wanyu/IteraTeR_v2 huggingface dataset:
Original:  We evaluate Private PGB on a Gaussian mixture dataset and two US Census datasets, and demonstrate that Private PGB improves upon the standard private GAN approach across a collection of quality measures.
Revised:  We evaluate Private PGB on a Gaussian mixture dataset and two US Census datasets, and demonstrate that Private PGB improves upon a standard private GAN approach across a collection of quality measures.
Edit intent: fluency
Original:  Finally, we provide a non-private variant of PGB that improves the data quality of standard GAN training.
Revised:  We also provide a non-private variant of PGB that improves the data quality of standard GAN training.
Edit intent: coherence
Original: This paper presents a mathematical model and a parameter estimation technique for predicting the evolution of COVID-19 pandemic .
Revised: This paper presents the assessment and forecasts of the transmission dynamics of COVID-19 pandemic .
Edit intent: clarity
Original:  First, a modified SEIR (susceptible, exposed, infectious, and recovered) is introduced.
Revised:  First, a modified SEIRD (susceptible, exposed, infectious, and recovered) is introduced.
Edit intent: fluency
Original:  First, a modified SEIR (susceptible, exposed, infectious, and recovered) is introduced.
Revised:  First, a modified SEIR (susceptible, exposed, infectious, recovered, and death) model is introduced.
Edit intent: clarity
Original: This paper presents the assessment and forecasts of the transmission dynamics of  COVID-19 with the effects of time-dependent control actions using the data in Kazakhstan as a case study.
Revised: This paper presents the assessment of COVID-19 with the effects of time-dependent control actions using the data in Kazakhstan as a case study.
Edit intent: clarity
Original:  The reproduction number alongside other key parameters of the model can  be estimated by fitting the model to real-world data using numerical optimisation techniques  .
Revised:  The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques .
Edit intent: coherence
Original:  Specifically, we assume each agent can only receive allocations from a subset of the total supply for each resource.
Revised:  Unlike existing settings, we allow each agent to specify a constraint to only accept allocations from a subset of the total supply for each resource.
Edit intent: clarity
Original:  Such constraints often arise from location constraints (e.g. among all of the volunteer nurses, only a subset of them can work at hospital A due to commute constraints.
Revised:  Such constraints often arise from location constraints (e.g. among all of the volunteer nurses, only those who live nearby can work at hospital A due to commute constraints.
Edit intent: clarity
Original:    We propose a new mechanism called Group Dominant Resource Fairness which determines the allocations by solving a small number of linear programs.
Revised:  or We propose a new mechanism called Group Dominant Resource Fairness which determines the allocations by solving a small number of linear programs.
Edit intent: fluency
Original:  Finally, although motivated by the problem of medical resource allocation in a pandemic, our mechanism can be applied more broadly to resource allocation under Leontief utilities with external constraints.
Revised:  Finally, although motivated by the problem of medical resource allocation in a pandemic, our mechanism can be applied more broadly to resource allocation under Leontief utilities with accessibility constraints.
Edit intent: clarity
Original:  Unlike existing settings, we allow each agent to specify a constraint to only accept allocations from a subset of the total supply for each resource.
Revised:  Unlike existing settings, we allow each agent to specify requirements to only accept allocations from a subset of the total supply for each resource.
Edit intent: clarity
Original:  Such constraints often arise from location constraints (e.g. among all of the volunteer nurses, only those who live nearby can work at hospital A due to commute constraints.
Revised:  These requirements can take form in location constraints (e.g. among all of the volunteer nurses, only those who live nearby can work at hospital A due to commute constraints.
Edit intent: clarity
Original:  Such constraints often arise from location constraints (e.g. among all of the volunteer nurses, only those who live nearby can work at hospital A due to commute constraints.
Revised:  Such constraints often arise from location constraints (e.g. A hospital can only accept volunteers who live nearby can work at hospital A due to commute constraints.
Edit intent: clarity
Original:  Such constraints often arise from location constraints (e.g. among all of the volunteer nurses, only those who live nearby can work at hospital A due to commute constraints.
Revised:  Such constraints often arise from location constraints (e.g. among all of the volunteer nurses, only those who live nearby due to commute constraints.
Edit intent: clarity
Original:  This can also model a type of substitute effect where some agents need 1 unit of resource A or B, but some other agents specifically want A, and some specifically want B.
Revised:  This can also model a type of substitution effect where some agents need 1 unit of resource A or B, but some other agents specifically want A, and some specifically want B.
Edit intent: fluency
Original:  This can also model a type of substitute effect where some agents need 1 unit of resource A or B, but some other agents specifically want A, and some specifically want B.
