wanyu/IteraTeR_v2 huggingface dataset:
Original:  The derived plasticity learning rule is specific to each neuron model producing a theoretical prediction which can be verified experimentally .
Revised:  The derived plasticity learning rule is specific to each neuron model producing a theoretical prediction to be experimentally verified .
Edit intent: clarity
Original: Recurrent spiking neural networks (RSNN) in the  brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of time and energy consumption .
Revised: Recurrent spiking neural networks (RSNN) in the human brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of time and energy consumption .
Edit intent: clarity
Original:  Our model is general and it can be applied to a wide variety of network architectures and types of biological neurons.
Revised:  Our model can be applied to a wide variety of network architectures and types of biological neurons.
Edit intent: coherence
Original:  Our model is general and it can be applied to a wide variety of network architectures and types of biological neurons.
Revised:  Our model is general and it can be applied to different types of biological neurons.
Edit intent: clarity
Original:  The  derived plasticity learning rule is specific to each neuron model and can produce a theoretical prediction to be experimentally verified .
Revised:  The derived plasticity learning rule is specific to each neuron model and can produce a theoretical prediction for experimental validation .
Edit intent: clarity
Original:  After being trained on the large database of scPDB, DeepSurf demonstrates superior performance on two diverse testing datasets, by surpassing all its main deep learning-based competitors  .
Revised:  After being trained on the large database of scPDB, DeepSurf demonstrates superior results on three diverse testing datasets, by surpassing all its main deep learning-based competitors .
Edit intent: clarity
Original: We provide an original reformulation of the IAM, an abstract machine introduced by Danos and Regnier based on Girard's Geometry of Interaction .
Revised: This paper revisits the Interaction Abstract Machine (IAM), a machine based on Girard's Geometry of Interaction .
Edit intent: clarity
Original:  In this paper , I address the impact of  intraspecific competition on a modelling framework for banded vegetation patterns.
Revised:  In this chapter , I address the impact of intraspecific competition on a modelling framework for banded vegetation patterns.
Edit intent: clarity
Original:  Our approach integrates information extraction and clustering with simulation and formal analysis to allow for  automated iterative process that includes assembling, testing and selecting  most relevant models, given a set of desired system properties.
Revised:  Our approach integrates information extraction and clustering with simulation and formal analysis to allow for an automated iterative process that includes assembling, testing and selecting most relevant models, given a set of desired system properties.
Edit intent: fluency
Original:  Our approach integrates information extraction and clustering with simulation and formal analysis to allow for  automated iterative process that includes assembling, testing and selecting  most relevant models, given a set of desired system properties.
Revised:  Our approach integrates information extraction and clustering with simulation and formal analysis to allow for automated iterative process that includes assembling, testing and selecting the most relevant models, given a set of desired system properties.
Edit intent: fluency
Original:  We demonstrate our methodology on a model of the circuitry that controls T cell differentiation.
Revised:  We applied our methodology to a model of the circuitry that controls T cell differentiation.
Edit intent: clarity
Original:  We demonstrate our methodology on a model of the circuitry that controls T cell differentiation.
Revised:  We demonstrate our methodology on a model of the circuitry that con-trols T cell differentiation.
Edit intent: fluency
Original:  Besides being able to automatically and rapidly reconstruct the manually extended model , ACCORDION can provide multiple viable extended model versions .
Revised:  Besides demonstrating automated and rapid reconstruction of a model that was previously built manually , ACCORDION can provide multiple viable extended model versions .
Edit intent: clarity
Original:  Besides being able to automatically and rapidly reconstruct the manually extended model , ACCORDION can provide multiple viable extended model versions .
Revised:  Besides being able to automatically and rapidly reconstruct the manually extended model , ACCORDION can assemble multiple models that satisfy desired properties .
Edit intent: clarity
Original:  As such, it replaces large number of tedious or even impractical manual experiments and guides alternative interventions in real biological systems.
