wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Through the proposed methodology we have defined the core of a DSS for designing novel incentive mechanisms by operating on the policies in terms of QoI and user reputation scores .
Revised:  Through the proposed methodology we have defined the core of a DSS for designing novel incentive mechanisms by operating on the policies in terms of users' reputation scores, that also incorporate users' behaviours other than quality and quantity of contributions. Experimentally, we have considered the Waze dataset on vehicular traffic monitoring application and derived the disbursement of incentives comparing our method with baselines. Results demonstrate that our methodology, which also includes the local (microscopic) spatio-temporal distribution of behaviours, is able to better discriminate users' behaviours. This multi-scale characterisation of users represents a novel research direction and paves the way for novel policies on mobile crowdsensing systems .
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Original:  We show that considering deep uncertainties surrounding flood hazards, the discount rate, the house lifetime, and the fragility increases the economically optimal house elevation to values well above the recommendation by FEMA . An improved decision-support for home-owners has the potential to drastically improve decisions and outcomes .
Revised:  We show that considering deep uncertainties surrounding flood hazards, the discount rate, the house lifetime, and the fragility increases the economically optimal house elevation to values well above FEMA recommendation .
Edit intent: coherence
Original:  A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections ( i.e., Multiblock-VIOP or MB-VIOP) is explained in this paper.
Revised: For multivariate data analysis involving only two input matrices, the previously published methods for variable influence on projection (e.g., VIPOPLS or VIPO2PLS) are widely used for variable selection purposes, including (i) variable importance assessment, (ii) dimensionality reduction of big data and (iii) interpretation enhancement of PLS, OPLS and O2PLS models. For multiblock analysis, the OnPLS models find relationships among multiple data matrices by calculating latent variables; however, a method for improving the interpretation of these latent variables by assessing the importance of the input variables was not available up to now. A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections ( i.e., Multiblock-VIOP or MB-VIOP) is explained in this paper.
Edit intent: coherence
Original:  Multiblock-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of a large number of data matrices according to their importance for both simplification and interpretation of the total OnPLS model, and also of the unique, local and global model components separately.
Revised:  MB-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of a large number of data matrices according to their importance for both simplification and interpretation of the total OnPLS model, and also of the unique, local and global model components separately.
Edit intent: clarity
Original:  Multiblock-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of a large number of data matrices according to their importance for both simplification and interpretation of the total OnPLS model, and also of the unique, local and global model components separately.
Revised:  Multiblock-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of more than two data matrices according to their importance for both simplification and interpretation of the total OnPLS model, and also of the unique, local and global model components separately.
Edit intent: meaning-changed
Original:  Multiblock-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of a large number of data matrices according to their importance for both simplification and interpretation of the total OnPLS model, and also of the unique, local and global model components separately.
Revised:  Multiblock-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of a large number of data matrices according to their importance for both simplification and interpretation of the total multiblock model, and also of the unique, local and global model components separately.
Edit intent: clarity
Original:  The previously published OnPLS algorithm finds the relationships among multiple datamatrices (blocks) by calculating latent variables ; however, a method for improving the interpretation of these latent variables by assessing the importance of the input variables was missing.
Revised:  MB-VIOP has been tested using three multiblock datasets. MB-VIOP assesses the variable importance in any type of data. MB-VIOP connects the input variables of different data matrices according to their relevance for the interpretation of these latent variables by assessing the importance of the input variables was missing.
Edit intent: coherence
Original:  however, a method for improving the interpretation of these latent variables by assessing the importance of the input variables was missing. In this paper, we provide evidence for the usefulness, the efficiency and the reliability of MB-VIOP  for the abovementioned purposes by means of three examples, (i) a synthetic four-block dataset, (ii) a real three-block omics dataset related to plant sciences, and (iii) a real six-block dataset related to the food industry .
Revised:  however, a method for improving the interpretation of each latent variable, yielding enhanced interpretability for each OnPLS model component. Besides, MB-VIOP for the abovementioned purposes by means of three examples, (i) a synthetic four-block dataset, (ii) a real three-block omics dataset related to plant sciences, and (iii) a real six-block dataset related to the food industry .
Edit intent: clarity
Original:  In this paper, we provide evidence for the usefulness, the efficiency and the reliability of MB-VIOP  for the abovementioned purposes by means of three examples, (i) a synthetic four-block dataset, (ii) a real three-block omics dataset related to plant sciences, and (iii) a real six-block dataset related to the food industry .
Revised:  In this paper, we provide evidence for the usefulness, the efficiency and the reliability of MB-VIOP can deal with strong overlapping of types of variation, as well as with many data blocks with very different dimensionality. The ability of MB-VIOP for the abovementioned purposes by means of three examples, (i) a synthetic four-block dataset, (ii) a real three-block omics dataset related to plant sciences, and (iii) a real six-block dataset related to the food industry .
