wanyu/IteraTeR_v2 huggingface dataset:
Original: Backtesting risk measure forecasts requires identifiability (for model calibration and validation) and elicitability (for model comparison).
Revised: Backtesting risk measure forecasts requires identifiability (for model validation) and elicitability (for model comparison).
Edit intent: clarity
Original:  Therefore, we introduce a completely novel notion of multivariate scoring functions equipped with some order, which are therefore called multi-objective scores.
Revised:  Therefore, we introduce a completely novel notion of multi-objective scores.
Edit intent: coherence
Original: An idealized demonstrator of an civil aircraft wing spoiler in scale 1:2 is developed to evaluate strain-based structural health monitoring (SHM) methods under realistic loading conditions.
Revised: An idealized 1:2 is developed to evaluate strain-based structural health monitoring (SHM) methods under realistic loading conditions.
Edit intent: clarity
Original:  Thus, using the developed idealized spoiler demonstrator strain-based SHM systems can be tested under loading conditions similar to realistic operational loads by significantly reduced test effort and costs  .
Revised:  Thus, using the developed idealized demonstrator, strain-based SHM systems can be tested under loading conditions similar to realistic operational loads by significantly reduced test effort and costs .
Edit intent: clarity
Original:  Thus, using the developed idealized spoiler demonstrator strain-based SHM systems can be tested under loading conditions similar to realistic operational loads by significantly reduced test effort and costs  .
Revised:  Thus, using the developed idealized spoiler demonstrator strain-based SHM systems can be tested under conditions that reflect operational aerodynamic pressure loads, while the test effort and costs .
Edit intent: clarity
Original:  Although it is common to address these issues through pre-training components of RNN-T with other criteria or frame-wise alignment guidance, the alignment is not easily available in end-to-end manner.
Revised:  Although it is common to address this issue through pre-training components of RNN-T with other criteria or frame-wise alignment guidance, the alignment is not easily available in end-to-end manner.
Edit intent: fluency
Original:  To our best knowledge, this is the first work to simulate HMM-based frame-wise label using CTC model  . Experiments conducted on LibriSpeech and MLS English tasks show the proposed pre-training procedure, compared with random initialization, reduces the WER by relatively 5\%~11\% and the emission latency by 60 ms.
Revised:  To our best knowledge, this is the first work to simulate HMM-based frame-wise label using CTC model for pre-training . Experiments conducted on LibriSpeech and MLS English tasks show the proposed pre-training procedure, compared with random initialization, reduces the WER by relatively 5\%~11\% and the emission latency by 60 ms.
Edit intent: clarity
Original: As the role of online platforms has become increasingly prominent for communication , toxic behaviors, such as cyberbullying and harassment , have been rampant in the last decade.
Revised: Online platforms have become an increasingly prominent means of communication. Despite the obvious benefits to the expanded distribution of content, the last decade has resulted in disturbing toxic communication , toxic behaviors, such as cyberbullying and harassment , have been rampant in the last decade.
Edit intent: coherence
Original: As the role of online platforms has become increasingly prominent for communication , toxic behaviors, such as cyberbullying and harassment , have been rampant in the last decade.
Revised: As the role of online platforms has become increasingly prominent for communication , such as cyberbullying and harassment , have been rampant in the last decade.
Edit intent: clarity
Original:  On the other hand, online toxicity is  multi-dimensional and sensitive in nature, which makes its detection challenging.
Revised:  On the other hand, online toxicity is challenging due to its multi-dimensional and sensitive in nature, which makes its detection challenging.
Edit intent: clarity
Original:  As the impact of exposure to online toxicity can lead to serious implications for individuals and communities , reliable models and algorithms are required for detecting and understanding such communications .
Revised:  As the impact of exposure to online toxicity can have serious social consequences , reliable models and algorithms are required for detecting and understanding such communications .
Edit intent: clarity
Original:  As the impact of exposure to online toxicity can lead to serious implications for individuals and communities , reliable models and algorithms are required for detecting and understanding such communications .
Revised:  As the impact of exposure to online toxicity can lead to serious implications for individuals and communities , reliable models and algorithms are required for detecting and analyzing such communication across the vast and growing space of social media .
