wanyu/IteraTeR_full_sent huggingface dataset:
Original: Instrumental variable identification is a strategy in causal statistics for estimating the counterfactual effect of treatment D on output Y controlling for covariates \mathbf{X We introduce a de-biased machine learning (DML) approach to estimating complier parameters with high-dimensional data.
Revised:  We introduce a de-biased machine learning (DML) approach to estimating complier parameters with high-dimensional data.
Edit intent: coherence
Original:  We first verify its superior performance on a simulated scenario inspired by real data and then apply it to a large proprietary database from BNP Paribas Corporate and Institutional Banking.
Revised:  We first verify its superior performance on a simulated scenario inspired by real data and then apply it to two real-world databases, a publicly available dataset about the position of investors in Spanish stock market and proprietary data from BNP Paribas Corporate and Institutional Banking.
Edit intent: meaning-changed
Original: Some species of bacteria exhibit quorum sensing, a phenomenon in which gene expression and resultant behavior undergo a phase change at some threshold population density. We show here that even very simple nanodevices , modeled as chemical reaction networks, can by a different mechanism also sense their own population densities and change their behaviors dramatically at certain thresholds.
Revised: We show that very simple molecular systems , modeled as chemical reaction networks, can by a different mechanism also sense their own population densities and change their behaviors dramatically at certain thresholds.
Edit intent: clarity
Original:  Moreover, the magnitudes of these thresholds can thwart attempts to use  model checking, simulation, or approximation by differential equations to formally verify the behaviors of such nanodevices at realistic population densities .
Revised:  Moreover, the magnitudes of these thresholds can thwart attempts to use simulation, model checking, simulation, or approximation by differential equations to formally verify the behaviors of such nanodevices at realistic population densities .
Edit intent: fluency
Original:  Moreover, the magnitudes of these thresholds can thwart attempts to use  model checking, simulation, or approximation by differential equations to formally verify the behaviors of such nanodevices at realistic population densities .
Revised:  Moreover, the magnitudes of these thresholds can thwart attempts to use model checking, or approximation by differential equations to formally verify the behaviors of such nanodevices at realistic population densities .
Edit intent: clarity
Original:  A quantile regression analysis to USFS timber is found more appropriate than the homogenized bid methodology and illustrates the contribution of each explanatory variables to the private value distribution .
Revised:  A quantile regression analysis to USFS timber is found more appropriate than the homogenized bid methodology and illustrates the contribution of each explanatory variables to the private-value distribution. Linear interactive sieve extensions are proposed and studied in the Appendices .
Edit intent: meaning-changed
Original:  Formant transitions in speech are characterised by short rising and falling FM-sweeps in the kilohertz frequency range.
Revised:  Formant transitions in speech are characterised by short rising and falling FM-sweeps called formant transitions characterise individual speech sounds. There are two representations of FM in the ascending auditory pathway: a spectral representation, holding the instantaneous frequency of the stimuli; and a sweep representation, consisting of neurons that respond selectively to FM direction. To-date computational models use feedforward mechanisms to explain FM encoding. However, from neuroanatomy we know that there are massive feedback projections in the auditory pathway. Here, we found that a classical FM-sweep perceptual effect, the kilohertz frequency range.
Edit intent: meaning-changed
Original:  Formant transitions in speech are characterised by short rising and falling FM-sweeps in the kilohertz frequency range. These sounds elicit a pitch percept that deviates from their average frequency. This study uses this perceptual effect, termed here the sweep pitch shift, to inform a model characterising the neural encoding of FM.
Revised:  Formant transitions in speech are characterised by short rising and falling FM-sweeps in the sweep pitch shift, to inform a model characterising the neural encoding of FM.
Edit intent: clarity
Original:  This study uses this perceptual effect, termed here the sweep pitch shift, to inform a model characterising the neural encoding of FM. First, a reexamination of the classical effect, consisting of two perceptual experiments, provides a quantitative characterisation of the dependence of the sweep pitch shift with the properties of the sweeps.
Revised:  This study uses this perceptual effect, termed here the sweep pitch shift with the properties of the sweeps.
