wanyu/IteraTeR_full_sent huggingface dataset:
Original: High-resolution digital microscopy provides powerful tools for probing the real-time dynamics of subcellular structures, and adequate record-keeping is necessary to evaluate results, share data, and allow experiments to be repeated. In addition to advances in microscopic techniques, post-acquisition procedures such as image-data processing and analysis are often required for the reproducible and quantitative interpretationof images. While these techniques increase the usefulness of microscopy data, the limits to which quantitative results may be interpreted are often poorly quantified and documented. Keeping notes on microscopy experiments and calibration procedures should be relatively unchallenging, as the microscope is a machine whose performance should be easy to assess. Nevertheless, to this date, no widely adopted data provenance and quality control metadata guidelines to be recorded or published with imaging data exist. Metadata automatically recorded by microscopes from different companies vary widely and pose a substantial challenge for microscope users to create a good faith record of their work. Similarly, the complexity and aim of experiments using microscopes vary, leading to different reporting and quality control requirements from the simple description of a sample to the need to document the complexities of sub-diffraction resolution imaging in living cells and beyond. To solve this problem, the 4DN Imaging Standards Working Group has put forth a tiered system of microscopy calibration and metadata standards for images obtained through fluorescence microscopy.
Revised: High-resolution digital microscopy provides powerful tools for probing the real-time dynamics of subcellular structures, and adequate record-keeping is necessary to evaluate results, share data, and allow experiments to be reproducibly repeated, and promote the proper evaluation, interpretation, comparison, and re-use. To this end, microscopy images should be accompanied by complete descriptions detailing experimental procedures, biological samples, microscope hardware specifications, image acquisition parameters, and image analysis procedures, as well as metrics accounting for instrument performance and calibration. However, universal, community-accepted Microscopy Metadata standards and reporting specifications that would result in Findable Accessible Interoperable and Reproducible (FAIR) microscopy data have not yet been established. To understand this shortcoming and to propose a way forward, here we provide an overview of the nature of microscopy metadata and its importance for fostering data quality, reproducibility, scientific rigor, and sharing value in light microscopy.
Edit intent: meaning-changed
Original:  The proposal is an extension of the OME data model and aims at increasing data fidelity, ease future analysis, and facilitate objective comparison of different datasets, experimental setups, and essays ]   .
Revised:  The proposal is an extension of the OME data model and aims at increasing data fidelity, ease future analysis, and facilitate objective comparison of different datasets, experimental setups, and essays 1-3] .
Edit intent: meaning-changed
Original:  The proposal is an extension of the OME data model and aims at increasing data fidelity, ease future analysis, and facilitate objective comparison of different datasets, experimental setups, and essays ]   .
Revised:  The proposal is an extension of the OME data model and aims at increasing data fidelity, ease future analysis, and facilitate objective comparison of different datasets, experimental setups, and essays ] as well as a suite of three complementary and interoperable tools are being developed to facilitate the process of image data documentation and are presented in related manuscripts .
Edit intent: meaning-changed
Original:  The proposal is an extension of the OME data model and aims at increasing data fidelity, ease future analysis, and facilitate objective comparison of different datasets, experimental setups, and essays ]   .
Revised:  The proposal is an extension of the OME data model and aims at increasing data fidelity, ease future analysis, and facilitate objective comparison of different datasets, experimental setups, and essays ] 4-6 .
Edit intent: meaning-changed
Original:  This enables the utilization and exploration of tiered molecular latent spaces, either individually -- the node (atom) tier, the group tier, or the graph (molecule) tier -- or jointly, as well as navigation across the tiers.
Revised:  This enables the utilization and exploration of tiered molecular latent spaces, either individually - the node (atom) tier, the group tier, or the graph (molecule) tier -- or jointly, as well as navigation across the tiers.
Edit intent: fluency
Original:  This enables the utilization and exploration of tiered molecular latent spaces, either individually -- the node (atom) tier, the group tier, or the graph (molecule) tier -- or jointly, as well as navigation across the tiers.
Revised:  This enables the utilization and exploration of tiered molecular latent spaces, either individually -- the node (atom) tier, the group tier, or the graph (molecule) tier - or jointly, as well as navigation across the tiers.
Edit intent: fluency
Original:  Further, tiered graph autoencoders and graph classification together provide effective, efficient and interpretable deep learning for molecular graphs, with the former providing unsupervised, transferable learning and the latter providing supervised, task-optimized learning.