Edit intent: clarity
Original:  In this paper, we define toxicity to provide a foundation drawing social theories .
Revised:  In this paper, we draw on psychological and social theory to define toxicity .
Edit intent: clarity
Original:  SGs/VSGs are able to provide inertia while droop-controlled inverters are generally inertia-less .
Revised:  SGs/VSGs are able to provide inertia while droop-controlled inverters are generally inertialess .
Edit intent: fluency
Original:  More importantly , two significant differences from conventional power systems are discovered.
Revised:  Moreover , two significant differences from conventional power systems are discovered.
Edit intent: coherence
Original:  The second is that the frequency state variable exhibits an abrupt change at the moment of fault disturbances, thus impacting the post-fault initial-state location and stability assessment.
Revised:  The second is that the frequency state variable exhibits a jump at the moment of fault disturbances, thus impacting the post-fault initial-state location and stability assessment.
Edit intent: clarity
Original:  The underlying mechanism behind the two new characteristics is clarified and the impact on the transient stability  is analyzed and verified in this paper .
Revised:  The underlying mechanism behind the two new characteristics is clarified and the impact on the transient stability performance is analyzed and verified in this paper .
Edit intent: clarity
Original:  The underlying mechanism behind the two new characteristics is clarified and the impact on the transient stability  is analyzed and verified in this paper .
Revised:  The underlying mechanism behind the two new characteristics is clarified and the impact on the transient stability is analyzed and verified .
Edit intent: coherence
Original:  The findings provide new insights into the stability of power systems hosting heterogeneous devices.
Revised:  The findings provide new insights into transient stability of power systems hosting heterogeneous devices.
Edit intent: clarity
Original:  While having many attractive features, state-of-the-art PINNs surrogate a specific realization of a PDE system and hence are problem-specific.
Revised:  While state-of-the-art PINNs have many attractive features, state-of-the-art PINNs surrogate a specific realization of a PDE system and hence are problem-specific.
Edit intent: fluency
Original:  While having many attractive features, state-of-the-art PINNs surrogate a specific realization of a PDE system and hence are problem-specific.
Revised:  While having many attractive features, they approximate a specific realization of a PDE system and hence are problem-specific.
Edit intent: clarity
Original:  That is,  each time the boundary conditions  and domain shape change, the model needs to be re-trained .
Revised:  That is, the model needs to be re-trained each time the boundary conditions and domain shape change, the model needs to be re-trained .
Edit intent: fluency
Original:  That is,  each time the boundary conditions  and domain shape change, the model needs to be re-trained .
Revised:  That is, each time the boundary conditions and domain shape /size change .
Edit intent: clarity
Original:  This limitation prohibits the application of PINNs in realistic or large-scale engineering problems especially since the costs and efforts associated with their training are considerable.
Revised:  This limitation prohibits the application of PINNs to realistic or large-scale engineering problems especially since the costs and efforts associated with their training are considerable.
Edit intent: fluency
Original:  This paper introduces a transferable framework for solving boundary value problems (BVPs) via deep neural networks which can be trained once and used forever for various domains of unseen sizes, shapes, and boundary conditions.
Revised:  We introduce a transferable framework for solving boundary value problems (BVPs) via deep neural networks which can be trained once and used forever for various domains of unseen sizes, shapes, and boundary conditions.
Edit intent: coherence
Original:  First, we introducegenomic flow network (GFNet), a neural network that can infer the solution of a BVP across arbitrary boundary conditions on a small square domain called genome .
Revised:  First, we introducegenomic flow network (GFNet), a neural network that can infer the solution of a BVP across arbitrary BCson a small square domain called genome .
Edit intent: clarity
Original:  First, we introducegenomic flow network (GFNet), a neural network that can infer the solution of a BVP across arbitrary boundary conditions on a small square domain called genome .
Revised:  First, we introducegenomic flow network (GFNet), a neural network that can infer the solution of a BVP across arbitrary boundary conditions on a small square domain called genome .