Edit intent: coherence
Original:  First, a reexamination of the classical effect, consisting of two perceptual experiments, provides a quantitative characterisation of the dependence of the sweep pitch shift with the properties of the sweeps. Next, simulations carried on the new experimental data show that classical temporal and spectral models of pitch processing cannot explain the pitch shift. Conversely, a modified spectral model considering a predictive interaction between frequency and FM encoding fully reproduces our and previous experimental data .
Revised:  First, a reexamination of the classical effect, consisting of two perceptual experiments, provides a quantitative characterisation of the dependence of the sweep pitch shift , cannot be explained by standard feedforward processing models. We hypothesised that the sweep pitch shift is caused by a predictive interaction between frequency and FM encoding fully reproduces our and previous experimental data .
Edit intent: clarity
Original:  Conversely, a modified spectral model considering a predictive interaction between frequency and FM encoding fully reproduces our and previous experimental data .
Revised:  Conversely, a modified spectral model considering a predictive interaction between the sweep and the spectral representation. To test this hypothesis, we developed a novel model of FM encoding incorporating a predictive feedback mechanism. The model fully accounted for experimental data that we acquired in a perceptual experiment with human participants as well as previously published experimental results. We also designed a new family of stimuli for a second perceptual experiment to further validate the model .
Edit intent: meaning-changed
Original: Simultaneous real-time fMRI and EEG neurofeedback (rtfMRI-EEG-nf) is an emerging neuromodulation approach, that enables simultaneous volitional regulation of both hemodynamic (BOLD fMRI) and electrophysiological (EEG) regional brain activities.
Revised: Simultaneous real-time fMRI and EEG neurofeedback (rtfMRI-EEG-nf) is an emerging neuromodulation approach, that enables simultaneous volitional regulation of both hemodynamic (BOLD fMRI) and electrophysiological (EEG) brain activities.
Edit intent: meaning-changed
Original:  In this proof-of-concept study, MDD patients in the experimental group (n=16) used rtfMRI-EEG-nf during a happy emotion induction task to simultaneously upregulate four brain activity measures relevant to MDD.
Revised:  In this proof-of-concept study, MDD patients in the experimental group (n=16) used rtfMRI-EEG-nf during a happy emotion induction task to simultaneously upregulate two fMRI and two EEG activity measures relevant to MDD.
Edit intent: meaning-changed
Original:   The experimental group participants achieved significant increases in the LA BOLD activity, FAA, and FBA during the rtfMRI-EEG-nf task, as well as significant enhancement in fMRI connectivity between the LA and left rACC.
Revised:  An advanced procedure for improved real-time EEG-fMRI artifact correction was implemented. The experimental group participants achieved significant increases in the LA BOLD activity, FAA, and FBA during the rtfMRI-EEG-nf task, as well as significant enhancement in fMRI connectivity between the LA and left rACC.
Edit intent: meaning-changed
Original:   The experimental group participants achieved significant increases in the LA BOLD activity, FAA, and FBA during the rtfMRI-EEG-nf task, as well as significant enhancement in fMRI connectivity between the LA and left rACC.
Revised:  The experimental group participants demonstrated significant upregulation of the LA BOLD activity, FAA, and FBA during the rtfMRI-EEG-nf task, as well as significant enhancement in fMRI connectivity between the LA and left rACC.
Edit intent: clarity
Original:   Temporal correlations between the FAA and FBA time courses and the LA BOLD activity were significantly enhanced during the rtfMRI-EEG-nf task.
Revised:  Average individual FAA changes during the rtfMRI-EEG-nf task positively correlated with depression and anhedonia severities, and negatively correlated with after-vs-before changes in depressed mood ratings. Temporal correlations between the FAA and FBA time courses and the LA BOLD activity were significantly enhanced during the rtfMRI-EEG-nf task.
Edit intent: meaning-changed
Original: Decision making is a dynamical phenomenon and thus the transition from indecision to decision can be described well by bifurcation theory. Agreement, or consensus, is only one of many ways in which collective decisions can be made. However, theories of multi-agent multi-option decision making either focus on agreement decisions or average at the agent level to achieve a mean-field description of the decision dynamics. Introducing the agent level back into collective decision-making models uncovers a plethora of novel collective behaviors beyond agreement and mean-field descriptions of disagreement and polarization. These include uniform and moderate-extremist disagreement, and switchy-and-fast versus continuous-and-slow transitions from indecision to decision. Which of these behaviors emerge depends on the model parameters, in particular, the number of agents and the number of options.Our study is grounded in Equivariant Bifurcation Theory, which allows us to formulate model-independent predictions and to develop a constructive sensitivity analysis of the decision dynamics at URLanizing equivariant singularity.
