wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the 'digital divide' . In this paper we present a  method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia.
Revised:  This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the 'digital divide' . In this paper we present a machine learning method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia.
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Original:  A predictive machine learning approach can capture up to 40 \% of data variance, compared to existing approaches which only explain up to 20\% of the data variance .
Revised:  A predictive machine learning approach consistently outperforms baseline models which use population density or nightlight luminosity, with an improvement in data variance prediction of at least 40 \% of data variance, compared to existing approaches which only explain up to 20\% of the data variance .
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Original:  In a bid to resolve this conundrum we are inspired by de Finetti's Theorem, and we exhibit equivalence classes of meta-population models that are orbits of the symmetric group.
Revised:  This paper utilizes a result from the theory of interest rate modeling, namely a bond pricing formula of Vasicek, and an approach inspired by a foundational result in statistics, de Finetti's Theorem, and we exhibit equivalence classes of meta-population models that are orbits of the symmetric group.
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Original:  In a bid to resolve this conundrum we are inspired by de Finetti's Theorem, and we exhibit equivalence classes of meta-population models that are orbits of the symmetric group. We illustrate with a mixture of stochastic SIR models in which growth can be inferred from a classic bond pricing formula of Vasicek. This approach exploits the symmetry of model observables, and then uses convexity adjustments to directly determine the degree of variation that is needed to locate the orbit of nature's model . Convexity adjustments are also useful, and material, for cross-sectional comparison.
Revised:  In a bid to resolve this conundrum we are inspired by de Finetti's Theorem, to show how the modeling discrepancy can be explained. Moreover the difference between predictions of classic constant parameter epidemiological models and those with variation and stochastic evolution can be reduced to simple "convexity" formulas. A novel feature of this approach is that we do not attempt to locate a true model but only a model that is equivalent after permutations . Convexity adjustments are also useful, and material, for cross-sectional comparison.
Edit intent: coherence
Original:  This approach exploits the symmetry of model observables, and then uses convexity adjustments to directly determine the degree of variation that is needed to locate the orbit of nature's model . Convexity adjustments are also useful, and material, for cross-sectional comparison. We consider some stylized population density profiles and derive easy to use rules of thumb for estimating threshold infection level in one region given knowledge of threshold infection in another.
Revised:  This approach exploits the symmetry of model observables, and then uses convexity adjustments to directly determine the degree of variation that is needed to locate the orbit of nature's model . Convexity adjustments can also be used for cross sectional comparisons and we derive easy to use rules of thumb for estimating threshold infection level in one region given knowledge of threshold infection in another.
Edit intent: coherence
Original:  We demonstrate the validity of the approach on several synthetic datasets and on calcium recordings from the ellipsoid body of Drosophila melanogaster . This circuit is known to encode head direction, and mGPLVM correctly recovers the ring topology expected from a neural population representing a single angular variable.
Revised:  We demonstrate the validity of the approach on several synthetic datasets and on calcium recordings from the ellipsoid body of Drosophila melanogaster and extracellular recordings from the mouse anterodorsal thalamic nucleus. These circuits are both known to encode head direction, and mGPLVM correctly recovers the ring topology expected from a neural population representing a single angular variable.
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Original:  An elementary proof of a constant in the strong discrete Stechkin inequality given by Bennett is provided, and we improve the constants given by Stechkin and Copson. By means of convex optimization, we compute two decimal places of the optimal constant in the classical version of the strong discrete Stechkin inequality .
Revised:  An elementary proof of a constant in the strong discrete Stechkin inequality given by Bennett is provided, and we improve the constants given by Levin and Stechkin and by Copson .
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Original: Advances in interface design using touch surfaces creates greater obstacles for blind and visually impaired users of technology. Conversational user interfaces  offer a reasonable alternative for interactions and enable greater access and most importantly greater independence for the blind .
Revised: Advances in interface design using touch surfaces creates greater obstacles for blind and visually impaired users and contribute to an environment of dependency in work settings. We believe that offer a reasonable alternative for interactions and enable greater access and most importantly greater independence for the blind .
