wanyu/IteraTeR_v2 huggingface dataset:
Original:  In networks employing ideal sector antennas without sidelobes, we find that the moderate assumption of a concave orientation error distribution is sufficient to prove monotonicity and quasi-concavity of outage-unconstrained and outage-constrained throughput metrics , respectively.
Revised:  In networks employing ideal sector antennas without sidelobes, we find that concavity of the orientation error distribution is sufficient to prove monotonicity and quasi-concavity of outage-unconstrained and outage-constrained throughput metrics , respectively.
Edit intent: clarity
Original: Spontaneous cortical activity -- the ongoing cortical activities in absence of  sensory input -- are considered to play a vital role in many aspects of both normal brain functions and mental dysfunctions.
Revised: Spontaneous cortical activity -- the ongoing cortical activities in absence of sensory input -- is considered to play a vital role in many aspects of both normal brain functions and mental dysfunctions.
Edit intent: fluency
Original:  We present a centered Gaussian-binary deep Boltzmann machine (GDBM) for modeling the activity in  visual cortex and relate the random sampling in DBMs to the spontaneous cortical activity.
Revised:  We present a centered Gaussian-binary Deep Boltzmann Machine (GDBM) for modeling the activity in visual cortex and relate the random sampling in DBMs to the spontaneous cortical activity.
Edit intent: fluency
Original:  We present a centered Gaussian-binary deep Boltzmann machine (GDBM) for modeling the activity in  visual cortex and relate the random sampling in DBMs to the spontaneous cortical activity.
Revised:  We present a centered Gaussian-binary deep Boltzmann machine (GDBM) for modeling the activity in visual cortex and relate the random sampling in GDBMs to the spontaneous cortical activity.
Edit intent: clarity
Original:  After training  on natural image patches, the proposed model is able to learn the filters similar to the receptive fields of simple cells in V1.
Revised:  After training the proposed model on natural image patches, the proposed model is able to learn the filters similar to the receptive fields of simple cells in V1.
Edit intent: fluency
Original:  Furthermore, we show that the samples collected from random sampling in the centered GDBMs encompass similar activity patterns as found in the spontaneous cortical activity of the visual cortex.
Revised:  Furthermore, we show that the samples collected from random sampling in the centered GDBMs encompass similar activity patterns as found in the spontaneous cortical activity of visual cortex.
Edit intent: fluency
Original:  Our work demonstrates the homeostasis learned by the centered GDBMand its potential for modeling visual cortical activity .
Revised:  Our work demonstrates the centered GDBM's potential for modeling visual cortical activity .
Edit intent: clarity
Original:  Our work demonstrates the homeostasis learned by the centered GDBMand its potential for modeling visual cortical activity .
Revised:  Our work demonstrates the homeostasis learned by the centered GDBMand its potential for modeling cortical activity in early visual cortex .
Edit intent: clarity
Original:  We present a centered Gaussian-binary Deep Boltzmann Machine (GDBM) for modeling the activity in early visual cortex and relate the random sampling in GDBMs to the spontaneous cortical activity.
Revised:  We present a centered Gaussian-binary Deep Boltzmann Machine (GDBM) for modeling the activity in early cortical visual areas and relate the random sampling in GDBMs to the spontaneous cortical activity.
Edit intent: clarity
Original:  After training the proposed model on natural image patches, we show that the samples collected from the model's probability distribution encompass similar activity patterns as found in the spontaneous cortical activityof visual cortex . Specifically, filters having the same orientation preference tend to be active together during random sampling.
Revised:  After training the proposed model on natural image patches, we show that the samples collected from the model's probability distribution encompass similar activity patterns as found in the spontaneous activity . Specifically, filters having the same orientation preference tend to be active together during random sampling.
Edit intent: clarity
Original:  Our work demonstrates the centered GDBM 's potential for modeling cortical activity in early visual cortex .
Revised:  Our work demonstrates the centered GDBM is a meaningful model approach for basic receptive field properties and the emergence of spontaneous activity patterns in early cortical visual areas .
Edit intent: clarity
Original:  Besides, we show empirically that centered GDBMs do not suffer from  difficulties during training as GDBMs do and can be properly trained without  layer-wise pretraining.
Revised:  Besides, we show empirically that centered GDBMs do not suffer from the difficulties during training as GDBMs do and can be properly trained without layer-wise pretraining.
Edit intent: fluency
Original:  Besides, we show empirically that centered GDBMs do not suffer from  difficulties during training as GDBMs do and can be properly trained without  layer-wise pretraining.
Revised:  Besides, we show empirically that centered GDBMs do not suffer from difficulties during training as GDBMs do and can be properly trained without the layer-wise pretraining.
