wanyu/IteraTeR_full_sent huggingface dataset:
Original:  effect and utility of one compartment directly or indirectly on another, identify the system efficiency and stress, measure the compartmental exposure to system flows, determine the residence time and compartmental activity levels, and ascertain the restoration time and resilience in the case of disturbances.
Revised:  effect and utility of one compartment directly or indirectly on another, identify the system efficiency and stress, measure the compartmental exposure to system flows, determine the residence time and compartmental activity levels, and ascertain the system resilience and resistance in the case of disturbances.
Edit intent: clarity
Original: The seminal idea of quantum money due to Stephen Wiesner, the money not URLeable due to laws of Quantum Mechanics  , has laid foundations for the Quantum Information Theory in early '70s.
Revised: The seminal idea of quantum money due to Stephen Wiesner, the money not URLeable due to laws of Quantum Mechanics proposed by Stephen Wiesner , has laid foundations for the Quantum Information Theory in early '70s.
Edit intent: meaning-changed
Original:  As quantum technology develops, in parallel to crypto-currencies entering market, various other schemes for quantum currencies are being proposednowadays. The schemes put forward so far relay on assumption that the mint does not cooperate with the counterfeiter who may use malicious terminal for verification.
Revised:  As quantum technology develops, in parallel to crypto-currencies entering market, various other schemes for quantum currencies have been proposed, all however relying on the assumption that the mint does not cooperate with the counterfeiter who may use malicious terminal for verification.
Edit intent: clarity
Original:  The schemes put forward so far relay on assumption that the mint does not cooperate with the counterfeiter who may use malicious terminal for verification. Here we introduce a semi-device independent scheme for money partially relaxing this assumption .
Revised:  The schemes put forward so far relay on assumption that the mint does not cooperate with the counterfeiter . Drawing inspirations from the semi-device independent scheme for money partially relaxing this assumption .
Edit intent: coherence
Original:  Here we introduce a semi-device independent scheme for money partially relaxing this assumption . We prove that it is protected against URLery under qubit by qubit copying in case when the banknote is verified at the Bank by untrusted terminal. In case of verification by classical communication, protection against a URLery cheating independently on each qubit is also provided. The scheme bases on the preamble of semi-device independent protocol of Paw\l{ Phys.
Revised:  Here we introduce a semi-device independent Phys.
Edit intent: coherence
Original:  The scheme bases on the preamble of semi-device independent protocol of Paw\l{ Phys. Rev. A 84, 010302 (2011)%DIFDELCMD < ] %%% with more restrictive condition for acceptance.
Revised:  The scheme bases on the preamble of semi-device independent protocol of Paw\l{ %DIFDELCMD < ] %%% with more restrictive condition for acceptance.
Edit intent: coherence
Original:  Rev. A 84, 010302 (2011)%DIFDELCMD < ] %%% with more restrictive condition for acceptance. This is in correspondence with more complex proof of security originating from a two-party cryptographic nature of the money schemes. The lower bound on the size of the banknote is ruled only by the law of large numbers as the secure key is not physically generated during the protocol. Due to the current burst out of the new quantum-crypto currencies, on the basis of the presented protocol we finally introduce and discuss possible quantum analogue of the Oresme-Copernicus-Gresham's law of economy.
Revised:  Rev. A 84, 010302 (2011)%DIFDELCMD < ] %%% quantum key distribution protocol, we introduce the first scheme of quantum money with this assumption partially relaxed, along with the proof of its URLeability. Significance of this protocol is supported by an impossibility result, which we prove, stating that there is no both fully device independent and secure money scheme. Finally, we formulate a quantum analogue of the Oresme-Copernicus-Gresham's law of economy.
Edit intent: meaning-changed
Original: Predicting future clinical events , such as death, is an important task in medicine that helps physicians guide appropriate action. Neural networks have particular promise to assist with medical prediction tasks related to clinical imaging by learning patterns from large datasets.
Revised: Predicting future clinical events , such as death, is an important task in medicine that helps physicians guide appropriate intervention. Machine learning has tremendous promise to assist with medical prediction tasks related to clinical imaging by learning patterns from large datasets.
Edit intent: clarity
Original:  Neural networks have particular promise to assist with medical prediction tasks related to clinical imaging by learning patterns from large datasets. Significant advances have been made in predicting complex diagnoses from medical imaging 1-5%DIFDELCMD < ]%%% . Predicting future events, then, is a natural but relatively unexplored extension of those efforts.
Revised:  Neural networks have particular promise to assist 1-5%DIFDELCMD < ]%%% . Predicting future events, then, is a natural but relatively unexplored extension of those efforts.
Edit intent: clarity
Original:  Significant advances have been made in predicting complex diagnoses from medical imaging 1-5%DIFDELCMD < ]%%% . Predicting future events, then, is a natural but relatively unexplored extension of those efforts.