Revised: We develop a model-independent predictions and to develop a constructive sensitivity analysis of the decision dynamics at URLanizing equivariant singularity.
Edit intent: meaning-changed
Original: Our study is grounded in Equivariant Bifurcation Theory, which allows us to formulate model-independent predictions and to develop a constructive sensitivity analysis of the decision dynamics at URLanizing equivariant singularity. The localized sensitivity analysis reveals how collective decision making can be both flexible and robust in response to subtle changes in the environment or in the deciding agent interactions. Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES\R\otimes}\R.
Revised: Our study is grounded in Equivariant Bifurcation Theory, which allows us to formulate model-independent framework to study the dynamics of decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES\R\otimes}\R.
Edit intent: clarity
Original:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES\R\otimes}\R.
Revised:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making in opinion networks for an arbitrary number of agents and an arbitrary number of options. Model-independence means that the analysis is not performed on a specific set of equations, in contrast to classical approaches to decision making that fix a specific model and analyze it. Rather, the general features of decision making in dynamical opinion networks can be derived starting from empirically testable hypotheses about the deciding agents, the available options, and the interactions among them. After translating these empirical hypotheses into algebraic ones, we use the tools of equivariant bifurcation theory to uncover model-independent properties of dynamical opinion networks. The model-independent results are illustrated on a novel analytical model that is constructed by plugging a generic sigmoidal nonlinearity, modeling boundedness of opinions and opinion perception, into the model-independent equivariant structure. Our analysis reveals richer and more flexible opinion-formation behavior as compared to model-dependent approaches. For instance, analysis reveals the possibility of switching between consensus and various forms of dissensus by modulation of the level of agent cooperativity and without requiring any particular ad-hoc interaction topology (e.g., structural balance). From a theoretical viewpoint, we prove new results in equivariant bifurcation theory. We construct an exhaustive list of axial subgroups for the action of\ES\ES\R\otimes}\R.
Edit intent: meaning-changed
Original:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES\R\otimes}\R.
Revised:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES_n \times\ES\R\otimes}\R.
Edit intent: meaning-changed
Original:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES\R\otimes}\R.
Revised:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES_3 on\R\otimes}\R.
Edit intent: meaning-changed
Original:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES\R\otimes}\R.
Revised:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES\R^{n-1\otimes}\R.
Edit intent: meaning-changed
Original:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES\R\otimes}\R.
Revised:  Equivariant Bifurcation Theory also guides the construction of new multi-agent multi-option decision-making dynamics, for an arbitrary number of agents and an arbitrary number of options, and with fully controllable dynamical behaviors\ES\ES\R\otimes}\R^{2.
Edit intent: meaning-changed
Original:  In this review, we first give a comprehensive theoretical account of the stationary distributions and their relation to the long-term behaviour of the Markov chain, which is readily accessible to non-experts and free of irreducibility assumptions made in standard texts.
Revised:  In this review, we first give a comprehensive theoretical account of the stationary distributions and their relation to the long-term behaviour of CTMCs that is readily accessible to non-experts and free of irreducibility assumptions made in standard texts.
Edit intent: meaning-changed
Original:  We then review truncation-based approximation schemes  paying particular attention to their convergence and to the errors they introduce, and we illustrate their performance with an example of a stochastic reaction network of relevance in biology and chemistry.
Revised:  We then review truncation-based approximation schemes for CTMCs with infinite state spaces paying particular attention to their convergence and to the errors they introduce, and we illustrate their performance with an example of a stochastic reaction network of relevance in biology and chemistry.
Edit intent: meaning-changed
Original:  We then review truncation-based approximation schemes  paying particular attention to their convergence and to the errors they introduce, and we illustrate their performance with an example of a stochastic reaction network of relevance in biology and chemistry.
Revised:  We then review truncation-based approximation schemes paying particular attention to the schemes' convergence and the errors they introduce, and we illustrate their performance with an example of a stochastic reaction network of relevance in biology and chemistry.
Edit intent: clarity
Original:  We also find biomass and diversity to be less dependent on the symmetry of interactions with functional response, and co-operation to no longer have a detrimental effect on stability .
