wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper, we focus on feedforward ReLU networks, and prove fundamental barriers to proving such results beyond depths 4, by reduction to open problems and natural-proof barriers in circuit complexity.
Revised:  In this paper, we focus on feedforward ReLU networks, and prove fundamental barriers to proving such results beyond depth 4, by reduction to open problems and natural-proof barriers in circuit complexity.
Edit intent: fluency
Original: We introduce a deterministic model that partitions the total population into the susceptible, infected, quarantined, and those traced after exposure,  recovered and the deceased.
Revised: We introduce a deterministic model that partitions the total population into the susceptible, infected, quarantined, and those traced after exposure, the recovered and the deceased.
Edit intent: fluency
Original:  We introduce the concept of 'accessible population for transmission of the disease' , which can be a small fraction of the total population, for instance when interventions are in force.
Revised:  We hypothesize 'accessible population for transmission of the disease' , which can be a small fraction of the total population, for instance when interventions are in force.
Edit intent: clarity
Original:  We introduce the concept of 'accessible population for transmission of the disease' , which can be a small fraction of the total population, for instance when interventions are in force.
Revised:  We introduce the concept of 'accessible population for transmission of the disease' to be a small fraction of the total population, for instance when interventions are in force.
Edit intent: coherence
Original:  This assumption , together with the structure of the set of coupled nonlinear ordinary differential equations for the populations, allows us to decouple the equations into just two equations.
Revised:  This hypothesis , together with the structure of the set of coupled nonlinear ordinary differential equations for the populations, allows us to decouple the equations into just two equations.
Edit intent: clarity
Original:  The validity of the 'accessible population' assumption and the efficacy of the reduced logistic model is demonstrated by the ease of fitting the United Kingdom data for the total number of infected cases.
Revised:  The validity of the 'accessible population' hypothesis and the efficacy of the reduced logistic model is demonstrated by the ease of fitting the United Kingdom data for the total number of infected cases.
Edit intent: clarity
Original:  The active infected population of the model is seen to exhibit a turning point around mid-May, suggesting the beginning of a slow-down in the spread of infections.
Revised:  The active infected population of the model is seen to exhibit a turning point , suggests the beginning of a slow-down in the spread of infections.
Edit intent: clarity
Original: This paper concerns optimal error estimates for Legendre polynomial expansions of singular functions whose regularities are naturally characterised by a certain fractional Sobolev-type space introduced in 34, Math.
Revised: This paper concerns optimal error estimates for Legendre polynomial expansions of singular functions whose 34, Math.
Edit intent: clarity
Original: We introduce a new problem that generalizes some previous attempts of covering locally checkable problems under the same umbrella.
Revised: By providing a new framework, we extend previous results on locally checkable problems under the same umbrella.
Edit intent: clarity
Original:  As a consequence, we obtain polynomial-time algorithms to solve,  for bounded treewidth graphs, Grundy domination and double Roman domination  , among other problems for which no such algorithm was previously known.
Revised:  As a consequence, we show how to solve, for bounded treewidth graphs, Grundy domination and double Roman domination , among other problems for which no such algorithm was previously known.
Edit intent: clarity
Original:  As a consequence, we obtain polynomial-time algorithms to solve,  for bounded treewidth graphs, Grundy domination and double Roman domination  , among other problems for which no such algorithm was previously known.
Revised:  As a consequence, we obtain polynomial-time algorithms to solve, for bounded treewidth graphs, double Roman domination , among other problems for which no such algorithm was previously known.
Edit intent: clarity
Original:  Moreover, by proving that (fixed ) powers of bounded degree and bounded treewidth graphs are also bounded degree and bounded treewidth graphs, we can enlarge the family of problems that can be solved in polynomial time for these graph classes, including distance coloring problems and distance domination problems (for bounded distances).
Revised:  Moreover, by proving that fixed powers of bounded degree and bounded treewidth graphs are also bounded degree and bounded treewidth graphs, we can enlarge the family of problems that can be solved in polynomial time for these graph classes, including distance coloring problems and distance domination problems (for bounded distances).
Edit intent: fluency
Original: The X-ray transform models a forward projection operator of image formation, which has been widely used for tomographic image reconstruction.
Edit intent: coherence
Original:  The algorithm is easy to be implemented, and the computational cost is optimal.
Edit intent: coherence
Original:  Moreover, we discuss the intrinsic ambiguities of the problem itself that perhaps happen , and present a solution.
Revised:  Moreover, we discuss the intrinsic ambiguities of the problem itself , and present a solution.
Edit intent: coherence
Original:  The algorithm not only possesses the adaptability with regard to the center position, scale and size of the image, and the scanning geometry as well, but also is quite suited to parallelize with optimality.
Revised:  The algorithm not only possesses the adaptability with regard to the center position, scale and size of the image, but also is quite suited to parallelize with optimality.