Revised:  As such, it replaces large number of tedious or even imprac-tical manual experiments and guides alternative interventions in real biological systems.
Edit intent: fluency
Original:  As such, it replaces large number of tedious or even impractical manual experiments and guides alternative interventions in real biological systems.
Revised:  As such, it replaces large number of tedious or even impractical manual experiments and guides alternative hypotheses and interventions in biological systems.
Edit intent: clarity
Original:  Our system is real-time capable with a soft deadline of 20ms . Moreover, it is robust against changing lighting conditions and reflections.
Revised:  Our system is real-time capable with a soft deadline of 20 ms . Moreover, it is robust against changing lighting conditions and reflections.
Edit intent: fluency
Original:  In this paper, we present AnyHLS, a library to synthesize FPGA designs in a modular and abstract way.
Revised:  In this paper, we present AnyHLS, an approach to synthesize FPGA designs in a modular and abstract way.
Edit intent: clarity
Original:  AnyHLS resorts to standard programming language features such as types and higher-order functions to accomplish this as follows: First, partial evaluation specializes and optimizes the user application based on a library of abstractions.
Revised:  AnyHLS resorts to standard programming language features such as types and higher-order functions as follows: First, partial evaluation specializes and optimizes the user application based on a library of abstractions.
Edit intent: coherence
Original:  AnyHLS resorts to standard programming language features such as types and higher-order functions to accomplish this as follows: First, partial evaluation specializes and optimizes the user application based on a library of abstractions.
Revised:  AnyHLS resorts to standard programming language features such as types and higher-order functions to accomplish this as follows: It relies on partial evaluation to specialize and to optimize the user application based on a library of abstractions.
Edit intent: clarity
Original:  Ultimately, the backend of AnyHLS generates vendor-specific HLS code  for Intel and Xilinx FPGAs.
Revised:  Then, vendor-specific HLS code for Intel and Xilinx FPGAs.
Edit intent: clarity
Original:  Ultimately, the backend of AnyHLS generates vendor-specific HLS code  for Intel and Xilinx FPGAs.
Revised:  Ultimately, the backend of AnyHLS generates vendor-specific HLS code is generated for Intel and Xilinx FPGAs.
Edit intent: clarity
Original: We study the problem of augmenting online algorithms with machine learned (ML) predictions .
Revised: We study the problem of augmenting online algorithms with machine learned (ML) advice .
Edit intent: clarity
Original:  These online algorithms have no knowledge about the quality or the prediction error type of the ML predictions .
Revised:  These online algorithms have no knowledge about the quality or the prediction error type of the ML prediction .
Edit intent: fluency
Original:  We numerically evaluate the performance of our proposed online algorithms in practice .
Revised:  Extensive experiments using both synthetic and real world data traces verify our theoretical observations and show better performance against algorithms that purely rely on online decision making .
Edit intent: clarity
Original:  We port a popular distributed computing framework, Ray , on atop of Hoplite .
Revised:  We port a popular distributed computing framework, Ray .
Edit intent: coherence
Original:  We definelatent games , a new class of game where agents are mappings that transform latent distributions.
Revised:  We define , a new class of game where agents are mappings that transform latent distributions.
Edit intent: fluency
Original:  We show that minimax equilibria in latent games can be approximated by asingle pair of dense neural networks .
Revised:  We show that minimax equilibria in latent games can be approximated by a pair of dense neural networks .
Edit intent: fluency
Original: Experimental measurements of physical systems often have a finite number of independent channels, causing essential dynamical variables to remain unobserved.
Revised: Experimental measurements of physical systems often have a limited number of independent channels, causing essential dynamical variables to remain unobserved.
Edit intent: clarity
Original:  We first apply our technique to a variety of synthetic and real-world datasets with known strange attractors, and we use established and novel measures of attractor fidelity to show that our method successfully reconstructs attractors better than existing techniques .
