wanyu/IteraTeR_full_sent huggingface dataset:
Original:  {\it   In the second part, we incorporate in the model, a consideration of artificial herd immunity and show how we can decrease the peak time of infection with a subsequent decrease in the maximum number of infected people . Finally, we estimate a critical value of the rate of vaccination by a statistical method such that we proposed a way of possible eradication of the epidemic in a short time by effectively providing the vaccine to a population .
Revised:  {\it In the second part, we incorporate in the model, a consideration of artificial herd immunity and show how we can decrease the peak time of infection with a subsequent decrease in the maximum number of infected people .
Edit intent: coherence
Original:  The system is made up of two blocks - the first block gen-erates a transcript of discovered phonetic units for the inputaudio , and the second block which performs intent classifi-cation from the generated phonemic transcripts.
Revised:  The system is made up of two blocks - the first block is a universal phone recognition system that generates a transcript of discovered phonetic units for the inputaudio , and the second block which performs intent classifi-cation from the generated phonemic transcripts.
Edit intent: meaning-changed
Original:  The system is made up of two blocks - the first block gen-erates a transcript of discovered phonetic units for the inputaudio , and the second block which performs intent classifi-cation from the generated phonemic transcripts.
Revised:  The system is made up of two blocks - the first block gen-erates a transcript of discovered phonetic units for the input audio , and the second block which performs intent classifi-cation from the generated phonemic transcripts.
Edit intent: fluency
Original:  The system is made up of two blocks - the first block gen-erates a transcript of discovered phonetic units for the inputaudio , and the second block which performs intent classifi-cation from the generated phonemic transcripts.
Revised:  The system is made up of two blocks - the first block gen-erates a transcript of discovered phonetic units for the inputaudio , and the second block performs intent classification from the generated phonemic transcripts.
Edit intent: clarity
Original:  The system is made up of two blocks - the first block gen-erates a transcript of discovered phonetic units for the inputaudio , and the second block which performs intent classifi-cation from the generated phonemic transcripts. Our workpresents results for such a system for two languages families- Indic languages and Romance languages, for two differentintent recognition tasks.
Revised:  The system is made up of two blocks - the first block gen-erates a transcript of discovered phonetic units for the inputaudio , and the second block which performs intent classifi-cation from the generated phonetic transcripts. We propose a CNN+LSTM based architecture and present results for two languages families - Indic languages and Romance languages, for two differentintent recognition tasks.
Edit intent: meaning-changed
Original:  In these problems, we are often given a metric space , and asked to open a set of at most k facilities and provide a certain assignment of these facilities to a set of clients, in order to minimize the ordered weighted sum of the induced service costs from all facility-client assignments c is minimized.
Revised:  In ordered k-median, given a metric space , and asked to open a set of at most k facilities and provide a certain assignment of these facilities to a set of clients, in order to minimize the ordered weighted sum of the induced service costs from all facility-client assignments c is minimized.
Edit intent: meaning-changed
Original:  In these problems, we are often given a metric space , and asked to open a set of at most k facilities and provide a certain assignment of these facilities to a set of clients, in order to minimize the ordered weighted sum of the induced service costs from all facility-client assignments c is minimized.
Revised:  In these problems, we are often given a metric space (X,d), we need to open a set of at most k facilities and provide a certain assignment of these facilities to a set of clients, in order to minimize the ordered weighted sum of the induced service costs from all facility-client assignments c is minimized.
Edit intent: meaning-changed
Original:  In these problems, we are often given a metric space , and asked to open a set of at most k facilities and provide a certain assignment of these facilities to a set of clients, in order to minimize the ordered weighted sum of the induced service costs from all facility-client assignments c is minimized.
Revised:  In these problems, we are often given a metric space , and asked to open a set of k facilities S\subseteq X, such that if c is minimized.