Revised:  Significant advances have been made in predicting complex diagnoses from medical imaging %DIFDELCMD < ]%%% . Predicting future events, then, is a natural but relatively unexplored extension of those efforts.
Edit intent: coherence
Original:  Significant advances have been made in predicting complex diagnoses from medical imaging 1-5%DIFDELCMD < ]%%% . Predicting future events, then, is a natural but relatively unexplored extension of those efforts. Moreover, neural networks have not yet been applied to medical videos on a large scale, such as ultrasound of the heart (echocardiography).
Revised:  Significant advances have been made in predicting complex diagnoses from medical imaging 1-5%DIFDELCMD < ]%%% physicians with predictions based on the discovery of complex patterns from historical data, such as large, longitudinal electronic health records (EHR). This study is a first attempt to demonstrate such capabilities using raw echocardiographic videos of the heart (echocardiography).
Edit intent: meaning-changed
Original:  Moreover, neural networks have not yet been applied to medical videos on a large scale, such as ultrasound of the heart (echocardiography). Here we show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( approx. 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year survival with good accuracy .
Revised:  Moreover, neural networks have not yet been applied to medical videos on a large scale, such as ultrasound of the heart . We show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( approx. 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year survival with good accuracy .
Edit intent: clarity
Original:  Here we show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( approx. 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year survival with good accuracy .
Revised:  Here we show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( ~ 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year survival with good accuracy .
Edit intent: clarity
Original:  Here we show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( approx. 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year survival with good accuracy .
Revised:  Here we show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( approx. 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year mortality with good accuracy .
Edit intent: clarity
Original:  Here we show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( approx. 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year survival with good accuracy . We also demonstrate that prediction accuracy can be further improved by adding highly predictive clinical variables from the electronic health record .
Revised:  Here we show that a large dataset of 723,754 clinically-acquired echocardiographic videos ( approx. 45 million images) linked to longitudinal follow-up data in 27,028 patients can be used to train a deep neural network to predict 1-year survival with good accuracy (area under the curve (AUC) in an independent test set = 0.839). Prediction accuracy was further improved by adding highly predictive clinical variables from the electronic health record .
Edit intent: meaning-changed
Original: Populations of nonlinear oscillators are found in a variety of applications from physics, chemistry, biology, and engineering \mbox{%DIFAUXCMD physics,kuramoto,winfree,Keener2009 The collective behavior of such oscillators varies, and includes synchronization, desynchronization , and clustering.
Revised:  The collective behavior of such oscillators varies, and includes synchronization, desynchronization , and clustering.
Edit intent: clarity
Original: Populations of nonlinear oscillators are found in a variety of applications from physics, chemistry, biology, and engineering \mbox{%DIFAUXCMD physics,kuramoto,winfree,Keener2009 The collective behavior of such oscillators varies, and includes synchronization, desynchronization , and clustering. For example, synchronization in beta cells is crucial for efficient insulin secretion \mbox{%DIFAUXCMD beta_synch the beating of the heart is regulated by constant pacing of synchronized cardiac pacemaker cells \mbox{%DIFAUXCMD sn_synch1,sn_synch20pt%DIFAUXCMD , and neural synchrony is essential in visual and odor processing \mbox{%DIFAUXCMD visual,odor}0pt%DIFAUXCMD , } and also in learning and memory recall \mbox{%DIFAUXCMD learn,memory as well.
Revised: Populations of nonlinear oscillators are found in a variety of applications from physics, chemistry, biology, and engineering \mbox{%DIFAUXCMD physics,kuramoto,winfree,Keener2009 The collective behavior of biological oscillators has been recognized as an important problem for several decades, but its control has come into limelight only recently. Much of the focus for control has been on desynchronization of an oscillator population, motivated by the pathological neural synchrony present in essential and parkinsonian tremor. Other applications, such as the beating of the heart is regulated by constant pacing of synchronized cardiac pacemaker cells \mbox{%DIFAUXCMD sn_synch1,sn_synch20pt%DIFAUXCMD , and neural synchrony is essential in visual and odor processing \mbox{%DIFAUXCMD visual,odor}0pt%DIFAUXCMD , } and also in learning and memory recall \mbox{%DIFAUXCMD learn,memory as well.
Edit intent: coherence
Original:  For example, synchronization in beta cells is crucial for efficient insulin secretion \mbox{%DIFAUXCMD beta_synch the beating of the heart is regulated by constant pacing of synchronized cardiac pacemaker cells \mbox{%DIFAUXCMD sn_synch1,sn_synch20pt%DIFAUXCMD , and neural synchrony is essential in visual and odor processing \mbox{%DIFAUXCMD visual,odor}0pt%DIFAUXCMD , } and also in learning and memory recall \mbox{%DIFAUXCMD learn,memory as well.
