wanyu/IteraTeR_v2 huggingface dataset:
Original:  In line with the well-established idea that sensory processing is adapted to the statistics of the natural environment, the GSM has also been considered a model of the early visual system, as a reasonable "first-order" approximation to the internal model that the primary visual cortex (V1) inverts .
Revised:  In line with the well-established idea that sensory processing is adapted to the statistics of the natural environment, the GSM has also been considered a model of the early visual system, as a reasonable "first-order" approximation to the internal model that the primary visual cortex (V1) implements .
Edit intent: clarity
Original:  Thus, for a deeper insight into the realm of possible behaviours the GSM can (and cannot) exhibit and predict, here we present analytical derivations for the limiting behaviour of the mean and (co)variance of the GSM posterior at very low and  high contrast levels.
Revised:  Thus, for a deeper insight into the realm of possible behaviours the GSM can (and cannot) exhibit and predict, here we present analytical derivations for the limiting behaviour of the mean and (co)variance of the GSM posterior at very low and very high contrast levels.
Edit intent: fluency
Original:  Over and above competitive F1 score, AQQUCN gets the best entity ranking accuracy on two syntax rich and two syntax poor public query workloads  amounting to over 8,000 queries , with 16 to 18 percent absolute improvement in mean average precision (MAP), compared to recent systems .
Revised:  Over and above competitive F1 score, AQQUCN gets the best entity ranking accuracy on two syntax rich and two syntax poor public query workloads , amounting to over 8,000 queries , with 16 to 18 percent absolute improvement in mean average precision (MAP), compared to recent systems .
Edit intent: fluency
Original:  QA systems based on precise parsing tend to be brittle: minor variations in the question may dramatically change the output .
Revised:  QA systems based on precise parsing tend to be brittle: minor syntax variations may dramatically change the output .
Edit intent: clarity
Original:  QA systems based on precise parsing tend to be brittle: minor variations in the question may dramatically change the output .
Revised:  QA systems based on precise parsing tend to be brittle: minor variations in the question may dramatically change the response .
Edit intent: clarity
Original:  A large corpus may have supporting information , but in an unstructured, unreliable form.
Revised:  At the other extreme, a large corpus may have supporting information , but in an unstructured, unreliable form.
Edit intent: clarity
Original:  A large corpus may have supporting information , but in an unstructured, unreliable form.
Revised:  A large corpus may provide broader coverage , but in an unstructured, unreliable form.
Edit intent: clarity
Original:  Our system, called AQQUCN , accepts a broad spectrum of queries , between well-formed questions to short `telegraphic' keyword sequences.
Revised:  Our system, called AQQUCN , accepts a broad spectrum of query syntax , between well-formed questions to short `telegraphic' keyword sequences.
Edit intent: clarity
Original:  On four public query workloads, amounting to over 8,000 queries in different query formats , we see 5--16\% absolute improvement in mean average precision (MAP), compared to the entity ranking performance of recent systems.
Revised:  On four public query workloads, amounting to over 8,000 queries with diverse query syntax , we see 5--16\% absolute improvement in mean average precision (MAP), compared to the entity ranking performance of recent systems.
Edit intent: clarity
Original:  The dynamics of this Markov chain is governed by two types of events: vertex duplication, where at constant rate a pair of vertices is sampled uniformly , one of these vertices loses its incident edges and is rewired to the other vertex and its neighbors;
Revised:  The dynamics of this Markov chain is governed by two types of events: vertex duplication, where at constant rate a pair of vertices is sampled uniformly and one of these vertices loses its incident edges and is rewired to the other vertex and its neighbors;
Edit intent: fluency
Original: Image compositing is a popular and successful method used to generate realistic yet fake imagery .
Revised: Image compositing is a method used to generate realistic yet fake imagery .
Edit intent: clarity
Original:  However, most previous work does not investigate the topic of automatically selecting semantically compatible segments and predicting their locations and sizes given a background image.
Edit intent: coherence
Original:  We tackle this by developing a novel Convolutional Neural Network (CNN) model that jointly predicts the potential location and size of the person segment.
Revised:  We tackle this by developing a novel Convolutional Neural Network (CNN) that jointly predicts the potential location and size of the person segment.
Edit intent: clarity
Original:  Experimental results show our system can generate composite images that look semantically and visually convincing.
Revised:  Experimental results show that our model can generate composite images that look semantically and visually convincing.
Edit intent: clarity
Original:  Experimental results show our system can generate composite images that look semantically and visually convincing.
Revised:  Experimental results show our system can generate composite images that look visually convincing.
Edit intent: clarity
Original:  We also develop a proof-of-concept user interface to demonstrate the potential application of our method.
Revised:  We also develop a user interface to demonstrate the potential application of our method.
