wanyu/IteraTeR_v2 huggingface dataset:
Original:  More specifically, the proposed model consists of three components: an image CNN to extract the image representation , one sentence CNN to encode the question, and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words.
Revised:  More specifically, the proposed model consists of three CNNs: one image CNN to extract the image representation , one sentence CNN to encode the question, and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words.
Edit intent: clarity
Original:  More specifically, the proposed model consists of three components: an image CNN to extract the image representation , one sentence CNN to encode the question, and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words.
Revised:  More specifically, the proposed model consists of three components: an image CNN to encode the image content , one sentence CNN to encode the question, and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words.
Edit intent: clarity
Original:  More specifically, the proposed model consists of three components: an image CNN to extract the image representation , one sentence CNN to encode the question, and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words.
Revised:  More specifically, the proposed model consists of three components: an image CNN to extract the image representation , one sentence CNN to compose the words of the question, and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words.
Edit intent: clarity
Original:  More specifically, the proposed model consists of three components: an image CNN to extract the image representation , one sentence CNN to encode the question, and one multimodal convolution layer to fuse the multimodal input of the image and question to obtain the joint representation for the classification in the space of candidate answer words.
Revised:  More specifically, the proposed model consists of three components: an image CNN to extract the image representation , one sentence CNN to encode the question, and one multimodal convolution layer to learn their joint representation for the classification in the space of candidate answer words.
Edit intent: clarity
Original:  We demonstrate the efficacy of our proposed model on  DAQUAR and COCO-QA datasets, two datasets recently created for the image question answering (QA), with performance substantially outperforming the state-of-the-arts .
Revised:  We demonstrate the efficacy of our proposed model on the DAQUAR and COCO-QA datasets, two datasets recently created for the image question answering (QA), with performance substantially outperforming the state-of-the-arts .
Edit intent: fluency
Original:  We demonstrate the efficacy of our proposed model on  DAQUAR and COCO-QA datasets, two datasets recently created for the image question answering (QA), with performance substantially outperforming the state-of-the-arts .
Revised:  We demonstrate the efficacy of our proposed model on DAQUAR and COCO-QA datasets, which are two benchmark datasets for the image question answering (QA), with performance substantially outperforming the state-of-the-arts .
Edit intent: clarity
Original:  We demonstrate the efficacy of our proposed model on  DAQUAR and COCO-QA datasets, two datasets recently created for the image question answering (QA), with performance substantially outperforming the state-of-the-arts .
Revised:  We demonstrate the efficacy of our proposed model on DAQUAR and COCO-QA datasets, two datasets recently created for the image QA, with the performances significantly outperforming the state-of-the-art .
Edit intent: clarity
Original: Learning and inferring features that generate sensory input are crucial information processing tasks continuously performed by cortical networks .
Revised: Learning and inferring features that generate sensory input is a task continuously performed by cortical networks .
Edit intent: clarity
Original: Learning and inferring features that generate sensory input are crucial information processing tasks continuously performed by cortical networks .
Revised: Learning and inferring features that generate sensory input are crucial information processing tasks continuously performed by cortex .
Edit intent: clarity
Original:  In recent years, novel algorithms and learning rules have been proposed that allow neuronal networks to learn such features from complex datasets .
Revised:  In recent years, novel algorithms and learning rules have been proposed that allow neural network models to learn such features from complex datasets .
Edit intent: clarity
Original:  These networks involve layers of hidden neurons leading to a remarkable increase in their ability to explain complex sensory inputs .
Revised:  These networks involve layers of hidden neurons .
Edit intent: clarity
Original:  Here we review recent advancements in this line of research emphasizing, amongst other things, the processing of dynamical inputs by networks with hidden nodes .
Revised:  Here we review recent advancements in this area emphasizing, amongst other things, the processing of dynamical inputs by networks with hidden nodes .
