wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (}  the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). }  Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Revised:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (} the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). } Numerical results show that the new method is more robust than those methods for the deficient rank problem with the small noise data .
Edit intent: clarity
Original:  This makes it hard for a decision maker to meaningfully compare these notions and their resulting trade-offs across different applications. We address these shortcomings by extending the principles of cardinal welfare to the influence maximization setting, which is underlain by complex connections between members of different communities.
Revised:  Second, these measures are typically imposed as strict constraints leading to undesirable properties such as wastage of resources. To address these shortcomings by extending the principles of cardinal welfare to the influence maximization setting, which is underlain by complex connections between members of different communities.
Edit intent: meaning-changed
Original:  We address these shortcomings by extending the principles of cardinal welfare to the influence maximization setting, which is underlain by complex connections between members of different communities. We generalize the theory regarding these principles and show under what circumstances these principles can be satisfied by a welfare function.
Revised:  We address these shortcomings , we provide a principled characterization of the properties that a fair influence maximization algorithm should satisfy. In particular, we propose a framework based on social welfare theory, wherein the cardinal utilities derived by each community are aggregated using the isoelastic social welfare functions. Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter. We then show under what circumstances these principles can be satisfied by a welfare function.
Edit intent: meaning-changed
Original:  We then propose a family of welfare functions that are governed by a single inequity aversion parameter which allows a decision maker to study task-dependent trade-offs between fairness and total influence and effectively trade off quantities like influence gap by varying this parameter. We use these welfare functions as a fairness notion to rule out undesirable allocations. We show that the resulting optimization problem is monotone and submodular and can be solved  with optimality guarantees.
Revised:  The resulting optimization problem is monotone and submodular and can be solved with optimality guarantees.
Edit intent: coherence
Original:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Revised:  To help financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Edit intent: fluency
Original:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Revised:  To help the financial regulatory authorities and banks manage the risk associated with networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Edit intent: clarity
Original:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Revised:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk , a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Edit intent: fluency
Original:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Revised:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue in developing preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Edit intent: clarity
Original:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Revised:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and established iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Edit intent: clarity
Original:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool , to facilitate the closed-loop analysis process.
Revised:  To help the financial regulatory authorities and banks manage the risk brought by the networked loans, we identified the default contagion risk as a pivotal issue to take preventive measures, and develop iConVis, an interactive visual analysis tool that facilitates the closed-loop analysis process.
Edit intent: coherence
Original:  In this work we propose an alternative approach to building low-resource Named Entity Recognition (NER) models using " non-speaker " (NS) annotations, provided by annotators with no prior experience in the target language.
Revised:  In this work we propose a complementary approach to building low-resource Named Entity Recognition (NER) models using " non-speaker " (NS) annotations, provided by annotators with no prior experience in the target language.
Edit intent: meaning-changed
Original:  In this work we propose an alternative approach to building low-resource Named Entity Recognition (NER) models using " non-speaker " (NS) annotations, provided by annotators with no prior experience in the target language.
Revised:  In this work we propose an alternative approach to building low-resource Named Entity Recognition (NER) models using `` non-speaker " (NS) annotations, provided by annotators with no prior experience in the target language.
Edit intent: fluency
Original:  In this work we propose an alternative approach to building low-resource Named Entity Recognition (NER) models using " non-speaker " (NS) annotations, provided by annotators with no prior experience in the target language.
Revised:  In this work we propose an alternative approach to building low-resource Named Entity Recognition (NER) models using " non-speaker '' (NS) annotations, provided by annotators with no prior experience in the target language.
Edit intent: fluency
Original:  We recruit 30 participants to annotate unfamiliar languages in a carefully controlled annotation experiment , using Indonesian, Russian, and Hindi as target languages. Our results show that use of non-speaker annotators produces results that approach or match performance of fluent speakers.
Revised:  We recruit 30 participants to annotate unfamiliar languages in a carefully controlled annotation experiment , using Indonesian, Russian, and Hindi . We show that use of non-speaker annotators produces results that approach or match performance of fluent speakers.
Edit intent: clarity
Original:  Our results show that use of non-speaker annotators produces results that approach or match performance of fluent speakers. NS results are also consistently on par or better than cross-lingual methods built on modern contextual representations, and have the potential to further outperform with additional effort.
Revised:  Our results show that use of non-speaker annotators produces results that are consistently on par or better than cross-lingual methods built on modern contextual representations, and have the potential to further outperform with additional effort.
Edit intent: clarity
Original: Recent work showed that the initial COVID-19 data from China  followed a subexponential power-law increase in the number of confirmed cases. This was attributed to a combination of effective containment and mitigation measures employed as well as behaviorial changes by the population.
Revised: Recent work showed that the initial COVID-19 data from China followed a subexponential power-law increase , with a growth exponent of around 2.2. The power-law behavior was attributed to a combination of effective containment and mitigation measures employed as well as behaviorial changes by the population.
