wanyu/IteraTeR_full_sent huggingface dataset:
Original: Group equivariant convolutional neural networks (G-CNNs) have recently emerged as a very effective model class for learning from signals in the context of known symmetries. A wide variety of equivariant layers has been proposed for signals on 2D and 3D Euclidean space , graphs, and the sphere , and it has become difficult to see how all of these methods are related, and how they may be generalized.
Revised: Group equivariant convolutional neural networks (G-CNNs) on homogeneous spaces such as Euclidean space and 3D Euclidean space , graphs, and the sphere , and it has become difficult to see how all of these methods are related, and how they may be generalized.
Edit intent: clarity
Original:  A wide variety of equivariant layers has been proposed for signals on 2D and 3D Euclidean space , graphs, and the sphere , and it has become difficult to see how all of these methods are related, and how they may be generalized.
Revised:  A wide variety of equivariant layers has been proposed for signals on 2D and the sphere , and it has become difficult to see how all of these methods are related, and how they may be generalized.
Edit intent: clarity
Original:  A wide variety of equivariant layers has been proposed for signals on 2D and 3D Euclidean space , graphs, and the sphere , and it has become difficult to see how all of these methods are related, and how they may be generalized. In this paper, we present a fairly general theory of equivariant convolutional networks . Convolutional feature spaces are described as fields over a homogeneous base space, such as the plane \mathbb{R .
Revised:  A wide variety of equivariant layers has been proposed for signals on 2D and 3D Euclidean space , graphs, and the sphere . Feature maps in these networks represent fields on a homogeneous base space, such as the plane \mathbb{R .
Edit intent: clarity
Original:  In this paper, we present a fairly general theory of equivariant convolutional networks . Convolutional feature spaces are described as fields over a homogeneous base space, such as the plane \mathbb{R .
Revised:  In this paper, we present a fairly general theory of equivariant convolutional networks . Convolutional feature spaces are described as fields over a homogeneous base space, and layers are equivariant maps between spaces of fields .
Edit intent: meaning-changed
Original:  The theory enables a systematic classification of all existing G-CNNs in terms of their group of symmetry , base space, and field type (e. g. scalar, vector, or tensor field, etc.). In addition to this classification, we use Mackey theory to show that convolutions with equivariant kernels are the most general class of equivariant maps between such fields, thus establishing G-CNNs as a universal class of equivariant networks.
Revised:  The theory enables a systematic classification of all existing G-CNNs in terms of their group of symmetry , base space, and field type . We also consider a fundamental question: what is the most general class of equivariant maps between such fields, thus establishing G-CNNs as a universal class of equivariant networks.
Edit intent: clarity
Original:  In addition to this classification, we use Mackey theory to show that convolutions with equivariant kernels are the most general class of equivariant maps between such fields, thus establishing G-CNNs as a universal class of equivariant networks. The theory also explains how the space of equivariant kernelscan be parameterized for learning, thereby simplifying the development of G-CNNs for new spaces and symmetries. Finally, the theory introduces a rich geometric semantics to learned feature spaces, thus improving interpretability of deep networks, and establishing a connection to central ideas in mathematics and physics .
Revised:  In addition to this classification, we use Mackey theory to show that convolutions with equivariant kernels are the most general kind of equivariant linear map between feature spaces (fields) of given types? Following Mackey, we show that such maps correspond one-to-one with convolutions using equivariant kernels, and characterize the space of such kernels .
Edit intent: meaning-changed
Original:  When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNN with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex.
Revised:  When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex.
Edit intent: fluency
Original:  Moreover, when CNNs are transferred between different types of input images, here white noise v.s. natural images, transfer learning shows a good performance, which implies that CNN indeed captures the full computational ability of a single retinal cell for different inputs.
Revised:  Moreover, when CNNs are transferred between different types of input images, here white noise v.s. natural images, transfer learning shows a good performance, which implies that CNNs indeed capture the full computational ability of a single retinal cell for different inputs.
Edit intent: fluency
Original: Latent variable models have been widely applied for the analysis and visualization of large datasets. In the case of sequential data, closed-form inference is possible when the transition and observation functions are linear. However, approximate inference techniques are usually necessary when dealing with nonlinear dynamics and observation functions. Here, we propose a novel variational inference framework for the explicit modeling of time series, Variational Inference for Nonlinear Dynamics (VIND),  that is able to uncover nonlinear observation and transition functions from sequential data.
