wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In the context of trade liberalisation and market harmonisation in the European markets, accurate price forecasting becomes even more difficult to obtain. The increasing power market integration has complicated the forecasting process, where electricity forecasting requires considering features from both the local market and ever-growing coupling markets.
Revised:  In the context of trade liberalisation and market harmonisation in the European markets, accurate price forecasting becomes difficult for electricity market participants to obtain because electricity forecasting requires considering features from both the local market and ever-growing coupling markets.
Edit intent: clarity
Original:  The results show that feature selection is essential to achieving accurate prediction . Superior feature selection algorithms filter meaningful information, eliminate irrelevant information, and further improve the forecasting accuracy of LSTM-based deep neural networks. The proposed models obtain considerably accurate results .
Revised:  The results show that feature selection is essential to achieving accurate prediction , and features from integrated markets have an impact on prediction. The feature importance analysis implies that the German market has a salient role in the price generation of Nord Pool .
Edit intent: meaning-changed
Original:  Using in-person user study and large-scale randomized experiments across three tasks, we demonstrate a clear difference between in-distribution and out-of-distribution, and observe mixed results for interactive explanations: while interactive explanations improve human perception of AI assistance's usefulness, they may magnify human biases and lead to limited performance improvement.
Revised:  Using virtual pilot studies and large-scale randomized experiments across three tasks, we demonstrate a clear difference between in-distribution and out-of-distribution, and observe mixed results for interactive explanations: while interactive explanations improve human perception of AI assistance's usefulness, they may magnify human biases and lead to limited performance improvement.
Edit intent: meaning-changed
Original:  Using in-person user study and large-scale randomized experiments across three tasks, we demonstrate a clear difference between in-distribution and out-of-distribution, and observe mixed results for interactive explanations: while interactive explanations improve human perception of AI assistance's usefulness, they may magnify human biases and lead to limited performance improvement.
Revised:  Using in-person user study and large-scale randomized experiments across three tasks, we demonstrate a clear difference between in-distribution and out-of-distribution, and observe mixed results for interactive explanations: while interactive explanations improve human perception of AI assistance's usefulness, they may reinforce human biases and lead to limited performance improvement.
Edit intent: clarity
Original:  Since transcribing audio signals is a costly process, Automatic WER Evaluation (e-WER) methods have been developed which attempt to predict the WER of a Speech system by only relying on the transcription and the speech signal features.
Revised:  Since transcribing audio signals is a costly process, Automatic WER Evaluation (e-WER) methods have been developed to automatically predict the WER of a Speech system by only relying on the transcription and the speech signal features.
Edit intent: clarity
Original:  Since transcribing audio signals is a costly process, Automatic WER Evaluation (e-WER) methods have been developed which attempt to predict the WER of a Speech system by only relying on the transcription and the speech signal features.
Revised:  Since transcribing audio signals is a costly process, Automatic WER Evaluation (e-WER) methods have been developed which attempt to predict the WER of a speech system by only relying on the transcription and the speech signal features.
Edit intent: fluency
Original:  Drawing analogies from discrete and graph signal processing, we introduce the building blocks for processing data on simplicial complexes and hypergraphs, two common abstractions of higher-order networks that can incorporate polyadic relationships.
Revised:  Drawing analogies from discrete and graph signal processing, we introduce the building blocks for processing data on simplicial complexes and hypergraphs, two common abstractions of higher-order network abstractions that can incorporate polyadic relationships.
Edit intent: fluency
Original:  We provide basic introductions to simplicial complexes and hypergraphs, making special emphasis on the concepts needed for processing signals on them. Leveraging these concepts , we discuss Fourier analysis, signal denoising, signal interpolation, node embeddings, and non-linear processing through neural networks in these two representations of polyadic relational structures .
Revised:  We provide basic introductions to simplicial complexes and hypergraphs, making special emphasis on the concepts needed for the processing of signals supported on these structures. Specifically , we discuss Fourier analysis, signal denoising, signal interpolation, node embeddings, and non-linear processing through neural networks in these two representations of polyadic relational structures .
Edit intent: clarity
Original:  Leveraging these concepts , we discuss Fourier analysis, signal denoising, signal interpolation, node embeddings, and non-linear processing through neural networks in these two representations of polyadic relational structures .
