wanyu/IteraTeR_full_sent huggingface dataset:
Original:  They noted the presence or absence of macula edema or subretinal fluid , then compared their assessment to that obtained from Fluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans.
Revised:  They noted the presence or absence of ME or SRF , then compared their assessment to that obtained from Fluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans.
Edit intent: clarity
Original:  Setting: Clinic setting with PACS access to OCT images of patients with or without retinal fluid. Participants: Board-certified ophthalmologists with fellowship training in retina retrospectively selected retinal OCT images in a consecutive fashion. Effort was made by each center to balance the number of scans with retinal fluid and scans without.
Revised:  Investigators consecutively selected retinal OCTs, while making effort to balance the number of scans with retinal fluid and scans without.
Edit intent: clarity
Original:  Main outcome and Measures: Accuracy in the form of sensitivity and specificity of the AI mobile app compared against assessments of board-certified retina specialists .
Revised:  Main outcome and Measures: Accuracy in the form of sensitivity and specificity of the AI mobile App was determined by comparing its assessments to those of the retina specialists. At the time of this submission, five centers have completed their initial studies. This consists of a total of 283 OCT scans of which 155 had either ME or SRF ("wet") and 128 did not ("dry"). The sensitivity ranged from 82.5\% to 97\% with a weighted average of 89.3\%. The specificity ranged from 52\% to 100\% with a weighted average of 81.23\%. CONCLUSION: Cloud-based Mobile AI technology is feasible for the detection retinal disease. In particular, Fluid Intelligence (alpha version), is sufficiently accurate as a screening tool for SRF and ME, especially in underserved areas. Further studies and technology development is needed .
Edit intent: meaning-changed
Original: We design envy-free mechanisms for the allocation of rooms and rent payments among roommates. We achieve four objectives: (1) each agent is allowed to make a report that expresses her preference about violating her budget constraint, a feature not achieved by mechanisms that only elicit quasi-linear reports; (2) these reports are finite dimensional; (3) computation is feasible in polynomial time; and (4) incentive properties of envy-free mechanisms that elicit quasi-linear reports are preserved .
Revised: We design envy-free mechanisms for the allocation of rooms and payments of rent among financially constrained roommates. Each agent reports her values for rooms, her housing earmark (soft budget), and an index that reflects the difficulty the agent experiences from having to pay over this amount. Then an envy-free allocation for these reports is recommended. We identify conditions under which the complete information non-cooperative outcomes of these mechanisms are exactly the envy-free allocations with respect to true preferences .
Edit intent: meaning-changed
Original:  In this context, we derive an upper bound on the growth rate of the synergistic Susceptible-Infected-Susceptible model in terms of the eigenvalues of a matrix whose size grows quadratically with the number of the nodes in the network.
Revised:  Although synergistic effects play key roles in various biological and social phenomena, their analyses have been often performed by means of approximation techniques and for limited types of networks. In order to address this limitation, this paper proposes a rigorous approach to quantitatively understand the effect of synergy in the Susceptible-Infected-Susceptible model taking place in an arbitrary complex network. We derive an upper bound on the growth rate of the synergistic Susceptible-Infected-Susceptible model in terms of the eigenvalues of a matrix whose size grows quadratically with the number of the nodes in the network.
Edit intent: meaning-changed
Original:  In these conditions, our results unveil an eco-evolutionary feedback between species spatial mixing and predators perceptual range: different levels of species mixing select for different perceptual ranges, which in turn reshape the spatial distribution of preys and their interaction with predators.
Revised:  In these conditions, our results unveil an eco-evolutionary feedback between species spatial mixing and predators perceptual range: different levels of mixing select for different perceptual ranges, which in turn reshape the spatial distribution of preys and their interaction with predators.
Edit intent: fluency
Original:  In these conditions, our results unveil an eco-evolutionary feedback between species spatial mixing and predators perceptual range: different levels of species mixing select for different perceptual ranges, which in turn reshape the spatial distribution of preys and their interaction with predators.
Revised:  In these conditions, our results unveil an eco-evolutionary feedback between species spatial mixing and predators perceptual range: different levels of species mixing select for different perceptual ranges, which in turn reshape the spatial distribution of prey and its interaction with predators.
Edit intent: fluency
Original: Recent vibrational energy exchange experiments on a protein have been explained employing the relation, not new in itself,between protein and fractal structure. The differnce in the scaling exponent of specific part of the protein entails a distinct vibrational localization property and consequentely a discrepancy in the vibrational energy exchange ) with d \sim 1.8, whereas those associated to non-bonded contacts result simply localized with d \sim 1.}
Revised: Recent vibrational energy exchange experiments on a protein showed the diffusion of vibrational energy does not occur along the backbone interaction,as it might be expected, but prevalently on non-bonded contacts. These results are explained presenting a theoretical picture, supported by computational calculations, that accounts for these different behaviors in vibrational energy exchange ) with d \sim 1.8, whereas those associated to non-bonded contacts result simply localized with d \sim 1.}
Edit intent: meaning-changed
Original:  The differnce in the scaling exponent of specific part of the protein entails a distinct vibrational localization property and consequentely a discrepancy in the vibrational energy exchange ) with d \sim 1.8, whereas those associated to non-bonded contacts result simply localized with d \sim 1.}
Revised:  The differnce in the scaling exponent of specific part of the protein entails a distinct vibrational localization property and consequentely a discrepancy in the vibrational energy exchange process showing the collective motions on the backbone present a superlocalized nature as their decay with the distance r is exp(-r^{d) with d \sim 1.8, whereas those associated to non-bonded contacts result simply localized with d \sim 1.}
Edit intent: meaning-changed
Original: There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines , let alone ordinary classical computers . During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind.