Edit intent: clarity
Original:  We identify issues which in our view should be explored to achieve a better understanding of the relationship between machine learning approaches to learning in networks with hidden nodes and learning in cortical circuits.
Revised:  We identify issues which in our view should be explored to achieve a better understanding of the relationship between machine learning approaches to learning with hidden nodes and learning in cortical circuits.
Edit intent: clarity
Original:  We identify issues which in our view should be explored to achieve a better understanding of the relationship between machine learning approaches to learning in networks with hidden nodes and learning in cortical circuits.
Revised:  We identify issues which in our view should be explored to achieve a better understanding of the relationship between machine learning approaches to learning in networks with hidden nodes and those in cortical circuits.
Edit intent: clarity
Original: It has been exiting that nearly all ion activities analyzed thus far exhibit oscillations which are tightly coupled to growth.
Revised: It has been interesting that nearly all ion activities analyzed thus far exhibit oscillations which are tightly coupled to growth.
Edit intent: coherence
Original: It has been exiting that nearly all ion activities analyzed thus far exhibit oscillations which are tightly coupled to growth.
Revised: It has been exiting that nearly all of the ion activities that have been analysed thus far have exhibited oscillations that are tightly coupled to growth.
Edit intent: clarity
Original:  Here, we account on discrete Fourier transform (DFT) spectra with  finite sampling of tip-growing cells and organs  obtained from voltage measurements of  elongating coleoptiles of maize in situ.
Revised:  Here, we present discrete Fourier transform (DFT) spectra with finite sampling of tip-growing cells and organs obtained from voltage measurements of elongating coleoptiles of maize in situ.
Edit intent: clarity
Original:  Here, we account on discrete Fourier transform (DFT) spectra with  finite sampling of tip-growing cells and organs  obtained from voltage measurements of  elongating coleoptiles of maize in situ.
Revised:  Here, we account on discrete Fourier transform (DFT) spectra with a finite sampling of tip-growing cells and organs obtained from voltage measurements of elongating coleoptiles of maize in situ.
Edit intent: fluency
Original:  Here, we account on discrete Fourier transform (DFT) spectra with  finite sampling of tip-growing cells and organs  obtained from voltage measurements of  elongating coleoptiles of maize in situ.
Revised:  Here, we account on discrete Fourier transform (DFT) spectra with finite sampling of tip-growing cells and organs that were obtained from voltage measurements of elongating coleoptiles of maize in situ.
Edit intent: clarity
Original:  Here, we account on discrete Fourier transform (DFT) spectra with  finite sampling of tip-growing cells and organs  obtained from voltage measurements of  elongating coleoptiles of maize in situ.
Revised:  Here, we account on discrete Fourier transform (DFT) spectra with finite sampling of tip-growing cells and organs obtained from voltage measurements of the elongating coleoptiles of maize in situ.
Edit intent: fluency
Original:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, where the solenoid is initially placed at the specimen tip and then is co-moving, changing its position upon growth (so EMF can be measured first at the tip, then at  sub-apical part and finally at  shank).
Revised:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, in which the solenoid was initially placed at the specimen tip and then is co-moving, changing its position upon growth (so EMF can be measured first at the tip, then at sub-apical part and finally at shank).
Edit intent: coherence
Original:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, where the solenoid is initially placed at the specimen tip and then is co-moving, changing its position upon growth (so EMF can be measured first at the tip, then at  sub-apical part and finally at  shank).
Revised:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, where the solenoid is initially placed at the tip of the specimen and then was moved thus changing its position upon growth (so EMF can be measured first at the tip, then at sub-apical part and finally at shank).
Edit intent: clarity
Original:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, where the solenoid is initially placed at the specimen tip and then is co-moving, changing its position upon growth (so EMF can be measured first at the tip, then at  sub-apical part and finally at  shank).
Revised:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, where the solenoid is initially placed at the specimen tip and then is co-moving, changing its position in relation to growth ( EMF can be measured first at the tip, then at sub-apical part and finally at shank).