Edit intent: coherence
Original:  The algorithm not only possesses the adaptability with regard to the center position, scale and size of the image, and the scanning geometry as well, but also is quite suited to parallelize with optimality.
Revised:  The algorithm not only possesses the adaptability with regard to the center position, scale and size of the image, and the scanning geometry as well, but also is suited to parallelize with optimality.
Edit intent: coherence
Original:  we analyze the collective motion of a population of %DIFDELCMD < {\em %%% Escherichia coli bacteria in response to two stimuli,
Revised:  we analyze the collective motion of a population of %DIFDELCMD < {\em %%% Escherichia coli bacteria in response to two stimuli,
Edit intent: fluency
Original:  Finally, we examine our theory with Monte-Carlo agent-based simulations  .
Revised:  Finally, we examine the theory with Monte-Carlo agent-based simulations .
Edit intent: clarity
Original: We introduce AdaHessian ,
Revised: We introduce ADAHESSIAN ,
Edit intent: fluency
Original:  To address these, we incorporate several novel approaches in AdaHessian , including: (i) a new variance reduction estimate of the Hessian diagonal with low computational overhead;
Revised:  To address these, we incorporate several novel approaches in ADAHESSIAN , including: (i) a new variance reduction estimate of the Hessian diagonal with low computational overhead;
Edit intent: fluency
Original:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on  ResNets20/32 on Cifar10, and 5.55 %DIFDELCMD < \\%%% %DIF < higher accuracy on ImageNet as compared to ADAM ;
Revised:  We show that ADAHESSIAN achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55 %DIFDELCMD < \\%%% %DIF < higher accuracy on ImageNet as compared to ADAM ;
Edit intent: fluency
Original:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on  ResNets20/32 on Cifar10, and 5.55 %DIFDELCMD < \\%%% %DIF < higher accuracy on ImageNet as compared to ADAM ;
Revised:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of Adam . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55 %DIFDELCMD < \\%%% %DIF < higher accuracy on ImageNet as compared to ADAM ;
Edit intent: fluency
Original:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that AdaHessian : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on  ResNets20/32 on Cifar10, and 5.55 %DIFDELCMD < \\%%% %DIF < higher accuracy on ImageNet as compared to ADAM ;
Revised:  We show that AdaHessian achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods, including variants of ADAM . In particular, we perform extensive tests on CV, NLP, and recommendation system tasks and find that ADAHESSIAN : (i) achieves 1.80 %DIFDELCMD < \\%%% %DIF < /1.45\\% higher accuracy on ResNets20/32 on Cifar10, and 5.55 %DIFDELCMD < \\%%% %DIF < higher accuracy on ImageNet as compared to ADAM ;
Edit intent: fluency
Original:  (ii) outperforms ADAMW for transformers by 0.27 /0.33 BLEU score on IWSLT14/WMT14 and 1.8 /1.0 PPL on PTB/Wikitext-103;
Revised:  (ii) outperforms AdamW for transformers by 0.27 /0.33 BLEU score on IWSLT14/WMT14 and 1.8 /1.0 PPL on PTB/Wikitext-103;
Edit intent: fluency
Original:  and (iii)  achieves 0.032 %DIFDELCMD < \\%%% %DIF <  better score than AdaGrad for DLRM on the Criteo Ad Kaggle dataset.
Revised:  (iii) achieves 0.032 %DIFDELCMD < \\%%% %DIF < better score than AdaGrad for DLRM on the Criteo Ad Kaggle dataset.
Edit intent: coherence
Original:  Importantly, we show that the cost per iteration of AdaHessian is comparable to first-order methods, and that it exhibits robustness towards its hyperparameters .
Revised:  Importantly, we show that the cost per iteration of ADAHESSIAN is comparable to first-order methods, and that it exhibits robustness towards its hyperparameters .
Edit intent: fluency
Original:  Importantly, we show that the cost per iteration of AdaHessian is comparable to first-order methods, and that it exhibits robustness towards its hyperparameters .
Revised:  Importantly, we show that the cost per iteration of AdaHessian is comparable to first order methods, and that it exhibits robustness towards its hyperparameters .
Edit intent: fluency
Original: Stabilized Runge-Kutta (aka Chebyshev) methods are especially efficient for the numerical solution of large systems of stiff  differential equations because they are fully explicit ;
Revised: Stabilized Runge-Kutta methods are especially efficient for the numerical solution of large systems of stiff differential equations because they are fully explicit ;
Edit intent: coherence
Original:  For semi-discrete parabolic (or diffusion dominated) problems, for instance, stabilized Runge-Kutta methods overcome the stringent stability condition of standard methods without sacrificing explicitness.
Revised:  For semi-discrete parabolic problems, for instance, stabilized Runge-Kutta methods overcome the stringent stability condition of standard methods without sacrificing explicitness.