Edit intent: fluency
Original: Deep learning has recently lead to great successes in tasks such as image recog- nition (e.g Krizhevsky et al., 2012).
Revised: Deep learning has recently lead to great successes in tasks such as image recognition (e.g Krizhevsky et al., 2012).
Edit intent: fluency
Original:  The challenge is to identify which neural mechanisms are relevant, and to find suitable abstractions to model them.
Revised:  The challenge is to identify which neuronal mechanisms are relevant, and to find suitable abstractions to model them.
Edit intent: fluency
Original: Deep learning has recently lead to great successes in tasks such as image recognition (e.g Krizhevsky et al., 2012).
Revised: Deep learning has recently led to great successes in tasks such as image recognition (e.g Krizhevsky et al., 2012).
Edit intent: fluency
Original:  However, deep networks are still outmatched by the power and versatility of the brain, perhaps in part due to the richer neuronal computations available to real cortical circuits.
Revised:  However, deep networks are still outmatched by the power and versatility of the brain, perhaps in part due to the richer neuronal computations available to cortical circuits.
Edit intent: clarity
Original:  Here, we show how aspects of spike timing, long hypothesized to play a crucial role in cortical information processing, could be incorporated into deep networks to build richer, versatile deep representations.
Revised:  Here, we show how aspects of spike timing, long hypothesized to play a crucial role in cortical information processing, could be incorporated into deep networks to build richer, versatile representations.
Edit intent: coherence
Original:  Focusing on the latter aspect , we demonstrate the potential of the approach in several simple experiments.
Revised:  Focusing on the latter , we demonstrate the potential of the approach in several simple experiments.
Edit intent: clarity
Original:  Thus, synchrony could implement a flexible mechanism that fulfills multiple functional roles in deep networks.
Revised:  Thus, neuronal synchrony could be a flexible mechanism that fulfills multiple functional roles in deep networks.
Edit intent: clarity
Original:  Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed remains finite: for a special class of initial conditions on the weights, very deep networks incur only a finite  delay in learning speed relative to shallow networks.
Revised:  Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed can nevertheless remain finite: for a special class of initial conditions on the weights, very deep networks incur only a finite delay in learning speed relative to shallow networks.
Edit intent: clarity
Original:  We further show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, thereby providing analytical insight into the success of unsupervised pretraining in deep supervised learning tasks .
Revised:  We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, thereby providing analytical insight into the success of unsupervised pretraining in deep supervised learning tasks .
Edit intent: clarity
Original:  We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, while scaled random Gaussian initilizations cannot.
Revised:  We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, while scaled random Gaussian initializations cannot.
Edit intent: fluency
Original:  We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times , and we show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate just beyond the edge of chaos.
Revised:  We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times . We further show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate just beyond the edge of chaos.
Edit intent: coherence
Original:  We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times , and we show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate just beyond the edge of chaos.
Revised:  We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times , and we show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate in a special regime known as the edge of chaos.
Edit intent: clarity
Original: This paper presents an unsupervised multi-modal learning system that learns associative representation from two input modalities (channels) such that input on one channel will correctly generate the associated response at the other channel and vice versa.
Revised: This paper presents an unsupervised multi-modal learning system that learns associative representation from two input modalities , or channels, such that input on one channel will correctly generate the associated response at the other channel and vice versa.
Edit intent: clarity
Original: This paper presents an unsupervised multi-modal learning system that learns associative representation from two input modalities (channels) such that input on one channel will correctly generate the associated response at the other channel and vice versa.
Revised: This paper presents an unsupervised multi-modal learning system that learns associative representation from two input modalities (channels) such that input on one channel will correctly generate the associated response at the other and vice versa.
Edit intent: fluency
Original:  Specifically, we find that we can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error.
Revised:  We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error.
Edit intent: coherence
Original:  Such multistability is used to encode phenotypic states in processes ranging from nutrient uptake and persistence in bacteria to cell cycle control  and development.
Revised:  Such multistability is used to encode phenotypic states in processes including nutrient uptake and persistence in bacteria to cell cycle control and development.
Edit intent: clarity
Original:  Such multistability is used to encode phenotypic states in processes ranging from nutrient uptake and persistence in bacteria to cell cycle control  and development.
Revised:  Such multistability is used to encode phenotypic states in processes ranging from nutrient uptake and persistence in bacteria to cell cycle control , and development.
Edit intent: fluency
Original:  We confirm this notion in strains with a high expression level of the repressor lacI (leading to shorter periods over which the lac-operon remains unbound), which show a reduced transition rate.