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Original:  In this paper, we provide evidence for the usefulness, the efficiency and the reliability of MB-VIOP  for the abovementioned purposes by means of three examples, (i) a synthetic four-block dataset, (ii) a real three-block omics dataset related to plant sciences, and (iii) a real six-block dataset related to the food industry .
Revised:  In this paper, we provide evidence for the usefulness, the efficiency and the reliability of MB-VIOP for generating dimensionality reduced models with high interpretability makes this method ideal for big data mining, multi-omics data integration and any study that requires exploration and interpretation of large streams of data .
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Original: In this paper, we demonstrate the interpretative power of partial least square models and how robust interpretability can lead to new quantitative insights.
Revised: In this paper, we ask if it is possible to increase the interpretability in multivariate analysis by aligning and projecting covariates onto comparative subspaces. We demonstrate our method as well as the interpretative power of partial least square models and how robust interpretability can lead to new quantitative insights.
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Original:  We present hierarchical pathway enrichment results stemming from aligned p-values, which are compared with results derived from enrichment analysis, as an external validation of our method.
Revised:  The applicability of this approach within life science is also demonstrated by applying it to three use cases of publically available datasets. Further we present hierarchical pathway enrichment results stemming from aligned p-values, which are compared with results derived from enrichment analysis, as an external validation of our method.
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Original:  The applicability of this approach is further demonstrated by applying it to both publically available microarray data from multiple sclerosis and diabetes patients as well as RNA sequencing data from breast cancer patients .
Revised:  We find that the method can uncover known results from genomics for all of the studied use cases, i.e. microarray data from multiple sclerosis and diabetes patients as well as RNA sequencing data from breast cancer patients .
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Original:  The applicability of this approach is further demonstrated by applying it to both publically available microarray data from multiple sclerosis and diabetes patients as well as RNA sequencing data from breast cancer patients . Our method confers more global, physiologically relevant, knowledge about the datasets than traditional and more myopic methods .
Revised:  The applicability of this approach is further demonstrated by applying it to both publically available microarray data from multiple sclerosis and diabetes patients as well as RNA sequencing data from breast cancer patients .
Edit intent: coherence
Original:  The dynamics is analysed by means of time series of selected topological indicators of the MSTs  . Our empirical results show the usefulness of the proposed approach to the analysis of systemic risk in the insurance sector.
Revised:  The dynamics is analysed by means of time series of selected topological indicators of the MSTs in the years 2005-2019 . Our empirical results show the usefulness of the proposed approach to the analysis of systemic risk in the insurance sector.
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Original:  However some applications like automatic journalism or information retrieval from social media, require more information about entities mentioned in groups of social media posts . Conventional methods applied to structured and well typed documents provide acceptable results while compared to new user generated media, these methods are not satisfactory. One valuable piece of information about an entity is the related image to the text. Combining this multimodal data reduces ambiguity and provides wider information about the entities mentioned. In order to address this issue, we propose a novel deep learning approach utilizing multimodal deep learning. Our solution is able to provide more accurate resultson named entity recognition task. Experimental results, namely the precision, recall and F1 score metrics show the superiority of our work compared to other state-of-the-art NER solutions.
Revised:  We propose two novel deep learning approaches utilizing multimodal deep learning and Transformers. Both of our approaches use image features from short social media posts to provide better results on the NER task. On the first approach, we extract image features using InceptionV3 and use fusion to combine textual and image features. This presents more reliable name entity recognition when the images related to the entities are provided by the user. On the second approach, we use image features combined with text and feed it into a BERT like Transformer. The experimental results, namely, the precision, recall and F1 score metrics show the superiority of our work compared to other state-of-the-art NER solutions.
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Original:  Each city (vertex) represents the collection of all firms within that city. Edge weights are modeled by the total amount of wire transfersthat arise due to business transactions between firms localized at different cities. The rationale is that the more they transact with each other, the more dependent they become in the economic sense.
Revised:  Each city (vertex) represents the collection of all firms in that location and links denote intercity wire transfers.
Edit intent: clarity
Original:  Our results suggest that recessions have a large impact on the trade network with meaningful and heterogeneous economic consequences across municipalities  .
Revised:  Our results suggest that recessions have a large impact on the trade network with meaningful and heterogeneous economic consequences across municipalities . We run econometric exercises and find that courts efficiency plays a dual role. From the customer perspective, it plays an important role in reducing contractual frictions as it increases economic transactions between different cities. From the supplier perspective, cities that are central suppliers to the supply chain seem to use courts inefficiency as a lawsuit barrier from their customers .
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Original:  To accommodate model ambiguity we introduce appropriate notions of history-consistent updating and of transition consistency for risk mappings on nested probability spaces .
