wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We present INtERAcT, a novel approach to infer interactions between molecular entities extracted from literature using an unsupervised procedure that takes advantage of recent developments in automatic text mining and text analysis. INtERAcT implements a new metric acting on the vector space of word representations to estimate an interaction score between two molecules .
Revised:  We present INtERAcT, a novel approach to extract protein-protein interactions from a corpus of biomedical articles related to a broad range of scientific domains in a completely unsupervised way. INtERAcT exploits vector representation of words, computed on a corpus of domain specific knowledge, and implements a new metric acting on the vector space of word representations to estimate an interaction score between two molecules .
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Original:  INtERAcT implements a new metric acting on the vector space of word representations to estimate an interaction score between two molecules . We show that the proposed metric outperforms other metrics at the task of identifying known molecular interactions reported in publicly available databases for different cancer types.
Revised:  INtERAcT implements a new metric acting on the vector space of word representations to estimate an interaction score between two molecules in the space where the corresponding words are embedded. We demonstrate the power of INtERAcT by reconstructing the molecular pathways associated to 10 different cancer types using a corpus of disease-specific articles for each cancer type. We evaluate INtERAcT using STRING database as a benchmark, and show that our metric outperforms currently adopted approaches for similarity computation at the task of identifying known molecular interactions reported in publicly available databases for different cancer types.
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Original:  Furthermore, our approach does not require text annotation, manual curation or the definition of semantic rules based on expert knowledge, and hence it can be readily and efficiently applied to different scientific domains , enabling the automatic reconstruction of domain-specific networks of molecular interactions .
Revised:  Furthermore, our approach does not require text annotation, manual curation or the definition of semantic rules based on expert knowledge, and hence it can be easily and efficiently applied to different scientific domains , enabling the automatic reconstruction of domain-specific networks of molecular interactions .
Edit intent: clarity
Original:  Furthermore, our approach does not require text annotation, manual curation or the definition of semantic rules based on expert knowledge, and hence it can be readily and efficiently applied to different scientific domains , enabling the automatic reconstruction of domain-specific networks of molecular interactions .
Revised:  Furthermore, our approach does not require text annotation, manual curation or the definition of semantic rules based on expert knowledge, and hence it can be readily and efficiently applied to different scientific domains . Our findings suggest that INtERAcT may increase our capability to summarize the understanding of a specific disease using the published literature in an automated and completely unsupervised fashion .
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Original: In this work we propose a data-driven modelization approach for the management of advertising investments of a firm. First, we propose an application of dynamic linear models to the prediction of an economic variable, such as global sales, which can use information from the environment and the investment levels of the company in different channels. After we build a robust and precise model, we propose a metric of risk ,   which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
Revised: We propose a robust implementation of the Nerlove--Arrow model using a Bayesian structural time series model to explain the relationship between advertising expenditures of a country-wide fast-food franchise network with its weekly sales. Thanks to the flexibility and modularity of the model , which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
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Original:  After we build a robust and precise model, we propose a metric of risk ,   which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
Revised:  After we build a robust and precise model, we propose a metric of risk , it is well suited to generalization to other markets or situations. Its Bayesian nature facilitates incorporating which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
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Original:  After we build a robust and precise model, we propose a metric of risk ,   which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
Revised:  After we build a robust and precise model, we propose a metric of risk , a priori which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
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Original:  After we build a robust and precise model, we propose a metric of risk ,   which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
Revised:  After we build a robust and precise model, we propose a metric of risk , information (the manager's views), which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
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Original:  After we build a robust and precise model, we propose a metric of risk ,   which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
Revised:  After we build a robust and precise model, we propose a metric of risk , which can be updated with relevant data. This aspect of the model will be used to present a strategy of budget scheduling across time and channels .
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Original:  But, an increasing number of RNA molecules are now known to fold into multiple stable structures. Discrete optimization methods are commonly used to predict foldings, and adding experimental data as auxiliary information improves prediction accuracy when there is a single dominant conformation .