Edit intent: clarity
Original: Personalized PageRank (PPR) is a measure of the importance of a node in a graph from the perspective of another node (we call these nodes the target and the source  , respectively).
Revised: Personalized PageRank (PPR) is a measure of the importance of a node from the perspective of another node (we call these nodes the target and the source , respectively).
Edit intent: clarity
Original: Personalized PageRank (PPR) is a measure of the importance of a node in a graph from the perspective of another node (we call these nodes the target and the source  , respectively).
Revised: Personalized PageRank (PPR) is a measure of the importance of a node in a graph from the perspective of another (we call these nodes the target and the source , respectively).
Edit intent: coherence
Original: Personalized PageRank (PPR) is a measure of the importance of a node in a graph from the perspective of another node (we call these nodes the target and the source  , respectively).
Revised: Personalized PageRank (PPR) is a measure of the importance of a node in a graph from the perspective of another node (we call these nodes the target , respectively).
Edit intent: clarity
Original:  PPR has been used in many applications, such as offering a Twitter user (the source) personalized recommendations of who to follow (targets deemed important by PPR) . Computing PPR at scale is infeasible for  networks like Twitter, so many estimation algorithms have been proposed.
Revised:  PPR has been used in many applications, such as offering a Twitter user (the source) recommendations of who to follow (targets deemed important by PPR) . Computing PPR at scale is infeasible for networks like Twitter, so many estimation algorithms have been proposed.
Edit intent: clarity
Original:  PPR has been used in many applications, such as offering a Twitter user (the source) personalized recommendations of who to follow (targets deemed important by PPR) . Computing PPR at scale is infeasible for  networks like Twitter, so many estimation algorithms have been proposed.
Revised:  PPR has been used in many applications, such as offering a Twitter user (the source) personalized recommendations of who to follow (targets deemed important by PPR) . Computing PPR at scale is infeasible for large networks like Twitter, so many estimation algorithms have been proposed.
Edit intent: clarity
Original: Upcoming wireless deployments, such as C-RAN and Massive-MIMO, rely on real-time transport of baseband samples.
Revised: In wireless deployments, such as C-RAN and Massive-MIMO, rely on real-time transport of baseband samples.
Edit intent: clarity
Original: Upcoming wireless deployments, such as C-RAN and Massive-MIMO, rely on real-time transport of baseband samples.
Revised: Upcoming wireless deployments, such as Massive-MIMO, rely on real-time transport of baseband samples.
Edit intent: clarity
Original:  We also characterize the maximum wireless capacity using DISTRO while meeting the real-time constraints of baseband processing .
Revised:  We further characterize the wireless capacity using DISTRO while meeting the real-time constraints of baseband processing .
Edit intent: clarity
Original:  We also characterize the maximum wireless capacity using DISTRO while meeting the real-time constraints of baseband processing .
Revised:  We also characterize the maximum wireless capacity using DISTRO and provide an efficient search algorithm for the design of a capacity achieving baseband transport .
Edit intent: clarity
Original:  As concrete examples we identify a number of crystallographic protein structures in Protein Data Bank (PDB) that display exceptional atomic positions around their %DIFDELCMD < {\it %DIFDELCMD < %%% cis peptide planes.
Revised:  As concrete examples we identify a number of crystallographic protein structures in Protein Data Bank (PDB) that display exceptional atomic positions around their %DIFDELCMD < {\it %DIFDELCMD < %%% cis peptide planes.
Edit intent: fluency
Original: The efficient solution of discretizations of coupled systems of partial differential equations (PDEs) is at the core of much of numerical simulation.
Revised: The efficient solution of discretisations of coupled systems of partial differential equations (PDEs) is at the core of much of numerical simulation.
Edit intent: fluency
Original:  With few exceptions, the reported numerical implementation of such solution strategies is specific to a particular model setup, and intimately ties the solver strategy to the discretization and PDE, especially when the preconditioner requires auxiliary operators.
Revised:  With few exceptions, the reported numerical implementation of such solution strategies is specific to a particular model setup, and intimately ties the solver strategy to the discretisation and PDE, especially when the preconditioner requires auxiliary operators.
Edit intent: fluency
Original:  In this paper, we present recent improvements in the Firedrake finite element library that allow for straightforward development of the building blocks of extensible, composable , preconditioners that decouple the solver from the model formulation.
Revised:  In this paper, we present recent improvements in the Firedrake finite element library that allow for straightforward development of the building blocks of extensible, composable preconditioners that decouple the solver from the model formulation.
Edit intent: fluency
Original:  Our implementation extends the algebraic composability of linear solvers offered by the PETSc library by augmenting operators, and hence preconditioners, with the ability to assemble any necessary auxiliary operators.