Revised: Latent variable models have been widely applied for the analysis of time series resulting from experimental neuroscience techniques. In these datasets, observations are relatively smooth and possibly nonlinear. We present Variational Inference for Nonlinear Dynamics (VIND), that is able to uncover nonlinear observation and transition functions from sequential data.
Edit intent: clarity
Original:  Here, we propose a novel variational inference framework for the explicit modeling of time series, Variational Inference for Nonlinear Dynamics (VIND),  that is able to uncover nonlinear observation and transition functions from sequential data.
Revised:  Here, we propose a novel variational inference framework for the explicit modeling of time series, Variational Inference for Nonlinear Dynamics (VIND), a variational inference framework that is able to uncover nonlinear observation and transition functions from sequential data.
Edit intent: fluency
Original:  Here, we propose a novel variational inference framework for the explicit modeling of time series, Variational Inference for Nonlinear Dynamics (VIND),  that is able to uncover nonlinear observation and transition functions from sequential data.
Revised:  Here, we propose a novel variational inference framework for the explicit modeling of time series, Variational Inference for Nonlinear Dynamics (VIND), that is able to uncover nonlinear , smooth latent dynamics from sequential data.
Edit intent: meaning-changed
Original:  We apply the method to several datasets and show that it is able to accurately infer the underlying dynamics of these systems , in some cases substantially outperforming state-of-the-art methods.
Revised:  We apply VIND to electrophysiology, single-cell voltage and widefield imaging datasets with state-of-the-art results in reconstruction error. In single-cell voltage data, VIND finds a 5D latent space, with variables akin to those of Hodgkin-Huxley-like models. VIND's learned dynamics are further quantified by predicting future neural activity. VIND excels in this task , in some cases substantially outperforming state-of-the-art methods.
Edit intent: meaning-changed
Original: Biochemical networks, under mass-action kinetics, containing reactions with three or more reactants (called high-molecular reactions) are investigated. An algorithm for stochastically approximating the high-molecular reactions with a set of bi-molecular ones, involving at most two reactants, is presented.
Revised: Biochemical networks, under mass-action kinetics, containing reactions with three or more reactants , called higher-molecular reactions, play an important role in theoretical systems and synthetic biology. In particular, such reactions underpin a variety of important bio-dynamical phenomena, such as multi-stability/multi-modality, oscillations, bifurcations, and noise-induced effects. However, only reactions with at most two reactants, is presented.
Edit intent: meaning-changed
Original:  An algorithm for stochastically approximating the high-molecular reactions with a set of bi-molecular ones, involving at most two reactants, is presented.
Revised:  An algorithm for stochastically approximating the high-molecular reactions with a set of bi-molecular ones, involving at most two reactants, called bi-molecular reactions, are experimentally feasible. To bridge the gap, in this paper we put forward an algorithm for systematically approximating arbitrary higher-molecular reactions with bi-molecular ones, while preserving the underlying stochastic dynamics.
Edit intent: meaning-changed
Original:  We propose three properties that are related to the stationary population identity (SPI) of population biology by connecting it with stationary populations and non-stationary populations which are approaching stationarity.
Revised: A population is considered stationary if the growth rate is zero and the age structure is constant. It thus follows that a population is considered non-stationary if either its growth rate is non-zero and/or its age structure is non-constant. We propose three properties that are related to the stationary population identity (SPI) of population biology by connecting it with stationary populations and non-stationary populations which are approaching stationarity.
Edit intent: coherence
Original:   These properties provide deeper insights into cohort formation in real-world populations and the length of the duration for which stationary and non-stationary conditions hold.
Revised:  One of these important properties is that SPI can be applied to partition a population into stationary and non-stationary components. These properties provide deeper insights into cohort formation in real-world populations and the length of the duration for which stationary and non-stationary conditions hold.
Edit intent: meaning-changed
Original:  The new concepts are based on the time gap between the occurrence of stationary and non-stationary populations within the SPI framework that we refer to as Oscillatory SPI and the Amplitude of SPI. 
Revised:  The new concepts are based on the time gap between the occurrence of stationary and non-stationary populations within the SPI framework that we refer to as Oscillatory SPI and the Amplitude of SPI. This article will appear in Bulletin of Mathematical Biology (Springer)
Edit intent: meaning-changed
Original:  From a sample of 296 stocks of the S\&P 500 index, the periods 2000 to 2007 and 2007 through 2009 are used to develop networks of stocks.
