wanyu/IteraTeR_v2 huggingface dataset:
Original:  By focusing on the Compute-and-Forward framework, we show that through carefully choosing the IRS parameters, such a scenario's computation rate will be significantly improved.
Revised:  Considering the compute-and-forward framework, we show that through carefully choosing the IRS parameters, such a scenario's computation rate will be significantly improved.
Edit intent: coherence
Original:  By focusing on the Compute-and-Forward framework, we show that through carefully choosing the IRS parameters, such a scenario's computation rate will be significantly improved.
Revised:  By focusing on the Compute-and-Forward framework, we show that through carefully designing the IRS parameters, such a scenario's computation rate will be significantly improved.
Edit intent: clarity
Original:  By focusing on the Compute-and-Forward framework, we show that through carefully choosing the IRS parameters, such a scenario's computation rate will be significantly improved.
Revised:  By focusing on the Compute-and-Forward framework, we show that through carefully choosing the IRS parameters, such a scenario's computation rate can be significantly improved.
Edit intent: clarity
Original:  More specifically, we formulate an optimization problem  to maximize the computation rate and tackle the problem via an alternating optimization (AO) approach .
Revised:  More specifically, we formulate an optimization problem which aims to maximize the computation rate and tackle the problem via an alternating optimization (AO) approach .
Edit intent: clarity
Original:  Our resultsconfirm the usefulness of IRS technology for  future wireless networks -- such as 6G -- with massive computation requirements  .
Revised:  Our resultsconfirm the usefulness of IRS technology for future wireless networks with massive computation requirements .
Edit intent: clarity
Original:  Our resultsconfirm the usefulness of IRS technology for  future wireless networks -- such as 6G -- with massive computation requirements  .
Revised:  Our resultsconfirm the usefulness of IRS technology for future wireless networks -- such as 6G -- with massive computation requirements , such as 6G .
Edit intent: fluency
Original: High-order numerical methods are applied to the shallow-water equations on the sphere  .
Revised: A novel numerical approach to solving the shallow-water equations on the sphere .
Edit intent: clarity
Original:  The spatial discretization is done with the direct flux reconstruction method, which is an alternative formulation to the Discontinuous Galerkin approach.
Revised:  The spatial discretization is done with the direct flux reconstruction method, which is an alternative formulation to the discontinuous Galerkin approach.
Edit intent: fluency
Original:  It is well known that the time step of traditional explicit methods is limited by the phase speed of the fastest waves.
Revised:  It is well known that the time step of traditional explicit methods is limited by the phase velocity of the fastest waves.
Edit intent: clarity
Original:  Sobolev spaces are the correct framework in which most  partial derivative equations may be statedand solved .
Revised:  Sobolev spaces are the mathematical framework in which most partial derivative equations may be statedand solved .
Edit intent: clarity
Original:  Sobolev spaces are the correct framework in which most  partial derivative equations may be statedand solved .
Revised:  Sobolev spaces are the correct framework in which most partial derivative equations are stated, and where solutions are sought .
Edit intent: clarity
Original:  In recent works [ RR9334 ], we presented the mathematical theory needed to establish the development of compilers and tools for DAE-based physical modeling languages on solid mathematical grounds.
Revised:  In recent works [ RR-9334 ], we presented the mathematical theory needed to establish the development of compilers and tools for DAE-based physical modeling languages on solid mathematical grounds.
Edit intent: fluency
Original: At the core of this analysis sits the so-called structural analysis , whose purpose, at compile time, is to either identify under- and over-specified subsystems (if any), or to rewrite the model in a form amenable of existing DAE solvers, including the handling of mode change events.
Revised: At the core of this analysis sits the so-called *structural analysis* , whose purpose, at compile time, is to either identify under- and over-specified subsystems (if any), or to rewrite the model in a form amenable of existing DAE solvers, including the handling of mode change events.
Edit intent: fluency
Original:  The notion of "structure" collects, for each mode and mode change event, the variables and equations involved, as well as the latent equations (additional equations redundant with the system), needed to prepare the code submitted to the solver.
Revised:  The notion of "structure" collects, for each mode and mode change event, the variables and equations involved, as well as the *latent equations* (additional equations redundant with the system), needed to prepare the code submitted to the solver.
Edit intent: fluency
Original:  The notion of DAE index (the minimal number of times any equation has to be possibly differentiated) is part of this structural analysis.
Revised:  The notion of DAE *index* (the minimal number of times any equation has to be possibly differentiated) is part of this structural analysis.