Edit intent: fluency
Original:  Then, we propose\emph{mosaic flow (MF) predictor, a novel iterative algorithm that assembles or stitches the GFNet's inferences to obtain the solution of BVPs on unseen, large domains while preserving the spatial regularity of the solution.
Revised:  Then, we \emph{mosaic flow (MF) predictor, a novel iterative algorithm that assembles or stitches the GFNet's inferences to obtain the solution of BVPs on unseen, large domains while preserving the spatial regularity of the solution.
Edit intent: clarity
Original:  Then, we propose\emph{mosaic flow (MF) predictor, a novel iterative algorithm that assembles or stitches the GFNet's inferences to obtain the solution of BVPs on unseen, large domains while preserving the spatial regularity of the solution.
Revised:  Then, we propose\emph{proposed mosaic flow (MF) predictor, a novel iterative algorithm that assembles or stitches the GFNet's inferences to obtain the solution of BVPs on unseen, large domains while preserving the spatial regularity of the solution.
Edit intent: fluency
Original:  Then, we propose\emph{mosaic flow (MF) predictor, a novel iterative algorithm that assembles or stitches the GFNet's inferences to obtain the solution of BVPs on unseen, large domains while preserving the spatial regularity of the solution.
Revised:  Then, we propose\emph{mosaic flow (MF) predictor, a novel iterative algorithm that assembles the GFNet's inferences to obtain the solution of BVPs on unseen, large domains while preserving the spatial regularity of the solution.
Edit intent: clarity
Original:  We demonstrate that our framework can estimate the solution of Laplace and Navier-Stokes equations in domains of unseen shapes and boundary conditions that are, respectively, 1200 and 12 times larger than the domains where training is performed .
Revised:  We demonstrate that our framework can estimate the solution of Laplace and Navier-Stokes equations in domains of unseen shapes and BCs that are, respectively, 1200 and 12 times larger than the domains where training is performed .
Edit intent: clarity
Original:  We demonstrate that our framework can estimate the solution of Laplace and Navier-Stokes equations in domains of unseen shapes and boundary conditions that are, respectively, 1200 and 12 times larger than the domains where training is performed .
Revised:  We demonstrate that our framework can estimate the solution of Laplace and Navier-Stokes equations in domains of unseen shapes and boundary conditions that are, respectively, 1200 and 12 times larger than the training domains .
Edit intent: clarity
Original:  Since our framework eliminates the need to re-train  , it demonstrates up to 3 orders of magnitude speedups compared to the state-of-the-art.
Revised:  Since our framework eliminates the need to re-train , it demonstrates up to 3 orders-of-magnitude speedups compared to the state-of-the-art.
Edit intent: fluency
Original:  Building on the Bayesian epidemiological model of Anderson et al. (2020), we propose a hierarchical  Bayesian model with time-varying regional parameters to account for the relative reduction in contact due to physical distancing and increased testing from March to December of 2020.
Revised:  Building on the Bayesian epidemiological model of Anderson et al. (2020), we propose a hierarchical Bayesian model with time-varying regional parameters between March to December of 2020.
Edit intent: clarity
Original:  The first approach reduces number of binary variables, which is related to the collision avoidance of delivery objects, by reformulating them as continuous variables with additional hard constraints.
Revised:  The first approach reduces number of binary variables, which are related to the collision avoidance of delivery objects, by reformulating them as continuous variables with additional hard constraints.
Edit intent: fluency
Original:  These hard constraints maintain consistency by conditionally propagating binary values, which is related to the carry action state and collision avoidance of robots, to the reformulated continuous variables.
Revised:  These hard constraints maintain consistency by conditionally propagating binary values, which are related to the carry action state and collision avoidance of robots, to the reformulated continuous variables.
Edit intent: fluency
Original:  We demonstrate the effectiveness of the proposed models with a modern MILP solver.
Revised:  We demonstrate the effectiveness of the proposed approaches with a modern MILP solver.
Edit intent: clarity
Original:  It is much more challenging compared to conventional 2D case due to its inherent ill-posed property, which is mainly reflected on the lack of depth information.
Revised:  It is much more challenging than conventional 2D case due to its inherent ill-posed property, which is mainly reflected on the lack of depth information.