Revised:  Further, tiered graph autoencoders and graph prediction together provide effective, efficient and interpretable deep learning for molecular graphs, with the former providing unsupervised, transferable learning and the latter providing supervised, task-optimized learning.
Edit intent: clarity
Original:   In this paper, we propose a generic strategy for heritability inference, termed as "boosting heritability" , by combining several advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model.
Revised:  Results: In this paper, we propose a generic strategy for heritability inference, termed as "boosting heritability" , by combining several advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model.
Edit intent: fluency
Original:   In this paper, we propose a generic strategy for heritability inference, termed as "boosting heritability" , by combining several advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model.
Revised:  In this paper, we propose a generic strategy for heritability inference, termed as ``boosting heritability" , by combining several advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model.
Edit intent: fluency
Original:   In this paper, we propose a generic strategy for heritability inference, termed as "boosting heritability" , by combining several advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model.
Revised:  In this paper, we propose a generic strategy for heritability inference, termed as "boosting heritability" , by combining the advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model.
Edit intent: clarity
Original:  We use  antibiotic resistance data from a major human pathogen, Sptreptococcus pneumoniae , to demonstrate the applicability of our inference strategy  .
Revised:  We use antibiotic resistance data from a major human pathogen, Sptreptococcus pneumoniae , to demonstrate the applicability of our inference strategy . Conclusions: Boosting is shown to offer a reliable and practically useful tool for inference about heritability .
Edit intent: meaning-changed
Original:  This approach is particularly useful when  data noise can be represented as a probabilistic distribution: from the point of view of nonparametric regression, the wQISA estimator is robust to random data perturbation such as noise and outliers.
Revised:  This approach is particularly useful when the data noise can be represented as a probabilistic distribution: from the point of view of nonparametric regression, the wQISA estimator is robust to random data perturbation such as noise and outliers.
Edit intent: fluency
Original:  This approach is particularly useful when  data noise can be represented as a probabilistic distribution: from the point of view of nonparametric regression, the wQISA estimator is robust to random data perturbation such as noise and outliers.
Revised:  This approach is particularly useful when data noise can be represented as a probabilistic distribution: from the point of view of nonparametric regression, the wQISA estimator is robust to random perturbations, such as noise and outliers.
Edit intent: fluency
Original:  We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation, in stress scenarios, faced by the system as a whole and the individual banks, and (2) the value of providing bailouts and bail-ins to consider when such notions are financially advisable.
Revised:  We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation, in stress scenarios, faced by the system as a whole and the individual banks, and (2) the value of providing bailouts to consider when such notions are financially advisable.
Edit intent: clarity
Original:  Our formulation circumvents the first-stage regression which can be a bottleneck in modern two-stage procedures for IV regression. We also show that our framework is closely related to the generalized method of moments (GMM) with specific assumptions. This highlights the fundamental connection between GMM and two-stage procedures in IV literature. Using the proposed framework , we develop a simple kernel-based algorithm with consistency guarantees.
Revised:  Our formulation circumvents the first-stage regression which is a potential bottleneck in real-world applications. Based on this new approach , we develop a simple kernel-based algorithm with consistency guarantees.
Edit intent: clarity
Original:  Based on this new approach, we develop a simple kernel-based algorithm with a closed-form solution . Empirical results show that we are competitive to existing, more complicated algorithms for  instrumental variable regression.
Revised:  Based on this new approach, we develop a simple kernel-based algorithm with an analytic solution based on this formulation . Empirical results show that we are competitive to existing, more complicated algorithms for instrumental variable regression.
Edit intent: meaning-changed
Original:  Based on this new approach, we develop a simple kernel-based algorithm with a closed-form solution . Empirical results show that we are competitive to existing, more complicated algorithms for  instrumental variable regression.
Revised:  Based on this new approach, we develop a simple kernel-based algorithm with a closed-form solution . Empirical results show that we are competitive to existing, more complicated algorithms for non-linear instrumental variable regression.
Edit intent: meaning-changed
Original:  Here, we present a neural network architecture modelled on the  corticothalamic connections and the behaviour of the thalamus: a corticothalamic neural network (CTNN) .
Revised:  Here, we present a neural network architecture modelled on the top-down corticothalamic connections and the behaviour of the thalamus: a corticothalamic neural network (CTNN) .