Revised:  But, an increasing number of RNA molecules are now known to have distributions of multiple stable sequences. Recent algorithms use the list of nucleotides in a sequence and auxiliary experimental data to predict such distributions. Although the algorithms are largely successful in identifying a distribution's constituent structures, it remains challenging to recover their relative weightings .
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Original:  In this paper, we analyze the outputs of existing structural prediction models when they receive auxiliary data derived from a mixture of structures. Under a binary model of auxiliary data, we find that current structural prediction methods typically favor distributions with one dominant structure , and hence cannot guarantee accurate reconstruction of multimodal distributions. Additionally, we analyze empirical distributions of auxiliary data used in current prediction models. We show that even when the structures in a distribution are known in advance, it is difficult to determine the weightings of the structures using auxiliary data.
Revised:  In this paper, we quantify this issue using a total variation distance. Then, we prove under a Nussinov-Jacobson model that a large proportion of RNA structure pairs cannot be jointly reconstructed with low total variation distance. Finally, we characterize the uncertainty in predicting conformational ratios by analyzing the amount of information in the auxiliary data.
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Original: We present an O(n) expected time algorithm and an O(n \log n) deterministic time algorithm to find a set of five points that stab a set of n pairwise intersecting disks in the plane.
Revised: Suppose we are given a set \mathcal{D O(n \log n) deterministic time algorithm to find a set of five points that stab a set of n pairwise intersecting disks in the plane.
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Original: We present an O(n) expected time algorithm and an O(n \log n) deterministic time algorithm to find a set of five points that stab a set of n pairwise intersecting disks in the plane. We also give a simple construction with 13 pairwise intersecting disks that cannot be stabbed by three  points.
Revised: We present an O(n) expected time algorithm and an O(n \log n) deterministic time algorithm to find a set of five points that stab \mathcal{D give a simple construction with 13 pairwise intersecting disks that cannot be stabbed by three points.
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Original:  We also give a simple construction with 13 pairwise intersecting disks that cannot be stabbed by three  points.
Revised:  We also give a simple construction with 13 pairwise intersecting disks that cannot be stabbed by three points. This provides a simple-albeit slightly weaker-algorithmic version of a classical result by Danzer that such a set \mathcal{D points.
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Original: Inspired by the works of Forman on discrete Morse theory, which is a combinatorial adaptation to cell complexes of classical Morse theory on manifolds, we introduce a discrete analogue of the stratified Morse theory of Goresky and MacPherson (1988) .
Revised: Inspired by the works of Forman on discrete Morse theory, which is a combinatorial adaptation to cell complexes of classical Morse theory on manifolds, we introduce a discrete analogue of the stratified Morse theory of Goresky and MacPherson .
Edit intent: coherence
Original: Computer Aided Diagnosis (CAD) tools are often needed for fast and accurate detection, characterization, and risk assessment of different tumors from radiology images.
Revised: Cancer is among the leading causes of death worldwide. Risk stratification of cancer tumors in radiology images can be improved with computer-aided diagnosis (CAD) tools are often needed for fast and accurate detection, characterization, and risk assessment of different tumors from radiology images.
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Original: Computer Aided Diagnosis (CAD) tools are often needed for fast and accurate detection, characterization, and risk assessment of different tumors from radiology images. Any improvement in robust and accurate image-based tumor characterization can assist in determining non-invasive cancer stage, prognosis, and  personalized treatment planning as a part of precision medicine.
Revised: Computer Aided Diagnosis (CAD) tools which can be made faster and more accurate. Tumor characterization through CADs can enable non-invasive cancer stage, prognosis, and personalized treatment planning as a part of precision medicine.
Edit intent: clarity
Original:  Our first approach is based on supervised learning for which we demonstrate significant gains in deep learning algorithms, particularly Convolutional Neural Network (CNN), by utilizing completely 3D approach and transfer learningto address the requirements of volumetric and large amount of training data, respectively .