Edit intent: fluency
Original:  It is much more challenging compared to conventional 2D case due to its inherent ill-posed property, which is mainly reflected on the lack of depth information.
Revised:  It is much more challenging compared to conventional 2D cases due to its inherent ill-posed property, which is mainly reflected on the lack of depth information.
Edit intent: fluency
Original:  It is much more challenging compared to conventional 2D case due to its inherent ill-posed property, which is mainly reflected on the lack of depth information.
Revised:  It is much more challenging compared to conventional 2D case due to its inherent ill-posed property, which is mainly reflected in the lack of depth information.
Edit intent: fluency
Original:  In this technical report , we study this problem with a practice built on  fully convolutional single-stage detector and propose a general framework FCOS3D.
Revised:  In this paper , we study this problem with a practice built on fully convolutional single-stage detector and propose a general framework FCOS3D.
Edit intent: clarity
Original:  In this technical report , we study this problem with a practice built on  fully convolutional single-stage detector and propose a general framework FCOS3D.
Revised:  In this technical report , we study this problem with a practice built on a fully convolutional single-stage detector and propose a general framework FCOS3D.
Edit intent: fluency
Original:  Specifically, we first transform the commonly defined 7-DoF 3D targets to  image domain and decouple it as 2D and 3D attributes.
Revised:  Specifically, we first transform the commonly defined 7-DoF 3D targets to the image domain and decouple it as 2D and 3D attributes.
Edit intent: fluency
Original:  Specifically, we first transform the commonly defined 7-DoF 3D targets to  image domain and decouple it as 2D and 3D attributes.
Revised:  Specifically, we first transform the commonly defined 7-DoF 3D targets to image domain and decouple them as 2D and 3D attributes.
Edit intent: fluency
Original:  Then the objects are distributed to different feature levels with the consideration of their 2D scales and assigned only according to the projected 3D-center for  training procedure.
Revised:  Then the objects are distributed to different feature levels with consideration of their 2D scales and assigned only according to the projected 3D-center for training procedure.
Edit intent: fluency
Original:  Then the objects are distributed to different feature levels with the consideration of their 2D scales and assigned only according to the projected 3D-center for  training procedure.
Revised:  Then the objects are distributed to different feature levels with the consideration of their 2D scales and assigned only according to the projected 3D-center for the training procedure.
Edit intent: fluency
Original:  Furthermore, the center-ness is redefined with a 2D Guassian distribution based on the 3D-center to fit the 3D target formulation.
Revised:  Furthermore, the center-ness is redefined with a 2D Gaussian distribution based on the 3D-center to fit the 3D target formulation.
Edit intent: fluency
Original:  The functional magnetic resonance imaging data of fifteen participants were collected while they were listening to two complete breakup songs and two complete rebellious songs currently popular in Taiwan.
Revised:  Functional magnetic resonance imaging data of fifteen participants were collected while they were listening to two complete breakup songs and two complete rebellious songs currently popular in Taiwan.
Edit intent: clarity
Original:  Experiments on the LibriSpeech dataset show that gender recognition and speaker verification can be reduced to a random guess, protecting against classification-based attacks , while maintaining the utility of the signal for speech recognition .
Revised:  Experiments on the LibriSpeech dataset show that gender recognition and speaker verification can be reduced to a random guess, protecting against classification-based attacks .
Edit intent: coherence
Original: The Python language has extension frameworks such as NumPy and SciPy for providing functionality involving numerical arrays and scientific computing .
Revised: While the Python language has extension frameworks such as NumPy and SciPy for providing functionality involving numerical arrays and scientific computing .
Edit intent: coherence
Original:  Objects for matrices and cubes are provided , as well as over 200 associated functions for manipulating data stored in the objects.
Revised:  Objects for matrices and cubes , as well as over 200 associated functions for manipulating data stored in the objects.
Edit intent: clarity
Original: PyArmadillo is a linear algebra and scientific computing library for the Python language, with the aim of closely mirroring the programming interface of the widely used Armadillo C++ library, which in turn is deliberately similar to Matlab.
Revised: PyArmadillo is a linear algebra library for the Python language, with the aim of closely mirroring the programming interface of the widely used Armadillo C++ library, which in turn is deliberately similar to Matlab.