Edit intent: meaning-changed
Original:  Here, we present a neural network architecture modelled on the  corticothalamic connections and the behaviour of the thalamus: a corticothalamic neural network (CTNN) . The CTNN presented in this paper consists of an auto-encoder connected to a difference engine , which is inspired by the behaviour of the thalamus .
Revised:  Here, we present a neural network architecture modelled on the corticothalamic connections and the behaviour of the thalamus: a corticothalamic neural network (CTNN) , consisting of an auto-encoder connected to a difference engine , which is inspired by the behaviour of the thalamus .
Edit intent: coherence
Original:  This research helps us understand how the human brain is able to provide contextual awareness to an object in the field of perception, handle robustness in a case of partial sensory occlusion, and achieve a high degree of autonomous behaviour while completing complex tasks such as driving a car .
Revised:  This increased efficiency could be highly significant in more complex implementations of this architecture, where the predictive nature of the cortex will allow most of the incoming data to be discarded .
Edit intent: coherence
Original:  These tests require us to overcome an identification problem  when the measures of central tendency are equal or in a local neighborhood of each other, as is the case for (exactly or nearly) symmetric and unimodal distributions.
Revised:  These tests require us to overcome an identification problem that arises when the measures of central tendency are equal or in a local neighborhood of each other, as is the case for (exactly or nearly) symmetric and unimodal distributions.
Edit intent: clarity
Original: Insurers record detailed information related to claims (e.g. the cause of the claim) and policies (e.g. the value of the insured risk) for pricing insurance contracts.
Revised: Traditional non-life reserving models largely neglect the vast amount of information collected over the lifetime of a claim. This information includes covariates describing the policy (e.g. the value of the insured risk) for pricing insurance contracts.
Edit intent: meaning-changed
Original: Insurers record detailed information related to claims (e.g. the cause of the claim) and policies (e.g. the value of the insured risk) for pricing insurance contracts. However, this information is largely neglected when estimating the reserve for future liabilities originating from past exposures.We present a flexible, yet highly interpretable framework for including these claimand policy-specific covariates in a reserving model. Our framework focuses on three building blocks in the development process of a claim: the time to settlement, the number of payments and the size of each payment.
Revised: Insurers record detailed information related to claims (e.g. the cause of the claim) and policies (e.g. the value of the insured risk) , claim cause (e.g. hail) as well as the detailed claim's history (e.g. settlement, payment, involvement lawyer). We present the hierarchical reserving model as a modular framework for integrating a claim's history and claim-specific covariates into the development process of a claim: the time to settlement, the number of payments and the size of each payment.
Edit intent: coherence
Original:  Our framework focuses on three building blocks in the development process of a claim: the time to settlement, the number of payments and the size of each payment. We carefully choose a generalized linear model (GLM) to model each of these stochastic building blocks in discrete time. Since GLMs are applied in the pricing of insurance contracts, our project bridges the gap between pricing and reserving methodology. We propose model selection techniques for GLMs adapted for censored data to select the relevant covariates in these modelsand demonstrate how the selected covariatesdetermine the granularity of our reserving model. At one extreme, including many covariates captures the heterogeneity in the development process of individual claims, while at the other extreme, including no covariatescorresponds to specifying a model for data aggregated in two-dimensional contingency tables, similar to the run-off triangles traditionally used by reservingactuaries. The set of selected covariates then naturally determines the position the actuary should take in between those two extremes .
Revised:  Our framework focuses on three building blocks in the development process of a claim: the time to settlement, the development process over time. Moreover, they are tailored to the portfolio at hand by adding a layer to the model for each of the registered events (e.g. settlement, payment). Layers are modelled with classical techniques (e.g. generalized linear models) or machine learning methods (e.g. gradient boosting machines) and using claim-specific covariates .
Edit intent: meaning-changed
Original:   We illustrate our method with case studies on real life insurance data sets.
Revised:  As a result of its flexibility, this framework incorporates many existing reserving models, ranging from aggregate models designed for runoff triangles to individual models using claim-specific covariates. This connection allows us to develop a data-driven strategy for choosing between aggregate and individual reserving; an important decision for reserving practitioners that is largely left unexplored in scientific literature. We illustrate our method with case studies on real life insurance data sets.
Edit intent: coherence
Original:   We illustrate our method with case studies on real life insurance data sets. These case studies provide new insights in the covariates driving the development of claims and demonstrate the accuracy and robustness of the reserving methodology over time.
Revised:  We illustrate our method with case studies on real life insurance data set. This case study provides new insights in the covariates driving the development of claims and demonstrate the accuracy and robustness of the reserving methodology over time.
