wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this work, we use Graph Neural Networks to model the 11,790 URN samples across 30 cities worldwide and use its predictability to define the spatial homogeneity .
Revised:  In this work, we use Graph Neural Networks to model the 11,790 URN samples across 30 cities worldwide to measure the spatial homogeneity .
Edit intent: clarity
Original:  Robust and easy-to-implement estimators for cardinality and joint quantities, as well as the ability to use SetSketch for similarity search, enable versatile applications.
Revised:  Fast, robust, and easy-to-implement estimators for cardinality and joint quantities, as well as the ability to use SetSketch for similarity search, enable versatile applications.
Edit intent: clarity
Original:  A resulting inter-personal equilibrium is then a Nash equilibrium between the two players, each of whom is restricted to her intra-personal equilibria .
Revised:  A resulting inter-personal equilibrium is then a Nash equilibrium between the two players, each of whom employs her best intra-personal equilibria .
Edit intent: clarity
Original:  While the propagation of conspiracy theories through social mediahas been studied, previous work has not evaluated their diffusion through text generation .
Revised:  While previous studies examine conspiracy theories in the context of social media, they have not evaluated their diffusion through text generation .
Edit intent: clarity
Original:  In this work, we investigate the propensity for language models to generate conspiracy theory text.
Revised:  In this work, we investigate the capability of language models to generate conspiracy theory text.
Edit intent: clarity
Original:  We also introduce a new dataset consisting of conspiracy theory topics , machine-generated conspiracy theories , and human-written conspiracy theories.
Revised:  We also introduce a new dataset consisting of conspiracy theory topics and machine-generated conspiracy theories , and human-written conspiracy theories.
Edit intent: fluency
Original:  The data-dependent nature of gradient-weighted normalization leads to powerful properties such as better stability against the perturbation and a sort of consistency in the scaling of input points, which cannot be attained by the conventional coefficient normalization.
Revised:  The data-dependent nature of gradient-weighted normalization leads to powerful properties such as better stability against perturbation and consistency in the scaling of input points, which cannot be attained by the conventional coefficient normalization.
Edit intent: coherence
Original:  We also provide an upper bound of the coefficient norm with respect to the gradient-weighted norm, which allows us to discuss the approximate border bases with gradient-weighted normalization from the perspective of the coefficient norm.
Revised:  We also provide an upper bound on the coefficient norm with respect to the gradient-weighted norm, which allows us to discuss the approximate border bases with gradient-weighted normalization from the perspective of the coefficient norm.
Edit intent: clarity
Original:  We also provide an upper bound of the coefficient norm with respect to the gradient-weighted norm, which allows us to discuss the approximate border bases with gradient-weighted normalization from the perspective of the coefficient norm.
Revised:  We also provide an upper bound of the coefficient norm based on the gradient-weighted norm, which allows us to discuss the approximate border bases with gradient-weighted normalization from the perspective of the coefficient norm.
Edit intent: clarity
Original: Broader transparency in descriptions of and communication regarding AI systems is widely considered desirable.
Revised: Broader disclosive transparency-truth and clarity in communication regarding the function of AI systems-is widely considered desirable.
Edit intent: clarity
Original:  However, previous work has suggested that a trade-off exists between greater system transparency and user confusion, where ` too much information' clouds a reader's understanding of what a system description means.
Revised:  However, previous work has suggested that a trade-off exists between greater disclosive transparency and user confusion, where ` too much information' clouds a reader's understanding of what a system description means.
Edit intent: clarity
Original:  However, previous work has suggested that a trade-off exists between greater system transparency and user confusion, where ` too much information' clouds a reader's understanding of what a system description means.
Revised:  However, previous work has suggested that a trade-off exists between greater system transparency and user confusion, where ' too much information' clouds a reader's understanding of what a system description means.
Edit intent: fluency
Original:  Previous work has suggested that a trade-off exists between greater disclosive transparencyand user confusion , where 'too much information' clouds a reader's understanding of what a system description means.
Revised:  This is problematic, as previous work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect , where 'too much information' clouds a reader's understanding of what a system description means.
Edit intent: clarity
Original:  Finally, we apply these metrics to study the relationships between transparency, confusion, and user perceptions in a corpus of NLP demo abstracts .
Revised:  Finally, we apply these metrics to study the relationships between transparency, confusion, and user perceptions in a corpus of real NLP system descriptions .
Edit intent: clarity
Original:  This is problematic, as previous work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where ' too much information ' clouds a reader's understanding of what a system description means.
Revised:  This is problematic, as previous work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where " too much information ' clouds a reader's understanding of what a system description means.