Edit intent: clarity
Original:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, where the solenoid is initially placed at the specimen tip and then is co-moving, changing its position upon growth (so EMF can be measured first at the tip, then at  sub-apical part and finally at  shank).
Revised:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, where the solenoid is initially placed at the specimen tip and then is co-moving, changing its position upon growth (so EMF can be measured first at the tip, then at the sub-apical part and finally at shank).
Edit intent: fluency
Original:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, where the solenoid is initially placed at the specimen tip and then is co-moving, changing its position upon growth (so EMF can be measured first at the tip, then at  sub-apical part and finally at  shank).
Revised:  The electromotive force (EMF) oscillations are } measured in a simple but highly sensitive RL circuit, where the solenoid is initially placed at the specimen tip and then is co-moving, changing its position upon growth (so EMF can be measured first at the tip, then at sub-apical part and finally at the shank).
Edit intent: fluency
Original:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Revised:  The influx- and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Edit intent: fluency
Original:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Revised:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^{+ densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Edit intent: coherence
Original:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Revised:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving the Nyquist theorem in order to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Edit intent: coherence
Original:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Revised:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum was calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Edit intent: fluency
Original:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Revised:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, which displayed a periodic sequence of pulses, are compared with the literature data.
Edit intent: coherence
Original:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, are compared with the literature data.
Revised:  The influx and efflux-induced oscillations of Ca^{2+}, along with H ^+, K^+ and Cl^- are densely sampled (preserving Nyquist theorem, to 'grasp the structure' of the pulse), the logarithmic amplitude of pulse spectrum is calculated, and the detected frequencies, displaying periodic sequence of pulses, were compared with the literature data.
Edit intent: fluency
Original:  A wide-band of life-vital individual pulses is obtained in a single run of experiment, not only allowing to determine fundamental frequencies (and processes' intensities ) but also permitting to establish phase relations .
Revised:  A band of life vital individual pulses was obtained in a single run of experiment, not only allowing to determine fundamental frequencies (and processes' intensities ) but also permitting to establish phase relations .
Edit intent: clarity
Original:  A wide-band of life-vital individual pulses is obtained in a single run of experiment, not only allowing to determine fundamental frequencies (and processes' intensities ) but also permitting to establish phase relations .
Revised:  A wide-band of life-vital individual pulses is obtained in a single run of the experiment, which not only allowed the fundamental frequencies (and processes' intensities ) but also permitting to establish phase relations .
Edit intent: coherence
Original:  A discrete frequency spectrum (like the hydrogen spectrum in quantum physics) is achieved for the first time for a growing plant .
Revised:  A discrete frequency spectrum (like the hydrogen spectrum in quantum physics) was achieved for a growing plant .
Edit intent: clarity
Original:  All metabolic and enzymatic functions of the life cell cycle are preserved in this totally non-invasive treatment.
Revised:  All metabolic and enzymatic functions of the life cell cycle were preserved using this totally non-invasive treatment.
Edit intent: clarity
Original: In this paper, we solve the problem of reconstructing or identifying the network topology from steady state flux measurements.
Revised: We solve the problem of reconstructing or identifying the network topology from steady state flux measurements.
Edit intent: clarity
Original: In this paper, we solve the problem of reconstructing or identifying the network topology from steady state flux measurements.
Revised: In this paper, we solve the problem of identifying (reconstructing) network topology from steady state flux measurements.
Edit intent: coherence
Original:  Specifically, given a data matrix X , where each column contains measurements corresponding to a distinct steady state configuration , we wish to estimate a model A which captures the approximate linear relationships between  different variables comprising X (i.e. of the form \mathbf{AX \approx 0 ) such that \mathbf{A is full rank (highest possible) and consistent with a network  incidence structure.