Revised:  Leveraging these concepts , we discuss Fourier analysis, signal denoising, signal interpolation, node embeddings, and nonlinear processing through neural networks in these two representations of polyadic relational structures .
Edit intent: fluency
Original:  Leveraging these concepts , we discuss Fourier analysis, signal denoising, signal interpolation, node embeddings, and non-linear processing through neural networks in these two representations of polyadic relational structures .
Revised:  Leveraging these concepts , we discuss Fourier analysis, signal denoising, signal interpolation, node embeddings, and non-linear processing through neural networks , using these two higher-order network models .
Edit intent: meaning-changed
Original:  Exponential time integration schemes remove this stability restriction and allow larger time steps. New multistep exponential propagation iterative methods of orders 4, 5 and 6 are introduced.
Revised:  Exponential integration is employed to enable integrations with significantly larger time step sizes and improve the efficiency of the overall time integration. New multistep-type exponential propagation iterative methods of orders 4, 5 and 6 are introduced.
Edit intent: clarity
Original:  New multistep exponential propagation iterative methods of orders 4, 5 and 6 are introduced. The complex-step approximation of the Jacobian is applied to the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm for computing matrix-vector products with \varphi%DIFDELCMD < }%%% -functions.
Revised:  New multistep exponential propagation iterative methods of orders 4, 5 and 6 are constructed and applied to integrate the shallow-water equations in time. These new schemes enable time integration with high-order accuracy but without significant increases in computational time compared to low-order methods. The exponential matrix functions-vector products used in the exponential schemes are approximated using the complex-step approximation of the Jacobian is applied to the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm for computing matrix-vector products with \varphi%DIFDELCMD < }%%% -functions.
Edit intent: meaning-changed
Original:  The complex-step approximation of the Jacobian is applied to the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm for computing matrix-vector products with \varphi%DIFDELCMD < }%%% -functions.
Revised:  The complex-step approximation of the Jacobian in the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm for computing matrix-vector products with \varphi%DIFDELCMD < }%%% -functions.
Edit intent: clarity
Original:  The complex-step approximation of the Jacobian is applied to the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm for computing matrix-vector products with \varphi%DIFDELCMD < }%%% -functions.
Revised:  The complex-step approximation of the Jacobian is applied to the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm \varphi%DIFDELCMD < }%%% -functions.
Edit intent: coherence
Original:  The complex-step approximation of the Jacobian is applied to the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm for computing matrix-vector products with \varphi%DIFDELCMD < }%%% -functions.
Revised:  The complex-step approximation of the Jacobian is applied to the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm for computing matrix-vector products with %DIFDELCMD < }%%% -functions.
Edit intent: coherence
Original:  The complex-step approximation of the Jacobian is applied to the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm for computing matrix-vector products with \varphi%DIFDELCMD < }%%% -functions. Results are evaluated using standard benchmarks .
Revised:  The complex-step approximation of the Jacobian is applied to the Krylov-based KIOPS (Krylov with incomplete orthogonalization procedure solver) algorithm for computing matrix-vector products with \varphi%DIFDELCMD < }%%% . Performance of the new numerical methods is evaluated using a set of standard benchmark tests .
Edit intent: clarity
Original:  CAW-N is evaluated to predict links over 6 real temporal networks and uniformly outperforms previous SOTA methods by averaged 15 \% AUC gain in the inductive setting.
Revised:  CAW-N is evaluated to predict links over 6 real temporal networks and uniformly outperforms previous SOTA methods by averaged 10 \% AUC gain in the inductive setting.
Edit intent: meaning-changed
Original:  Thus , we provide novel insight into the macroscopic effects of short-term synaptic plasticity in spiking neural networks, as well as two different mean-field descriptions for future investigations of such networks.
Revised:  Using bifurcation analysis, we find that QIF networks with pre-synaptic short-term plasticity can express regimes of periodic bursting activity as well as bi-stable regimes. Together , we provide novel insight into the macroscopic effects of short-term synaptic plasticity in spiking neural networks, as well as two different mean-field descriptions for future investigations of such networks.
Edit intent: meaning-changed
Original: We use categorical methods to define a new flavor of Petri nets which could be useful in modelling chemical reactions .
