wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We separate the analysis into students in grade 5 and above, for which previous achievement may be used as a predictor; and students in grade 3, which must rely on family- and school-level predictors only. We train and compare a set of classifiers for reading and numeracy learning areas respectively. The classifiers achieve good predictive power in terms of area under the ROC curve, suggesting that it is feasible for schools to more accurately screen a large number of students for academic risk .
Revised:  We show that machine learning models do not outperform logistic regression for detecting students who will perform in the `below standard' band of achievement upon sitting their next test .
Edit intent: clarity
Original:  In this article we apply popular machine learning models to a large dataset (n= 2.2 million) containing primary and middle school performance on NAPLAN, a test given annually to all Australian studentsin grades 3, 5, 7, and 9.
Revised:  In this article we apply popular machine learning models to a large dataset (n= 1.2 million) containing primary and middle school performance on NAPLAN, a test given annually to all Australian studentsin grades 3, 5, 7, and 9.
Edit intent: meaning-changed
Original:  In this article we apply popular machine learning models to a large dataset (n= 2.2 million) containing primary and middle school performance on NAPLAN, a test given annually to all Australian studentsin grades 3, 5, 7, and 9.
Revised:  In this article we apply popular machine learning models to a large dataset (n= 2.2 million) containing primary and middle school performance on a standardized test given annually to all Australian studentsin grades 3, 5, 7, and 9.
Edit intent: clarity
Original:  In this article we apply popular machine learning models to a large dataset (n= 2.2 million) containing primary and middle school performance on NAPLAN, a test given annually to all Australian studentsin grades 3, 5, 7, and 9.
Revised:  In this article we apply popular machine learning models to a large dataset (n= 2.2 million) containing primary and middle school performance on NAPLAN, a test given annually to Australian students.
Edit intent: clarity
Original:  We present a complete wireless neural stimulation system consisting of a 2.2 mm^3 wireless, batteryless, leadless implantable stimulator (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Revised:  We present a complete wireless neural stimulation system consisting of a 1.7 mm^3 wireless, batteryless, leadless implantable stimulator (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Edit intent: meaning-changed
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote,  enabling complex stimulation protocols with high-temporal resolution and  closed-loop capability while reducing power consumption and on-chip memory requirements . Uplink data indicates whether the mote is currently stimulating;
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, reducing power consumption and on-chip memory requirements and enabling complex stimulation protocols with high-temporal resolution and closed-loop capability while reducing power consumption and on-chip memory requirements . Uplink data indicates whether the mote is currently stimulating;
Edit intent: coherence
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote,  enabling complex stimulation protocols with high-temporal resolution and  closed-loop capability while reducing power consumption and on-chip memory requirements . Uplink data indicates whether the mote is currently stimulating;
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with high-temporal resolution and low-latency feedback for use in closed-loop capability while reducing power consumption and on-chip memory requirements . Uplink data indicates whether the mote is currently stimulating;
Edit intent: meaning-changed
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote,  enabling complex stimulation protocols with high-temporal resolution and  closed-loop capability while reducing power consumption and on-chip memory requirements . Uplink data indicates whether the mote is currently stimulating;
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with high-temporal resolution and closed-loop stimulation . Uplink data indicates whether the mote is currently stimulating;
Edit intent: clarity
Original:  We present a complete wireless neural stimulation system consisting of a 1.7 mm^3 wireless, batteryless, leadless implantable stimulator (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Revised:  We present a complete wireless neural stimulation system consisting of a 1.7 mm3 wireless, batteryless, leadless implantable stimulator (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Edit intent: clarity
Original:  We investigate the performance of the system with motes acutely implanted with a cuff on the sciatic nerve of anesthetized rats and show highly repeatable stimulation across a wide range of physiological responses.
Revised:  We demonstrate the in vivo performance of the system with motes acutely implanted with a cuff on the sciatic nerve of anesthetized rats and show highly repeatable stimulation across a wide range of physiological responses.
Edit intent: clarity
Original:  This result , which is new even under one-way clustering, implies simple central limit theorems for sample averages . It is also key for showing the asymptotic normality of nonlinear estimators such as GMM and smooth functionals of the empirical cumulative distribution function .
Revised:  This result , which is new even under one-way clustering, implies simple central limit theorems for sample averages but is also key for showing the asymptotic normality of nonlinear estimators such as GMM and smooth functionals of the empirical cumulative distribution function .