Edit intent: fluency
Original:  In particular,  for deterministic models it is well known that weak reversibility and a deficiency zero of the reaction network imply that the model is complex balanced .
Revised:  In particular, for deterministic models , which date back to the early 1970s, focus on models with a deficiency of zero and connect them to detailed-balanced models. Moreover, the largest open conjecture related to deterministic models, the so-called Global Attractor Conjecture, focuses on models with a deficiency of zero .
Edit intent: meaning-changed
Original:  In the stochastic setting it is known that weak reversibility and a deficiency of zero imply the existence of a stationary distribution that is a product of Poissons.
Revised:  In the stochastic setting it is known that weak reversibility and a deficiency of zero together with a certain connectivity property of the associated reaction network implies that the stationary distribution of the model is a product of Poissons.
Edit intent: meaning-changed
Original:  We establish a function, r(n), termed a threshold function , such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n) and converges to 0 if p_n \gg r(n).
Revised:  We establish a function, r(n), termed a threshold function , such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n) and converges to 0 if p_n \gg r(n).
Edit intent: fluency
Original: In the mathematical study of reaction networks, the most classical results are those pertaining to models that have a deficiency of zero, which, loosely, is a linear independence property of the associated reaction vectors. In particular, the classical results for deterministic models, which date back to the early 1970s, focus on models with a deficiency of zero andconnect them to detailed-balanced models. Moreover, the largest open conjecture related to deterministic models, the so-called Global Attractor Conjecture, focuses on models with a deficiency of zero.
Revised: Reaction networks are commonly used within the mathematical biology and mathematical chemistry communities to model the dynamics of interacting species. These models differ from the typical graphs found in random graph theory since their vertices are constructed from elementary building blocks, i.e., the so-called Global Attractor Conjecture, focuses on models with a deficiency of zero.
Edit intent: coherence
Original:  Moreover, the largest open conjecture related to deterministic models, the so-called Global Attractor Conjecture, focuses on models with a deficiency of zero. In the stochastic reaction network setting it has been shown that a deficiency of zero together with a certain connectivity property of the associated reaction network implies that the stationary distribution of the model is a product of Poissons.
Revised:  Moreover, the largest open conjecture related to deterministic models, the species. In this paper, we consider these networks in an Erd\H os-R\'enyi framework and, under suitable assumptions, derive a threshold function for the network to have a deficiency of zero together with a certain connectivity property of the associated reaction network implies that the stationary distribution of the model is a product of Poissons.
Edit intent: meaning-changed
Original:  In the stochastic reaction network setting it has been shown that a deficiency of zero together with a certain connectivity property of the associated reaction network implies that the stationary distribution of the model is a product of Poissons. Given that deficiency zero models play such a significant role in the mathematical study of reaction networks, a natural question is : how prevalent are they? In order to answer this question, we consider reaction networks under an Erdos-Renyi random graph framework. In particular, we start with n species, and then let our possible vertices be all zeroth, first, and second order complexes that can be produced from the n species. Edges, or reversible reactions, between two arbitrary vertices then occur independently with probability p_n. We establish a function, r(n), termed athreshold function , such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n ){r(n)}}\to \to  and converges to 0 if p_n \gg{r(n)}}\to \to  r(n)  .
Revised:  In the stochastic reaction network setting it has been shown that a deficiency of zero threshold function , such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n ){r(n)}}\to \to and converges to 0 if p_n \gg{r(n)}}\to \to r(n) .
Edit intent: meaning-changed
Original:  We establish a function, r(n), termed athreshold function , such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n ){r(n)}}\to \to  and converges to 0 if p_n \gg{r(n)}}\to \to  r(n)  .
Revised:  We establish a function, r(n), termed a , such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n ){r(n)}}\to \to and converges to 0 if p_n \gg{r(n)}}\to \to r(n) .
Edit intent: clarity
Original:  We establish a function, r(n), termed athreshold function , such that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n ){r(n)}}\to \to  and converges to 0 if p_n \gg{r(n)}}\to \to  r(n)  .
Revised:  We establish a function, r(n), termed athreshold function , which is a property of great interest in the reaction network community. Specifically, if the number of species is denoted by n and if the edge probability is denote by p_n, then we prove that the probability of the random network being deficiency zero converges to 1 if p_n \ll r(n ){r(n)}}\to \to and converges to 0 if p_n \gg{r(n)}}\to \to r(n) .
Edit intent: meaning-changed