Revised:  This can also model a type of substitute effect where some agents need 1 unit of resource A or B, but some other agents specifically want A, and others specifically want B.
Edit intent: clarity
Original:  We propose a new mechanism called Group Dominant Resource Fairness  which determines the allocations by solving a small number of linear programs.
Revised:  We propose a new mechanism called Dominant Resource Fairness which determines the allocations by solving a small number of linear programs.
Edit intent: clarity
Original:  We shall give some comments including some open problems and my conjectures.
Revised:  In the last section, we shall give some open problems and my conjectures.
Edit intent: clarity
Original:  We prove an equivalent result for the (provably) strictly less capable model of shrinking cellular automata (SCAs), which are cellular automata whose cells can spontaneously delete themselves.
Revised:  We prove an analogous result for the (provably) strictly less capable model of shrinking cellular automata (SCAs), which are cellular automata whose cells can spontaneously delete themselves.
Edit intent: fluency
Original:  We also show there is a language which cannot be accepted by any SCA in o(n / \log n) time, even though it admits an O(\log n)-space streaming algorithm with O(\log n) update time , where n is the input length .
Revised:  We also show there is a language which cannot be accepted by any SCA in o(n / \log n) time, even though it admits an O(\log n)-space streaming algorithm with O(\log n) update time .
Edit intent: clarity
Original:  We define execution semantics for this setting and prove chain growth, chain quality, and common prefix which together implies both safety and liveness.
Revised:  We define execution semantics for this setting and prove chain growth, chain quality, and common prefix which together imply both safety and liveness.
Edit intent: fluency
Original:  Our main results are focused on digraphs of bounded %DIFDELCMD < {\em %%% average outdegree d.
Revised:  Our main results are focused on digraphs of bounded %DIFDELCMD < {\em %%% average outdegree d.
Edit intent: fluency
Original:  The so called input forgetting property is identified as the key hypothesis that guarantees the existence and uniqueness (up to system isomorphisms) of canonical realizations for causal and time-invariant input/output systems with semi-infinite inputs.
Revised:  The so-called input forgetting property is identified as the key hypothesis that guarantees the existence and uniqueness (up to system isomorphisms) of canonical realizations for causal and time-invariant input/output systems with semi-infinite inputs.
Edit intent: fluency
Original:  A second result uses the notion of optimal reduction borrowed from the theory of symmetric Hamiltonian systems  to construct canonical realizations out of input forgetting but not necessarily canonical ones.
Revised:  Additionally, the notion of optimal reduction borrowed from the theory of symmetric Hamiltonian systems to construct canonical realizations out of input forgetting but not necessarily canonical ones.
Edit intent: coherence
Original:  A second result uses the notion of optimal reduction borrowed from the theory of symmetric Hamiltonian systems  to construct canonical realizations out of input forgetting but not necessarily canonical ones.
Revised:  A second result uses the notion of optimal reduction coming from the theory of symmetric Hamiltonian systems to construct canonical realizations out of input forgetting but not necessarily canonical ones.
Edit intent: clarity
Original:  These two procedures are implemented and studied in detail in the framework of linear fading memory input/output systems  .
Revised:  These two procedures are studied in detail in the framework of linear fading memory input/output systems .
Edit intent: clarity
Original:   In this paper  we focus on the popular representations of integers from bit strings, and ask how sensitive these particular representationsare to variation operators, and in particular to  single-bit mutations.
Revised:  In this paper , we focus on the popular representations of integers from bit strings, and ask how sensitive these particular representationsare to variation operators, and in particular to single-bit mutations.
Edit intent: fluency
Original:  More specifically, we prove that a neural network is a quiver representation with activation functions, a mathematical object that we represent using a %DIFDELCMD < {\em %%% network quiver . Also, we show that network quivers gently adapt to common neural network concepts such as fully-connected layers, convolution operations, residual connections, batch normalization, and pooling operations .
Revised:  More specifically, we prove that a neural network is a quiver representation with activation functions, a mathematical object that we represent using a %DIFDELCMD < {\em %%% network quiver . Also, we show that network quivers gently adapt to common neural network concepts such as fully-connected layers, convolution operations, residual connections, batch normalization, and pooling operations .
Edit intent: fluency
Original:  We show that a neural network saves the data as quiver representations, and maps it to a geometrical space called the %DIFDELCMD < {\em %%% moduli space , which is given in terms of the underlying oriented graph of the network .
Revised:  We show that a neural network saves the data as quiver representations, and maps it to a geometrical space called the %DIFDELCMD < {\em %%% moduli space , which is given in terms of the underlying oriented graph of the network .