Revised:  We confirm this notion in strains with a high expression level of the repressor lacI (leading to shorter periods over which the lac-operon remains unbound), which show a reduced switching rate.
Edit intent: clarity
Original:  Our techniques apply to multistable gene regulatory systems in general and allow to identify the molecular mechanisms behind stochastic transitions in gene regulatory circuits.
Revised:  Our techniques apply to multi-stable gene regulatory systems in general and allow to identify the molecular mechanisms behind stochastic transitions in gene regulatory circuits.
Edit intent: fluency
Original:  When trained on one task, then trained on a second task, many machine learning models "forget '' how to perform the first task.
Revised:  When trained on one task, then trained on a second task, many machine learning models "forget " how to perform the first task.
Edit intent: fluency
Original:  Ethical issues and complicated access to the IDU population make it difficult to gather epidemiological data.
Revised:  Ethical issues and complicated access to the PWID population make it difficult to gather epidemiological data.
Edit intent: clarity
Original:  This review presents compartmental and individual-based models in stochastic and deterministic frameworks in order to underline their strengths and limits in the context of HCV infection among IDUs . The final section discusses the main results of the papers.
Revised:  This review presents compartmental and individual-based models in order to underline their strengths and limits in the context of HCV infection among IDUs . The final section discusses the main results of the papers.
Edit intent: clarity
Original:  This review presents compartmental and individual-based models in stochastic and deterministic frameworks in order to underline their strengths and limits in the context of HCV infection among IDUs . The final section discusses the main results of the papers.
Revised:  This review presents compartmental and individual-based models in stochastic and deterministic frameworks in order to underline their strengths and limits in the context of HCV infection among PWID . The final section discusses the main results of the papers.
Edit intent: clarity
Original:  R. Soc. A  ). Here we show that the originally suggested fixation probability, 1- r^{-(H-2)}%DIFDELCMD < } %%% for a single mutant with fitness r on a superstar with structural parameter H and population size N%DIFDELCMD < \to%%% \infty, is indeed too optimistic.
Revised:  R. Soc. A ). Here we r^{-(H-2)}%DIFDELCMD < } %%% for a single mutant with fitness r on a superstar with structural parameter H and population size N%DIFDELCMD < \to%%% \infty, is indeed too optimistic.
Edit intent: coherence
Original:  R. Soc. A  ). Here we show that the originally suggested fixation probability, 1- r^{-(H-2)}%DIFDELCMD < } %%% for a single mutant with fitness r on a superstar with structural parameter H and population size N%DIFDELCMD < \to%%% \infty, is indeed too optimistic.
Revised:  R. Soc. A ). Here we show that the originally suggested fixation probability, 1- r^{-(H-2)}%DIFDELCMD < } %%% %DIFDELCMD < \to%%% \infty, is indeed too optimistic.
Edit intent: coherence
Original:  More specifically, we derive improved upper and lower bounds, which indicate that the fixation probability scales at best as 1-1/(r^4 H) for  N\to\infty and  H\gg1.
Revised:  More specifically, we derive improved upper and lower bounds, which indicate that the fixation probability remains close to 1-1/(r^4 H) for N\to\infty and H\gg1.
Edit intent: clarity
Original:  However, we also confirm that in the limit N,H\to\infty superstars remain capable of eliminating random drift and hence provide arbitrarily strong selective advantages to any beneficial mutation.
Revised:  We also confirm that in the limit N,H\to\infty superstars remain capable of eliminating random drift and hence provide arbitrarily strong selective advantages to any beneficial mutation.
Edit intent: coherence
Original:  However, we also confirm that in the limit N,H\to\infty superstars remain capable of eliminating random drift and hence provide arbitrarily strong selective advantages to any beneficial mutation.
Revised:  However, we also confirm that in the limit N,H\to\infty superstars remain capable of eliminating random drift and hence of providing arbitrarily strong selective advantages to any beneficial mutation.
Edit intent: coherence
Original:  In addition, we investigate the robustness of the dynamical features of superstars and find that evolutionary amplification appears to be fragile with respect to changes in selection or mutational process .
Revised:  In addition, we investigate the robustness of amplification in superstars and find that evolutionary amplification appears to be fragile with respect to changes in selection or mutational process .
Edit intent: clarity
Original:  In addition, we investigate the robustness of the dynamical features of superstars and find that evolutionary amplification appears to be fragile with respect to changes in selection or mutational process .
Revised:  In addition, we investigate the robustness of the dynamical features of superstars and find that it appears to be fragile with respect to changes in selection or mutational process .
Edit intent: clarity
Original:  In addition, we investigate the robustness of the dynamical features of superstars and find that evolutionary amplification appears to be fragile with respect to changes in selection or mutational process .