Revised: We use categorical methods to define a new flavor of Petri nets where transitions can only fire a limited number of times, specified by a quantity that we call mana. We do so with chemistry in mind, looking at ways of modelling the behavior of chemical reactions that depend on enzymes to work. We prove that such nets can be either obtained as a result of a comonadic construction, or by enriching them with extra information encoded into a functor. We then use a well-established categorical result to prove that the two constructions are equivalent, and generalize them to the case where the firing of some transitions can "regenerate" the mana of others. This allows us to represent the action of catalysts and also of biochemical processes where the byproducts of some chemical reaction are exactly the enzymes that another reaction needs to work .
Edit intent: meaning-changed
Original: Existing urban navigation algorithms employ integrity monitoring (IM) to mitigate the impact of measurement bias errors and determine system availability when estimating the position of a receiver. Many IM techniques, such as receiver autonomous integrity monitoring (RAIM), utilize measurement residuals associated with a single receiver position to provide integrity. However, identifying a single correct receiver position is often challenging in urban environments due to low satellite visibility and multiple measurements with bias errors. To address this, we propose Particle RAIM as a novel framework for robust state estimation and IM using GNSS and odometry measurements.
Revised: We propose a novel particle filter-based framework for robust state estimation and IM using GNSS and odometry measurements.
Edit intent: meaning-changed
Original:  Particle RAIM integrates residual-based RAIM with a particle filter and Gaussian mixture model likelihood to jointly perform state estimation and fault mitigation using a multimodal probability distribution of the receiver state.
Revised:  Particle RAIM integrates residual-based RAIM with a particle filter and Gaussian mixture model (GMM) for computing particle weights, we develop an expectation-maximization algorithm for jointly inferring GMM weight parameters and a robust particle distribution of receiver state.
Edit intent: meaning-changed
Original:  Our experiments on simulated and real-world data show that Particle RAIM achieves smaller positioning errors as well as smaller probability of false alarm and probability of missed-identification in determining system availability than existing urban localization and IM approachesin challenging environments with a relatively small computation overhead .
Revised:  From the inferred particle distribution and GMM, we determine the navigation system availability based on specified integrity requirements. Unlike traditional residual-based integrity monitoring algorithms that analyze measurement residuals from an estimated receiver position, we incorporate measurement residuals from multiple particles to estimate the receiver position and monitor integrity. Our method achieves small horizontal positioning errors compared to existing filter-based state estimation techniques on challenging simulated and real urban driving scenarios with multiple erroneous measurements. Through multiple simulations, we also show that our method determines system availability with comparable false alarms and missed identifications to best-performing existing integrity monitoring approaches .
Edit intent: meaning-changed
Original:  The experimental results on multiple real-world datasets show that the proposed model outperforms the SOTA methods, and the ablation study verifies the effectiveness of the multi-channel setting and the self-supervised task. 
Revised:  The experimental results on multiple real-world datasets show that the proposed model outperforms the SOTA methods, and the ablation study verifies the effectiveness of the multi-channel setting and the self-supervised task. The implementation of our model is available via URL
Edit intent: meaning-changed
Original:  In this paper, we present GeoSim, a geometry-aware image composition process that synthesizes novel urban driving scenes by augmenting existing images with dynamic objects extracted from other scenes and rendered at novel poses.
Revised:  In this paper, we present GeoSim, a geometry-aware image composition process which synthesizes novel urban driving scenes by augmenting existing images with dynamic objects extracted from other scenes and rendered at novel poses.
Edit intent: fluency
Original:  In this paper, we present GeoSim, a geometry-aware image composition process that synthesizes novel urban driving scenes by augmenting existing images with dynamic objects extracted from other scenes and rendered at novel poses.
Revised:  In this paper, we present GeoSim, a geometry-aware image composition process that synthesizes novel urban driving scenarios by augmenting existing images with dynamic objects extracted from other scenes and rendered at novel poses.
Edit intent: clarity
Original:  During simulation, we perform a novel geometry-aware simulation-by-composition procedure which 1) proposes plausible and realistic object placements into a given scene, 2) renders novel views of dynamic objects from the asset bank, and 3) composes and blends the rendered image segments.
Revised:  During simulation, we perform a novel geometry-aware simulation-by-composition procedure which 1) proposes plausible and realistic object placements into a given scene, 2) render novel views of dynamic objects from the asset bank, and 3) composes and blends the rendered image segments.