Revised:  We first apply our technique to a variety of synthetic and real-world systems better than existing techniques .
Edit intent: clarity
Original:  Inspired by classical techniques for studying the strange attractors of chaotic systems , we introduce a general embedding technique for  time series, consisting of an autoencoder trained with a novel latent-space loss function.
Revised:  Inspired by classical analysis techniques for partial observations of chaotic attractors , we introduce a general embedding technique for time series, consisting of an autoencoder trained with a novel latent-space loss function.
Edit intent: clarity
Original:  Despite of  advancement in recent years, the existing methods still suffer from two issues:  they are either designed without preserving sharp features or less robust in preserving geometric features;
Revised:  Despite of significant advancement in recent years, the existing methods still suffer from two issues: they are either designed without preserving sharp features or less robust in preserving geometric features;
Edit intent: clarity
Original:  Despite of  advancement in recent years, the existing methods still suffer from two issues:  they are either designed without preserving sharp features or less robust in preserving geometric features;
Revised:  Despite of advancement in recent years, the existing methods still suffer from two issues: they are either designed without preserving sharp features or less robust in feature preservation; and 2)
Edit intent: clarity
Original:  In this paper, we propose a novel deep learning approach that automatically and robustly filters point clouds with removing noise and preserving sharp features and geometric details .
Revised:  In this paper, we propose a novel deep learning approach that automatically and robustly filters point clouds by removing noise and preserving sharp features and geometric details .
Edit intent: fluency
Original:  In this paper, we propose a novel deep learning approach that automatically and robustly filters point clouds with removing noise and preserving sharp features and geometric details .
Revised:  In this paper, we propose a novel deep learning approach that automatically and robustly filters point clouds with removing noise and preserving their sharp features .
Edit intent: clarity
Original:  The encoder directly takes points (a point and its neighbors) as input, and learns a latent representation vector which is gone through the decoder and related to the ground-truth position .
Revised:  The encoder directly takes points (a point and its neighbors) as input, and learns a latent representation vector which goes through the decoder and related to the ground-truth position .
Edit intent: clarity
Original:  The encoder directly takes points (a point and its neighbors) as input, and learns a latent representation vector which is gone through the decoder and related to the ground-truth position .
Revised:  The encoder directly takes points (a point and its neighbors) as input, and learns a latent representation vector which is gone through the decoder to relate the ground-truth position .
Edit intent: clarity
Original:  Our trained network can automatically infer a corresponding quality point set to a noisy point cloud input.
Revised:  Our trained network can automatically generate a set of clean points from a noisy input.
Edit intent: clarity
Original:  Extensive evaluations show that our approach outperforms the state-of-the-art deep learning techniques in terms of  visual quality and  error metrics.
Revised:  Extensive experiments show that our approach outperforms the state-of-the-art deep learning techniques in terms of visual quality and error metrics.
Edit intent: clarity
Original:  Extensive evaluations show that our approach outperforms the state-of-the-art deep learning techniques in terms of  visual quality and  error metrics.
Revised:  Extensive evaluations show that our approach outperforms the state-of-the-art deep learning techniques in terms of both visual quality and error metrics.
Edit intent: clarity
Original:  We will make our code and dataset publicly available.
Revised:  The source code and dataset publicly available.
Edit intent: clarity
Original:  At the same time, we find that the influence networks associated to most topics exhibit small world properties, which we find to play a significant role towards the overall diversity of sentiment expressed about the topic by the news sources in the network.
Revised:  At the same time, we find that the influence networks associated with most topics exhibit small world properties, which we find to play a significant role towards the overall diversity of sentiment expressed about the topic by the news sources in the network.
Edit intent: fluency
Original:  In particular  we derive necessary conditions for this membership that have to be fulfilled by m, n and k, where m and n are the number of vertices on the two partition classes of the bipartite graph.
