wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of \text{CO emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050s . Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.
Revised:  Conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of CO2 emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050s . Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.
Edit intent: meaning-changed
Original:  Conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of \text{CO emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050s . Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.
Revised:  Conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of \text{CO emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050's . Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.
Edit intent: fluency
Original:  this is motivated bymany applications , e.g., sensitivity analysis and end-to-end learning . Our contribution is a theoretically guaranteed and widely applicable smoothing framework based on randomization.
Revised:  this is motivated bymany applications , e.g., sensitivity analysis and end-to-end learning , the demand for differentiable optimization algorithms has been significantly increasing. In this paper, we establish a theoretically guaranteed and widely applicable smoothing framework based on randomization.
Edit intent: meaning-changed
Original:  Our contribution is a theoretically guaranteed and widely applicable smoothing framework based on randomization. We prove that our smoothed greedy algorithm almost recovers original approximation guarantees in expectation for the cases of cardinality and \kappa-extensible system constrains.
Revised:  Our contribution is a theoretically guaranteed versatile framework that makes the greedy algorithm for monotone submodular function maximization differentiable. We smooth the greedy algorithm via randomization, and prove that it almost recovers original approximation guarantees in expectation for the cases of cardinality and \kappa-extensible system constrains.
Edit intent: meaning-changed
Original:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta  fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta)  ) in total variation distance.
Revised:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and whp in d^{\text{poly(k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta) ) in total variation distance.
Edit intent: meaning-changed
Original:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta  fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta)  ) in total variation distance.
Revised:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most k^{O(k)(\epsilon} +\eta fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta) ) in total variation distance.
Edit intent: meaning-changed
Original:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta  fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta)  ) in total variation distance.
Revised:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta ) fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta) ) in total variation distance.
Edit intent: fluency
Original:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta  fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta)  ) in total variation distance.
Revised:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta fraction of the points whenever every pair of mixture components are separated by 1-\exp(- poly (k/\eta) ) in total variation distance.
Edit intent: clarity
Original:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta  fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta)  ) in total variation distance.
Revised:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta fraction of the points whenever every pair of components is separated by 1-\exp(- \text{poly (k/\eta) ) in total variation distance.
Edit intent: meaning-changed
Original:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta  fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta)  ) in total variation distance.
Revised:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta) ^k ) in total variation distance.
Edit intent: meaning-changed
Original:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta  fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta)  ) in total variation distance. This is the statistically weakest possible notion of separation and allows, for e.g., clusteringof mixtures with components with the same mean with covariances differing in a single unknown direction or separated in Frobenius distance.
Revised:  Concretely, our algorithm takes input an \epsilon-corrupted sample from a k-GMM and (k/\eta)} time, } outputs an approximate clustering that misclassifies at most O(k\epsilon)(\epsilon} +\eta fraction of the points whenever every pair of components is separated by 1-\exp(- poly (k/\eta) ) in total variation (TV) distance. Such a result was not previously known even for k=2. TV separation is the statistically weakest possible notion of separation and allows, for e.g., clusteringof mixtures with components with the same mean with covariances differing in a single unknown direction or separated in Frobenius distance.
Edit intent: meaning-changed
Original:  This is the statistically weakest possible notion of separation and allows, for e.g., clusteringof mixtures with components with the same mean with covariances differing in a single unknown direction or separated in Frobenius distance. The running time of our algorithm is d^{O(\log(\kappa)) \textrm{poly(k/\eta)} where \kappa is a measure of spread of the mixture in any direction.
Revised:  This is the statistically weakest possible notion of separation and (k/\eta)} where \kappa is a measure of spread of the mixture in any direction.
Edit intent: coherence
Original:  For k=2, our algorithms run in time and samples }poly (d) with no dependence on the spread \kappa. Such a results were not known prior to our work, even for k=2. More generally, our algorithm succeeds for mixtures of any distribution that satisfies two well-studied analytic assumptions - certifiable hypercontractivity and anti-concentration .
Revised:  For k=2, our algorithms run in time and samples }poly captures important special cases such as mixed linear regression and subspace clustering. Our main conceptual contribution is to distill two simple analytic properties - certifiable hypercontractivity and anti-concentration .
Edit intent: meaning-changed
Original:  More generally, our algorithm succeeds for mixtures of any distribution that satisfies two well-studied analytic assumptions - certifiable hypercontractivity and anti-concentration . Thus, they extend to clustering mixtures of arbitrary affine transforms of the uniform distribution on the d-dimensional unit sphere.
