labeling approaches of Latent Dirichlet allocation (LDA) when compared by pointwise mutual information (PMI) assessment .
Revised:  The results show both accurate label assignment to topics as well as improved topic generation than those obtained using various labeling approaches of Latent Dirichlet allocation (LDA) .
Edit intent: coherence
Original:  On the other hand , unsupervised learning exploits cheap unlabeled data and can help to solve the same tasks more efficiently.
Revised:  On the other hand , unsupervised learning exploits cheap unlabeled data and can help to solve these tasks more efficiently.
Edit intent: clarity