Revised:  In addition, we investigate the robustness of the dynamical features of superstars and find that evolutionary amplification appears to be a fragile phenomenon with respect to changes in selection or mutational process .
Edit intent: clarity
Original:  In addition, we investigate the robustness of the dynamical features of superstars and find that evolutionary amplification appears to be fragile with respect to changes in selection or mutational process .
Revised:  In addition, we investigate the robustness of the dynamical features of superstars and find that evolutionary amplification appears to be fragile with respect to changes in the selection or mutation processes .
Edit intent: fluency
Original:  In a series of computational experiments, we compare the performance of the MIP formulations as well as the heuristics , and we find small instances for which the heuristics fail to find an optimal solution .
Revised:  In a series of computational experiments, we compare the performance of the MIP formulations as well as the heuristics .
Edit intent: coherence
Original:  In the last decade, the fisheries stock assessment modeling framework Stock Synthesis (SS3) has become widely-used around the world.
Revised:  In the last decade, the fisheries stock assessment modeling framework Stock Synthesis (SS3) has become widely used around the world.
Edit intent: fluency
Original:  Here, we introduce ss3sim, an R package that facilitates large-scale, rapid, and reproducible end-to-end simulation testing with SS3. ss3sim requires an existing SS3 model configuration along with plain-text control files describing alternative population dynamics, fishery properties, sampling scenarios, and assessment approaches. ss3sim then generates an underlying truth , samples from that truth, modifies and runs an estimation model, and synthesizes the results.
Revised:  Here, we introduce ss3sim, an R package that facilitates reproducible, flexible, and rapid end-to-end simulation testing with SS3. ss3sim requires an existing SS3 model configuration along with plain-text control files describing alternative population dynamics, fishery properties, sampling scenarios, and assessment approaches. ss3sim then generates an underlying truth , samples from that truth, modifies and runs an estimation model, and synthesizes the results.
Edit intent: clarity
Original:  The simulations can be run in parallel, speeding computation , and the source code is free to be modified under an open-source MIT license. ss3sim is designed to explore structural differences between the underlying truth and assumptions of an estimation model, or between multiple estimation model configurations.
Revised:  The simulations can be run in parallel, reducing runtime , and the source code is free to be modified under an open-source MIT license. ss3sim is designed to explore structural differences between the underlying truth and assumptions of an estimation model, or between multiple estimation model configurations.
Edit intent: clarity
Original:  We demonstrate the software with a simple example, discuss how ss3sim complements other simulation software, and outline specific research questions that ss3sim could address.
Revised:  We demonstrate the software with an example, discuss how ss3sim complements other simulation software, and outline specific research questions that ss3sim could address.
Edit intent: clarity
Original: The Grundy Number of a graph G, denoted by \Gamma(G), is the largest k such that there exists a partition of V(G), into k independent sets V1,..., Vk and every vertex in Vi is adjacent to at least one vertex in Vj , for every j < i.
Revised: The Grundy number of a graph G, denoted by \Gamma(G), is the largest k such that there exists a partition of V(G), into k independent sets V1,..., Vk and every vertex in Vi is adjacent to at least one vertex in Vj , for every j < i.
Edit intent: fluency
Original: Various rod-shaped bacteria such as the canonical gram negative Escherichia coli or the well-studied gram positive \emph{Bacillus subtilis elongate while maintaining a constant radius.
Revised: Various rod-shaped bacteria such as the canonical gram negative Escherichia coli or the well-studied gram positive \emph{Bacillus subtilis elongate while maintaining a constant radius.
Edit intent: fluency
Original:  Budding Yeast ( Saccharomyces cerevisiae ) cells have a different mode of growth, where the division is asymmetric.
Revised:  Budding Yeast ( Saccharomyces cerevisiae ) cells have a different mode of growth, where the division is asymmetric.
Edit intent: fluency
Original:  Existing data for E. coli and budding yeast supports partial size control, and a particular explanation: a cell attempts to add a constant volume to its initial one.
Revised:  Existing data for E. coli and budding yeast supports partial size control, and a particular explanation: a cell attempts to add a constant volume to its initial one.
Edit intent: fluency
Original:  Budding Yeast (Saccharomyces cerevisiae) cells have a different mode of growth, where the division is asymmetric.
Edit intent: coherence
Original:  Here, we propose an analytically tractable model for cell size control in microorganisms, under the assumption that noise is additive to the inter-division time, and calculate the distributions of cell sizes and inter-division times .
Revised:  Here, we propose an analytically tractable model for cell size control , and calculate the distributions of cell sizes and inter-division times .
Edit intent: clarity