wanyu/IteraTeR_v2 huggingface dataset:
Original:  Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction q of newly infected individuals are not observed.
Revised:  Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed.
Edit intent: clarity
Original:  We propose to estimate of model parameters using Bayesian Monte-Carlo Markov Chain sampling of the posterior distribution.
Revised:  We propose to estimate of model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution.
Edit intent: fluency
Original:  We apply our model to estimate the infection rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight countries in North and South America.
Revised:  We use our model to estimate the infection rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight countries in North and South America.
Edit intent: fluency
Original:  We apply our model to estimate the infection rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight countries in North and South America.
Revised:  We apply our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight countries in North and South America.
Edit intent: clarity
Original:  Thus, these beliefs are implicit in a trial's presumed conformance to a community standard of safety, and may therefore to some extent be 'reverse engineered' from trial designs.
Revised:  Thus, these beliefs are implicit in a trial's presumed conformance to a community standard of safety, and may therefore to some extent be reverse-engineered from trial designs.
Edit intent: fluency
Original: We consider a semantic communication system for speech signals, named SCS . Motivated by the breakthroughs in deep learning , we explore the speech semantic to recover semantic meaning of the speech at the receiver, which aims to minimize the speech semantic error rather than the bit-error rate or symbol-error rate in traditional communications . Particularly, based on the attention mechanism squeeze-and-excitation (SE)
Revised: We consider a semantic communication system for speech signals, named SCS . Motivated by the breakthroughs in deep learning (DL), we make an effort to recover the transmitted speech signals in the semantic communication systems, which minimizes the error at the semantic level rather than the bit-error rate or symbol-error rate in traditional communications . Particularly, based on the attention mechanism squeeze-and-excitation (SE)
Edit intent: clarity
Original: We consider a semantic communication system for speech signals, named SCS . Motivated by the breakthroughs in deep learning , we explore the speech semantic to recover semantic meaning of the speech at the receiver, which aims to minimize the speech semantic error rather than the bit-error rate or symbol-error rate in traditional communications . Particularly, based on the attention mechanism squeeze-and-excitation (SE)
Revised: We consider a semantic communication system for speech signals, named SCS . Motivated by the breakthroughs in deep learning , we explore the speech semantic to recover semantic meaning of the speech at the receiver, which aims to minimize the speech semantic error rather than the bit level or symbol level as in the traditional communication systems . Particularly, based on the attention mechanism squeeze-and-excitation (SE)
Edit intent: clarity
Original: We consider a semantic communication system for speech signals, named SCS . Motivated by the breakthroughs in deep learning , we explore the speech semantic to recover semantic meaning of the speech at the receiver, which aims to minimize the speech semantic error rather than the bit-error rate or symbol-error rate in traditional communications . Particularly, based on the attention mechanism squeeze-and-excitation (SE)
Revised: We consider a semantic communication system for speech signals, named SCS . Motivated by the breakthroughs in deep learning , we explore the speech semantic to recover semantic meaning of the speech at the receiver, which aims to minimize the speech semantic error rather than the bit-error rate or symbol-error rate in traditional communications . Particularly, based on an attention mechanism employing squeeze-and-excitation (SE)
Edit intent: clarity
Original:  networks, we design the transceiver as an end-to-end (E2E) system, which extracts and learns the essential speech information.
Revised:  networks, we design the transceiver as an end-to-end (E2E) system, which learns and extracts the essential speech information.
Edit intent: fluency
Original:  The simulation results demonstrate that our proposed SCS is more robust to channel variations and outperforms  traditional communication systems, especially in the low signal-to-noise (SNR) regime.
Revised:  The simulation results demonstrate that our proposed SCS is more robust to channel variations and outperforms the traditional communication systems, especially in the low signal-to-noise (SNR) regime.
Edit intent: fluency
Original: Governments are increasingly turning to algorithmic risk assessments when making important decisions, believing that these algorithms will improve public servants ' ability to make policy-relevant predictions and thereby lead to more informed decisions.
Revised: Governments are increasingly turning to algorithmic risk assessments when making important decisions, such as whether to release criminal defendants before trial. Policymakers assert that providing public servants with algorithmic advice will improve human risk predictions and thereby lead to more informed decisions.
Edit intent: clarity
Original: Governments are increasingly turning to algorithmic risk assessments when making important decisions, believing that these algorithms will improve public servants ' ability to make policy-relevant predictions and thereby lead to more informed decisions.