Edit intent: clarity
Original:  This report complements [ RR9334 ] by collecting all the needed background on structural analysis.
Revised:  This report complements [ RR-9334 ] by collecting all the needed background on structural analysis.
Edit intent: fluency
Original:  This is achieved by introducing a relation \simeq, defined over a revised presentation of Parigot's \lambda\mu-calculus we dub \Lambda M.
Revised:  This is achieved by introducing a relation \simeq, defined over a revised presentation of \lambda\mu-calculus we dub \Lambda M.
Edit intent: clarity
Original:  Our result builds on three main ingredients which guide our semantical development: (1) factorization of Parigot's \lambda\mu-reduction into multiplicative and exponential steps by means of explicit operators, (2) adaptation of Laurent's original \simeq_\sigma-equivalence to \Lambda M, and (3) interpretation of \Lambda M into Laurent's polarized proof-nets (PPN).
Revised:  More precisely, we first identify the reasons behind Laurent's original \simeq_\sigma-equivalence to \Lambda M, and (3) interpretation of \Lambda M into Laurent's polarized proof-nets (PPN).
Edit intent: clarity
Original:  In particular, the modeled microstructure evolution, in form of diffusion-based and non-diffusional transformations, is a pure consequence of energy and mobility competitions among the different specifies , without the need for heuristic transformation criteria as often applied in existing models.
Revised:  In particular, the modeled microstructure evolution, in form of diffusion-based and non-diffusional transformations, is a pure consequence of energy and mobility competitions among the different species , without the need for heuristic transformation criteria as often applied in existing models.
Edit intent: fluency
Original:  In this work, we present a machine-learning method to solve this equation as an alternative to ABCs.
Revised:  In this work, we present a machine-learning method to solve this type of equations as an alternative to ABCs.
Edit intent: clarity
Original:  The accuracy is tested for both the second-order wave equation and the Schrodinger equation, including the nonlinear Schrodinger  .
Revised:  The accuracy is tested for both the second-order wave equation and the Schrodinger equation, including the nonlinear Schrodinger .
Edit intent: fluency
Original:  The accuracy is tested for both the second-order wave equation and the Schrodinger equation, including the nonlinear Schrodinger  .
Revised:  The accuracy is tested for both the second-order wave equation and the Schrodinger equation, including the nonlinear Schrodinger equation .
Edit intent: fluency
Original:  Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods . Simulators using explicit time-stepping scheme require tiny time steps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method to solve the expensive linearized dynamics.
Revised:  Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods : Simulators using explicit time-stepping scheme require tiny time steps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method to solve the expensive linearized dynamics.
Edit intent: fluency
Original:  Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods . Simulators using explicit time-stepping scheme require tiny time steps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method to solve the expensive linearized dynamics.
Revised:  Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods . Simulators using explicit time-stepping scheme require tiny time steps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method and solving the expensive linearized dynamics.
Edit intent: coherence
Original:  Inspired by Projective Dynamics (PD), we present DiffPD , an efficient differentiable soft-body simulator  with implicit time integration.
Revised:  Inspired by Projective Dynamics (PD), we present Differentiable Projective Dynamics (DiffPD) , an efficient differentiable soft-body simulator with implicit time integration.
Edit intent: clarity
Original:  We evaluate the performance of DiffPD and observe a speedup of 4-19 times  compared to the standard Newton's method in various applications including system identification, inverse design problems, trajectory optimization, and closed-loop control  .
Revised:  We evaluate the performance of DiffPD and observe it is 4-19 times compared to the standard Newton's method in various applications including system identification, inverse design problems, trajectory optimization, and closed-loop control .
Edit intent: clarity
Original:  Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods: Simulators using explicit time-stepping scheme require tiny time steps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method and solving the expensive linearized dynamics.
Revised:  Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods: Simulators using explicit time-stepping schemes require tiny time steps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method and solving the expensive linearized dynamics.
Edit intent: fluency
Original:  We evaluate the performance of DiffPD and observe it is 4-19 times faster compared to the standard Newton's method in various applications including system identification, inverse design problems, trajectory optimization, and closed-loop control.
Revised:  We evaluate the performance of DiffPD and observe it is 4-19 times faster compared with the standard Newton's method in various applications including system identification, inverse design problems, trajectory optimization, and closed-loop control.
Edit intent: fluency
Original:  We also apply DiffPD in a  real-to-sim  example with contact and collisions and show its capability of reconstructing a digital twin of real-world scenes.