Revised: There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines . During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind.
Edit intent: coherence
Original:  So, we redefine NA as a data-driven framework, called TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity .
Revised:  We redefine NA as a data-driven framework, called TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity .
Edit intent: coherence
Original:  So, we redefine NA as a data-driven framework, called TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity .
Revised:  So, we redefine NA as a data-driven framework, TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity .
Edit intent: coherence
Original:  So, we redefine NA as a data-driven framework, called TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity . TARA makes no assumptions about what nodes should be aligned, distinguishing it from existing NA methods . Specifically, TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns (features) .
Revised:  So, we redefine NA as a data-driven framework, called TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity , like traditional NA methods do. TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns (features) .
Edit intent: clarity
Original:  TARA makes no assumptions about what nodes should be aligned, distinguishing it from existing NA methods . Specifically, TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns (features) .
Revised:  TARA makes no assumptions about what nodes should be aligned, distinguishing it from existing NA methods . Specifically, TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns .
Edit intent: clarity
Original:  In this context, we find that TARA outperforms or complements existing state-of-the-art NA methods WAVE, SANA, and PrimAlign .
Revised:  Clearly, TARA as currently implemented uses topological but not protein sequence information for this task. We find that TARA outperforms or complements existing state-of-the-art NA methods WAVE, SANA, and PrimAlign .
Edit intent: meaning-changed
Original:  In this context, we find that TARA outperforms or complements existing state-of-the-art NA methods WAVE, SANA, and PrimAlign .
Revised:  In this context, we find that TARA outperforms or complements existing state-of-the-art NA methods WAVE, SANA, and even outperforms or complements a state-of-the-art NA method that uses both topological and sequence information, PrimAlign. Hence, adding sequence information to TARA, which is our future work, is likely to further improve its performance .
Edit intent: meaning-changed
Original:  In other words, the proposed performance model provides a systematic approach to measure the elasticity of the microservice platform by analyzing the provisioning performance at both  microservice platform and the back-end macroservice infrastructure .
Revised:  In other words, the proposed performance model provides a systematic approach to measure the elasticity of the microservice platform by analyzing the provisioning performance at both the microservice platform and the back-end macroservice infrastructure .
Edit intent: fluency
Original:  In other words, the proposed performance model provides a systematic approach to measure the elasticity of the microservice platform by analyzing the provisioning performance at both  microservice platform and the back-end macroservice infrastructure .
Revised:  In other words, the proposed performance model provides a systematic approach to measure the elasticity of the microservice platform by analyzing the provisioning performance at both microservice platform and the back-end macroservice infrastructures .
Edit intent: fluency
Original:  Our explanation models the visual mechanisms in low-level stages that can reveal tilt cues in Geometrical distortion illusions such as Tile illusions particularly Caf\'e Wall illusions.
Revised:  Our explanation models the visual mechanisms in low level stages and the lateral inhibition of simple cells that can reveal tilt cues in Geometrical distortion illusions such as Tile illusions particularly Caf\'e Wall illusions.
Edit intent: meaning-changed
Original:  For this, we simulate the activation of the retinal/cortical simple cells in responses to these patterns based on a Classical Receptive Field (CRF) model  to explain tilt effects in these illusions.
Revised:  For this, we simulate the activations of the retinal/cortical simple cells in responses to these patterns based on a Classical Receptive Field (CRF) model to explain tilt effects in these illusions.
Edit intent: fluency
Original:  For this, we simulate the activation of the retinal/cortical simple cells in responses to these patterns based on a Classical Receptive Field (CRF) model  to explain tilt effects in these illusions.
Revised:  For this, we simulate the activation of the retinal/cortical simple cells in responses to these patterns based on a Classical Receptive Field (CRF) model (referred to as Vis-CRF) to explain tilt effects in these illusions.
Edit intent: meaning-changed
Original:  In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects early-life stages dispersal, but also by the spawning stock attributes that affect reproductive output (e.g., passive eggs and larvae) and its survivorship, constituting the so-called spatial reproductive system.
Revised:  In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects early-life stages dispersal, but also by the spawning stock attributes that affect reproductive output (e.g., passive eggs and larvae) and its survivorship, constituting the so-called spatial temporal spawner-recruit system.
Edit intent: meaning-changed
Original:  Then, the painted comber, a sedentary and coastal fish with ca. 21 days of pelagic larval duration, was selected as model species to address questions related to a specific reproductive system.
Revised:  Then, the painted comber, a sedentary and coastal fish with ca. 21 days of pelagic larval duration, was selected as model species to address questions related to a specific spawner-recruit system.