Edit intent: meaning-changed
Original:  In view of this , we performed a Monte Carlo random walk study to better understand proximity-based infectious disease spread, particularly under restrictions. Our model is found to be rather robust and reproduces the observed power-law growth without relying on any external parameters. Three\langle \rangle growth regimes (quadratic,  power-law and exponential)  emerge naturally from our simulations.
Revised:  In view of this , we performed a Monte Carlo random walk study to better understand proximity-based infection spread. Social distancing is incorporated in the simulations through a single parameter, the size of each step in the random walk process. The step size l is taken to be a multiple of\langle \rangle growth regimes (quadratic, power-law and exponential) emerge naturally from our simulations.
Edit intent: meaning-changed
Original:  These\langle \rangle\lesssim \langle \rangle results suggest that the early containment of the disease within China was close to optimal and could not have been significantly improved upon .
Revised:  For l =\langle \rangle\lesssim \langle \rangle results suggest that the early containment of the disease within China was close to optimal and could not have been significantly improved upon .
Edit intent: meaning-changed
Original:  These\langle \rangle\lesssim \langle \rangle results suggest that the early containment of the disease within China was close to optimal and could not have been significantly improved upon .
Revised:  These\langle \rangle, we get intermediate power-law growth exponents that are in general agreement with available data from China. On the other hand, we obtain a quadratic growth for smaller step sizes l\lesssim \langle \rangle results suggest that the early containment of the disease within China was close to optimal and could not have been significantly improved upon .
Edit intent: meaning-changed
Original:  These\langle \rangle\lesssim \langle \rangle results suggest that the early containment of the disease within China was close to optimal and could not have been significantly improved upon .
Revised:  These\langle \rangle\lesssim \langle \rangle/2 , while for large l the growth is found to be exponential. Together with available data, these results suggest that the early containment of the disease within China was close to optimal and could not have been significantly improved upon .
Edit intent: meaning-changed
Original:  These\langle \rangle\lesssim \langle \rangle results suggest that the early containment of the disease within China was close to optimal and could not have been significantly improved upon .
Revised:  These\langle \rangle\lesssim \langle \rangle results suggest that the early containment of the disease within China was close to optimal . We further performed a comparative case study of data from three other countries, India, Brazil and South Africa .
Edit intent: meaning-changed
Original:  This network is built with Floor (\lfloor x\rfloor) and ReLU (\max\{0,x\}) activation functions and hence we call such networks as Floor-ReLU networks.
Revised:  This network is built with Floor (\lfloor x\rfloor) or ReLU (\max\{0,x\}) activation functions and hence we call such networks as Floor-ReLU networks.
Edit intent: fluency
Original:  This network is built with Floor (\lfloor x\rfloor) and ReLU (\max\{0,x\}) activation functions and hence we call such networks as Floor-ReLU networks.
Revised:  This network is built with Floor (\lfloor x\rfloor) and ReLU (\max\{0,x\}) activation function in each neuron and hence we call such networks as Floor-ReLU networks.
Edit intent: meaning-changed
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} it is shown that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N} ^{- \sqrt{L}}, where \mu is the Lipschitz constantof f. { More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: clarity
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can uniformly approximate a H } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N} ^{- \sqrt{L}}, where \mu is the Lipschitz constantof f. { More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: meaning-changed
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous \"o} function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N} ^{- \sqrt{L}}, where \mu is the Lipschitz constantof f. { More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: meaning-changed
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous }lder function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N} ^{- \sqrt{L}}, where \mu is the Lipschitz constantof f. { More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: meaning-changed
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an approximation rate 3 \mu\sqrt{d\,N} ^{- \sqrt{L}}, where \mu is the Lipschitz constantof f. { More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: meaning-changed
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \,N} ^{- \sqrt{L}}, where \mu is the Lipschitz constantof f. { More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: meaning-changed
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N} \lambda d^{\alpha/2 ^{- \sqrt{L}}, where \mu is the Lipschitz constantof f. { More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: meaning-changed
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N} ^{- \alpha \sqrt{L}}, where \mu is the Lipschitz constantof f. { More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: meaning-changed
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N} ^{- \sqrt{L}}, where \alpha \in(0,1 { More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: meaning-changed
Original: }}  that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N}  ^{-  \sqrt{L}}, where \mu is the Lipschitz constantof f. {  More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Revised: }} that Floor-ReLU networks with width \max\{d,\, 5N+13\} and depth 64dL+3 can pointwise approximate a Lipschitz continuous } function f on [0,1]^d with an exponential approximation rate 3 \mu\sqrt{d\,N} ^{- \sqrt{L}}, where \mu is the Lipschitz constantof f. and \lambda are the H{ More generally for an arbitrary continuous function f on [0,1]^d with a modulus of continuity \omega_f(\cdot), the constructive approximation rate is \omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}.
Edit intent: meaning-changed