Revised: Governments are increasingly turning to algorithmic risk assessments when making important decisions, believing that these algorithms will improve public servants ' ability to make policy-relevant predictions and thereby lead to better (e.g., fairer) decisions.
Edit intent: clarity
Original:  Yet because many policy decisions require balancing risk-minimization with competing social goals, evaluating the impacts of risk assessments requires considering how public servants are influenced by risk assessments when making policy decisions rather than just how accurately these algorithms make predictions.
Revised:  Yet because many policy decisions require balancing risk-reduction with competing goals, improving the accuracy of predictions may not necessarily improve the quality of decisions. If risk assessments make people more attentive to reducing risk at the expense of other values, these algorithms would diminish the implementation of public policy even as they lead to more accurate predictions.
Edit intent: clarity
Original:  Through an online experiment with 2,140 lay participants simulating two high-stakes government contexts, we provide the first large-scale evidence that risk assessments can systematically alter decision-making processes by increasing the salience of risk as a factor in decisions and that these shifts could exacerbate racial disparities.
Revised:  Through an experiment with 2,140 lay participants simulating two high-stakes government contexts, we provide the first large-scale evidence that risk assessments can systematically alter decision-making processes by increasing the salience of risk as a factor in decisions and that these shifts could exacerbate racial disparities.
Edit intent: clarity
Original:  Through an online experiment with 2,140 lay participants simulating two high-stakes government contexts, we provide the first large-scale evidence that risk assessments can systematically alter decision-making processes by increasing the salience of risk as a factor in decisions and that these shifts could exacerbate racial disparities.
Revised:  Through an online experiment with 2,140 lay participants simulating two high-stakes government contexts, we provide the first direct evidence that risk assessments can systematically alter decision-making processes by increasing the salience of risk as a factor in decisions and that these shifts could exacerbate racial disparities.
Edit intent: clarity
Original:  These results demonstrate that improving human prediction accuracy with algorithms does not necessarily improve human decisionsand highlight the need to experimentally test how government algorithms are used by human decision-makers .
Revised:  These results demonstrate the potential limits and harms of attempts to improve public policy by incorporating predictive algorithms into multifaceted policy decisions. If these observed behaviors occur in practice, presenting risk assessments to public servants would generate unexpected and unjust shifts in public policy without being subject to democratic deliberation or oversight .
Edit intent: clarity
Original:  In the model, longer lymphangion chains generally yield larger CMFRs, and they fail to generate positive CMFRs at higher adverse pressure differences than shorter chains.
Revised:  In the model, longer lymphangion chains generally yield larger CMFRs, and they fail to generate positive CMFRs at higher AAPDs than shorter chains.
Edit intent: clarity
Original:  Simultaneously contracting pumps generate the largest CMFRs at nearly every adverse pressure difference and for every chain length.
Revised:  Simultaneously contracting pumps generate the largest CMFRs at nearly every AAPD and for every chain length.
Edit intent: clarity
Original:  Existing methods for SBR leverage powerful representation learning approaches to encode sessions into a low dimensional space.
Revised:  Existing methods leverage powerful representation learning approaches to encode sessions into a low dimensional space.
Edit intent: clarity
Original:  Existing methods for SBR leverage powerful representation learning approaches to encode sessions into a low dimensional space.
Revised:  Existing methods for SBR leverage powerful representation learning approaches to encode sessions into a low-dimensional space.
Edit intent: fluency
Original:  However,  all the existing studies focus on the item transitions in the session , without modeling the behavior patterns, which are strong clues to capture the preference of users.
Revised:  However, despite such achievements, all the existing studies focus on the item transitions in the session , without modeling the behavior patterns, which are strong clues to capture the preference of users.
Edit intent: clarity
Original:  Extensive experiments conducted on two benchmark E-commerce datasets, Yoochoose and Diginetica demonstrate our proposed method outperforms the\ie \ie  state-of-the-art methods consistently .
Revised:  Extensive experiments conducted on two benchmark E-commerce datasets, Yoochoose and Diginetica demonstrate our proposed method outperforms the\ie \ie state-of-the-art performance in all the tests .
Edit intent: clarity
Original: In this paper, we analyze the number of neurons and training parameters that a neural networks needs to approximate multivariate functions of bounded second mixed derivatives -- Koborov functions.
Revised: In this paper, we analyze the number of neurons and training parameters that a neural networks needs to approximate multivariate functions of bounded second mixed derivatives -- Korobov functions.
Edit intent: fluency
Original:  We further prove that these bounds nearly match the minimal number of parameters any continuous function approximator needs to approximate Koborov functions, showing that neural networks are near-optimal function approximators.