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Original:  Conversational user interfaces  offer a reasonable alternative for interactions and enable greater access and most importantly greater independence for the blind . This paper presents a case study of our work to develop a conversational user interface for accessibility for multifunction printers (MFP).
Revised:  Conversational user interfaces offer a reasonable alternative for interactions and enable greater access and most importantly greater independence for blind knowledge workers. We present a case study of our work to develop a conversational user interface for accessibility for multifunction printers (MFP).
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Original:  This paper presents a case study of our work to develop a conversational user interface for accessibility for multifunction printers (MFP). It describes our approach to conversational interfaces in general and the specifics of the solution we created for MFPs.
Revised:  This paper presents a case study of our work to develop a conversational user interface for accessibility for multifunction printers . We also describe our approach to conversational interfaces in general and the specifics of the solution we created for MFPs.
Edit intent: clarity
Original:  It describes our approach to conversational interfaces in general and the specifics of the solution we created for MFPs. It also presents a user study we performed to assess the solution and guide our future efforts  .
Revised:  It describes our approach to conversational interfaces in general and the specifics of the solution we created for multifunction printers. To guide our design, we worked with a group of blind and visually impaired individuals starting with focus group sessions to ascertain the challenges our target users face in their professional lives. We followed our technology development with a user study to assess the solution and guide our future efforts .
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Original:  Finally, for some deficient rank problems with or without the small noisy data from the NETLIB collection, we compare it with other two popular interior-point methods, i.e. the subroutine pathfollow.m and the built-in subroutine linprog.m of the MATLAB environment.
Revised:  Finally, for some rank-deficient problems with or without the small noisy data from the NETLIB collection, we compare it with other two popular interior-point methods, i.e. the subroutine pathfollow.m and the built-in subroutine linprog.m of the MATLAB environment.
Edit intent: clarity
Original:  When using individual node centrality indicators to predict which nodes will be among the top influencers or spreaders in a large network, no single centrality has consistently good predictive poweracross a set of 60 finite, diverse, static real-world topologies from six categories of social networks .
Revised:  When using individual node centrality indicators to predict which nodes will be among the top influencers or spreaders in a large network, no single centrality has consistently good ranking power .
Edit intent: clarity
Original:  We show that multi-centrality statistical classifiers , trained on a sample of the nodes from each network, are instead consistently predictive across diversenetwork cases . Certain pairs of centralities cooperate particularly well in statistically drawing the class boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood combine well with global centralities such as the eigenvector centrality, closeness, or the core number.
Revised:  We show that statistical classifiers using two or more centralities as input are instead consistently predictive across diversenetwork cases . Certain pairs of centralities cooperate particularly well in statistically drawing the class boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood combine well with global centralities such as the eigenvector centrality, closeness, or the core number.
Edit intent: clarity
Original:  We show that multi-centrality statistical classifiers , trained on a sample of the nodes from each network, are instead consistently predictive across diversenetwork cases . Certain pairs of centralities cooperate particularly well in statistically drawing the class boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood combine well with global centralities such as the eigenvector centrality, closeness, or the core number.
Revised:  We show that multi-centrality statistical classifiers , trained on a sample of the nodes from each network, are instead consistently predictive over many diverse, static real-world topologies . Certain pairs of centralities cooperate particularly well in statistically drawing the class boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood combine well with global centralities such as the eigenvector centrality, closeness, or the core number.
Edit intent: clarity
Original:  We show that multi-centrality statistical classifiers , trained on a sample of the nodes from each network, are instead consistently predictive across diversenetwork cases . Certain pairs of centralities cooperate particularly well in statistically drawing the class boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood combine well with global centralities such as the eigenvector centrality, closeness, or the core number.
Revised:  We show that multi-centrality statistical classifiers , trained on a sample of the nodes from each network, are instead consistently predictive across diversenetwork cases . Certain pairs of centralities cooperate particularly well in statistically drawing the boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood combine well with global centralities such as the eigenvector centrality, closeness, or the core number.
Edit intent: fluency
Original:  We show that multi-centrality statistical classifiers , trained on a sample of the nodes from each network, are instead consistently predictive across diversenetwork cases . Certain pairs of centralities cooperate particularly well in statistically drawing the class boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood combine well with global centralities such as the eigenvector centrality, closeness, or the core number.
