wanyu/IteraTeR_full_sent huggingface dataset:
Original: Progress to-date towards the ambitious global 2030 agenda for sustainable development has been limited, and upheaval from the COVID-19 pandemic will further exacerbate the already significant challenges to Sustainable Development Goal (SDG) achievement. Here, we undertake a model-based global integrated assessment to characterise alternative pathways towards 36 time-bound, science-driven targets by 2030, 2050, and 2100.
Revised: Progress to-date towards the ambitious global 2030 has fallen short of expectations. We undertake a model-based global integrated assessment to characterise alternative pathways towards 36 time-bound, science-driven targets by 2030, 2050, and 2100.
Edit intent: clarity
Original:  Here, we undertake a model-based global integrated assessment to characterise alternative pathways towards 36 time-bound, science-driven targets by 2030, 2050, and 2100. We show that it will be unlikely to jointly achieve socioeconomic and environmental targets by 2030, even under the most optimistic pathways and the least ambitious targets. Nonetheless, humanity can still avoid destabilisation of the Earth system and increase socioeconomic prosperity post-2030 via a Green Recovery pathway. A Green Recovery by mid- and end of the century requires reducing global population by 5\% and 26\%, empowering sustainable economic development by 32\% and 52\%, increasing education availability by 10\% and 40\%, reducing the total global fossil energy production by 36\% and 80\%, reducing agricultural land area by 7\% and 10\%, and promoting healthy and sustainable lifestyles by lowering consumption of animal-based foods (i.e., meat and dairy)
Revised:  Here, we undertake a model-based global integrated assessment to characterise alternative pathways towards 36 time-bound, science-driven targets by 2030, 2050, and 2100 and to characterise the transformations needed to deliver the global Sustainable Development Goals and an increasingly ambitious 21st century sustainability agenda. Our results quantify the scale and pace of transformations required through eight key entry points: increasing education access, powering sustainable economic development by 32\% and 52\%, increasing education availability by 10\% and 40\%, reducing the total global fossil energy production by 36\% and 80\%, reducing agricultural land area by 7\% and 10\%, and promoting healthy and sustainable lifestyles by lowering consumption of animal-based foods (i.e., meat and dairy)
Edit intent: meaning-changed
Original:  A Green Recovery by mid- and end of the century requires reducing global population by 5\% and 26\%, empowering sustainable economic development by 32\% and 52\%, increasing education availability by 10\% and 40\%, reducing the total global fossil energy production by 36\% and 80\%, reducing agricultural land area by 7\% and 10\%, and promoting healthy and sustainable lifestyles by lowering consumption of animal-based foods (i.e., meat and dairy) by 39\% and 50\%, compared to the business-as-usual trajectories for 2050 and 2100, respectively. Our results show that the combination of these changes together towards extended, more ambitious goals by 2050 and 2100 is central to the transformative change needed to ensure that both people and planet prosper in medium- and long-term futures .
Revised:  A Green Recovery by mid- and end of the century requires reducing global population by 5\% and 26\%, empowering sustainable economic development , controlling global population growth, lowering energy intensity across sectors, decarbonising energy systems, promoting healthy food diets, limiting agricultural land expansion, and 2100 is central to the transformative change needed to ensure that both people and planet prosper in medium- and long-term futures .
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Original:  Our results show that the combination of these changes together towards extended, more ambitious goals by 2050 and 2100 is central to the transformative change needed to ensure that both people and planet prosper in medium- and long-term futures .
Revised:  Our results show that the combination of these changes together towards extended, more ambitious goals by 2050 and reducing global emissions intensity. Our findings indicate many actions that appear to make a limited contribution to initial progress are in fact vital for accelerating change towards sustainable development later in the century .
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Original:  Specifically, an investor considers alternative strategies that co-move with the benchmark and whose terminal wealth lies within a Wasserstein ball surrounding  it.
Revised:  Specifically, an investor considers alternative strategies that co-move with the benchmark and whose terminal wealth lies within a Wasserstein ball surrounding a benchmark's -- being distributionally close -- and that have a specified dependence/copula -- tying state-by-state outcomes -- to it.
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Original:   Finally, we illustrate how investors with different  risk preferences invest and improve upon the benchmark using the Tail Value-at-Risk, inverse S-shaped distortion risk measures , and lower- and upper-tail  risk measures as examples.
Revised:  We further propose a simulation approach to calculate the optimal strategy's terminal wealth, making our approach applicable to a wide range of market models. Finally, we illustrate how investors with different risk preferences invest and improve upon the benchmark using the Tail Value-at-Risk, inverse S-shaped distortion risk measures , and lower- and upper-tail risk measures as examples.
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Original:   Finally, we illustrate how investors with different  risk preferences invest and improve upon the benchmark using the Tail Value-at-Risk, inverse S-shaped distortion risk measures , and lower- and upper-tail  risk measures as examples.