Edit intent: clarity
Original:  However, when much of the stiffness is only induced by a few components, as in the presence of spatially local mesh refinement, their efficiency deteriorates.
Revised:  However, when stiffness is only induced by a few components, as in the presence of spatially local mesh refinement, their efficiency deteriorates.
Edit intent: clarity
Original:  Although political affinity is thought to affect the consumption of misinformation, there is very few evidence that political orientation of the information source plays a role during the propagation of conspiracy information.
Revised:  Although political affinity is thought to affect the consumption of misinformation, there is very little evidence that political orientation of the information source plays a role during the propagation of conspiracy information.
Edit intent: fluency
Original:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Revised:  2 %DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Edit intent: clarity
Original:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Revised:  2 , stochastic elements%DIFDELCMD < [%%% , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Edit intent: coherence
Original:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Revised:  2 , stochastic elements%DIFDELCMD < [%%% 3 %DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Edit intent: clarity
Original:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Revised:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Edit intent: coherence
Original:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Revised:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 %DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Edit intent: clarity
Original:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Revised:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Edit intent: coherence
Original:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Revised:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Edit intent: coherence
Original:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 , or photonics%DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Revised:  2 , stochastic elements%DIFDELCMD < [%%% 3 , nonlinear dissipative dynamics%DIFDELCMD < [%%% 4-8 , in-memory operations%DIFDELCMD < [%%% 9 , 10 %DIFDELCMD < [%%% 11-14 . A paradigmatic optimization problem is finding low-energy states in classical spin systems with fully-random interactions 15-18 . To date no alternative computing platform can address such spin-glass problems on a large scale.
Edit intent: coherence
Original:  Our results  provide a general route towards large-scale computing that exploits speed, parallelism and coherence of light 19-23 .
Revised:  Our results provide a general route towards large-scale computing that exploits speed, parallelism and coherence of light .
Edit intent: clarity
Original: Time-Scale Modification (TSM) is a well-researched field , however no effective objective measure of quality exists.
Revised: Time Scale Modification (TSM) is a well-researched field , however no effective objective measure of quality exists.
Edit intent: fluency
Original: Time-Scale Modification (TSM) is a well-researched field , however no effective objective measure of quality exists.
Revised: Time-Scale Modification (TSM) is a well-researched field ; however, no effective objective measure of quality exists.
Edit intent: fluency
Original:  This paper details the creation, subjective evaluation  and analysis of a dataset , for use in the development of an objective measure of quality for TSM.
Revised:  This paper details the creation, subjective evaluation , and analysis of a dataset , for use in the development of an objective measure of quality for TSM.
Edit intent: fluency
Original:  This paper details the creation, subjective evaluation  and analysis of a dataset , for use in the development of an objective measure of quality for TSM.
Revised:  This paper details the creation, subjective evaluation and analysis of a dataset for use in the development of an objective measure of quality for TSM.
Edit intent: fluency
Original:  Comprising two parts, the training component contains 88 source files processed using six TSM methods at 10 time-scales , while the testing component contains 20 source files processed using three additional methods at four time-scales .
Revised:  Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time-scales , while the testing component contains 20 source files processed using three additional methods at four time-scales .
Edit intent: fluency
Original:  Comprising two parts, the training component contains 88 source files processed using six TSM methods at 10 time-scales , while the testing component contains 20 source files processed using three additional methods at four time-scales .
Revised:  Comprising two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales , while the testing component contains 20 source files processed using three additional methods at four time-scales .
Edit intent: fluency
Original:  Comprising two parts, the training component contains 88 source files processed using six TSM methods at 10 time-scales , while the testing component contains 20 source files processed using three additional methods at four time-scales .
Revised:  Comprising two parts, the training component contains 88 source files processed using six TSM methods at 10 time-scales , while the testing component contains 20 source files processed using three additional methods at four time scales .
Edit intent: fluency
Original:  The source material contains speech, solo harmonic and percussive instruments, sound effects  and a range of music genres.
Revised:  The source material contains speech, solo harmonic and percussive instruments, sound effects , and a range of music genres.
Edit intent: fluency
Original:   42 , 529 ratings were collected from 633 sessions using laboratory and remote collection methods.
Revised:  Ratings ( 42 , 529 ratings were collected from 633 sessions using laboratory and remote collection methods.
Edit intent: coherence
Original:   42 , 529 ratings were collected from 633 sessions using laboratory and remote collection methods.
Revised:  42 529 ratings were collected from 633 sessions using laboratory and remote collection methods.
Edit intent: fluency
Original:   42 , 529 ratings were collected from 633 sessions using laboratory and remote collection methods.
Revised:  42 , 529 ) were collected from 633 sessions using laboratory and remote collection methods.
Edit intent: clarity