Revised:  From a sample of 296 stocks of the S\&P 500 index, distinct periods in between 2004 and 2011 are used to develop networks of stocks.
Edit intent: meaning-changed
Original:  The Minimal Spanning Tree analysis of those time-varying networks of stocks demonstrates that the crisis drove the market from a star-like random network in the pre-crisis period, to a highly clustered community structure during theGreat Recession .
Revised:  The Minimal Spanning Tree analysis of those time-varying networks of stocks demonstrates that the crises of 2007-2008 and 2010-2011 drove the market from a star-like random network in the pre-crisis period, to a highly clustered community structure during theGreat Recession .
Edit intent: meaning-changed
Original:  The Minimal Spanning Tree analysis of those time-varying networks of stocks demonstrates that the crisis drove the market from a star-like random network in the pre-crisis period, to a highly clustered community structure during theGreat Recession .
Revised:  The Minimal Spanning Tree analysis of those time-varying networks of stocks demonstrates that the crisis drove the market from a star-like random network in the pre-crisis period, to a highly clustered community structure during the .
Edit intent: clarity
Original:  The Minimal Spanning Tree analysis of those time-varying networks of stocks demonstrates that the crisis drove the market from a star-like random network in the pre-crisis period, to a highly clustered community structure during theGreat Recession . A comparison with theGeneral Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to  restore the stock market's ceased order of the pre-crisis era  .
Revised:  The Minimal Spanning Tree analysis of those time-varying networks of stocks demonstrates that the crisis drove the market from a star-like random network in the pre-crisis period, to a highly clustered community structure during theGreat Recession General Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to restore the stock market's ceased order of the pre-crisis era .
Edit intent: clarity
Original:  A comparison with theGeneral Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to  restore the stock market's ceased order of the pre-crisis era  .
Revised:  A comparison with theGeneral Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to clustered community structures in both periods, helping to restore the stock market's ceased order of the pre-crisis era .
Edit intent: meaning-changed
Original:  A comparison with theGeneral Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to  restore the stock market's ceased order of the pre-crisis era  .
Revised:  A comparison with theGeneral Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to restore the stock market's ceased order of the pre-crises era. However, a comparison of the emergent clusters with the .
Edit intent: meaning-changed
Original:  A comparison with theGeneral Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to  restore the stock market's ceased order of the pre-crisis era  .
Revised:  A comparison with theGeneral Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to restore the stock market's ceased order of the pre-crisis eraGeneral Industry Classification Standard .
Edit intent: fluency
Original:  A comparison with theGeneral Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to  restore the stock market's ceased order of the pre-crisis era  .
Revised:  A comparison with theGeneral Industry Classification Standard conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to restore the stock market's ceased order of the pre-crisis era conveys the impression that industry sectors do not play a major role in that order .
Edit intent: meaning-changed
Original: We numerically investigated the role of degenerate self-loops on the attractors and its basin size using the budding yeast cell-cycle network model.
Revised: Study of network dynamics is very active area in biological and social sciences. However, the relationship between the network structure and the attractors of the dynamics has not been fully understood yet. In this study, we numerically investigated the role of degenerate self-loops on the attractors and its basin size using the budding yeast cell-cycle network model.
Edit intent: meaning-changed
Original:   It is found that there is a simple division rule of the state space by removing the self-loops when the attractors consist only of point attractors.
Revised:  In the network, all self-loops negatively surpress the node (self-inhibition loops) and the attractors are only fixed points, i.e. point attractors. It is found that there is a simple division rule of the state space by removing the self-loops when the attractors consist only of point attractors.
Edit intent: meaning-changed
Original:  The point attractor with largest basin size is robust against the change the self-loop .
Revised:  The point attractor with largest basin size is robust against the change of the self-inhibition loop. Furthermore, some limit cycles of period 2 appear as new attractor when a self-activation loop is added to the original network. It is also shown that even in that case, the point attractor with largest basin size is robust .
Edit intent: meaning-changed
Original:   These results imply that distance-based folding is an efficient and accurate method for protein structure prediction .
Revised:  Further experimental validation in CAMEO shows that our ab initio folding server predicted correct fold for a membrane protein of new fold with 200 residues and 229 sequence homologs while all the other servers failed. These results imply that distance-based folding is an efficient and accurate method for protein structure prediction .
Edit intent: meaning-changed
Original:  In fact, we show in Doval and Skreta (2018) that they can be used to provide an upper bound on the number of posteriors a designer with limited commitment uses in his optimal mechanism .