Edit intent: meaning-changed
Original:  In these problems, we are often given a metric space , and asked to open a set of at most k facilities and provide a certain assignment of these facilities to a set of clients, in order to minimize the ordered weighted sum of the induced service costs from all facility-client assignments c is minimized.
Revised:  In these problems, we are often given a metric space , and asked to open a set of at most k facilities and provide a certain assignment of these facilities to a set of clients, in order to minimize the ordered weighted sum of the induced service costs from all facility-client assignments :=\{d(j,S):j\in X\} is the service cost vector of connection distances, the objective w^Tc is minimized.
Edit intent: meaning-changed
Original:  In these problems, we are often given a metric space , and asked to open a set of at most k facilities and provide a certain assignment of these facilities to a set of clients, in order to minimize the ordered weighted sum of the induced service costs from all facility-client assignments c is minimized.
Revised:  In these problems, we are often given a metric space , and asked to open a set of at most k facilities and provide a certain assignment of these facilities to a set of clients, in order to minimize the ordered weighted sum of the induced service costs from all facility-client assignments c^{\downarrow is minimized.
Edit intent: meaning-changed
Original:  Here w\inR^{|X|} is a predefined non-increasing non-negative vector, and }c  . In the fault-tolerant problem, every client jhas a requirement r_j and needs to be assigned r_j distinct facilities.
Revised:  Here w\inR^{|X|} is a predefined non-increasing non-negative vector, and }c^{\downarrow . In the fault-tolerant problem, every client jhas a requirement r_j and needs to be assigned r_j distinct facilities.
Edit intent: meaning-changed
Original:  Here w\inR^{|X|} is a predefined non-increasing non-negative vector, and }c  . In the fault-tolerant problem, every client jhas a requirement r_j and needs to be assigned r_j distinct facilities.
Revised:  Here w\inR^{|X|} is a predefined non-increasing non-negative vector, and }c , therefore multiplying larger costs with larger weights . In the fault-tolerant problem, every client jhas a requirement r_j and needs to be assigned r_j distinct facilities.
Edit intent: meaning-changed
Original:  Here w\inR^{|X|} is a predefined non-increasing non-negative vector, and }c  . In the fault-tolerant problem, every client jhas a requirement r_j and needs to be assigned r_j distinct facilities.
Revised:  Here w\inR^{|X|} is a predefined non-increasing non-negative vector, and }c . In the fault-tolerant variant of ordered k-median, every j\in X needs to be assigned r_j distinct facilities.
Edit intent: meaning-changed
Original:  Here w\inR^{|X|} is a predefined non-increasing non-negative vector, and }c  . In the fault-tolerant problem, every client jhas a requirement r_j and needs to be assigned r_j distinct facilities. The cost of client j is the sum of distances to its assigned facilities .
Revised:  Here w\inR^{|X|} is a predefined non-increasing non-negative vector, and }c . In the fault-tolerant problem, every client jhas a requirement r_j and needs to be assigned r_j \geq 1 distinct facilities in S. The service cost of j is now the sum of distances to its assigned facilities .
Edit intent: meaning-changed
Original:  The cost of client j is the sum of distances to its assigned facilities . In the robust problem,\vec{c} a parameter m is given and we need to assign some facility to at least m clients.
Revised:  The cost of client j is the sum of distances to its assigned facilities , and the objective is still w^T\vec{c} a parameter m is given and we need to assign some facility to at least m clients.
Edit intent: meaning-changed
Original:  In the robust problem,\vec{c} a parameter m is given and we need to assign some facility to at least m clients. We give polynomial-time\vec{c} constant-factor  approximation algorithms for both problems, which use standard sparsification as well as iterative rounding techniques for LP relaxations .
Revised:  In the robust problem,\vec{c} a parameter m is given and we need to connect the nearest open facility to m points in X. In this case, the service cost vector\vec{c} constant-factor approximation algorithms for both problems, which use standard sparsification as well as iterative rounding techniques for LP relaxations .