Revised:  We further prove that these bounds nearly match the minimal number of parameters any continuous function approximator needs to approximate Korobov functions, showing that neural networks are near-optimal function approximators.
Edit intent: fluency
Original: The Fitzhugh-Nagumo system is a prototypical model for simulating different modes of neuronal spiking activity.
Revised: The FitzHugh-Nagumo system is a prototypical model for simulating different modes of neuronal spiking activity.
Edit intent: fluency
Original: The Fitzhugh-Nagumo system is a prototypical model for simulating different modes of neuronal spiking activity.
Revised: The Fitzhugh-Nagumo system is a widely used model for describing various modes of neuronal spiking activity.
Edit intent: clarity
Original:  We introduce a minimal model for synaptic integration  of action potentials and explore the basic properties of postsynaptic neurons thus defined.
Revised:  We introduce a minimal model for synaptic integration of postsynaptic neurons thus defined.
Edit intent: clarity
Original:  Overall, the model offers a simple and intuitive test bed to explore various patterns of dendritic stimulation in simple neurons .
Revised:  The model requires a small number of parameters and is especially useful to isolate the role of integration mechanisms that rely on summation of inputs with little dendritic processing .
Edit intent: clarity
Original:  Notably, the [BGM20] paper included an elegant polynomial time algorithm for the same estimation task with 2D circuits, which makes a novel use of 1D Matrix Product States  carefully tailored to the 2D geometry of the circuit in question.
Revised:  Notably, the [BGM20] paper included an elegant polynomial time algorithm for this estimation task restricted to 2D circuits, which makes a novel use of 1D Matrix Product States carefully tailored to the 2D geometry of the circuit in question.
Edit intent: clarity
Original:  Notably, the [BGM20] paper included an elegant polynomial time algorithm for the same estimation task with 2D circuits, which makes a novel use of 1D Matrix Product States  carefully tailored to the 2D geometry of the circuit in question.
Revised:  Notably, the [BGM20] paper included an elegant polynomial time algorithm for the same estimation task with 2D circuits, which makes a novel use of 1D Matrix Product States (MPS) carefully tailored to the 2D geometry of the circuit in question.
Edit intent: clarity
Original:  In order to surpass the technical barriers encountered by previously known techniques we are forced to pursue a novel approach : Our algorithm has a Divide-and-Conquer structure, constructing a recursive sub-division of the given 3D circuit using carefully designed block-encodings, each creating a 3D-local circuit on at most half the number of qubits as the original.
Revised:  In order to surpass the technical barriers encountered by previously known techniques we are forced to pursue a novel approach . Our algorithm has a Divide-and-Conquer structure, constructing a recursive sub-division of the given 3D circuit using carefully designed block-encodings, each creating a 3D-local circuit on at most half the number of qubits as the original.
Edit intent: fluency
Original:  In order to surpass the technical barriers encountered by previously known techniques we are forced to pursue a novel approach : Our algorithm has a Divide-and-Conquer structure, constructing a recursive sub-division of the given 3D circuit using carefully designed block-encodings, each creating a 3D-local circuit on at most half the number of qubits as the original.
Revised:  In order to surpass the technical barriers encountered by previously known techniques we are forced to pursue a novel approach : Our algorithm has a Divide-and-Conquer structure, constructing a recursive sub-division of the given 3D circuit using carefully designed block-encodings, each creating a 3D-local circuits on about half the number of qubits as the original.
Edit intent: clarity
Original:  This division step is then applied recursively, expressing the original quantity as a weighted sum of smaller and smaller 3D-local quantum circuits.
Revised:  This division step is then applied recursively, expressing the original quantity as a weighted combination of smaller and smaller 3D-local quantum circuits.
Edit intent: clarity
Original:  A central technical challenge is to control correlations arising from the entanglement that may exist between the different circuit " pieces" produced this way.
Revised:  A central technical challenge is to control correlations arising from entanglement that may exist between the different circuit " pieces" produced this way.
Edit intent: fluency
Original:  A central technical challenge is to control correlations arising from the entanglement that may exist between the different circuit " pieces" produced this way.
Revised:  A central technical challenge is to control correlations arising from the entanglement that may exist between the different circuit `` pieces" produced this way.
Edit intent: fluency
Original:  A well-known perceptual consequence of categorization in humans and other animals, called categorical perception, is  characterized by a within-category compression and a between-category separation: two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories.
Revised:  A well-known perceptual consequence of categorization in humans and other animals, called categorical perception, is notably characterized by a within-category compression and a between-category separation: two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories.