Edit intent: coherence
Original:  Monte Carlo simulations suggest that inference based on either asymptotic normality and our variance estimator or the pigeonhole bootstrap may be accurate even with a very small number of clustersin each dimension .
Revised:  Monte Carlo simulations suggest that inference based on either asymptotic normality and our variance estimator or the pigeonhole bootstrap may be accurate even with very few clusters, and significantly improve upon inference based on Cameron et al. (2011) .
Edit intent: meaning-changed
Original:  It is often assumed that neurons are connected at random when their actual connectivity structure is unknown. Such models are then often approximated by replacing the random network by ans-R\'enyi (ER) networks, regular random (RR) networks, ring lattices, and } all-to-all network, where every neuron is connected to all other neurons.
Revised:  In this paper we study how network topology underpins the emergence of different dynamical behaviors in neuronal networks. In particular, we consider neuronal network dynamics on Erd\H{os-R\'enyi (ER) networks, regular random (RR) networks, ring lattices, and } all-to-all network, where every neuron is connected to all other neurons.
Edit intent: meaning-changed
Original:  Such models are then often approximated by replacing the random network by ans-R\'enyi (ER) networks, regular random (RR) networks, ring lattices, and } all-to-all network, where every neuron is connected to all other neurons. This mean-field approximation is a common approach in statistical physics. In this paper we show that such approximation can be invalid .
Revised:  Such models are then often approximated by replacing the random network by ans-R\'enyi (ER) networks, regular random (RR) networks, ring lattices, and } all-to-all networks .
Edit intent: coherence
Original:  We solve analytically a neuronal network model with  binary-state neurons in both random and all-to-all networks . We find strikingly different phase diagrams corresponding to each networkstructure . Neuronal network dynamics is not only different within certain parameter ranges, but it also undergoes different bifurcations .
Revised:  We solve analytically a neuronal network model with binary-state neurons in all the network topologies, except ring lattices. Given that apart from network structure, all four models are equivalent, this allows us to understand the role of network structure in neuronal network dynamics. Whilst ER and RR networks are characterized by similar phase diagrams, we find strikingly different phase diagrams corresponding to each networkstructure . Neuronal network dynamics is not only different within certain parameter ranges, but it also undergoes different bifurcations .
Edit intent: meaning-changed
Original:  We find strikingly different phase diagrams corresponding to each networkstructure . Neuronal network dynamics is not only different within certain parameter ranges, but it also undergoes different bifurcations . Our results therefore suggest cautiousness when using mean-field models based on all-to-all network topologies to represent random networks .
Revised:  We find strikingly different phase diagrams corresponding to each networkstructure . Neuronal network dynamics is not only different within certain parameter ranges, but it also undergoes different bifurcations (with a richer repertoire of bifurcations in ER and RR compared to all-to-all network topologies to represent random networks .
Edit intent: meaning-changed
Original:  Our results therefore suggest cautiousness when using mean-field models based on all-to-all network topologies to represent random networks .
Revised:  Our results therefore suggest cautiousness when using mean-field models based on all-to-all networks). This suggests that local heterogeneity in the ratio between excitation and inhibition plays a crucial role on emergent dynamics. Furthermore, we also observe one subtle discrepancy between ER and RR networks, namely ER networks undergo a neuronal activity jump at lower noise levels compared to RR networks, presumably due to the degree heterogeneity in ER networks that is absent in RR networks. Finally, a comparison between network oscillations in RR networks and ring lattices shows the importance of small-world properties in sustaining stable network oscillations .
Edit intent: meaning-changed
Original:  To wit, we solve for the competitive equilibrium prices at which liquidity takers' demands are absorbed by dealers with quadratic inventory costs, who can in turn gradually transfer these positions to an end-user market .
Revised:  To wit, we solve for the competitive equilibrium prices at which liquidity takers' demands are absorbed by dealers with quadratic inventory costs, who can in turn gradually transfer these positions to an exogenous open market with finite liquidity .
Edit intent: meaning-changed
Original:  This endogenously leads to a model with transient price impact  . Smooth, diffusive, and discrete trades all incur finite but nontrivial liquidity costs, and can arise naturally from the liquidity takers' optimization.