Revised:  In particular , we derive necessary conditions for this membership that have to be fulfilled by m, n and k, where m and n are the number of vertices on the two partition classes of the bipartite graph.
Edit intent: fluency
Original:  Furthermore  we show that B_k \not\subseteq B_{2k-9}^m holds for all k\geqslant 5.
Revised:  Furthermore , we show that B_k \not\subseteq B_{2k-9}^m holds for all k\geqslant 5.
Edit intent: fluency
Original:  For any (randomized) algorithm, there are input graphs with n nodes and maximum degree \Delta on which \Omega(\min\{\sqrt{\log n/\log \log n},\log \Delta/\log \log \Delta\}) (expected) communication rounds are required to obtain polylogarithmic approximations to a minimum vertex cover, minimum dominating set, or maximum matching , respectively . Via reduction, this hardness extends to symmetry breaking tasks like finding maximal independent sets or maximal matchings.
Revised:  For any (randomized) algorithm, there are input graphs with n nodes and maximum degree \Delta on which \Omega(\min\{\sqrt{\log n/\log \log n},\log \Delta/\log \log \Delta\}) (expected) communication rounds are required to obtain polylogarithmic approximations to a minimum vertex cover, minimum dominating set, or maximum matching . Via reduction, this hardness extends to symmetry breaking tasks like finding maximal independent sets or maximal matchings.
Edit intent: coherence
Original: While probabilistic models describe the dependence structure between observed variables, causal models go one step further: they predict  how cognitive functions are affected by external interventions that perturb neuronal activity.
Revised: While probabilistic models describe the dependence structure between observed variables, causal models go one step further: they predict , for example, how cognitive functions are affected by external interventions that perturb neuronal activity.
Edit intent: coherence
Original:  Inferring causal relationships from data  is an ambitious task that is particularly challenging in cognitive neuroscience.
Revised:  Inferring causal relationships from data . Causal inference is an ambitious task that is particularly challenging in cognitive neuroscience.
Edit intent: clarity
Original:  Here, we discuss two difficulties in more detail: the scarcity of interventional data and the challenge of finding the right variables.
Revised:  We discuss two difficulties in more detail: the scarcity of interventional data and the challenge of finding the right variables.
Edit intent: coherence
Original:  Modelling a target variable using the correct set of causal variables yields a model that generalises across environments or subjects (if these environments leave the causal mechanisms intact ).
Revised:  Robustness (or invariance) is a fundamental principle underlying causal methodology. A causal model of a target variable generalises across environments or subjects (if these environments leave the causal mechanisms intact ).
Edit intent: clarity
Original:  Modelling a target variable using the correct set of causal variables yields a model that generalises across environments or subjects (if these environments leave the causal mechanisms intact ).
Revised:  Modelling a target variable using the correct set of causal variables yields a model that generalises across environments or subjects as long as these environments leave the causal mechanisms intact ).
Edit intent: clarity
Original:  Conversely , if a candidate model does not generalise, then either it includes non-causes of the target variable  or the underlying variables are wrongly defined .
Revised:  Conversely , if a candidate model does not generalise, then either it does not consist of the target variable or the underlying variables are wrongly defined .
Edit intent: clarity
Original:  In this sense, generalisability may serve as a guiding principle when defining relevant variables and can be used to partially compensate for the lack of interventional data.
Revised:  In this sense, assessing generalisability may be useful when defining relevant variables and can be used to partially compensate for the lack of interventional data.
Edit intent: clarity
Original: In recent years there has been a push to integrate symbolic AI and deep learning , as it is  argued that the strengths and weaknesses of these approaches are complementary.
Revised: The AI community is increasingly putting its attention towards combining symbolic and neural approaches , as it is argued that the strengths and weaknesses of these approaches are complementary.
Edit intent: clarity
Original: In recent years there has been a push to integrate symbolic AI and deep learning , as it is  argued that the strengths and weaknesses of these approaches are complementary.