Edit intent: meaning-changed
Original:  However, under-reporting is commonly prevalent in the early stages of an epidemic. In this study, confirmed cases of healthcare workers are considered as accurate small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives: parameter estimation , spatiotemporal correlation, and spatiotemporal lag .
Revised:  However, under-reporting of cases is very common in fields associated with public health. It is also possible to draw biased inferences if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, infection data of healthcare workers are considered as accurate small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives: parameter estimation , spatiotemporal correlation, and spatiotemporal lag .
Edit intent: coherence
Original:  In this study, confirmed cases of healthcare workers are considered as accurate small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives: parameter estimation , spatiotemporal correlation, and spatiotemporal lag .
Revised:  In this study, confirmed cases of healthcare workers in Wuhan and Hubei (excluding Wuhan) were considered as a small sample, and a novel framework was proposed to explore the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives: parameter estimation , spatiotemporal correlation, and spatiotemporal lag .
Edit intent: meaning-changed
Original:  In this study, confirmed cases of healthcare workers are considered as accurate small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives: parameter estimation , spatiotemporal correlation, and spatiotemporal lag .
Revised:  In this study, confirmed cases of healthcare workers are considered as accurate small samples to analyze the impact of under-reporting on COVID-19 from three perspectives: parameter estimation , spatiotemporal correlation, and spatiotemporal lag .
Edit intent: clarity
Original:  In this study, confirmed cases of healthcare workers are considered as accurate small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives: parameter estimation , spatiotemporal correlation, and spatiotemporal lag .
Revised:  In this study, confirmed cases of healthcare workers are considered as accurate small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 infection peak and duration estimation in Wuhan and Hubei (excluding Wuhan) .
Edit intent: meaning-changed
Original:  The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the  reported cases lagged the peak infection time of the healthcare worker cases , and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases.
Revised:  The results show that the reported cases lagged the peak infection time of the healthcare worker cases , and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases.
Edit intent: clarity
Original:  (2) the estimated peak infection time of the  reported cases lagged the peak infection time of the healthcare worker cases , and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases.
Revised:  (2) the estimated peak infection time of the infection peak estimated by the reported cases lagged the peak infection time of the healthcare worker cases , and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases.
Edit intent: meaning-changed
Original:  (2) the estimated peak infection time of the  reported cases lagged the peak infection time of the healthcare worker cases , and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases.
Revised:  (2) the estimated peak infection time of the reported cases in Wuhan may lag the actual infection peak by 9~10 days , and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases.
Edit intent: meaning-changed
Original:  (2) the estimated peak infection time of the  reported cases lagged the peak infection time of the healthcare worker cases , and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases.
Revised:  (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases , and the infection duration estimated by the reported cases was smaller than that of the healthcare worker cases.
Edit intent: clarity
Original:  (2) the estimated peak infection time of the  reported cases lagged the peak infection time of the healthcare worker cases , and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages , and the impact on the early onset area was greater than that on the late onset area.
Revised:  (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases , and the estimated infection time interval of the reported cases in Wuhan may be 16 days shorter than the actual infection duration. The infection peak estimated by the reported cases in Hubei (excluding Wuhan) may lag the actual infection peak by 1~2 days , and the impact on the early onset area was greater than that on the late onset area.
Edit intent: meaning-changed
Original:  (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages , and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases .
Revised:  (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages , and the infection duration estimated by the reported cases in Hubei (excluding Wuhan) may be 12.1 days shorter than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases .
Edit intent: meaning-changed
Original: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U)  where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Revised: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra-Itzykson-Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U) where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Edit intent: fluency
Original: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U)  where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Revised: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) distribution on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U) where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Edit intent: clarity
Original: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U)  where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Revised: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \text{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U) where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Edit intent: meaning-changed
Original: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U)  where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Revised: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \text{tr (U\Lambda U^*Y)}d\mu(U) where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Edit intent: meaning-changed
Original: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U)  where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Revised: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U) , where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Edit intent: fluency
Original: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U)  where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Revised: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U) where \mu is the Haar measure on \text{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Edit intent: meaning-changed
Original: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U)  where \mu is the Haar measure on \mathrm{U (n). (U\Lambda U^*Y)} is known as the HCIZ density.