Revised:  We illustrate our results with applications to mechanism design with limited commitment ( Doval and Skreta (2018) that they can be used to provide an upper bound on the number of posteriors a designer with limited commitment uses in his optimal mechanism .
Edit intent: meaning-changed
Original: Liquid democracy allows an agent to either vote directly over the available alternatives(candidates) of an election, or to delegate her voting rights to another agent (her guru) who can then vote on her behalf. In the academic literature and industrial applications of liquid democracy, each agent is usually allowed to nominate only one guru per election.
Revised: Liquid democracy allows an agent to either vote directly over the available alternatives(candidates) of an election, or to delegate her voting rights to someone they trust. Most of the liquid democracy literature and implementations allow each voter to nominate only one guru per election.
Edit intent: clarity
Original:  To solve this issue, firstly we introduce a general framework for voting systems that allow delegation of voting rights . The key feature of this framework is the delegation rule function, which when instantiated details who receives each delegated vote. Secondly, we propose a delegationrule function instantiation that we call Breadth First Delegation. Given that personal rankings over gurus are declared, this is the first rule where every agent weakly prefers receiving a delegated vote .
Revised:  To solve this issue, we propose a new method to delegate voting rights in an election, called breadth-first delegation. Additionally, the proposed method prioritises assigning voting rights to individuals closely connected to the voters who delegate .
Edit intent: clarity
Original:  A limit distribution is provided under a in-fill asymptotic framework which verifies that the new estimator outperforms the least-squares estimator  .
Revised:  A limit distribution is provided under a in-fill asymptotic framework which verifies that the new estimator outperforms the least-squares estimator . I apply the method to estimate the break date in U.S. real GDP growth and U.S. and UK stock return prediction models .
Edit intent: meaning-changed
Original: Recent characterizations of data movement performance have evaluated optimizations for dense and blocked accesses used by accelerators like GPUs and Xeon Phi, but sparse access patterns like scatter and gather are still not well understood across current and emerging architectures. We propose a tunable benchmark suite , Spatter, that allows users to characterize scatter, gather , and related sparse access patterns at a low level across multiple backends, including CUDA, OpenCL, and OpenMP. Spatter also allows users to vary the block size and amount of data that is moved to create a more comprehensive picture of sparse access patterns and to model patternsthat are found in real applications.
Revised: This paper describes a new benchmark tool , Spatter, that allows users to characterize scatter, gather , and related sparse access patterns at a low level across multiple backends, including CUDA, OpenCL, and OpenMP. Spatter also allows users to vary the block size and amount of data that is moved to create a more comprehensive picture of sparse access patterns and to model patternsthat are found in real applications.
Edit intent: clarity
Original:  We propose a tunable benchmark suite , Spatter, that allows users to characterize scatter, gather , and related sparse access patterns at a low level across multiple backends, including CUDA, OpenCL, and OpenMP. Spatter also allows users to vary the block size and amount of data that is moved to create a more comprehensive picture of sparse access patterns and to model patternsthat are found in real applications.
Revised:  We propose a tunable benchmark suite , Spatter, for assessing memory system architectures in the context of a specific category of indexed accesses known as gather and scatter. These types of operations are increasingly used to express sparse and irregular data access patterns, and they have widespread utility in many modern HPC applications including scientific simulations, data mining and analysis computations, and graph processing. However, many traditional benchmarking tools like STREAM, STRIDE, and GUPS focus on characterizing only uniform stride or fully random accesses despite evidence that modern applications use varied set of more complex access patterns. Spatter provides a tunable and configurable framework to benchmark a variety of indexed access patterns, including variations of gather/scatter that are seen in HPC mini-apps evaluated in this work. The design of Spatter includes tunable backends for OpenMP and CUDA, OpenCL, and OpenMP. Spatter also allows users to vary the block size and amount of data that is moved to create a more comprehensive picture of sparse access patterns and to model patternsthat are found in real applications.
Edit intent: meaning-changed
Original:  We propose a tunable benchmark suite , Spatter, that allows users to characterize scatter, gather , and related sparse access patterns at a low level across multiple backends, including CUDA, OpenCL, and OpenMP. Spatter also allows users to vary the block size and amount of data that is moved to create a more comprehensive picture of sparse access patterns and to model patternsthat are found in real applications.
Revised:  We propose a tunable benchmark suite , Spatter, that allows users to characterize scatter, gather , and related sparse access patterns at a low level across multiple backends, including CUDA, and to model patternsthat are found in real applications.
Edit intent: coherence