Revised:  More generally, our algorithm succeeds for mixtures of any distribution that satisfies two well-studied analytic assumptions - certifiable hypercontractivity and anti-concentration - that are necessary and sufficient for mixture models to be (efficiently) clusterable. As a consequence, our results extend to clustering mixtures of arbitrary affine transforms of the uniform distribution on the d-dimensional unit sphere.
Edit intent: meaning-changed
Original:  Our main conceptual contribution is to distill two simple analytic properties - (certifiable) hypercontractivity and  anti-concentration  - that are necessary and sufficient for mixture models to be (efficiently) clusterable.
Revised:  Our main conceptual contribution is to distill simple analytic properties - (certifiable) hypercontractivity and anti-concentration - that are necessary and sufficient for mixture models to be (efficiently) clusterable.
Edit intent: clarity
Original:  Our main conceptual contribution is to distill two simple analytic properties - (certifiable) hypercontractivity and  anti-concentration  - that are necessary and sufficient for mixture models to be (efficiently) clusterable.
Revised:  Our main conceptual contribution is to distill two simple analytic properties - (certifiable) hypercontractivity and bounded variance of degree 2 polynomials and anti-concentration - that are necessary and sufficient for mixture models to be (efficiently) clusterable.
Edit intent: meaning-changed
Original:  Our main conceptual contribution is to distill two simple analytic properties - (certifiable) hypercontractivity and  anti-concentration  - that are necessary and sufficient for mixture models to be (efficiently) clusterable.
Revised:  Our main conceptual contribution is to distill two simple analytic properties - (certifiable) hypercontractivity and anti-concentration of linear projections - that are necessary and sufficient for mixture models to be (efficiently) clusterable.
Edit intent: meaning-changed
Original:  Our algorithms build on the recent sequence of works relying on certifiable anti-concentration first introduced in KKK'19, RY'20 . Our techniques expand the sum-of-squares toolkit to show robust certifiability of TV-separated Gaussian clusters in data.
Revised:  Our algorithms build on the recent sequence of works relying on certifiable anti-concentration first introduced in . Our techniques expand the sum-of-squares toolkit to show robust certifiability of TV-separated Gaussian clusters in data.
Edit intent: clarity
Original:  Our algorithms build on the recent sequence of works relying on certifiable anti-concentration first introduced in KKK'19, RY'20 . Our techniques expand the sum-of-squares toolkit to show robust certifiability of TV-separated Gaussian clusters in data.
Revised:  Our algorithms build on the recent sequence of works relying on certifiable anti-concentration first introduced in KKK'19, RY'20 the works of Karmarkar, Klivans, and Kothari and Raghavendra, and Yau in 2019. Our techniques expand the sum-of-squares toolkit to show robust certifiability of TV-separated Gaussian clusters in data.
Edit intent: meaning-changed
Original:  It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 5 ) evaluates the consensus between user-selected algorithms and 6 ) allows users to review details and iteratively update the acquired knowledge.
Revised:  It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 4 ) evaluates the consensus between user-selected algorithms and 6 ) allows users to review details and iteratively update the acquired knowledge.
Edit intent: meaning-changed
Original:  It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 5 ) evaluates the consensus between user-selected algorithms and 6 ) allows users to review details and iteratively update the acquired knowledge.
Revised:  It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 5 ) evaluates the consensus between user-selected algorithms , and 5 ) allows users to review details and iteratively update the acquired knowledge.
Edit intent: coherence
Original:  We include example calculations, where we indicate which testing architectures yield the greatest efficiency gains in various settings. We find that for identification of individuals with COVID-19, array testing is usually the best choice  , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests  .
Revised:  We find that in practical settings, for identification of individuals with COVID-19, array testing is usually the best choice , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests .
Edit intent: clarity
Original:  We find that for identification of individuals with COVID-19, array testing is usually the best choice  , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests  .
Revised:  We find that for identification of individuals with COVID-19, Dorfman testing is the best choice , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests .
Edit intent: meaning-changed
Original:  We find that for identification of individuals with COVID-19, array testing is usually the best choice  , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests  .
Revised:  We find that for identification of individuals with COVID-19, array testing is usually the best choice at prevalences up to 30\% , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests .
Edit intent: meaning-changed
Original:  We find that for identification of individuals with COVID-19, array testing is usually the best choice  , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests  .
Revised:  We find that for identification of individuals with COVID-19, array testing is usually the best choice , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing is the best choice at prevalences up to 30\% given a fixed and relatively small number of tests .
Edit intent: meaning-changed
Original:  We find that for identification of individuals with COVID-19, array testing is usually the best choice  , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests  .