Edit intent: clarity
Original:  The maximum average degree mad(G) of a graph G is the maximum of the average degrees of all the subgraphs of G. We denote M_k the class of signed graphs with maximum average degree less than k and P_g the class of planar signed graphs with girth at least g.
Revised:  The maximum average degree mad(G) of a graph G is the maximum of the average degrees of all the subgraphs of G. We denote M_k the class of signed graphs with maximum average degree less than k and P_g the class of planar signed graphs of girth at least g.
Edit intent: clarity
Original:  We prove: \chi_s(\mathcal{M_{8{3}}) = 4 which implies \chi_s(P_{8}) = 4, } \chi_s(P_{7}) \le 5, \chi_s(M_{17{5}}) \le 10 which implies \chi_s(P_{5}) \le 10, \chi_s(M_{4-8{q+3}}) \le q+1 with q a prime power congruent to 1 modulo 4.
Revised:  We prove: _{8{3}}) = 4 which implies \chi_s(P_{8}) = 4, } \chi_s(P_{7}) \le 5, \chi_s(M_{17{5}}) \le 10 which implies \chi_s(P_{5}) \le 10, \chi_s(M_{4-8{q+3}}) \le q+1 with q a prime power congruent to 1 modulo 4.
Edit intent: coherence
Original:  Furthermore, we study a modality-agnostic  single-backbone Transformer by sharing weights among the three modalities.
Revised:  Furthermore, we study a modality-agnostic , single-backbone Transformer by sharing weights among the three modalities.
Edit intent: fluency
Original:  In this work, we present a hyperbolicity-preserving stochastic Galerkin formulation by carefully selecting the polynomial chaos approximations to the nonlinear terms of (q^x)^2/h, (q^y)^2/h, and (q^xq^y)/h in the shallow water equations.
Revised:  In this work, we present a hyperbolicity-preserving stochastic Galerkin formulation by carefully selecting the polynomial chaos approximations to the nonlinear terms in the shallow water equations.
Edit intent: clarity
Original:  To mitigate this issue, CNNs are often accompanied by techniques like transfer, multi-task, or ensemble learning that often provide high accuracy at the cost of high computational complexity.
Revised:  To mitigate this issue, CNNs are often accompanied by techniques like transfer, multi-task, or ensemble learning that often provide high accuracy at the cost of increased computational complexity.
Edit intent: clarity
Original:  In this work, we propose a Part-based Ensemble Transfer Learning network , which models how humans recognize facial expressions by correlating the spatial orientation pattern of the facial features with a specific expression.
Revised:  In this work, we propose a Part-based Ensemble Transfer Learning network that models how humans recognize facial expressions by correlating the spatial orientation pattern of the facial features with a specific expression.
Edit intent: coherence
Original:  It consists of 5 sub-networks, in which each sub-network performs transfer learning from one of the five subsets of facial landmarks: eyebrows, eyes, nose, mouth, or jaw to expression classification.
Revised:  It consists of 5 sub-networks, and each sub-network performs transfer learning from one of the five subsets of facial landmarks: eyebrows, eyes, nose, mouth, or jaw to expression classification.
Edit intent: coherence
Original:  Additionally, it consists of a total of 1.65M model parameters and requires only 3.28 \times 10^{6 real-time deployment.
Revised:  Additionally, it consists of a total of 1.65M model parameters , ensuring computational efficiency during training and real-time deployment.
Edit intent: clarity
Original:  The class of algebraic combinatory models admits finite axiomatisation with seven closed equations, and it can be characterised as the retracts of combinatory models.
Revised:  The class of algebraic combinatory models admits finite axiomatisation with seven closed equations, and they are shown to be exactly the retracts of combinatory models.
Edit intent: clarity
Original:  Lambda algebras are then characterised as algebraic combinatory models which are stable , and there is a canonical construction of a lambda algebra from an algebraic combinatory model.
Revised:  Lambda algebras are then characterised as algebraic combinatory models which are stable ; moreover there is a canonical construction of a lambda algebra from an algebraic combinatory model.
Edit intent: coherence