Revised:  For example, synchronization in beta cells is crucial for efficient insulin secretion \mbox{%DIFAUXCMD beta_synch the beating of the heart 0pt%DIFAUXCMD , and neural synchrony is essential in visual and odor processing \mbox{%DIFAUXCMD visual,odor}0pt%DIFAUXCMD , } and also in learning and memory recall \mbox{%DIFAUXCMD learn,memory as well.
Edit intent: clarity
Original:  For example, synchronization in beta cells is crucial for efficient insulin secretion \mbox{%DIFAUXCMD beta_synch the beating of the heart is regulated by constant pacing of synchronized cardiac pacemaker cells \mbox{%DIFAUXCMD sn_synch1,sn_synch20pt%DIFAUXCMD , and neural synchrony is essential in visual and odor processing \mbox{%DIFAUXCMD visual,odor}0pt%DIFAUXCMD , } and also in learning and memory recall \mbox{%DIFAUXCMD learn,memory as well.
Revised:  For example, synchronization in beta cells is crucial for efficient insulin secretion \mbox{%DIFAUXCMD beta_synch the beating of the heart is regulated by constant pacing of synchronized cardiac pacemaker cells \mbox{%DIFAUXCMD sn_synch1,sn_synch20pt%DIFAUXCMD , and neural synchrony is essential in visual and odor processing \mbox{%DIFAUXCMD visual,odor}0pt%DIFAUXCMD , } and insulin secretion, require synchronization, and recently there has been interest in forming clusters within an oscillator population as well.
Edit intent: meaning-changed
Original:  For example, pathological neural synchronization in the thalamus and the subthalamic nucleus (STN) brain region is hypothesized to be one of the causes of motor symptoms for essential and parkinsonian tremor, respectively~\mbox{%DIFAUXCMD thalamus_syncro,STN_syncro to desynchronize an oscillator population.
Revised:  In this article, we devise a unified control framework to achieve all of these distinct collective behaviors observed in biological oscillators. The control algorithm is based on the partial differential equation governing the evolution of the phase distribution of a population of identical, uncoupled oscillators. Motivated by pathological neural synchrony, we apply our control to desynchronize an oscillator population.
Edit intent: meaning-changed
Original:  For example, pathological neural synchronization in the thalamus and the subthalamic nucleus (STN) brain region is hypothesized to be one of the causes of motor symptoms for essential and parkinsonian tremor, respectively~\mbox{%DIFAUXCMD thalamus_syncro,STN_syncro to desynchronize an oscillator population. Recently there has also been focus on achieving partial synchrony through clustering instead of complete neural desynchronization \mbox{%DIFAUXCMD tim,Matchen2018,clustered\hspace{0pt}%DIFAUXCMD . One motivation behind such clustering is to rewire neural connections by }  enhancing spike time dependent plasticity which potentiates intra-cluster synaptic connections and depresses inter-cluster connections.
Revised:  For example, pathological neural synchronization in the thalamus and the subthalamic nucleus (STN) brain region is hypothesized to be one of the causes of motor symptoms for essential and parkinsonian tremor, respectively~\mbox{%DIFAUXCMD thalamus_syncro,STN_syncro to desynchronize an \hspace{0pt}%DIFAUXCMD . One motivation behind such clustering is to rewire neural connections by } enhancing spike time dependent plasticity which potentiates intra-cluster synaptic connections and depresses inter-cluster connections.
Edit intent: meaning-changed
Original:  Recently there has also been focus on achieving partial synchrony through clustering instead of complete neural desynchronization \mbox{%DIFAUXCMD tim,Matchen2018,clustered\hspace{0pt}%DIFAUXCMD . One motivation behind such clustering is to rewire neural connections by }  enhancing spike time dependent plasticity which potentiates intra-cluster synaptic connections and depresses inter-cluster connections.
Revised:  Recently there has also been focus on achieving partial synchrony through clustering instead of complete neural desynchronization \mbox{%DIFAUXCMD tim,Matchen2018,clustered\hspace{0pt}%DIFAUXCMD . One motivation behind such clustering is to rewire neural connections by } initially synchronized neural population. Given the proposed importance of enhancing spike time dependent plasticity which potentiates intra-cluster synaptic connections and depresses inter-cluster connections.
Edit intent: meaning-changed
Original:  Recently there has also been focus on achieving partial synchrony through clustering instead of complete neural desynchronization \mbox{%DIFAUXCMD tim,Matchen2018,clustered\hspace{0pt}%DIFAUXCMD . One motivation behind such clustering is to rewire neural connections by }  enhancing spike time dependent plasticity which potentiates intra-cluster synaptic connections and depresses inter-cluster connections. This potentially helps in long-term stabilizability of the clusters in the presence of noise .