Edit intent: fluency
Original:  In this paper, we present LookOut, an approach to jointly perceive the environmentand predict a diverse set of futures from sensor data, estimate their probability, and optimize a contingency plan over these diverse future realizations.
Revised:  In this paper, we present LookOut, a novel autonomy system that perceives the environment, predicts a diverse set of futures from sensor data, estimate their probability, and optimize a contingency plan over these diverse future realizations.
Edit intent: meaning-changed
Original:  In this paper, we present LookOut, an approach to jointly perceive the environmentand predict a diverse set of futures from sensor data, estimate their probability, and optimize a contingency plan over these diverse future realizations.
Revised:  In this paper, we present LookOut, an approach to jointly perceive the environmentand predict a diverse set of futures of how the scene might unroll and estimates the trajectory of the SDV by optimizing a set of contingency plans over these future realizations.
Edit intent: meaning-changed
Original:  In particular, we learn a diverse joint distribution over multi-agent future trajectories in a traffic scene that allows us to cover a wide range of future modes with high sample efficiency while leveraging the expressive power of generative models.
Revised:  In particular, we learn a diverse joint distribution over multi-agent future trajectories in a traffic scene that covers a wide range of future modes with high sample efficiency while leveraging the expressive power of generative models.
Edit intent: coherence
Original:  Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset as well as safer and more comfortable motion plans in long-term closed-loop simulations than current state-of-the-art models.
Revised:  Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset as well as safer and less-conservative motion plans in long-term closed-loop simulations than current state-of-the-art models.
Edit intent: clarity
Original:  Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset as well as safer and more comfortable motion plans in long-term closed-loop simulations than current state-of-the-art models.
Revised:  Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset as well as safer and more comfortable motion plans in long-term closed-loop simulations when compared to current state-of-the-art models.
Edit intent: clarity
Original: This work considers the following extension of the tree-depth problem : for a given input graph G and integers k and b, find a rooted forest F of height at most k and width at most b (defined as the maximum number of vertices allowed in a level of F) such that G is a subgraph of the closure of F . We are interested in the case when G is a line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive b-approximation algorithm.
Revised: The tree-depth problem : for a given input graph G and integers k and b, find a rooted forest F of height at most k and width at most b (defined as the maximum number of vertices allowed in a level of F) such that G is a subgraph of the closure of F . We are interested in the case when G is a line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive b-approximation algorithm.
Edit intent: clarity
Original: This work considers the following extension of the tree-depth problem : for a given input graph G and integers k and b, find a rooted forest F of height at most k and width at most b (defined as the maximum number of vertices allowed in a level of F) such that G is a subgraph of the closure of F . We are interested in the case when G is a line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive b-approximation algorithm.
Revised: This work considers the following extension of the tree-depth problem can be seen as finding an elimination tree of minimum height for a given input graph G and integers k and b, find a rooted forest F of height at most k and width at most b (defined as the maximum number of vertices allowed in a level of F) such that G is a subgraph of the closure of F . We are interested in the case when G is a line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive b-approximation algorithm.
Edit intent: meaning-changed
Original: This work considers the following extension of the tree-depth problem : for a given input graph G and integers k and b, find a rooted forest F of height at most k and width at most b (defined as the maximum number of vertices allowed in a level of F) such that G is a subgraph of the closure of F . We are interested in the case when G is a line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive b-approximation algorithm.
Revised: This work considers the following extension of the tree-depth problem : for a given input graph G . We introduce a bicriteria generalization in which additionally the width of the elimination tree needs to be bounded by some input integer b . We are interested in the case when G is a line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive b-approximation algorithm.
Edit intent: meaning-changed
Original: This work considers the following extension of the tree-depth problem : for a given input graph G and integers k and b, find a rooted forest F of height at most k and width at most b (defined as the maximum number of vertices allowed in a level of F) such that G is a subgraph of the closure of F . We are interested in the case when G is a line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive b-approximation algorithm.
Revised: This work considers the following extension of the tree-depth problem : for a given input graph G and integers k and b, find a rooted forest F of height at most k and width at most b (defined as the maximum number of vertices allowed in a level of F) such that G is a subgraph of the closure of F . We are interested in the case when G is the line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive b-approximation algorithm.
Edit intent: fluency