Edit intent: fluency
Original:  This is problematic, as previous work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where ' too much information ' clouds a reader's understanding of what a system description means.
Revised:  This is problematic, as previous work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where ' too much information " clouds a reader's understanding of what a system description means.
Edit intent: fluency
Original: Multi-hop reasoning (i.e., reasoning across two or more documents) at scale is a key step toward NLP models that can exhibit broad world knowledgeby leveraging large collections of documents.
Revised: Multi-hop reasoning (i.e., reasoning across two or more documents) is a key step toward NLP models that can exhibit broad world knowledgeby leveraging large collections of documents.
Edit intent: clarity
Original: Multi-hop reasoning (i.e., reasoning across two or more documents) at scale is a key step toward NLP models that can exhibit broad world knowledgeby leveraging large collections of documents.
Revised: Multi-hop reasoning (i.e., reasoning across two or more documents) at scale is a key ingredient for NLP models that can exhibit broad world knowledgeby leveraging large collections of documents.
Edit intent: clarity
Original:  We propose Baleen, a system that improves the robustness and scalability of multi-hop reasoning over current approaches.
Revised:  We propose Baleen, a system that improves the accuracy and robustness of multi-hop reasoning over current approaches.
Edit intent: clarity
Original:  We evaluate Baleen on the new many-hop claim verification dataset HoVer , establishing state-of-the-art performance.
Revised:  We evaluate Baleen on the new many-hop claim verification , establishing state-of-the-art performance.
Edit intent: clarity
Original:  To enable a capacity-optimal network, a novel formulation of random channel access with NOMA is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
Revised:  To enable a capacity-optimal network, a novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
Edit intent: clarity
Original:  To this end, two algorithms, i.e., a centralized model-based algorithm and a scalable distributed model-free algorithm, are proposed to optimally tune the transmission probabilities of IoT devices to attain the maximum capacity.
Revised:  To this end, two algorithms, i.e., a centralized model-based algorithm and a scalable distributed model-free algorithm, are proposed to optimally tune the transmission probabilities of IoT devices and attain the maximum capacity.
Edit intent: coherence
Original:  Finally, we apply the algorithm to the neuronal network of C. elegans in one dataset, and find its all cliques and some cavities of different orders therein, providing a basis for further mathematical analysis and computation of the structure and function of the C. elegans neuronal network.
Revised:  Finally, we apply the algorithm to the neuronal network of C. elegans in one dataset, and find all of its cliques and some cavities of different orders therein, providing a basis for further mathematical analysis and computation of the structure and function of the C. elegans neuronal network.
Edit intent: fluency
Original: Complex networks have complete subgraphs such as nodes, edges, triangles, etc., referred to as  cliques of different orders.
Revised: Complex networks contain complete subgraphs such as nodes, edges, triangles, etc., referred to as cliques of different orders.
Edit intent: clarity
Original:  Notably, cavities consisting of higher-order cliques have been found playing an important role in brain functions.
Revised:  Notably, cavities consisting of higher-order cliques play an important role in brain functions.
Edit intent: clarity
Original:  Since searching for the maximum clique in a large network is an NP-complete problem, we propose using k-core decomposition to determine the computability of a given network subject to limited computing resources .
Revised:  Since searching for maximum cliques is an NP-complete problem, we propose using k-core decomposition to determine the computability of a given network subject to limited computing resources .
Edit intent: clarity
Original:  Since searching for the maximum clique in a large network is an NP-complete problem, we propose using k-core decomposition to determine the computability of a given network subject to limited computing resources .
Revised:  Since searching for the maximum clique in a large network is an NP-complete problem, we use k-core decomposition to determine the computability of a given network subject to limited computing resources .
Edit intent: clarity
Original:  Since searching for the maximum clique in a large network is an NP-complete problem, we propose using k-core decomposition to determine the computability of a given network subject to limited computing resources .
Revised:  Since searching for the maximum clique in a large network is an NP-complete problem, we propose using k-core decomposition to determine the computability of a given network .
Edit intent: clarity
Original:  For a computable network, we design a search  algorithm for finding cliques of different orders, which also provides the Euler characteristic number.
Revised:  For a computable network, we design a search algorithm for finding cliques of different orders, obtaining also the Euler characteristic number.
Edit intent: clarity
Original:  Then, we compute the Betti number by using the ranks of the boundary matrices of adjacent cliques.
Revised:  Then, we compute the Betti numbers by using the ranks of the boundary matrices of adjacent cliques.
Edit intent: fluency
Original:  Then, we compute the Betti number by using the ranks of the boundary matrices of adjacent cliques.
Revised:  Then, we compute the Betti number by using the ranks of boundary matrices of adjacent cliques.