Revised:  Recently there has also been focus on achieving partial synchrony through clustering instead of complete neural desynchronization \mbox{%DIFAUXCMD tim,Matchen2018,clustered\hspace{0pt}%DIFAUXCMD . One motivation behind such clustering is to rewire neural connections by } enhancing spike time dependent plasticity to stabilize neural clusters and counteract pathological neural synchronization, we formulate the phase difference distribution in terms of the phase distribution, and prove some of its fundamental properties, and in turn apply our control to transform the neural phase distribution to form clusters. Finally, motivated by eliminating cardiac alternans, we apply our control to phase shift a synchronous cardiac pacemaker cell population. To demonstrate the effectiveness of our control for each of these applications, we show that a population of 100 phase oscillators with the applied control mimics the desired phase distribution .
Edit intent: meaning-changed
Original:   Motivated by pathological neural synchrony, we apply our control to desynchronize an initially synchronized neural population.
Revised:  Our first two control algorithms are Lyapunov-based, which work by decreasing a positive definite Lyapunov function towards zero. Our third control is an optimal control algorithm, which minimizes the control energy consumption while achieving the desired collective behavior of an oscillator population. Motivated by pathological neural synchrony, we apply our control to desynchronize an initially synchronized neural population.
Edit intent: meaning-changed
Original:  Finally, motivated by eliminating cardiac alternans, we apply our control to phase shift a synchronous cardiac pacemaker cell population . To demonstrate the effectiveness of our control for each of these applications, we show that a population of 100 phase oscillators with the applied control mimics the desired phase distribution .
Revised:  Finally, motivated by eliminating cardiac alternans, we apply our control to phase shift a synchronous cardiac pacemaker cell population .
Edit intent: coherence
Original:  In consumer demand this form of measurement error is present because the price an individual pays is often measured by the  average price paid by individuals in a specified group (e.g., a county)  .
Revised:  In consumer demand this form of measurement error is present because the price an individual pays is often measured by the average price paid by individuals in a specified group (e.g., a county) , rather than the true transaction price .
Edit intent: meaning-changed
Original:  Examining the demand for gasoline in the U.S., accounting for Berkson errors is found to be quantitatively important for estimating price effects and for welfare calculations.
Revised:  Examining the demand for gasoline in the U.S., we document substantial within-market price variability, and show that there are significant spatial differences in the magnitude of Berkson errors across regions of the U.S. Accounting for Berkson errors is found to be quantitatively important for estimating price effects and for welfare calculations.
Edit intent: meaning-changed
Original:  They accelerate protein engineering by learning from information contained in all measured variants and using it to select variants that are likely to be improved.
Revised:  To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using it to select variants that are likely to be improved.
Edit intent: meaning-changed
Original:  In this review, we introduce the steps required to collect protein data, train machine-learning models, and use trained models to guide engineering. We make recommendations at each stage and look to future opportunities for machine learning to enable the discovery of new protein functions and uncover the relationship between protein sequence and function.
Revised:  We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities for machine learning to enable the discovery of new protein functions and uncover the relationship between protein sequence and function.
Edit intent: clarity
Original:  In particular, we develop a numerical approach for estimating genotypes' fitnesses in locked-box  models describing bacterial evolution in experimental setups similar to the celebrated Lenski experiment . Our approach is based is on a nonlinear least squares optimization procedure which takes into account both the first and the  second mutation events.
Revised:  In particular, we develop a numerical approach for estimating genotypes' fitnesses in locked-box models describing bacterial evolution in experimental setups similar to the celebrated Lenski experiment on Escherichia Coli. Our algorithm first analyses emergence of new mutant strains for each individual trajectory. For each trajectory our analysis is progressive in time, and successively focuses on the second mutation events.
Edit intent: meaning-changed
Original:  Our approach is based is on a nonlinear least squares optimization procedure which takes into account both the first and the  second mutation events. The methodology developed in this paper is confirmed with numerical simulations with realistic parameter values .
Revised:  Our approach is based is on a nonlinear least squares optimization procedure which takes into account both the first and the second mutation event. The basic principle applied here is to minimize (for each trajectory) the mean squared errors of prediction w(t) - W(t) where the observed white cell frequencies w(t) are predicted by W(t), which is computed as the conditional expectation of w(t) given the available information at time (t-1). The pooling of all selective advantages estimates across all trajectories provides histograms on which we perform a precise peak analysis to compute final estimates of selective advantages. We validate our approach using ensembles of simulated trajectories .
Edit intent: meaning-changed
Original:  Later, we investigate the case where the trader is creating transient market impact . We show that strategies in which the trader is observing the signal a number of times during the trading period, can dramatically reduce the transaction costs and improve the performance of the optimal static strategy .
Revised:  In the same spirit, in the case of transient impact it is shown that strategies that observe the signal a finite number of times during the trading period, can dramatically reduce the transaction costs and improve the performance of the optimal static strategy .
Edit intent: clarity