Revised:  We find that for identification of individuals with COVID-19, array testing is usually the best choice , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests . For instance, at a prevalence of up to 2\%, Dorfman testing gives an efficiency gain of 3.5--8; at 1\% prevalence, Gibbs-Gower testing gives an efficiency gain of 18, even when capping the pool size at a feasible number .
Edit intent: meaning-changed
Original:  We present Monte Carlo simulation results of a Susceptible- Infected-Removed (SIR) model on a network, where individuals are limited in the number of other people they can interact with  .
Revised:  We present Monte Carlo simulation results of a capacity constrained Susceptible-Infected-Removed (SIR) model on a network, where individuals are limited in the number of other people they can interact with .
Edit intent: meaning-changed
Original:  We present Monte Carlo simulation results of a Susceptible- Infected-Removed (SIR) model on a network, where individuals are limited in the number of other people they can interact with  .
Revised:  We present Monte Carlo simulation results of a Susceptible- Infected-Removed (SIR) model . The key modelling feature is that individuals are limited in the number of other people they can interact with .
Edit intent: clarity
Original:  We present Monte Carlo simulation results of a Susceptible- Infected-Removed (SIR) model on a network, where individuals are limited in the number of other people they can interact with  .
Revised:  We present Monte Carlo simulation results of a Susceptible- Infected-Removed (SIR) model on a network, where individuals are limited in the number of other people they can interact with , thereby constraining disease transmission to an infectious subnetwork of the original social network .
Edit intent: meaning-changed
Original:  In this work, we seek to determine whether one can walk in a room and detect any wireless sensor monitoring an individual. As such, we propose SnoopDog, a framework to not only detect  wireless sensors that are actively monitoring a user, but also classify and localize each device.
Revised:  In this work, we propose SnoopDog, a framework to not only detect wireless sensors that are actively monitoring a user, but also classify and localize each device.
Edit intent: coherence
Original:  We evaluated SnoopDog across several devices and various modalities and were able to detect causality 96.6\% percent of the time , classify suspicious devices with 100\% accuracy, and localize devices to a sufficiently reduced sub-space.
Revised:  We evaluated SnoopDog across several devices and various modalities and were able to detect causality for snooping devices 95.2\% of the time , classify suspicious devices with 100\% accuracy, and localize devices to a sufficiently reduced sub-space.
Edit intent: meaning-changed
Original:  We evaluated SnoopDog across several devices and various modalities and were able to detect causality 96.6\% percent of the time , classify suspicious devices with 100\% accuracy, and localize devices to a sufficiently reduced sub-space.
Revised:  We evaluated SnoopDog across several devices and various modalities and were able to detect causality 96.6\% percent of the time and localize devices to a sufficiently reduced sub-space.
Edit intent: clarity
Original:  By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the region , in the network parameter space, where bipartite networks can support extensive gene expression , as required in stable cell types.
Revised:  By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the necessary conditions , in the network parameter space, where bipartite networks can support extensive gene expression , as required in stable cell types.
Edit intent: clarity
Original:  By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the region , in the network parameter space, where bipartite networks can support extensive gene expression , as required in stable cell types.
Revised:  By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the region , in the network parameter space, under which bipartite networks can support extensive gene expression , as required in stable cell types.
Edit intent: clarity
Original:  By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the region , in the network parameter space, where bipartite networks can support extensive gene expression , as required in stable cell types.
Revised:  By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the region , in the network parameter space, where bipartite networks can support a multiplicity of stable gene expression patterns, under noisy conditions, as required in stable cell types.
Edit intent: meaning-changed
Original:  In particular, the analysis reveals the possibility of a bi-stability region, where extensive gene expression can be either sustained or not sustained, depending on the initial network state, which is remarkably different from the transition observed in standard percolation theory.
Revised:  In particular, the analysis reveals the possibility of a bi-stability region, where the extensive percolating cluster is or is not resilient to perturbations. This is remarkably different from the transition observed in standard percolation theory.
Edit intent: clarity
Original:  The superiority of the proposed ContextNet model is also verified on a much larger internal dataset.abstract 
Revised:  The superiority of the proposed ContextNet model is also verified on a much larger internal dataset.abstract \%/4.6\% without external language model (LM), 1.9\%/4.1\% with LM and 2.9\%/7.0\% with only 10M parameters on the clean/noisy LibriSpeech test sets. This compares to the previous best published system of 2.0\%/4.6\% with LM and 3.9\%/11.3\% with 20M parameters. The superiority of the proposed ContextNet model is also verified on a much larger internal dataset.
Edit intent: meaning-changed