Revised:  Our implementation extends the algebraic composability of linear solvers offered by the PETSc library by augmenting operators, and hence preconditioners, with the ability to provide any necessary auxiliary operators.
Edit intent: clarity
Original:  Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been shown to be a safer alternative to  electrical cortical stimulation mapping (ESM), which is currently the clinical/gold standard.
Revised:  Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been shown to be a safer alternative to the electrical cortical stimulation mapping (ESM), which is currently the clinical/gold standard.
Edit intent: fluency
Original:   In this study, we address the  limitation of the current RTFM signal estimation methods by analyzing the full frequency spectrum of the signal and applying machine learning algorithms, specifically random forest (RF)  . We train RF with power spectral density of the time-series RTFM signal in supervised learning framework where ground truth labels are obtained from the ESM.
Revised:  In this study, we address the accuracy limitation of the current RTFM signal estimation methods by analyzing the full frequency spectrum of the signal and applying machine learning algorithms, specifically random forest (RF) . We train RF with power spectral density of the time-series RTFM signal in supervised learning framework where ground truth labels are obtained from the ESM.
Edit intent: clarity
Original:   In this study, we address the  limitation of the current RTFM signal estimation methods by analyzing the full frequency spectrum of the signal and applying machine learning algorithms, specifically random forest (RF)  . We train RF with power spectral density of the time-series RTFM signal in supervised learning framework where ground truth labels are obtained from the ESM.
Revised:  In this study, we address the limitation of the current RTFM signal estimation methods by analyzing the full frequency spectrum of the signal and replacing signal power estimation methods with machine learning algorithms, specifically random forest (RF) . We train RF with power spectral density of the time-series RTFM signal in supervised learning framework where ground truth labels are obtained from the ESM.
Edit intent: clarity
Original:  Experimental results obtained from RTFM of six adult patients in a strictly controlled experimental setup reveal the state of the art detection accuracy of \approx 78\\%DIF < for the language comprehension task, an improvement of 23\\% over the conventional %DIF > for the \\%DIF > over the conventional RTFM estimation method.
Revised:  Results obtained from RTFM of six adult patients in a strictly controlled experimental setup reveal the state of the art detection accuracy of \approx 78\\%DIF < for the language comprehension task, an improvement of 23\\% over the conventional %DIF > for the \\%DIF > over the conventional RTFM estimation method.
Edit intent: clarity
Original:  Experimental results obtained from RTFM of six adult patients in a strictly controlled experimental setup reveal the state of the art detection accuracy of \approx 78\\%DIF < for the language comprehension task, an improvement of 23\\% over the conventional %DIF > for the \\%DIF > over the conventional RTFM estimation method.
Revised:  Experimental results obtained from RTFM of six adult patients in a strictly controlled experimental setup reveal the state of the art detection accuracy of \approx 78\\%DIF < for the language comprehension task, an improvement of 23\\% %DIF > for the \\%DIF > over the conventional RTFM estimation method.
Edit intent: fluency
Original:  Experimental results obtained from RTFM of six adult patients in a strictly controlled experimental setup reveal the state of the art detection accuracy of \approx 78\\%DIF < for the language comprehension task, an improvement of 23\\% over the conventional %DIF > for the \\%DIF > over the conventional RTFM estimation method.
Revised:  Experimental results obtained from RTFM of six adult patients in a strictly controlled experimental setup reveal the state of the art detection accuracy of \approx 78\\%DIF < for the language comprehension task, an improvement of 23\\% over the conventional %DIF > for the language comprehension task, an improvement of 23\\%DIF > over the conventional RTFM estimation method.
Edit intent: fluency
Original: We study distributed algorithms implemented in a simplified but biologically plausible model for stochastic spiking neural networks.
Revised: We study distributed algorithms implemented in a simplified biologically inspired model for stochastic spiking neural networks.
Edit intent: clarity
Original:  Using a combination of Yao's minimax principle and a VC dimension-based argument, we show that the tradeoff between runtime and network size in our neuro-RAM is nearly optimal.
Revised:  Using a VC dimension-based argument, we show that the tradeoff between runtime and network size in our neuro-RAM is nearly optimal.
Edit intent: coherence
Original:  Our result has several implications -- since our neuro-RAM construction can be implemented with deterministic threshold gates, it demonstrates that, in contrast to similarity testing, randomness does not provide significant computational advantages for this problem.
Revised:  Our result has several implications -- since our neuro-RAM can be implemented with deterministic threshold gates, it demonstrates that, in contrast to similarity testing, randomness does not provide significant computational advantages for this problem.
Edit intent: clarity
Original:  Our result has several implications -- since our neuro-RAM construction can be implemented with deterministic threshold gates, it demonstrates that, in contrast to similarity testing, randomness does not provide significant computational advantages for this problem.