Revised:  Specifically, given a data matrix X , where each column contains measurements corresponding to a distinct steady state configuration , we wish to estimate a model A which captures the approximate linear relationships between the different variables comprising X (i.e. of the form \mathbf{AX \approx 0 ) such that \mathbf{A is full rank (highest possible) and consistent with a network incidence structure.
Edit intent: fluency
Original:  Hence the network topology can be obtained just by looking at this estimated model.
Edit intent: coherence
Original:  The problem is solved through a sequence of steps including estimating approximate linear relationships between variables using Principal Component Analysis (PCA), obtaining fundamental cut-sets from the model estimated by PCA ,  and graph realization from f-cut-sets (or equivalently f-circuits).
Revised:  The problem is solved through a sequence of steps like estimating approximate linear relationships between variables using Principal Component Analysis (PCA), obtaining fundamental cut-sets from the model estimated by PCA , and graph realization from f-cut-sets (or equivalently f-circuits).
Edit intent: clarity
Original:  The problem is solved through a sequence of steps including estimating approximate linear relationships between variables using Principal Component Analysis (PCA), obtaining fundamental cut-sets from the model estimated by PCA ,  and graph realization from f-cut-sets (or equivalently f-circuits).
Revised:  The problem is solved through a sequence of steps including estimating approximate linear relationships using Principal Component Analysis (PCA), obtaining fundamental cut-sets from the model estimated by PCA , and graph realization from f-cut-sets (or equivalently f-circuits).
Edit intent: clarity
Original:  The problem is solved through a sequence of steps including estimating approximate linear relationships between variables using Principal Component Analysis (PCA), obtaining fundamental cut-sets from the model estimated by PCA ,  and graph realization from f-cut-sets (or equivalently f-circuits).
Revised:  The problem is solved through a sequence of steps including estimating approximate linear relationships between variables using Principal Component Analysis , and graph realization from f-cut-sets (or equivalently f-circuits).
Edit intent: clarity
Original:  The problem is solved through a sequence of steps including estimating approximate linear relationships between variables using Principal Component Analysis (PCA), obtaining fundamental cut-sets from the model estimated by PCA ,  and graph realization from f-cut-sets (or equivalently f-circuits).
Revised:  The problem is solved through a sequence of steps including estimating approximate linear relationships between variables using Principal Component Analysis (PCA), obtaining fundamental cut-sets from the model estimated by PCA , obtaining f-cut-sets from these approximate relationships, and graph realization from f-cut-sets (or equivalently f-circuits).
Edit intent: clarity
Original:  The method is illustrated using an example of water network reconstruction using steady-state flow rate measurements .
Revised:  The method is illustrated by identifying topology of a water distribution network .
Edit intent: clarity
Original:  We also establish hard limits on what can be, and cannot be done with steady-state data.
Revised:  We also study the extent of identifiability from steady-state data.
Edit intent: clarity
Original:  We formulate the problem of estimating the collective judgement of a rumour in social media as a supervised learning using annotated examples.
Revised:  We formulate the problem of classifying tweet level judgements of rumours as a supervised learning using annotated examples.
Edit intent: clarity
Original:  We consider both supervised and unsupervised domain adaptation  , in which rumour predictions are made on the basis of other annotated rumours.
Revised:  We consider both supervised and unsupervised domain adaptation are considered , in which rumour predictions are made on the basis of other annotated rumours.
Edit intent: clarity
Original:  We consider both supervised and unsupervised domain adaptation  , in which rumour predictions are made on the basis of other annotated rumours.
Revised:  We consider both supervised and unsupervised domain adaptation , in which tweets from a rumour are classified on the basis of other annotated rumours.
Edit intent: clarity
Original: The increasing reliance on online social networks for user engagement motivates the need to understand and counter reputation manipulation activities, such as boosting the number of likes of a Facebook page .
Revised: As businesses increasingly rely on social networking sites to engage with their customers, it is crucial to understand and counter reputation manipulation activities, such as boosting the number of likes of a Facebook page .
Edit intent: clarity