Revised:  Our first approach is based on supervised learning for which we demonstrate significant gains in deep learning algorithms, particularly by utilizing a 3D approach and transfer learningto address the requirements of volumetric and large amount of training data, respectively .
Edit intent: coherence
Original:  Our first approach is based on supervised learning for which we demonstrate significant gains in deep learning algorithms, particularly Convolutional Neural Network (CNN), by utilizing completely 3D approach and transfer learningto address the requirements of volumetric and large amount of training data, respectively .
Revised:  Our first approach is based on supervised learning for which we demonstrate significant gains in deep learning algorithms, particularly Convolutional Neural Network (CNN), by utilizing completely 3D Convolutional Neural Network along with transfer learning .
Edit intent: clarity
Original:  Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a graph regularized sparse Multi-Task Learning (MTL)  framework.
Revised:  Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a "graph-regularized sparse Multi-Task Learning (MTL) framework.
Edit intent: fluency
Original:  Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a graph regularized sparse Multi-Task Learning (MTL)  framework.
Revised:  Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a graph regularized sparse Multi-Task Learning (MTL) " framework.
Edit intent: fluency
Original:  In this second approach, we also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification . Finally, we study the effect of unsupervised representation learning using Generative Adversarial Networks (GAN) on classification performance .
Revised:  In this second approach, we also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification .
Edit intent: coherence
Original:  Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a " graph-regularized sparse Multi-Task Learning (MTL) " framework.
Revised:  Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a graph-regularized sparse Multi-Task Learning (MTL) " framework.
Edit intent: fluency
Original:  Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a " graph-regularized sparse Multi-Task Learning (MTL) " framework.
Revised:  Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a " graph-regularized sparse Multi-Task Learning (MTL) framework.
Edit intent: fluency
Original:  We evaluate our proposed approaches (both supervised and unsupervised ) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively .
Revised:  We evaluate our proposed approaches (both supervised and unsupervised ) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans , respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems .
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Original:  This study aimed to investigate whether a multivariable paradigm that combined resting state functional magnetic resonance imaging and clinical characteristics could predict the outcome at an individual level and identify the patient who would later recover consciousness.
Revised:  This study aimed to develop a multidomain prognostic model that combines resting state functional magnetic resonance imaging and clinical characteristics could predict the outcome at an individual level and identify the patient who would later recover consciousness.
Edit intent: clarity
Original:  This study aimed to investigate whether a multivariable paradigm that combined resting state functional magnetic resonance imaging and clinical characteristics could predict the outcome at an individual level and identify the patient who would later recover consciousness. Three datasets comprising a total of 112 patients with disorders of consciousness from two medical centers in China were involved in the study. Of the three datasets, one was used as the training dataset to establish a prediction model, and the other two were used only for validation. Combining the features extracted from the resting state functional magnetic resonance imaging and three clinical characteristics , i.e. etiology, incidence age and duration of unconsciousness, we built a regression model to predict the prognosis of the patients at the individual level. Each patient was then classified based on whether or not they were expected to recover consciousness. The classification accuracy was assessed by comparing these results with the actual outcome during at least 12 months follow-up. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90\% . Notably, our method could accurately identify the patients who were initially diagnosed as being in a vegetative state/unresponsive wakefulness syndrome but subsequently recovered consciousness.
Revised:  This study aimed to investigate whether a multivariable paradigm that combined resting state functional MRI with three clinical characteristics to predict one year outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90\% . Notably, our method could accurately identify the patients who were initially diagnosed as being in a vegetative state/unresponsive wakefulness syndrome but subsequently recovered consciousness.
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Original:  The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90\% . Notably, our method could accurately identify the patients who were initially diagnosed as being in a vegetative state/unresponsive wakefulness syndrome but subsequently recovered consciousness. The presented model was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics.
Revised:  The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90\% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics.