Revised:  This endogenously leads to transient price impact . Smooth, diffusive, and discrete trades all incur finite but nontrivial liquidity costs, and can arise naturally from the liquidity takers' optimization.
Edit intent: clarity
Original:  This endogenously leads to a model with transient price impact  . Smooth, diffusive, and discrete trades all incur finite but nontrivial liquidity costs, and can arise naturally from the liquidity takers' optimization.
Revised:  This endogenously leads to a model with transient price impact in the dealer market . Smooth, diffusive, and discrete trades all incur finite but nontrivial liquidity costs, and can arise naturally from the liquidity takers' optimization.
Edit intent: meaning-changed
Original: Dynamics of complex biological systems is driven by intricate networks, the current knowledge of which are often incomplete. The traditional systems biology modeling usually implements an ad hoc fixed set of differential equations with predefined function forms. Such an approach often suffers from overfitting or underfitting and thus inadequate predictive power, especially when dealing with systems of high complexity. This problem could be overcome by deep neuron network (DNN) . Choosing pattern formation of the gap genes in Drosophila early embryogenesis as an example, we established a differential equation model whose synthesis term is expressed as a DNN.
Revised: Complex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN) . Choosing pattern formation of the gap genes in Drosophila early embryogenesis as an example, we established a differential equation model whose synthesis term is expressed as a DNN.
Edit intent: coherence
Original:  This problem could be overcome by deep neuron network (DNN) . Choosing pattern formation of the gap genes in Drosophila early embryogenesis as an example, we established a differential equation model whose synthesis term is expressed as a DNN. The model yields perfect fitting and impressively accurate predictions on mutant patterns. We further mapped the trained DNN into a simplified conventional regulation network, which is consistent with the existing body of knowledge. The DNN model could lay a foundation of "in-silico-embryo", which can regenerate a great variety of interesting phenomena, and on which one can perform all kinds of perturbations to discover underlying mechanisms.
Revised:  This problem could be overcome by deep neuron network (DNN) , a form of machine learning or artificial intelligence. Specifically, the DNN model could lay a foundation of "in-silico-embryo", which can regenerate a great variety of interesting phenomena, and on which one can perform all kinds of perturbations to discover underlying mechanisms.
Edit intent: clarity
Original:  The DNN model could lay a foundation of "in-silico-embryo", which can regenerate a great variety of interesting phenomena, and on which one can perform all kinds of perturbations to discover underlying mechanisms.
Revised:  The DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform all kinds of perturbations to discover underlying mechanisms.
Edit intent: meaning-changed
Original:  The DNN model could lay a foundation of "in-silico-embryo", which can regenerate a great variety of interesting phenomena, and on which one can perform all kinds of perturbations to discover underlying mechanisms. This approach can be readily applied to a variety of complex biological systems .
Revised:  The DNN model could lay a foundation of "in-silico-embryo", which can regenerate a great variety of interesting phenomena, and on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation - the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN "black box". Our interpretable DNN approach should have broad applications in genotype-phenotype mapping .
Edit intent: meaning-changed
Original:  This sampling scheme is widely believed to be a wiring constraint whereby high resolution at the centre is achieved by sacrificing spatial acuityin the periphery. Here we propose that this sampling scheme may be optimal for object recognition because the relevant spatial content is dense near an object and sparse in the surrounding vicinity.
Revised:  This sampling scheme is widely believed that this high foveal resolution evolved at the expense of peripheral acuity. But what if this sampling scheme may be optimal for object recognition because the relevant spatial content is dense near an object and sparse in the surrounding vicinity.
Edit intent: clarity
Original:  Here we propose that this sampling scheme may be optimal for object recognition because the relevant spatial content is dense near an object and sparse in the surrounding vicinity. We tested this hypothesisby training deep convolutional neural networks on full-resolution and foveatedimages.
Revised:  Here we propose that this sampling scheme may be optimal for object recognition ? To test this hypothesis, we trained deep neural networks on full-resolution and foveatedimages.
Edit intent: clarity
Original:  We tested this hypothesisby training deep convolutional neural networks on full-resolution and foveatedimages. Our main finding is that networks trained on images with foveated sampling show better object classification compared to networks trained on full resolution images. Importantly, blurring images according to the human blur function yielded the best performance compared to images with shallower or steeper blurring.