Revised:  Finally, we illustrate how investors with different copula and risk preferences invest and improve upon the benchmark using the Tail Value-at-Risk, inverse S-shaped distortion risk measures , and lower- and upper-tail risk measures as examples.
Edit intent: meaning-changed
Original:   Finally, we illustrate how investors with different  risk preferences invest and improve upon the benchmark using the Tail Value-at-Risk, inverse S-shaped distortion risk measures , and lower- and upper-tail  risk measures as examples.
Revised:  Finally, we illustrate how investors with different risk preferences invest and improve upon the benchmark using the Tail Value-at-Risk, inverse S-shaped , and lower- and upper-tail risk measures as examples.
Edit intent: clarity
Original:   Finally, we illustrate how investors with different  risk preferences invest and improve upon the benchmark using the Tail Value-at-Risk, inverse S-shaped distortion risk measures , and lower- and upper-tail  risk measures as examples.
Revised:  Finally, we illustrate how investors with different risk preferences invest and improve upon the benchmark using the Tail Value-at-Risk, inverse S-shaped distortion risk measures , and lower- and upper-tail distortion risk measures as examples.
Edit intent: fluency
Original:  Our experimental results on various domains show that GYC generates counterfactual text samples exhibiting the above four properties . \%The generated counterfactuals can then be fed complementary to the existing data augmentation for improving the debiasing algorithms performance as compared to existing counterfactuals generated by token substitution . GYC generates counterfactuals that can act as test cases to evaluate a model and any text debiasing algorithm.
Revised:  Our experimental results on various domains show that GYC generates counterfactual text samples exhibiting the above four properties . GYC generates counterfactuals that can act as test cases to evaluate a model and any text debiasing algorithm.
Edit intent: coherence
Original:  Using this, we demonstrate how compositionality can enable agents to not only interact with unseen objects , but also transfer skills from one task to other in zero-shot (Can an agent, trained to pull and push twice, pull twice?)
Revised:  Using this, we demonstrate how compositionality can enable agents to not only interact with unseen objects but also transfer skills from one task to other in zero-shot (Can an agent, trained to pull and push twice, pull twice?)
Edit intent: fluency
Original:  Using this, we demonstrate how compositionality can enable agents to not only interact with unseen objects , but also transfer skills from one task to other in zero-shot (Can an agent, trained to pull and push twice, pull twice?)
Revised:  Using this, we demonstrate how compositionality can enable agents to not only interact with unseen objects , but also transfer skills from one task to another in a zero-shot (Can an agent, trained to pull and push twice, pull twice?)
Edit intent: coherence
Original:  Using this, we demonstrate how compositionality can enable agents to not only interact with unseen objects , but also transfer skills from one task to other in zero-shot (Can an agent, trained to pull and push twice, pull twice?)
Revised:  Using this, we demonstrate how compositionality can enable agents to not only interact with unseen objects , but also transfer skills from one task to other in zero-shot (Can an agent, trained to pull and push twice, pull twice?)Can an agent, trained to `pull' and `push twice', `pull twice'?
Edit intent: fluency
Original:  Using this, we demonstrate how compositionality can enable agents to not only interact with unseen objects , but also transfer skills from one task to other in zero-shot (Can an agent, trained to pull and push twice, pull twice?)
Revised:  Using this, we demonstrate how compositionality can enable agents to not only interact with unseen objects , but also transfer skills from one task to other in zero-shot (Can an agent, trained to pull and push twice, pull twice?).
Edit intent: coherence
Original:  The choice of 0<p<1 , fits the nature of the constraints to which quadrature rules are subject, thus providing a more suitable formulation for sparse quadrature recovery than the one based on \ell^1-norm minimization.
Revised:  The choice of 0<p<1 fits the nature of the constraints to which quadrature rules are subject, thus providing a more suitable formulation for sparse quadrature recovery than the one based on \ell^1-norm minimization.
Edit intent: fluency
Original:  The choice of 0<p<1 , fits the nature of the constraints to which quadrature rules are subject, thus providing a more suitable formulation for sparse quadrature recovery than the one based on \ell^1-norm minimization.
Revised:  The choice of 0<p<1 , fits the nature of the constraints to which quadrature rules are subject, thus providing a more natural formulation for sparse quadrature recovery than the one based on \ell^1-norm minimization.
Edit intent: clarity
Original:  The choice of 0<p<1 , fits the nature of the constraints to which quadrature rules are subject, thus providing a more suitable formulation for sparse quadrature recovery than the one based on \ell^1-norm minimization.
Revised:  The choice of 0<p<1 , fits the nature of the constraints to which quadrature rules are subject, thus providing a more suitable formulation for sparse quadrature recovery compared to the one based on \ell^1-norm minimization.