Revised:  We also apply DiffPD in a reality-to-simulation ( real-to-sim example with contact and collisions and show its capability of reconstructing a digital twin of real-world scenes.
Edit intent: clarity
Original:  We also apply DiffPD in a  real-to-sim  example with contact and collisions and show its capability of reconstructing a digital twin of real-world scenes.
Revised:  We also apply DiffPD in a real-to-sim ) example with contact and collisions and show its capability of reconstructing a digital twin of real-world scenes.
Edit intent: fluency
Original: Low-dimensional descriptions of spiking neural network dynamics are an effective tool for bridging different scales of organization of brain structure and function.
Revised: Low-dimensional descriptions of neural network dynamics are an effective tool for bridging different scales of organization of brain structure and function.
Edit intent: clarity
Original:  Various hybrid system formalisms have been used to model and analyse this combination of dynamics, ranging from mathematical descriptions, e.g., using impulsive differential equations and switching, to automata-theoretic and language-based approaches.
Revised:  Various hybrid system formalisms have been used to model and analyze this combination of dynamics, ranging from mathematical descriptions, e.g., using impulsive differential equations and switching, to automata-theoretic and language-based approaches.
Edit intent: fluency
Original: Session types denote message protocols between concurrent processes, allowing a type-safe formalization of inter-process communication.
Revised: Session types denote message protocols between concurrent processes, allowing a type-safe expression of inter-process communication.
Edit intent: clarity
Original:  Although previous works demonstrate a well-defined notion of subtyping where processes have different perceptions of the protocol, these formulations were limited to linear session types where each channel of communication has a unique provider and client.
Revised:  Although previous work demonstrate a well-defined notion of subtyping where processes have different perceptions of the protocol, these formulations were limited to linear session types where each channel of communication has a unique provider and client.
Edit intent: fluency
Original:  In our work , we extend these previous formulations into the shared session type setting where channels can now have multiple clients instead of a single client.
Revised:  In this paper , we extend these previous formulations into the shared session type setting where channels can now have multiple clients instead of a single client.
Edit intent: clarity
Original:  In our work , we extend these previous formulations into the shared session type setting where channels can now have multiple clients instead of a single client.
Revised:  In our work , we extend subtyping to shared session types where channels can now have multiple clients instead of a single client.
Edit intent: clarity
Original:  We demonstrate that this allows shared sessions to be released at a different type, allowing the encoding of phases in a shared protocol to be manifest in the session type .
Revised:  We demonstrate that this generalization can statically capture protocol requirements that span multiple phases of interactions of a client with a shared service provider, something not possible in prior proposals. Moreover, the phases are manifest in the session type .
Edit intent: clarity
Original: A common approach to the automatic detection of mispronunciation works by recognizing the phonemes produced by a student and comparing it to the expected pronunciation of a native speaker.
Revised: A common approach to the automatic detection of mispronunciation in language learning is to recognize the phonemes produced by a student and comparing it to the expected pronunciation of a native speaker.
Edit intent: clarity
Original: A common approach to the automatic detection of mispronunciation works by recognizing the phonemes produced by a student and comparing it to the expected pronunciation of a native speaker.
Revised: A common approach to the automatic detection of mispronunciation works by recognizing the phonemes produced by a student and compare it to the expected pronunciation of a native speaker.
Edit intent: fluency
Original:  These assumptions do not always hold  which can result in a significant amount of false mispronunciation alarms.
Revised:  These assumptions do not always hold , which can result in a significant amount of false mispronunciation alarms.
Edit intent: fluency
Original: Multichannel blind  source separation aims to recover the latent sources from their multichannel mixture without priors.
Revised: Multichannel blind audio source separation aims to recover the latent sources from their multichannel mixture without priors.
Edit intent: clarity
Original:  A state-of-art blind source separation method called independent low-rank matrix analysis (ILRMA) unified independent vector analysis (IVA) and nonnegative matrix factorization (NMF).
Revised:  A state-of-art blind source separation method , named independent low-rank matrix analysis (ILRMA) unified independent vector analysis (IVA) and nonnegative matrix factorization (NMF).
Edit intent: fluency
Original:  A state-of-art blind source separation method called independent low-rank matrix analysis (ILRMA) unified independent vector analysis (IVA) and nonnegative matrix factorization (NMF).
Revised:  A state-of-art blind source separation method called independent low-rank matrix analysis (ILRMA) , unifies independent vector analysis (IVA) and nonnegative matrix factorization (NMF).