Revised:  We show that multi-centrality statistical classifiers , trained on a sample of the nodes from each network, are instead consistently predictive across diversenetwork cases . Certain pairs of centralities cooperate particularly well in statistically drawing the class boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood benefit from the addition of a global centrality such as the eigenvector centrality, closeness, or the core number.
Edit intent: clarity
Original:   As a result  , training classifiers with seven classical centralities leads to a nearly maximum average precision function (0.995)  .
Revised:  This is, intuitively, because a local centrality may rank highly some nodes which are located in dense, but peripheral regions of the network---a situation in which an additional global centrality indicator can help by prioritising nodes located more centrally. The nodes selected as superspreaders will usually jointly maximise the values of both centralities. As a result , training classifiers with seven classical centralities leads to a nearly maximum average precision function (0.995) .
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Original: We develop a method to help quantify the impact different levels of mobility restrictions could have had on COVID-19 related deaths across nations. Synthetic control (SC) has emerged as a standard tool in such scenarios to produce counterfactual estimates if a particular interventionhad not occurred, using just observational data.However, it remains an important open problem of how to extend SC to obtain counterfactual estimates if a particular intervention had occurred - this is exactly the question of the impact of mobility restrictions stated above. As our main contribution, we introduce\le  synthetic interventions (SI), which helps resolve this open problem by allowing one to produce counterfactual estimates if there are multiple interventions of interest .
Revised: Consider a panel data setting with observations of N units over T time steps. Each of the N units undergoes exactly one of D interventions at time step T_0, with 1\le synthetic interventions (SI), which helps resolve this open problem by allowing one to produce counterfactual estimates if there are multiple interventions of interest .
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Original:  As our main contribution, we introduce\le  synthetic interventions (SI), which helps resolve this open problem by allowing one to produce counterfactual estimates if there are multiple interventions of interest .
Revised:  As our main contribution, we introduce\le T_0 < T, prior to which all units experience no intervention, i.e., control. We present a causal framework, synthetic interventions (SI), which helps resolve this open problem by allowing one to produce counterfactual estimates if there are multiple interventions of interest .
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Original:  We prove SI produces consistent counterfactual estimates under a tensor factor model . Our finite sample analysis shows the test error decays as 1/T_0, where T_0 is the amount of observed pre-intervention data. As a special case, this improves upon the 1/\sqrt{T_0 , we establish the parameter estimation and test error for Principal Component Regression (a key subroutine in SI and several SCvariants)under the setting of error-in-variable regression decays as 1/T_0, where T_0 is the number of samples observed;
Revised:  We prove identification of the causal parameter of interest under a latent factor model across time, units, and interventions. We furnish an algorithm to estimate the causal parameter, which utilizes principal component regression (PCR) as a key subroutine. We argue that PCR implicitly de-noises the observations, which are corrupted by idiosyncratic measurement error, and thus advocate for its usage in panel data settings. Formally, we establish consistency and asymptotic normality of the estimated causal parameter. We then compare our assumptions and results with those in the synthetic control (SC) literature. In doing so , we establish the parameter estimation and test error for Principal Component Regression (a key subroutine in SI and several SCvariants)under the setting of error-in-variable regression decays as 1/T_0, where T_0 is the number of samples observed;
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Original:  As a special case, this improves upon the 1/\sqrt{T_0 , we establish the parameter estimation and test error for Principal Component Regression (a key subroutine in SI and several SCvariants)under the setting of error-in-variable regression decays as 1/T_0, where T_0 is the number of samples observed; this improves the best prior test error bound of 1/\sqrt{T_0 .
Revised:  As a special case, this improves upon the 1/\sqrt{T_0 , we establish identification and inference results for SC as well. We further introduce a novel hypothesis test, with provable guarantees, to validate when to use SI (and thereby SC). Empirically, we showcase the efficacy of the SI framework on synthetic and real-world data. Finally, we discuss connections between the SI causal framework and tensor estimation .