Revised:  We also find co-operation to no longer have a detrimental effect on stability with non-linear feedback, and the order parameters mean abundance and diversity to be less dependent on the symmetry of interactions with functional response, and co-operation to no longer have a detrimental effect on stability .
Edit intent: meaning-changed
Original: We propose a market solution to the problem of resource allocation subject to constraints , such as considerations of diversity or geographical distribution . Constraints give rise to pecuniary externalities, which are internalized via prices.
Revised: We propose a market solution to the problem of resource allocation subject to constraints . Our treatment of constraints is general: including bihierarchical constraints due to considerations of diversity or geographical distribution . Constraints give rise to pecuniary externalities, which are internalized via prices.
Edit intent: meaning-changed
Original: We propose a market solution to the problem of resource allocation subject to constraints , such as considerations of diversity or geographical distribution . Constraints give rise to pecuniary externalities, which are internalized via prices.
Revised: We propose a market solution to the problem of resource allocation subject to constraints , such as considerations of diversity in school choice, or scheduling in course allocation; and other forms of constraints needed to model, for example, the market for roommates, and combinatorial assignment problems . Constraints give rise to pecuniary externalities, which are internalized via prices.
Edit intent: meaning-changed
Original: Explainable machine learning seeks to provide various stakeholders with insights into model behavior via feature importance scores, counterfactual explanations, and influential samples, among other techniques. Recent advances in this line of work, however, have gone without surveys of URLanizations are using these techniques in practice.
Revised: Explainable machine learning seeks to provide various stakeholders with insights into model behavior via feature importance scores, counterfactual explanations, or influential training data. Yet there is little understanding of URLanizations use these methods in practice.
Edit intent: clarity
Original: Bioinformatics of high throughput omics data (e. g. microarrays and proteomics) has been plagued by uncountable issues with reproducibility at the start of the century. Concerns have motivated international initiatives such as the FDA's led MAQC Consortium , addressing reproducibility of predictive biomarkers by means of appropriate Data Analysis Plans (DAPs) .
Revised: Reproducibility of AI models on biomedical data still stays as a major concern for their acceptance into the clinical practice. Initiatives for reproducibility in the development of predictive biomarkers as the MAQC Consortium already underlined the importance of appropriate Data Analysis Plans (DAPs) .
Edit intent: clarity
Original:  Concerns have motivated international initiatives such as the FDA's led MAQC Consortium , addressing reproducibility of predictive biomarkers by means of appropriate Data Analysis Plans (DAPs) . For instance, repreated cross-validation is a standard procedure meant at mitigating the risk that information from held-out validation data may be used during model selection. We prove here that, many years later, Data Leakage can still be a non-negligible overfitting source in deep learning models for digital pathology. In particular, we evaluate the impact of (i) the presence of multiple images for each subject in histology collections; (ii) the systematic adoption of training over collection of subregions (i.e. "tiles" or "patches") extracted for the same subject.
Revised:  Concerns have motivated international initiatives such as the FDA's led MAQC Consortium , addressing reproducibility of predictive biomarkers by means of appropriate Data Analysis Plans (DAPs) to control for different types of bias, including data leakage from the training to the test set. In the context of digital pathology, the leakage typically lurks in weakly designed experiments not accounting for the subjects in their data partitioning schemes. This issue is then exacerbated when fractions or subregions of slides (i.e. "tiles" or "patches") extracted for the same subject.
Edit intent: coherence
Original:  (ii) the systematic adoption of training over collection of subregions (i.e. "tiles" or "patches") extracted for the same subject. We verify thataccuracy scores may be inflated up to 41\% , even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Revised:  (ii) the systematic adoption of training over collection of subregions (i.e. "tiles" ) are considered. Despite this aspect is largely recognized by the community, we argue that it is often overlooked. In this study, we assess the impact of data leakage on the performance of machine learning models trained and validated on multiple histology data collection. We prove that, even with a properly designed DAP (10x5 repeated cross-validation), predictive scores can be inflated up to 41\% , even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Edit intent: coherence
Original:  We verify thataccuracy scores may be inflated up to 41\% , even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Revised:  We verify thataccuracy scores may be inflated up to 41\% when tiles from the same subject are kept together either in the internal training or validation splits.
Edit intent: clarity