Edit intent: coherence
Original:  Finally  we present two numerical examples to illustrate some practical application: the space-time evaluation of the fundamental solution of  linear 1D Schr\"odinger equation where we compare our method with the one based on \ell^1-norm minimization ;
Revised:  Finally , we present two numerical examples to illustrate some practical application: the space-time evaluation of the fundamental solution of linear 1D Schr\"odinger equation where we compare our method with the one based on \ell^1-norm minimization ;
Edit intent: fluency
Original:  Finally  we present two numerical examples to illustrate some practical application: the space-time evaluation of the fundamental solution of  linear 1D Schr\"odinger equation where we compare our method with the one based on \ell^1-norm minimization ;
Revised:  Finally we present two numerical examples to illustrate some practical applications. The first concerns the fundamental solution of linear 1D Schr\"odinger equation where we compare our method with the one based on \ell^1-norm minimization ;
Edit intent: clarity
Original:  Finally  we present two numerical examples to illustrate some practical application: the space-time evaluation of the fundamental solution of  linear 1D Schr\"odinger equation where we compare our method with the one based on \ell^1-norm minimization ;
Revised:  Finally we present two numerical examples to illustrate some practical application: the space-time evaluation of the fundamental solution of the linear 1D Schr\"odinger equation where we compare our method with the one based on \ell^1-norm minimization ;
Edit intent: fluency
Original:  Finally  we present two numerical examples to illustrate some practical application: the space-time evaluation of the fundamental solution of  linear 1D Schr\"odinger equation where we compare our method with the one based on \ell^1-norm minimization ;
Revised:  Finally we present two numerical examples to illustrate some practical application: the space-time evaluation of the fundamental solution of linear 1D Schr\"odinger equation , the second example deals with the hyper-reduction of a partial differential equation modelling a nonlinear diffusion process in the framework of the reduced basis method. For both the examples we compare our method with the one based on \ell^1-norm minimization ;
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Original:  Finally  we present two numerical examples to illustrate some practical application: the space-time evaluation of the fundamental solution of  linear 1D Schr\"odinger equation where we compare our method with the one based on \ell^1-norm minimization ; the hyper-reduction of a nonlinear diffusion partial differential equation in the framework of reduced basis method .
Revised:  Finally we present two numerical examples to illustrate some practical application: the space-time evaluation of the fundamental solution of linear 1D Schr\"odinger equation where we compare our method with the one based on \ell^1-norm minimization and the one relaying on the use of the non-negative least square method. Matlab codes related to the numerical examples and the algorithms described are provided .
Edit intent: meaning-changed
Original:  The core idea is using negative sampling to keep the probability of training with unlabeled entities at a very low level . Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines.
Revised:  The key idea is to use negative sampling that, to a large extent, avoids training NER models with unlabeled entities at a very low level . Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines.
Edit intent: clarity
Original:  The core idea is using negative sampling to keep the probability of training with unlabeled entities at a very low level . Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines.
Revised:  The core idea is using negative sampling to keep the probability of training with unlabeled entities . Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines.
Edit intent: clarity
Original: The Fitzhugh-Nagumo system is a prototypical model for simulating different modes of neuronal spiking activity. We introduce a minimal model for synaptic integration  of action potentials and explore the basic properties of postsynaptic neurons thus defined.
Revised: The Fitzhugh-Nagumo system is a prototypical model for simulating different modes of neuronal spiking behaviour. We study the responses of FitzHugh-Nagumo neurons to rectangular current pulses and obtain the corresponding threshold curves for excitation. We then show how the geometry of threshold curves allows us to devise a simple model for synaptic integration of action potentials and explore the basic properties of postsynaptic neurons thus defined.
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Original:  We introduce a minimal model for synaptic integration  of action potentials and explore the basic properties of postsynaptic neurons thus defined.
Revised:  We introduce a minimal model for synaptic integration . The model is able to simulate the summation of presynaptic action potentials in qualitative agreement with biological neurons. We also explore the response of action potentials and explore the basic properties of postsynaptic neurons thus defined.
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Original:  We introduce a minimal model for synaptic integration  of action potentials and explore the basic properties of postsynaptic neurons thus defined. Our model supports two basic modes of summation that resemble the biological scenario. We compare presynaptic and postsynaptic neurons for basic noise enhancing properties such as stochastic resonance, coherence resonanceand firing rate statistics. The model can be used to investigate the response of postsynaptic neurons to different kinds of synaptic stimulation , especially in those cases where differences in synaptic connectivity are known to confer a significant functional advantage .
Revised:  We introduce a minimal model for synaptic integration of action potentials and explore the basic properties of postsynaptic neurons to two well known noise driven processes: stochastic and coherence resonance. Overall, the model offers a simple and intuitive test bed to explore various patterns of dendritic stimulation in simple neurons .