Edit intent: fluency
Original:  Finally, we apply the algorithm to the neuronal network of C. elegans in one dataset, and find all of its cliques and some cavities of different orders therein , providing a basis for further mathematical analysis and computation of the structure and function of the C. elegans neuronal network .
Revised:  Finally, we apply the algorithm to the neuronal network of C. elegans in one dataset, and find all of its cliques and some cavities of different orders , providing a basis for further mathematical analysis and computation of the structure and function of the C. elegans neuronal network .
Edit intent: clarity
Original:  Finally, we apply the algorithm to the neuronal network of C. elegans in one dataset, and find all of its cliques and some cavities of different orders therein , providing a basis for further mathematical analysis and computation of the structure and function of the C. elegans neuronal network .
Revised:  Finally, we apply the algorithm to the neuronal network of C. elegans in one dataset, and find all of its cliques and some cavities of different orders therein , providing a basis for further mathematical analysis and computation of its structure and function of the C. elegans neuronal network .
Edit intent: fluency
Original:  Finally, we apply the algorithm to the neuronal network of C. elegans in one dataset, and find all of its cliques and some cavities of different orders therein , providing a basis for further mathematical analysis and computation of the structure and function of the C. elegans neuronal network .
Revised:  Finally, we apply the algorithm to the neuronal network of C. elegans in one dataset, and find all of its cliques and some cavities of different orders therein , providing a basis for further mathematical analysis and computation of the structure and function .
Edit intent: fluency
Original: Koopman mode decomposition and tensor component analysis are two tools that decompose high dimensional data sets into low dimensional modes that capture  relevant features and/or dynamics.
Revised: Koopman mode decomposition and tensor component analysis are two tools that decompose high dimensional data sets into low dimensional modes that capture the most relevant features and/or dynamics.
Edit intent: clarity
Original: We consider communications  between an encoder and a decoder , viewed as two decision makers, which have subjective beliefs on the probabilistic model of the source distribution.
Revised: We consider communications between an encoder and a decoder which have subjective beliefs on the probabilistic model of the source distribution.
Edit intent: clarity
Original: We consider communications  between an encoder and a decoder , viewed as two decision makers, which have subjective beliefs on the probabilistic model of the source distribution.
Revised: We consider communications between an encoder and a decoder , viewed as two decision makers, which have subjective probabilistic models on the source distribution.
Edit intent: clarity
Original:  Even though the decision makers employ the same cost function,  induced expected costs are different from the perspective of the encoder and decoder due to their subjective probabilistic beliefs , which requires a game theoretic treatment.
Revised:  Although they consider the same cost function, induced expected costs are different from the perspective of the encoder and decoder due to their subjective probabilistic beliefs , which requires a game theoretic treatment.
Edit intent: clarity
Original:  Even though the decision makers employ the same cost function,  induced expected costs are different from the perspective of the encoder and decoder due to their subjective probabilistic beliefs , which requires a game theoretic treatment.
Revised:  Even though the decision makers employ the same cost function, the induced expected costs are different from the perspective of the encoder and decoder due to their subjective probabilistic beliefs , which requires a game theoretic treatment.
Edit intent: fluency
Original:  Even though the decision makers employ the same cost function,  induced expected costs are different from the perspective of the encoder and decoder due to their subjective probabilistic beliefs , which requires a game theoretic treatment.
Revised:  Even though the decision makers employ the same cost function, induced expected costs are misaligned due to their subjective probabilistic beliefs , which requires a game theoretic treatment.
Edit intent: clarity
Original:  We show that the Stackelberg equilibrium cost of the encoder is upper semi continuous, under the Wasserstein metric, as encoder's prior approaches to decoder's prior and in the particular case of Gaussian subjective priors it is also lower semi continuous , which proves the robustness of the equilibrium around the team setup.
Revised:  We show that the Stackelberg equilibrium cost of the encoder is upper semi continuous, under the Wasserstein metric, as encoder's prior approaches the decoder's prior and in the particular case of Gaussian subjective priors it is also lower semi continuous , which proves the robustness of the equilibrium around the team setup.
Edit intent: fluency
Original:  We show that the Stackelberg equilibrium cost of the encoder is upper semi continuous, under the Wasserstein metric, as encoder's prior approaches to decoder's prior and in the particular case of Gaussian subjective priors it is also lower semi continuous , which proves the robustness of the equilibrium around the team setup.
Revised:  We show that the Stackelberg equilibrium cost of the encoder is upper semi continuous, under the Wasserstein metric, as encoder's prior approaches to decoder's prior , and it is also lower semi continuous , which proves the robustness of the equilibrium around the team setup.