Revised:  We tested this hypothesisby training deep convolutional neural networks on 'foveated' images with high resolution near objects and increasingly sparse sampling into the periphery. Neural networks trained using a blur profile matching the human eye yielded the best performance compared to images with shallower or steeper blurring.
Edit intent: meaning-changed
Original:  Importantly, blurring images according to the human blur function yielded the best performance compared to images with shallower or steeper blurring. Taken together our results suggest that, peripheral blurring in our eyes may have evolved for optimal object recognition , rather than merely to satisfy wiring constraints.
Revised:  Importantly, blurring images according to the human blur function yielded the best performance compared to shallower and steeper blur profiles. Even in humans, categorization accuracy deteriorated only for steeper blur profiles. Thus, our blurry peripheral vision may have evolved for optimal object recognition , rather than merely to satisfy wiring constraints.
Edit intent: meaning-changed
Original:  Yet, despite their importance within applications, and in comparison to their counterpart stochastic models in genetics or mathematical biology, there exist few relevant approaches for transient inference and uncertainty quantification tasks in these systems. This is a consequence of strong computational impediments and distinctive properties of the Markov jump processes induced by queueing networks .
Revised:  Yet, despite their importance within applications, and in comparison to their counterpart stochastic models in genetics or mathematical biology, the stochastic (jump) processes induced by queueing networks .
Edit intent: clarity
Original:  This is a consequence of strong computational impediments and distinctive properties of the Markov jump processes induced by queueing networks . In this paper, we offer a comprehensive overview of the inferential challenge and its comparison to analogue tasks within related mathematical domains. We then discuss a model augmentation over an approximating network system, and present a flexible and scalable variational Bayesian framework , which is targeted at general-form open and closed queueing systems, with varied service disciplines and priorities. The inferential procedure is finally validated in a couple of uncertainty quantification tasks for network service rates .
Revised:  This is a consequence of strong computational impediments and distinctive properties of the Markov jump processes induced by queueing networks have distinctive coupling and synchronization properties. This has prevented the derivation of variational approximations for conditional representations of transient dynamics, which rely on simplifying independence assumptions. Here, we present a model augmentation to a multivariate counting process for interactions across service stations, and we enable the variational evaluation of mean-field measures for partially-observed multi-class networks. We also show that our framework offers an efficient and improved alternative for inference tasks, where existing variational or numerically intensive solutions do not work .
Edit intent: meaning-changed
Original:  This stands in contrast with structure prediction of proteins and of multi-protein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information.
Revised:  Our mutual information-based method also provides signatures of the existence of interactions between protein families. These results stand in contrast with structure prediction of proteins and of multi-protein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information.
Edit intent: meaning-changed
Original: We investigated frictional effects on the folding rates of a human Telomerase hairpin (hTR HP) and H-type pseudoknot from the Beet Western Yellow Virus (BWYV PK) using simulations of the Three Interaction Site (TIS) model for RNA. The heat capacity from TIS model simulations, calculated using temperature replica exchange simulations, reproduces nearly quantitatively the available experimental data for the hTR HP.
Revised: We investigated frictional effects on the folding rates of a human telomerase hairpin (hTR HP) and H-type pseudoknot from the Beet Western Yellow Virus (BWYV PK) using simulations of the Three Interaction Site (TIS) model for RNA. The heat capacity from TIS model simulations, calculated using temperature replica exchange simulations, reproduces nearly quantitatively the available experimental data for the hTR HP.
Edit intent: fluency
Original:  We calculated the folding rates (k_F s ) from more than 100 folding trajectories for each value of the solvent viscosity (\eta) at a fixed salt concentration of 200 mM.
Revised:  We calculated the folding rates (k_F ) from more than 100 folding trajectories for each value of the solvent viscosity (\eta) at a fixed salt concentration of 200 mM.
Edit intent: clarity
Original:  Using the theoretical estimate (\proptoN where N is  number of nucleotides) for folding free energy barrier, k_F data for both the RNAs are quantitatively fit using one dimensional Kramers' theory with two parameters specifying the curvatures in the unfolded basin and the barrier top.
Revised:  By using the theoretical estimate (\proptoN where N is number of nucleotides) for folding free energy barrier, k_F data for both the RNAs are quantitatively fit using one dimensional Kramers' theory with two parameters specifying the curvatures in the unfolded basin and the barrier top.
Edit intent: coherence