Revised: In recent years there has been a push to integrate symbolic AI and deep learning , as it is often argued that the strengths and weaknesses of these approaches are complementary.
Edit intent: clarity
Original:  One such trend in the literature are weakly supervised learning techniques that use operators from fuzzy logics.
Revised:  One recent trend in the literature are weakly supervised learning techniques that use operators from fuzzy logics.
Edit intent: clarity
Original:  One such trend in the literature are weakly supervised learning techniques that use operators from fuzzy logics.
Revised:  One such trend in the literature are weakly supervised learning techniques that employ operators from fuzzy logics.
Edit intent: fluency
Original:  They employ prior background knowledge described in logic to benefit the training of a neural network from unlabeled and noisy data.
Revised:  In particular, these use prior background knowledge described in logic to benefit the training of a neural network from unlabeled and noisy data.
Edit intent: coherence
Original:  They employ prior background knowledge described in logic to benefit the training of a neural network from unlabeled and noisy data.
Revised:  They employ prior background knowledge described in such logics to help the training of a neural network from unlabeled and noisy data.
Edit intent: clarity
Original:  We show that many of these operators, including some of the best known , are highly unsuitable for use in a differentiable learning setting.
Revised:  We show that many of these operators, including some of the most well-known , are highly unsuitable for use in a differentiable learning setting.
Edit intent: fluency
Original:  We show that many of these operators, including some of the best known , are highly unsuitable for use in a differentiable learning setting.
Revised:  We show that many of these operators, including some of the best known , are highly unsuitable in this setting.
Edit intent: clarity
Original:  A further finding concerns the treatment of implication in these fuzzy logics, with a strong imbalance between gradients driven by the antecedent and the consequent of the implication.
Revised:  A further finding concerns the treatment of implication in these fuzzy logics, and shows a strong imbalance between gradients driven by the antecedent and the consequent of the implication.
Edit intent: coherence
Original:  However , to achieve the most significant performance improvement over a supervised baseline, we have to resort to non-standard combinations of logical operators which perform well in learning, but which no longer satisfy the usual logical laws .
Revised:  However , to achieve the largest performance improvement over a supervised baseline, we have to resort to non-standard combinations of logical operators which perform well in learning, but which no longer satisfy the usual logical laws .
Edit intent: clarity
Original:  However , to achieve the most significant performance improvement over a supervised baseline, we have to resort to non-standard combinations of logical operators which perform well in learning, but which no longer satisfy the usual logical laws .
Revised:  However , to achieve the most significant performance improvement over a supervised baseline, we have to resort to non-standard combinations of logical operators which perform well in learning, but no longer satisfy the usual logical laws .
Edit intent: coherence
Original:  While previous research presented a broad overview, we have chosen a narrow setting to reach for even deeper insights .
Revised:  While previous research presented a broad overview, we have chosen a narrow setting to reach for even deeper insights :
Edit intent: fluency
Original:  The framework JHotDraw had been designed so thoughtfully that most smells may be expected to be false positives.
Revised:  The framework JHotDraw had been designed so thoughtfully that most smell warnings are expected to be false positives.
Edit intent: clarity
Original:  The "Law of Good Style", better known as the "Law of Demeter"  is a rather restrictive design rule so that we can still expected to find some potential bad smells, i.e. violations of this "Law".
Revised:  Nevertheless, the "Law of Good Style", better known as the "Law of Demeter" is a rather restrictive design rule so that we can still expected to find some potential bad smells, i.e. violations of this "Law".
Edit intent: coherence
Original:  The "Law of Good Style", better known as the "Law of Demeter"  is a rather restrictive design rule so that we can still expected to find some potential bad smells, i.e. violations of this "Law".
Revised:  The "Law of Good Style", better known as the "Law of Demeter" , is a rather restrictive design rule so that we can still expected to find some potential bad smells, i.e. violations of this "Law".
Edit intent: fluency