Edit intent: coherence
Original:  Furthermore , we show that there exist fully informative Nash and Stackelberg equilibria for the cheap talk problem (i.e., no additive noise term and no power constraint at the encoder) as in the team theoretic setup under an absolute continuity condition.
Revised:  Furthermore , we show existence of fully informative Nash and Stackelberg equilibria for the cheap talk problem (i.e., no additive noise term and no power constraint at the encoder) as in the team theoretic setup under an absolute continuity condition.
Edit intent: coherence
Original:  Furthermore , we show that there exist fully informative Nash and Stackelberg equilibria for the cheap talk problem (i.e., no additive noise term and no power constraint at the encoder) as in the team theoretic setup under an absolute continuity condition.
Revised:  Furthermore , we show that there exist fully informative Nash and Stackelberg equilibria for the cheap talk problem under an absolute continuity condition.
Edit intent: coherence
Original: Timeliness is an emerging requirement for  Internet of Things (IoT) applications.
Revised: Timeliness is an emerging requirement for many Internet of Things (IoT) applications.
Edit intent: clarity
Original:  In IoT networks, where large-volume of nodes are distributed, severe interference may incur  which causes age of information (AoI) degradation.
Revised:  In IoT networks, where a large-number of nodes are distributed, severe interference may incur which causes age of information (AoI) degradation.
Edit intent: clarity
Original:  In this paper, we aim to optimize the age of information (AoI ) in random access Poisson networks.
Revised:  In this paper, we aim to optimize the AoI in random access Poisson networks.
Edit intent: clarity
Original:  By considering spatio-temporal interaction amongst the transmitters, an expression of the peak AoI is derived, based on explicit exponentions of the optimal peak AoI and the corresponding optimal system parameters including the packet arrival rate and the channel access probability are further derived.
Revised:  By taking into account the spatio-temporal interaction amongst the transmitters, an expression of the peak AoI is derived, based on explicit exponentions of the optimal peak AoI and the corresponding optimal system parameters including the packet arrival rate and the channel access probability are further derived.
Edit intent: clarity
Original:  By considering spatio-temporal interaction amongst the transmitters, an expression of the peak AoI is derived, based on explicit exponentions of the optimal peak AoI and the corresponding optimal system parameters including the packet arrival rate and the channel access probability are further derived.
Revised:  By considering spatio-temporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on explicit exponentions of the optimal peak AoI and the corresponding optimal system parameters including the packet arrival rate and the channel access probability are further derived.
Edit intent: fluency
Original:  By considering spatio-temporal interaction amongst the transmitters, an expression of the peak AoI is derived, based on explicit exponentions of the optimal peak AoI and the corresponding optimal system parameters including the packet arrival rate and the channel access probability are further derived.
Revised:  By considering spatio-temporal interaction amongst the transmitters, an expression of the peak AoI is derived, based on explicit expressions of the optimal peak AoI and the corresponding optimal system parameters including the packet arrival rate and the channel access probability are further derived.
Edit intent: clarity
Original:  It is shown that with a given packet arrival rate (resp. a given channel access probability), the optimal channel access probability (resp. the optimal packet arrival rate), is equal to one under a small node deployment density, and decrease monotonically as the node deployment density increases due to the severe interference caused by spatio-temproal coupling between transmitters.
Revised:  It is shown that with a given packet arrival rate (resp. a given channel access probability), the optimal channel access probability (resp. the optimal packet arrival rate), is equal to one under a small node deployment density, and decrease monotonically as the spatial deployment density increases due to the severe interference caused by spatio-temproal coupling between transmitters.
Edit intent: fluency
Original:  When joint tuning of the packet arrival rate and the channel access probability is performed, the optimal channel access probability is always set to be one.
Revised:  When joint tuning of the packet arrival rate and channel access probability is performed, the optimal channel access probability is always set to be one.
Edit intent: fluency
Original:  We present an efficient algorithm to generate uniform random directions in n-dimensional cones, to aid searching and sampling tasks in high dimensions.
Revised:  We present an efficient algorithm to generate uniformly distributed random directions in n-dimensional cones, to aid searching and sampling tasks in high dimensions.
Edit intent: clarity
Original:  The methodology applies to both stationary and transient as well as linear/nonlinear PDEs.
Revised:  The method applies to both stationary and transient as well as linear/nonlinear PDEs.
Edit intent: clarity
Original:  We describe how the methodology can be implemented as an extension of the existing FEM framework FEniCS and its algorithmic differentiation tool dolfin-adjoint.
Revised:  We describe implementation of the approach as an extension of the existing FEM framework FEniCS and its algorithmic differentiation tool dolfin-adjoint.
Edit intent: clarity