Edit intent: clarity
Original: The FitzHugh-Nagumo system is a widely used model for describing various modes of neuronal spiking behaviour. We study the responses of FitzHugh-Nagumo neurons to rectangular current pulses and obtain the corresponding threshold curves for excitation. We then show how the geometry of threshold curves allows us to devise a simple model for synaptic integration.
Revised: Synaptic integration is a prominent aspect of neuronal information processing. The detailed mechanisms that modulate synaptic inputs determine the computational properties of any given neuron. We study a simple model for synaptic integration.
Edit intent: clarity
Original:  The model is able to simulate the summation of presynaptic action potentials in qualitative agreement with biological neurons. We also explore the response of postsynaptic neurons  to two well known noise driven processes: stochastic and coherence resonance.
Revised:  The model is able to simulate the summation of excitatory inputs from synapses and illustrate its use by characterizing some functional properties of postsynaptic neurons. In this regard, we study the response of postsynaptic neurons to two well known noise driven processes: stochastic and coherence resonance.
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Original:  We evolve two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: i) spectral normalization for stability and generalization guarantees of unseen data and ii) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness.
Revised:  We make use of two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: i) spectral normalization for stability and generalization guarantees of unseen data and ii) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness.
Edit intent: clarity
Original: Previous online 3D dense reconstruction methods often cost massive memory storage while achieving unsatisfactory surface qualitymainly due to the usage of stagnant underlying geometry representation, such as TSDF (truncated signed distance functions) or surfels, without any knowledge of the scene priors.
Revised: Previous online 3D dense reconstruction methods struggle to achieve the balance between memory storage and surface quality, largely due to the usage of stagnant underlying geometry representation, such as TSDF (truncated signed distance functions) or surfels, without any knowledge of the scene priors.
Edit intent: clarity
Original:  In this paper, we present DI-Fusion (Deep Implicit Fusion), based on a novel 3D representation, called Probabilistic Local Implicit Voxels (PLIVoxs), for online 3D reconstruction using a commodity RGB-D camera.
Revised:  In this paper, we present DI-Fusion (Deep Implicit Fusion), based on a novel 3D representation, i.e. Probabilistic Local Implicit Voxels (PLIVoxs), for online 3D reconstruction using a commodity RGB-D camera.
Edit intent: coherence
Original:  In this paper, we present DI-Fusion (Deep Implicit Fusion), based on a novel 3D representation, called Probabilistic Local Implicit Voxels (PLIVoxs), for online 3D reconstruction using a commodity RGB-D camera.
Revised:  In this paper, we present DI-Fusion (Deep Implicit Fusion), based on a novel 3D representation, called Probabilistic Local Implicit Voxels (PLIVoxs), for online 3D reconstruction with a commodity RGB-D camera.
Edit intent: fluency
Original:  With such deep priors, we demonstrate by extensive experiments that we are able to perform online implicit 3D reconstruction achieving state-of-the-art mapping quality and camera trajectory estimation accuracy , while taking much less storage compared with previous online 3D reconstruction approaches. 
Revised:  With such deep priors, we are able to perform online implicit 3D reconstruction achieving state-of-the-art mapping quality and camera trajectory estimation accuracy , while taking much less storage compared with previous online 3D reconstruction approaches. 
Edit intent: clarity
Original:  With such deep priors, we demonstrate by extensive experiments that we are able to perform online implicit 3D reconstruction achieving state-of-the-art mapping quality and camera trajectory estimation accuracy , while taking much less storage compared with previous online 3D reconstruction approaches. 
Revised:  With such deep priors, we demonstrate by extensive experiments that we are able to perform online implicit 3D reconstruction achieving state-of-the-art camera trajectory estimation accuracy , while taking much less storage compared with previous online 3D reconstruction approaches. 
Edit intent: clarity
Original:  With such deep priors, we demonstrate by extensive experiments that we are able to perform online implicit 3D reconstruction achieving state-of-the-art mapping quality and camera trajectory estimation accuracy , while taking much less storage compared with previous online 3D reconstruction approaches. 
Revised:  With such deep priors, we demonstrate by extensive experiments that we are able to perform online implicit 3D reconstruction achieving state-of-the-art mapping quality and camera trajectory estimation accuracy and mapping quality, while achieving better storage efficiency compared with previous online 3D reconstruction approaches. 
Edit intent: clarity
Original:  With such deep priors, we demonstrate by extensive experiments that we are able to perform online implicit 3D reconstruction achieving state-of-the-art mapping quality and camera trajectory estimation accuracy , while taking much less storage compared with previous online 3D reconstruction approaches. 
Revised:  With such deep priors, we demonstrate by extensive experiments that we are able to perform online implicit 3D reconstruction achieving state-of-the-art mapping quality and camera trajectory estimation accuracy , while taking much less storage compared with previous online 3D reconstruction approaches. Our implementation is available at URL
Edit intent: meaning-changed