Edit intent: clarity
Original: Bacteria such as %DIFDELCMD < {\it %%% Escherichia coli move about in a series of runs and tumbles: while a run state (straight motion) entails all the flagellar motors spinning in counterclockwise (CCW) mode, a tumble is caused by a shift in the state of one or more motors to clockwise (CW) spinning.
Revised: Bacteria such as %DIFDELCMD < {\it %%% Escherichia coli move about in a series of runs and tumbles: while a run state (straight motion) entails all the flagellar motors spinning in counterclockwise (CCW) mode, a tumble is caused by a shift in the state of one or more motors to clockwise (CW) spinning.
Edit intent: fluency
Original: Bacteria such as Escherichia coli move about in a series of runs and tumbles: while a run state (straight motion) entails all the flagellar motors spinning in counterclockwise (CCW) mode, a tumble is caused by a shift in the state of one or more motors to clockwise (CW) spinning .
Revised: Bacteria such as Escherichia coli move about in a series of runs and tumbles: while a run state (straight motion) entails all the flagellar motors spinning in counterclockwise mode, a tumble is caused by a shift in the state of one or more motors to clockwise (CW) spinning .
Edit intent: fluency
Original: Bacteria such as Escherichia coli move about in a series of runs and tumbles: while a run state (straight motion) entails all the flagellar motors spinning in counterclockwise (CCW) mode, a tumble is caused by a shift in the state of one or more motors to clockwise (CW) spinning .
Revised: Bacteria such as Escherichia coli move about in a series of runs and tumbles: while a run state (straight motion) entails all the flagellar motors spinning in counterclockwise (CCW) mode, a tumble is caused by a shift in the state of one or more motors to clockwise spinning mode .
Edit intent: clarity
Original:  The formalism is applied to gamma-distributed CW and CCW waiting time distributions in E. coli, as suggested by recent experimental observations, and used for predicting the dependence of drift velocity on clockwise bias .
Revised:  New results for gamma-distributed CW and CCW waiting time distributions in E. coli, as suggested by recent experimental observations, and used for predicting the dependence of drift velocity on clockwise bias .
Edit intent: clarity
Original: Given a finite honest time, we derive representations for the additive and multiplicativedecomposition of it's Az\'ema  supermartingale in terms of optional supermartingales and its running supremum.
Revised: Given a finite honest time, we first derive a representation of the additive (the multiplicative) decomposition of its Az\'ema supermartingale in terms of optional supermartingales and its running supremum.
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Original: Given a finite honest time, we derive representations for the additive and multiplicativedecomposition of it's Az\'ema  supermartingale in terms of optional supermartingales and its running supremum.
Revised: Given a finite honest time, we derive representations for the additive and multiplicativedecomposition of it's Az\'ema optional supermartingale in terms of optional supermartingales and its running supremum.
Edit intent: fluency
Original: Given a finite honest time, we derive representations for the additive and multiplicativedecomposition of it's Az\'ema  supermartingale in terms of optional supermartingales and its running supremum.
Revised: Given a finite honest time, we derive representations for the additive and multiplicativedecomposition of it's Az\'ema supermartingale in terms of the draw-down (the relative draw-down) of some optional supermartingales with continuous running supremum.
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Original:  We then extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part, allowing one to establish formulas similar to the Madan-Roynette-Yor option pricing formulas for larger class of processes.
Revised:  The multiplicative representation then allows us to give a characterisation of finite honest times using a family of non-negative optional supermartingales with continuous running supremum which converges to zero at infinity. Finally, we extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part, allowing one to establish formulas similar to the Madan-Roynette-Yor option pricing formulas for larger class of processes.
Edit intent: meaning-changed
Original:  We then extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part, allowing one to establish formulas similar to the Madan-Roynette-Yor option pricing formulas for larger class of processes.
Revised:  We then extend the notion of semimartingales of class-(\Sigma) to optional semimartingales with jumps in its finite variation part, allowing one to establish formulae similar to the Madan-Roynette-Yor option pricing formulas for larger class of processes.
Edit intent: clarity