Revised: Given two n \times n Hermitian matrices Y and \Lambda, the Harish-Chandra--Itzykson--Zuber (HCIZ) density on the unitary group \mathrm{U (n) is e^{ \mathrm{Tr (U\Lambda U^*Y)}d\mu(U) where \mu is the Haar measure on \mathrm{U (n). The density e^{\text{tr(U\Lambda U^*Y)} is known as the HCIZ density.
Edit intent: meaning-changed
Original:  The main contribution of this paper is an algorithm to sample a matrix from a distribution that is \varepsilon-close to the given HCIZ density and whose running time depends polylogarithmically on 1/ \varepsilon.
Revised:  We present two efficient algorithms to sample matrices from distributions that are close to the HCIZ distribution. The first algorithm outputs samples that are \xi-close in total variation distance and requires polynomially many arithmetic operations in \log 1/\xi and the number of bits needed to encode Y and \Lambda. The second algorithm comes with a stronger guarantee that the samples are \xi-close in infinity divergence, but the number of arithmetic operations depends polynomially on 1/ \varepsilon.
Edit intent: meaning-changed
Original:  The main contribution of this paper is an algorithm to sample a matrix from a distribution that is \varepsilon-close to the given HCIZ density and whose running time depends polylogarithmically on 1/ \varepsilon. Interestingly, HCIZ densities can also be viewed as exponential densities on \mathrm{U (n)-orbits, and in this setting, they have been widely studied .
Revised:  The main contribution of this paper is an algorithm to sample a matrix from a distribution that is \varepsilon-close to the given HCIZ density and whose running time depends polylogarithmically on 1/ \xi, the number of bits needed to encode Y and \Lambda, and the differences of the largest and the smallest eigenvalues of Y and \Lambda. HCIZ densities can also be viewed as exponential densities on \mathrm{U (n)-orbits, and in this setting, they have been widely studied .
Edit intent: meaning-changed
Original:  Interestingly, HCIZ densities can also be viewed as exponential densities on \mathrm{U (n)-orbits, and in this setting, they have been widely studied .
Revised:  Interestingly, HCIZ densities can also be viewed as exponential densities on \text{U (n)-orbits, and in this setting, they have been widely studied .
Edit intent: meaning-changed
Original:  Interestingly, HCIZ densities can also be viewed as exponential densities on \mathrm{U (n)-orbits, and in this setting, they have been widely studied . Thus, our result has several direct applications, including polynomial-time algorithms 1)  to sample from  matrix Langevin distributions studied in statistics, 2)  to sample from continuous maximum entropy distributions with applications to quantum inference , and 3)  for differentially private low-rank approximation.
Revised:  Interestingly, HCIZ densities can also be viewed as exponential densities on \mathrm{U (n)-orbits, and these densities have been studied in statistics, machine learning, and theoretical computer science. Thus our results have the following applications: 1) to sample from matrix Langevin distributions studied in statistics, 2) to sample from continuous maximum entropy distributions with applications to quantum inference , and 3) for differentially private low-rank approximation.
Edit intent: clarity
Original:  Thus, our result has several direct applications, including polynomial-time algorithms 1)  to sample from  matrix Langevin distributions studied in statistics, 2)  to sample from continuous maximum entropy distributions with applications to quantum inference , and 3)  for differentially private low-rank approximation.
Revised:  Thus, our result has several direct applications, including polynomial-time algorithms 1) an efficient algorithm to sample from matrix Langevin distributions studied in statistics, 2) to sample from continuous maximum entropy distributions with applications to quantum inference , and 3) for differentially private low-rank approximation.
Edit intent: meaning-changed