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Original:  We are the first, to the best of our knowledge, to prove that even the simplest hard-margin one-class SVM with outliers problem is NP-complete, and has no fully PTAS unless P=NP  . For the data sanitization defense, we link it to the intrinsic dimensionality of data;
Revised:  We are the first, to the best of our knowledge, to prove that even the simplest hard-margin one-class SVM with outliers problem is NP-complete, and has no fully PTAS unless P=NP (that means it is hard to achieve an even approximate algorithm) . For the data sanitization defense, we link it to the intrinsic dimensionality of data;
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Original: The geodesic distance-based dual-front curve evolution model is a powerful and efficient solution to the active contours and image segmentation issues. In its basic formulation , the dual-front model regards the meeting interfaces of two adjacent Voronoi regions as the evolving curves in the course of curve evolution.
Revised: The geodesic distance-based dual-front curve evolution model is a powerful and efficient way for addressing the image segmentation and domain partitioning problems. In the basic formulation of the dual-front model regards the meeting interfaces of two adjacent Voronoi regions as the evolving curves in the course of curve evolution.
Edit intent: clarity
Original:  In its basic formulation , the dual-front model regards the meeting interfaces of two adjacent Voronoi regions as the evolving curves in the course of curve evolution. One of the most crucial ingredients for the construction of Voronoi regions or Voronoi diagram is the geodesic metrics  and the corresponding geodesic distance .
Revised:  In its basic formulation , the dual-front model regards the meeting interfaces of two adjacent Voronoi regions . Among these dual-front models, a crucial ingredient is regarded as the geodesic metrics and the corresponding geodesic distance .
Edit intent: clarity
Original:  In this paper, we introduce a new type of geodesic metrics that encodes the edge-based anisotropy features, the region-based homogeneity penalization and asymmetric enhancement. In contrast to the original isotropic dual-front model, the use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries  .
Revised:  In this paper, we introduce a type of asymmetric quadratic metrics dual-front model, the use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries .
Edit intent: clarity
Original:  In contrast to the original isotropic dual-front model, the use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries  .
Revised:  In contrast to the original isotropic dual-front model. The metrics considered are built by the integration of the image features and a vector field derived from the evolving contours. The use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries .
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Original:  In contrast to the original isotropic dual-front model, the use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries  .
Revised:  In contrast to the original isotropic dual-front model, the use of the asymmetry enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries .
Edit intent: fluency
Original:  In contrast to the original isotropic dual-front model, the use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries  .
Revised:  In contrast to the original isotropic dual-front model, the use of the asymmetric enhancement can reduce the risk of contour shortcut or leakage problems especially when the initial curves are far away from the target boundaries .
Edit intent: meaning-changed
Original:  In contrast to the original isotropic dual-front model, the use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries  .
Revised:  In contrast to the original isotropic dual-front model, the use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial contours are far away from the target boundaries .
Edit intent: clarity
Original:  In contrast to the original isotropic dual-front model, the use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries  .
Revised:  In contrast to the original isotropic dual-front model, the use of the asymmetric enhancement can reduce the risk of shortcuts or leakage problems especially when the initial curves are far away from the target boundaries or the images have complicated intensity distributions .
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Original: Graph Neural Networks (GNNs) are known to have an expressive power bounded by that of the vertex coloring algorithm (Xu et al., 2019a; Morris et al., 2018). However, for rich node features, such a bound does not exist and GNNs can be shown to be universal, namely, have the theoretical ability to approximate arbitrary graph functions. It is well known, however, that expressive power alone does not imply good generalization . In an effort to improve generalization of GNNs we suggest the Low-Rank Global Attention (LRGA ) module , taking advantage of the efficiency of low rank matrix-vector multiplication, that improves the algorithmic alignment (Xu et al., 2019b) of GNNs with the 2-folklore Weisfeiler-Lehman ( FWL) algorithm;
Revised: Graph Neural Networks (GNNs) are known to have an expressive power bounded by that of the vertex coloring algorithm (Xu et al., 2017) , taking advantage of the efficiency of low rank matrix-vector multiplication, that improves the algorithmic alignment (Xu et al., 2019b) of GNNs with the 2-folklore Weisfeiler-Lehman ( FWL) algorithm;
Edit intent: meaning-changed