Revised:  We confirm that the Markov property holds for the standard measures of risk used in practice such as Value at Risk and Average Value at Risk. We analyse the dual representation for convex Markovian risk mappings and a representation in terms of their acceptance sets. The Markov property is formulated in several equivalent versions including a strong version, opening up additional risk-sensitive optimisation problems such as optimal stopping with exercise lag and optimal prediction. We demonstrate how such problems can be reduced to a risk-sensitive optimal stopping problem with intermediate costs, and derive the dynamic programming equations for the latter. Finally, we show how our results can be extended to partially observable Markov processes .
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Original:  A swap portfolio is a portfolio in which each one of a pair of assets is held at a weight proportional to the market value of the other . A reverse-weighted index portfolio is a portfolio in which the weights of the market portfolio are swapped pairwise by rank.
Revised:  Functionally generated portfolios are portfolios for which the logarithmic return relative to the market portfolio can be decomposed into a function of the market weights and a process of locally finite variation, and this decomposition is convenient for characterizing the long-term behavior of the portfolio. A permutation-weighted portfolio is a portfolio in which each one of a pair of assets is held at a weight proportional to the market value of the other . A reverse-weighted index portfolio is a portfolio in which the weights of the market portfolio are swapped pairwise by rank.
Edit intent: meaning-changed
Original:  A swap portfolio is a portfolio in which each one of a pair of assets is held at a weight proportional to the market value of the other . A reverse-weighted index portfolio is a portfolio in which the weights of the market portfolio are swapped pairwise by rank.
Revised:  A swap portfolio is a portfolio in which the assets are held at weights proportional to a permutation of their market values, and such a portfolio is functionally generated only for markets with two assets (except for the identity permutation) . A reverse-weighted index portfolio is a portfolio in which the weights of the market portfolio are swapped pairwise by rank.
Edit intent: meaning-changed
Original:  A swap portfolio is a portfolio in which each one of a pair of assets is held at a weight proportional to the market value of the other . A reverse-weighted index portfolio is a portfolio in which the weights of the market portfolio are swapped pairwise by rank.
Revised:  A swap portfolio is a portfolio in which each one of a pair of assets is held at a weight proportional to the market value of the other . A reverse-weighted portfolio is a portfolio in which the weights of the market portfolio are swapped pairwise by rank.
Edit intent: clarity
Original:  A swap portfolio is a portfolio in which each one of a pair of assets is held at a weight proportional to the market value of the other . A reverse-weighted index portfolio is a portfolio in which the weights of the market portfolio are swapped pairwise by rank. Swap portfolios are functionally generated, and in a coherent market they have higher asymptotic growth rates than the market portfolio. Although reverse-weighted portfolios with two or more pairs of assets are not functionally generated, in a market represented by a first-order model with symmetric variances, they will grow faster than the market portfolio .
Revised:  A swap portfolio is a portfolio in which each one of a pair of assets is held at a weight proportional to the market value of the other . A reverse-weighted index portfolio is a portfolio in which the asset with the greatest market weight is assigned the smallest market weight, the asset with the second-largest weight is assigned the second-smallest, and so forth. Although the reverse-weighted portfolios with two or more pairs of assets are not functionally generated, in a market represented by a first-order model with symmetric variances, they will grow faster than the market portfolio .
Edit intent: clarity
Original:  Although reverse-weighted portfolios with two or more pairs of assets are not functionally generated, in a market represented by a first-order model with symmetric variances, they will grow faster than the market portfolio .
Revised:  Although reverse-weighted portfolio in a market with four or more assets is not functionally generated, in a market represented by a first-order model with symmetric variances, they will grow faster than the market portfolio .
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Original:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Revised:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Edit intent: coherence
Original:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Revised:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% %DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Edit intent: fluency
Original:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Revised:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
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Original:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Revised:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% 8.86-52.00\% and 1.84-2.94\%, for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Edit intent: meaning-changed
Original:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Revised:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction across five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Edit intent: fluency
Original:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Revised:  Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 15 and 108 milliseconds .
Edit intent: meaning-changed
Original:  We extend this approach to a system consisting of R receptors and L ligands to derive the probability density function p(r;R,L) , to find r bound receptor-ligand complexes at thermal equilibrium.
Revised:  We extend this approach to a system consisting of R receptors and L ligands to derive the probability density function p(r;R,L) to find r bound receptor-ligand complexes at thermal equilibrium.
Edit intent: fluency
Original:  This extension allows us to illustrate two aspects of this problem which we have not seen mentioned in existing literature , namely, a) a symmetry to be expected in the equilibrium distribution of the number of bound complexes under exchange of R and L and b) the number of bound complexes obtained from chemical kinetic equations has an exact correspondence to the maximum probable value of r from the expression for p(r;R,L).
Revised:  This extension allows us to illustrate two aspects of this problem which are not apparent in the single receptor problem , namely, a) a symmetry to be expected in the equilibrium distribution of the number of bound complexes under exchange of R and L and b) the number of bound complexes obtained from chemical kinetic equations has an exact correspondence to the maximum probable value of r from the expression for p(r;R,L).
Edit intent: meaning-changed