Edit intent: fluency
Original:  However, speech spectra modeled by NMF may not find a compact representation and it may not guarantee that each source is identifiable .
Revised:  However, the spectra matrix produced from NMF may not find a compact representation and it may not guarantee that each source is identifiable .
Edit intent: clarity
Original:  However, speech spectra modeled by NMF may not find a compact representation and it may not guarantee that each source is identifiable .
Revised:  However, speech spectra modeled by NMF may not find a compact spectral basis. It may not guarantee that each source is identifiable .
Edit intent: clarity
Original:  However, speech spectra modeled by NMF may not find a compact representation and it may not guarantee that each source is identifiable .
Revised:  However, speech spectra modeled by NMF may not find a compact representation and it may not guarantee the identifiability of each source as well .
Edit intent: clarity
Original:  To address the problem, here we propose a modified blind source separation method that enhances the identifiability of the source model  .
Revised:  To address this problem, here we propose a modified blind source separation method that enhances the identifiability of the source model .
Edit intent: coherence
Original:  To address the problem, here we propose a modified blind source separation method that enhances the identifiability of the source model  .
Revised:  To address the problem, here we propose to enhance the identifiability of the source model .
Edit intent: clarity
Original:  It combines ILRMA with penalty item of volume constraint.
Edit intent: coherence
Original:  The proposed method is optimized by standard majorization-minimization framework based multiplication updating rule , which ensures the stability of  convergence.
Revised:  The proposed method is optimized by standard majorization-minimization framework based multiplication updating rule , which ensures the stability of the convergence.
Edit intent: fluency
Original:  That is because there are usually many noises in the setting of long-form text matching , and it is difficult for existing semantic text matching to capture the key matching signals from this noisy information .
Revised:  That is because there are many contexts in long-form text matching , and it is difficult for existing semantic text matching to capture the key matching signals from this noisy information .
Edit intent: clarity
Original:  That is because there are usually many noises in the setting of long-form text matching , and it is difficult for existing semantic text matching to capture the key matching signals from this noisy information .
Revised:  That is because there are usually many noises in the setting of long-form text matching , and it is difficult for existing models to capture the key matching signals from this noisy information .
Edit intent: clarity
Original:  That is because there are usually many noises in the setting of long-form text matching , and it is difficult for existing semantic text matching to capture the key matching signals from this noisy information .
Revised:  That is because there are usually many noises in the setting of long-form text matching , and it is difficult for existing semantic text matching to capture the key matching signals from such noisy data .
Edit intent: clarity
Original:  Besides, these models are computationally expensive because they simply use all textual data indiscriminately in the matching process .
Revised:  Besides, these models are computationally expensive for simply use all textual data indiscriminately in the matching process .
Edit intent: clarity
Original:  Besides, these models are computationally expensive because they simply use all textual data indiscriminately in the matching process .
Revised:  Besides, these models are computationally expensive because they simply use all textual data indiscriminately .
Edit intent: clarity
Original:  To tackle the effectiveness and efficiency problem, we propose a novel hierarchical noise filtering model in this paper , namely Match-Ignition.
Revised:  To tackle the effectiveness and efficiency problem, we propose a novel hierarchical noise filtering model , namely Match-Ignition.
Edit intent: coherence
Original:  The basic idea is to plug the well-known PageRank algorithm into the Transformer, to identify and filter both sentence and word level noisy information in the matching process.
Revised:  The main idea is to plug the well-known PageRank algorithm into the Transformer, to identify and filter both sentence and word level noisy information in the matching process.
Edit intent: coherence
Original:  Noisy sentences are usually easy to detect because the sentence is the basic unit of a long-form text , so we directly use PageRank to filter such information , based on a sentence similarity graph.
Revised:  Noisy sentences are usually easy to detect because the sentence is the basic unit of a long-form text , so we directly use PageRank to filter such information based on a sentence similarity graph.
Edit intent: fluency
Original:  While words need to rely on their contexts to express concrete meanings, so we propose to jointly learn the filtering process and the matching process, to reflect the contextual dependencies between words . Specifically, a word graph is first built based on the attention scores in each self-attention block of Transformer, and keywords are then selected by applying PageRank on this graph.
Revised:  While words need to rely on their contexts to express concrete meanings, so we propose to jointly learn the filtering and matching process, to reflect the contextual dependencies between words . Specifically, a word graph is first built based on the attention scores in each self-attention block of Transformer, and keywords are then selected by applying PageRank on this graph.
Edit intent: fluency