Revised:  Our result has several implications -- since our neuro-RAM construction can be implemented with deterministic threshold gates, it shows that, in contrast to similarity testing, randomness does not provide significant computational advantages for this problem.
Edit intent: coherence
Original:  It also establishes a separation between our networks , which spike with a sigmoidal probability function , and well-studied but less biologically plausible deterministic sigmoidal networks, whose gates output real number  values, and which can implement a neuro-RAM much more efficiently.
Revised:  It also establishes a separation between our networks , which spike with a sigmoidal probability function , and well-studied deterministic sigmoidal networks, whose gates output real number values, and which can implement a neuro-RAM much more efficiently.
Edit intent: clarity
Original:  This architecture exploits the sparsity of neuron activations in CNNs to accelerate the computation and reduce memory requirements.
Revised:  NullHop exploits the sparsity of neuron activations in CNNs to accelerate the computation and reduce memory requirements.
Edit intent: clarity
Original:  We show how in RTL simulations in a 28nm process with a clock frequency of 500MHz, the NullHop core is able to reach over 450 GOp/s and efficiency of 368\%, maintaining over 98\% utilization of the MAC units and achieving a power efficiency of over 3TOp/s/W in a core area of 5.8mm2
Revised:  We show how in RTL simulations in a 28nm process with a clock frequency of 500MHz, the NullHop core is able to reach over 450 GOp/s and efficiency of 368\%, maintains over 98\% utilization of the MAC units and achieving a power efficiency of over 3TOp/s/W in a core area of 5.8mm2
Edit intent: fluency
Original:  We show how in RTL simulations in a 28nm process with a clock frequency of 500MHz, the NullHop core is able to reach over 450 GOp/s and efficiency of 368\%, maintaining over 98\% utilization of the MAC units and achieving a power efficiency of over 3TOp/s/W in a core area of 5.8mm2
Revised:  We show how in RTL simulations in a 28nm process with a clock frequency of 500MHz, the NullHop core is able to reach over 450 GOp/s and efficiency of 368\%, maintaining over 98\% utilization of the MAC units , and achieves a power efficiency of over 3TOp/s/W in a core area of 5.8mm2
Edit intent: coherence
Original: We present SimDex, a new technique for serving exact top-K recommendations on matrix factorization models that measures and optimizes for the similarity between users in the model.
Revised: Making top-K recommendations on matrix factorization models that measures and optimizes for the similarity between users in the model.
Edit intent: coherence
Original: Making top-K predictions for state-of-the-art Matrix Factorization models requires solving the Maximum Inner Product Search problem.
Revised: Exact Maximum Inner Product Search problem.
Edit intent: clarity
Original:  Solving MIPS can be computationally expensive, thus spurring the recent development of several indexing techniques for this task.
Revised:  Solving MIPS can be computationally expensive, thus spurring recent development of several indexing techniques for this task.
Edit intent: fluency
Original:  These techniques generally exploit similarity between user or item weights in the models to accelerate predictions.
Edit intent: clarity
Original:  We provide evidence of a third universal property: three-point correlations that suppress the creation of triangles and signal the presence of hierarchy.
Revised:  In this paper, we study a third universal property: three-point correlations that suppress the creation of triangles and signal the presence of hierarchy.
Edit intent: clarity
Original: Vision research has been shaped by the seminal insight that we can understand  higher-tier visual cortex from the perspective of multiple functional pathways with different goals.
Revised: Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals.
Edit intent: fluency
Original:  In this paper  we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks.
Revised:  In this paper , we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks.
Edit intent: fluency
Original:  We hypothesise that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of the ventral pathway , dedicated to vision for perception and dorsal pathway, dedicated to vision for action.
Revised:  We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of the ventral pathway , dedicated to vision for perception and dorsal pathway, dedicated to vision for action.
Edit intent: fluency
Original:  We hypothesise that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of the ventral pathway , dedicated to vision for perception and dorsal pathway, dedicated to vision for action.
Revised:  We hypothesise that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception and dorsal pathway, dedicated to vision for action.
Edit intent: fluency
Original:  We hypothesise that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of the ventral pathway , dedicated to vision for perception and dorsal pathway, dedicated to vision for action.
Revised:  We hypothesise that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of the ventral pathway , dedicated to vision for perception , and a dorsal pathway dedicated to vision for action.
Edit intent: coherence
Original:  To evaluate the functional organization in multi-task deep neural networks  we propose a method that measures the contribution of a unit towards each task and apply it to two networks that have been trained on either two related or two unrelated tasks  using an identical stimulus set.
Revised:  To evaluate the functional organization in multi-task deep neural networks , we propose a method that measures the contribution of a unit towards each task and apply it to two networks that have been trained on either two related or two unrelated tasks using an identical stimulus set.
Edit intent: fluency