Edit intent: fluency
Original:  In this letter we propose Energy-aware RElay Positioning (EREP), an algorithm for positioning the FCR taking into account its energy consumption.
Revised:  In this article we propose Energy-aware RElay Positioning (EREP), an algorithm for positioning the FCR taking into account its energy consumption.
Edit intent: clarity
Original:  The obtained results show significant gains in the FCR lifetime .
Revised:  The obtained results show significant gains in the FCR endurance .
Edit intent: clarity
Original: Recently, multi-agent policy gradient (MAPG) methods  witness vigorous progress.
Revised: Multi-agent policy gradient (MAPG) methods witness vigorous progress.
Edit intent: coherence
Original:  However, there is a discrepancy between the performance of MAPG methods and state-of-the-art multi-agent value-based approaches.
Revised:  However, there is a significant performance discrepancy between MAPG methods and state-of-the-art multi-agent value-based approaches.
Edit intent: clarity
Original:  In this paper, we investigate the causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
Revised:  In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
Edit intent: fluency
Original:  In addition, empirical evaluations on the StarCraft II micromanagement benchmark and multi-agent particle environments demonstrate that our method significantly outperforms state-of-the-art value-based and policy-based multi-agent reinforcement learning algorithms.
Revised:  In addition, empirical evaluations on the StarCraft II micromanagement benchmark and multi-agent particle environments demonstrate that DOP significantly outperforms both state-of-the-art value-based and policy-based multi-agent reinforcement learning algorithms.
Edit intent: clarity
Original:  Intuitively, these operations " homogenize" marginal distributions by making them similar.
Revised:  Intuitively, these operations `` homogenize" marginal distributions by making them similar.
Edit intent: fluency
Original:  Finally, we  discuss the connection of our results to joint mixability and to merging p-values in multiple hypothesis testing .
Revised:  Finally, we discuss the connection of our results to joint mixability .
Edit intent: coherence
Original:  Besides the normal-normal component, another trace involving combination of first order derivatives of stress should be continuous across the face.
Revised:  Besides the normal-normal component, another trace involving combination of first order derivatives of the tensor should be continuous across the face.
Edit intent: clarity
Original:  Due to the rigid of polynomials, the symmetric stress tensor element is  continuous at vertices, and on the plane orthogonal to each edge.
Revised:  Due to the smoothness of polynomials, the symmetric stress tensor element is continuous at vertices, and on the plane orthogonal to each edge.
Edit intent: clarity
Original:  Due to the rigid of polynomials, the symmetric stress tensor element is  continuous at vertices, and on the plane orthogonal to each edge.
Revised:  Due to the rigid of polynomials, the symmetric tensor element is continuous at vertices, and on the plane orthogonal to each edge.
Edit intent: clarity
Original:  Due to the rigid of polynomials, the symmetric stress tensor element is  continuous at vertices, and on the plane orthogonal to each edge.
Revised:  Due to the rigid of polynomials, the symmetric stress tensor element is also continuous at vertices, and on the plane orthogonal to each edge.
Edit intent: clarity
Original:   Besides the normal-normal component, another trace involving combination of first order derivatives of the tensor should be continuous across the face.
Revised:  Besides the normal-normal component, another trace involving combination of first order derivatives of the tensor is continuous across the face.
Edit intent: clarity
Original:  Its dynamics are based on alignment mechanisms, which are often used in collective and consensus models .
Revised:  Its dynamics are based on alignment mechanisms, which are commonly used in large interacting agent systems in consensus formation .
Edit intent: clarity
Original:  In a two-wave longitudinal study (N ~ = ~ 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity.
Revised:  In a two-wave longitudinal study (N = ~ 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity.
Edit intent: fluency
Original:  In a two-wave longitudinal study (N ~ = ~ 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity.
Revised:  In a two-wave longitudinal study (N ~ = 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity.
Edit intent: fluency
Original:  This design allowed us to identify those variables that explain unique variance in well-being and productivity.
Revised:  This design allowed us to identify the variables that explained unique variance in well-being and productivity.
Edit intent: fluency
Original:  and (4) the longitudinal study did not provide evidence that any predictor variable causal explained variance in well-being and productivity.
Revised:  and (4) longitudinal analyses did not provide evidence that any predictor variable causal explained variance in well-being and productivity.
Edit intent: clarity
Original:  Our study can assess the effectiveness of current work-from-home and general well-being and productivity support guidelines and provide tailored insights for software professionals .
Revised:  Overall, we conclude that working from home was per se not a significant challenge for software engineers .
Edit intent: coherence
Original: We show that associative networks of threshold linear units endowed with Hebbian learning  can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound .
Revised: We derive the Gardner storage capacity for associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound .
Edit intent: clarity
Original: We show that associative networks of threshold linear units endowed with Hebbian learning  can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound .
Revised: We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to such Gardner bound than binary networks, and even surpass this bound .
Edit intent: clarity
Original: We show that associative networks of threshold linear units endowed with Hebbian learning  can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound .
Revised: We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass it .
Edit intent: clarity
Original:  As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.
Revised:  As reaching the optimal capacity via non-local learning rules like backpropagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.
Edit intent: fluency
Original:  Our key insight is that while untyped code cannot be trusted by a gradual type system, there is no need to consider only the worst case when optimizing a gradually-typed program.
Revised:  Our key insight is that while untyped code cannot be trusted by a gradual type system, there is no need to consider only the worst case when optimizing a gradually-typed program.
Edit intent: fluency
Original:  A commonly criticised point, however, is that the resulting trees may not necessarily be the best representation of the data in terms of accuracy , size, and other considerations such as fairness .
Revised:  A commonly criticised point, however, is that the resulting trees may not necessarily be the best representation of the data in terms of accuracy and size .
Edit intent: clarity
Original:  Our algorithm supports constraints on the depth of the tree and number of nodes and we argue it can be extended with other requirements .
Revised:  Our algorithm supports constraints on the depth of the tree and number of nodes .
Edit intent: coherence
Original:  This aspect remains in stark contrast to learning with error backpropagation through time (BPTT) applied to recurrent neural networks (RNNs), or recently even to biologically-inspired spiking neural networks (SNNs) , because the unrolling through timeof BPTT leads to system-locking problems .
Revised:  This aspect remains in stark contrast to learning with error backpropagation through time (BPTT) applied to recurrent neural networks (RNNs), or recently to biologically-inspired spiking neural networks (SNNs) , because the unrolling through timeof BPTT leads to system-locking problems .
Edit intent: clarity
Original:  Online learning has recently regained the attention of the research community, focusing either on approaches that approximate BPTT or on biologically-plausible schemes applied in SNNs. Here we present an alternative perspective that is based on a clear separation of spatial and temporal gradient components.
Revised:  Online learning has recently regained the attention of the research community, focusing either on approaches that approximate BPTT or on biologically-plausible schemes applied to SNNs. Here we present an alternative perspective that is based on a clear separation of spatial and temporal gradient components.
Edit intent: fluency
Original:  Combined with insights from biology, we derive from first principles a novel online learning algorithm  , called online spatio-temporal learning (OSTL) , which is gradient-equivalent to BPTT for shallow networks.
Revised:  Combined with insights from biology, we derive from first principles a novel online learning algorithm , called online spatio-temporal learning (OSTL) . For shallow networks, OSTL is gradient-equivalent to BPTT for shallow networks.
Edit intent: coherence
Original:  We apply OSTL to SNNs allowing them for the first time to be trained online with BPTT-equivalent gradients.
Revised:  We apply OSTL to SNNs allowing them for the first time online training of SNNs with BPTT-equivalent gradients.
Edit intent: clarity
Original:  In addition, the proposed formulation uncovers a class of SNN architectures trainable online at low  complexity.
Revised:  In addition, the proposed formulation unveils a class of SNN architectures trainable online at low complexity.
Edit intent: clarity
Original:  We demonstrate the operation of our algorithm on various tasks from language modelling to speech recognition , and obtain results on par with the BPTT baselines.
Revised:  We demonstrate the operation of our algorithm on various tasks from language modelling to speech recognition and obtain results on par with the BPTT baselines.
Edit intent: fluency
Original: Multigrid methods are among the most efficient iterative techniques for solving large-scale linear systems that arise from discretized partial differential equations.
Revised: Multigrid is one of the most efficient iterative techniques for solving large-scale linear systems that arise from discretized partial differential equations.
Edit intent: clarity
Original: Multigrid methods are among the most efficient iterative techniques for solving large-scale linear systems that arise from discretized partial differential equations.
Revised: Multigrid methods are among the most efficient methods for solving large-scale linear systems that arise from discretized partial differential equations.
Edit intent: clarity
Original:  Convergence analysis of exact two-grid methods (i.e., the Galerkin coarse-grid system is solved exactly) has been well developed: the convergence factor of exact two-grid methods can be characterized by an identity.
Revised:  Convergence analysis of exact two-grid methods with exact solution of the Galerkin coarse-grid system is solved exactly) has been well developed: the convergence factor of exact two-grid methods can be characterized by an identity.
Edit intent: clarity