Edit intent: clarity
Original:  Our formal and numerical analysis provides insights into the geometry of the neural representation in deep layers, with expansion of space near category boundaries and contraction far from category boundaries.
Revised:  Our formal and numerical analyses provide insights into the geometry of the neural representation in deep layers, with expansion of space near category boundaries and contraction far from category boundaries.
Edit intent: fluency
Original:  We investigate categorical representation by using two complementary approaches: one mimics experiments in psychophysics and cognitive neuroscience by means of morphed continua between stimuli of different categories, while the other introduces a categoricality index that  quantifies the separability of the classes at the population level (a given layer in the neural network) .
Revised:  We investigate categorical representation by using two complementary approaches: one mimics experiments in psychophysics and cognitive neuroscience by means of morphed continua between stimuli of different categories, while the other introduces a categoricality index that quantifies the separability of the categories at the neural population level .
Edit intent: clarity
Original:  An important outcome of our analysis is to provide a coherent and unifying view of the efficacy of different heuristic practices of the dropout regularization technique.
Revised:  As an outcome of our analysis is to provide a coherent and unifying view of the efficacy of different heuristic practices of the dropout regularization technique.
Edit intent: clarity
Original:  An important outcome of our analysis is to provide a coherent and unifying view of the efficacy of different heuristic practices of the dropout regularization technique.
Revised:  An important outcome of our study, we propose a coherent view of the efficacy of different heuristic practices of the dropout regularization technique.
Edit intent: clarity
Original:  Our views, which find echoes in the neuroscience literature, insist on the differential role of noise as a function of the level of representation and in the course of learning: noise injected in the hidden layers gets structured according to the organization of the categories , more variability being allowed within a category than across classes .
Revised:  More generally, our view, which finds echoes in the neuroscience literature, insist on the differential role of noise as a function of the level of representation and in the course of learning: noise injected in the hidden layers gets structured according to the organization of the categories , more variability being allowed within a category than across classes .
Edit intent: coherence
Original:  Our views, which find echoes in the neuroscience literature, insist on the differential role of noise as a function of the level of representation and in the course of learning: noise injected in the hidden layers gets structured according to the organization of the categories , more variability being allowed within a category than across classes .
Revised:  Our views, which find echoes in the neuroscience literature, insists on the differential role of noise as a function of the level of representation and in the course of learning: noise injected in the hidden layers gets structured according to the organization of the categories , more variability being allowed within a category than across classes .
Edit intent: fluency
Original:  Our views, which find echoes in the neuroscience literature, insist on the differential role of noise as a function of the level of representation and in the course of learning: noise injected in the hidden layers gets structured according to the organization of the categories , more variability being allowed within a category than across classes .
Revised:  Our views, which find echoes in the neuroscience literature, insist on the differential impact of noise in any given layer depending on the geometry of the course of learning: noise injected in the hidden layers gets structured according to the organization of the categories , more variability being allowed within a category than across classes .
Edit intent: clarity
Original:  Our views, which find echoes in the neuroscience literature, insist on the differential role of noise as a function of the level of representation and in the course of learning: noise injected in the hidden layers gets structured according to the organization of the categories , more variability being allowed within a category than across classes .
Revised:  Our views, which find echoes in the neuroscience literature, insist on the differential role of noise as a function of the level of representation and in the neural representation that is being learned, i.e. on how this geometry reflects the structure of the categories , more variability being allowed within a category than across classes .
Edit intent: clarity
Original:  Our views, which find echoes in the neuroscience literature, insist on the differential role of noise as a function of the level of representation and in the course of learning: noise injected in the hidden layers gets structured according to the organization of the categories , more variability being allowed within a category than across classes .
Revised:  Our views, which find echoes in the neuroscience literature, insist on the differential role of noise as a function of the level of representation and in the course of learning: noise injected in the hidden layers gets structured according to the organization of the categories .
Edit intent: clarity
Original:  In the final section, we also propose some questions deserved for future works } .
Revised:  In the final section, we also propose some questions deserved for future works \L} .
Edit intent: coherence
Original:  We prove that if T_1 and T_2 are the theories of temporal structures, i.e., structures where all relations have a first-order definition in (Q , <), then CSP(T_1 \cup T_2) is in P or NP-complete.
Revised:  We prove that if T_1 and T_2 are the theories of temporal structures, i.e., structures where all relations have a first-order definition in (Q ; <), then CSP(T_1 \cup T_2) is in P or NP-complete.
Edit intent: fluency