Edit intent: clarity
Original:  In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects early-life stages dispersal, but also by the spawning stock attributes that affect  reproductive output (e.g., passive eggs and larvae) and its survivorship , constituting the so-called spatial temporal spawner-recruit system.
Revised:  In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects early-life stages dispersal, but also from the spawning stock attributes that affect reproductive output (e.g., passive eggs and larvae) and its survivorship , constituting the so-called spatial temporal spawner-recruit system.
Edit intent: fluency
Original:  In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects early-life stages dispersal, but also by the spawning stock attributes that affect  reproductive output (e.g., passive eggs and larvae) and its survivorship , constituting the so-called spatial temporal spawner-recruit system.
Revised:  In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects early-life stages dispersal, but also by the spawning stock attributes that affect the reproductive output (e.g., passive eggs and larvae) and its survivorship , constituting the so-called spatial temporal spawner-recruit system.
Edit intent: fluency
Original:  Finally, a hypothetical scenario explored alternative MPA network configurations , demonstrating how simple spatial decisions based on connectivity measures could improve recruitment and average network connectivity  .
Revised:  Finally, a hypothetical scenario explored alternative MPA network configurations , demonstrating how simple spatial decisions based on connectivity measures could improve recruitment and average network connectivity . Results demonstrate the potential of graph theory to identify non-trivial egg/larval dispersal patterns and emerging collective properties of the MPA network which are relevant for increasing protection efficiency .
Edit intent: meaning-changed
Original: The predictive performance of point forecasts for a statistical functional, such as the mean, a quantile, or a certain risk measure, is commonly assessed in terms of scoring (or loss) functions. A scoring function should be (strictly) consistent for the functional of interest, that is, the expected score should be minimised by the correctly specified functional value. A functional is elicitable if it possesses a strictly consistent scoring function. In quantitative risk management, the elicitability of a risk measure is closely related to comparative backtesting procedures. As such, it has gained considerable interest in the debate about which risk measure to choose in practice .
Revised: The debate of what quantitative risk measure to choose in practice .
Edit intent: coherence
Original:  As such, it has gained considerable interest in the debate about which risk measure to choose in practice . While this discussion has mainly focused on the dichotomy between Value at Risk (VaR) - a quantile - and Expected Shortfall (ES) - a tail expectation , this paper is concerned with Range Value at Risk (RVaR) . RVaR can be regarded as an interpolation of VaR and ES , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Revised:  As such, it has gained considerable interest in the debate about which risk measure to choose in practice has mainly focused on the dichotomy between Value at Risk (VaR) - a quantile - and Expected Shortfall (ES) - a tail expectation , this paper is concerned with Range Value at Risk (RVaR) . RVaR can be regarded as an interpolation of VaR and ES , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Edit intent: clarity
Original:  While this discussion has mainly focused on the dichotomy between Value at Risk (VaR) - a quantile - and Expected Shortfall (ES) - a tail expectation , this paper is concerned with Range Value at Risk (RVaR) . RVaR can be regarded as an interpolation of VaR and ES , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Revised:  While this discussion has mainly focused on the dichotomy between Value at Risk (VaR) -- a quantile -- and Expected Shortfall (ES) - a tail expectation , this paper is concerned with Range Value at Risk (RVaR) . RVaR can be regarded as an interpolation of VaR and ES , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Edit intent: fluency
Original:  While this discussion has mainly focused on the dichotomy between Value at Risk (VaR) - a quantile - and Expected Shortfall (ES) - a tail expectation , this paper is concerned with Range Value at Risk (RVaR) . RVaR can be regarded as an interpolation of VaR and ES , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Revised:  While this discussion has mainly focused on the dichotomy between Value at Risk (VaR) - a quantile - and Expected Shortfall (ES) -- a tail expectation , this paper is concerned with Range Value at Risk (RVaR) . RVaR can be regarded as an interpolation of VaR and ES , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Edit intent: fluency
Original:  While this discussion has mainly focused on the dichotomy between Value at Risk (VaR) - a quantile - and Expected Shortfall (ES) - a tail expectation , this paper is concerned with Range Value at Risk (RVaR) . RVaR can be regarded as an interpolation of VaR and ES , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Revised:  While this discussion has mainly focused on the dichotomy between Value at Risk (VaR) - a quantile - and Expected Shortfall (ES) - a tail expectation . Range Value at Risk (RVaR) . RVaR can be regarded as an interpolation of VaR and ES , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Edit intent: clarity
Original:  While this discussion has mainly focused on the dichotomy between Value at Risk (VaR) - a quantile - and Expected Shortfall (ES) - a tail expectation , this paper is concerned with Range Value at Risk (RVaR) . RVaR can be regarded as an interpolation of VaR and ES , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Revised:  While this discussion has mainly focused on the dichotomy between Value at Risk (VaR) - a quantile - and Expected Shortfall (ES) - a tail expectation , this paper is concerned with Range Value at Risk (RVaR) is a natural interpolation between these two prominent risk measures , which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former . Recalling that RVaR is not elicitable , we show that a triplet of RVaR with two VaR components at different levels is elicitable.
Edit intent: clarity