wanyu/IteraTeR_v2 huggingface dataset:
Original:  We have also extracted bounds on the reachable values during the computation of the piecewise quadratic Lyapunov function .
Revised:  We have also extracted bounds on the reachable values set .
Edit intent: clarity
Original:  The control is as efficient for peripherial nodes as driver nodes as it is for  more central nodes.
Revised:  The control is as efficient when peripheral nodes are driver nodes as it is for more central nodes.
Edit intent: fluency
Original:  The control is as efficient for peripherial nodes as driver nodes as it is for  more central nodes.
Revised:  The control is as efficient for peripherial nodes as driver nodes as it is for the case of more central nodes.
Edit intent: clarity
Original:  If we assume a cost to utilize nodes which is proportional to their  degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network  .
Revised:  However, if we assume a cost to utilize nodes which is proportional to their degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network .
Edit intent: coherence
Original:  If we assume a cost to utilize nodes which is proportional to their  degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network  .
Revised:  If we assume a cost to utilize nodes that is proportional to their degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network .
Edit intent: fluency
Original:  If we assume a cost to utilize nodes which is proportional to their  degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network  .
Revised:  If we assume a cost to utilize nodes which is proportional to their overall degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network .
Edit intent: clarity
Original:  If we assume a cost to utilize nodes which is proportional to their  degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network  .
Revised:  If we assume a cost to utilize nodes which is proportional to their degree, utilizing peripheral nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network .
Edit intent: fluency
Original:  If we assume a cost to utilize nodes which is proportional to their  degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network  .
Revised:  If we assume a cost to utilize nodes which is proportional to their degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that performs best in controlling the two-layer network .
Edit intent: clarity
Original:  Empirical evaluation on CIFAR-10 validates the superior expressiveness of SimNets, showing a significant gain in accuracy over ConvNets when computational resources at run-time are limited.
Revised:  Empirical evaluation validates the superior expressiveness of SimNets, showing a significant gain in accuracy over ConvNets when computational resources at run-time are limited.
Edit intent: clarity
Original:  In contrast to the ubiquitous network access model inherent to many cloud-based applications, for which the web browser forms the user front end, opportunistic applications require installing software on mobile devices.
Revised:  In contrast to the ubiquitous network access model inherent to many cloud-based applications, for which the web browser forms the user front end, opportunistic applications require installing software on mobile devices.
Edit intent: fluency
Original:  Based on the metric distances between spike trains, recent investigations have introduced the notion of  average or prototype spike train to characterize the template pattern in the neural activity.
Revised:  Based on the metric distances between spike trains, recent investigations have introduced the notion of an average or prototype spike train to characterize the template pattern in the neural activity.
Edit intent: fluency
Original:  However, as those metrics lack certain Euclidean properties, the defined averages are non-unique , and do not share the conventional properties of a mean.
Revised:  However, as those metrics lack certain Euclidean properties, the defined averages are nonunique , and do not share the conventional properties of a mean.
Edit intent: fluency
Original:  In this article, we propose a new framework to define the mean spike train where we adopt an Euclidean-like metric from an L p family.
Revised:  In this article, we propose a new framework to define the mean spike train where we adopt a Euclidean-like metric from an L p family.
Edit intent: fluency
Original:  In this article, we propose a new framework to define the mean spike train where we adopt an Euclidean-like metric from an L p family.
Revised:  In this article, we propose a new framework to define the mean spike train where we adopt an Euclidean-like metric from an L ^p family.
Edit intent: fluency
Original:  Furthermore, we apply the new framework in a recording from rodent geniculate ganglion, where background firing activity is a common is- sue for neural coding.
Revised:  Furthermore, we apply the new framework in a recording from rodent geniculate ganglion, where background firing activity is a common issue for neural coding.
Edit intent: fluency
Original:  This allows  an efficient encoding of the problem, and reveals how reaction networks are naturally suited to statistical inference tasks.
Revised:  This allows for an efficient encoding of the problem, and reveals how reaction networks are naturally suited to statistical inference tasks.
Edit intent: fluency
Original:  This allows  an efficient encoding of the problem, and reveals how reaction networks are naturally suited to statistical inference tasks.
Revised:  This allows an efficient encoding of the problem, and reveals that reaction networks are naturally suited to statistical inference tasks.
Edit intent: coherence
Original:  This allows  an efficient encoding of the problem, and reveals how reaction networks are naturally suited to statistical inference tasks.
Revised:  This allows an efficient encoding of the problem, and reveals how reaction networks are superbly suited to statistical inference tasks.
Edit intent: clarity
Original:  In addition, such a scheme may  provide a template to understand how biochemical signaling pathways integrate extensive information about their environment and history.
Revised:  In addition, such a scheme may also provide a template to understand how biochemical signaling pathways integrate extensive information about their environment and history.
Edit intent: fluency
Original:  I show using exact analytical calculations and numerical simulations that limiting dissipation qualitatively changes the kinetics in single cells marked by emergence of slow kinetics, large cell-tocell variations of copy numbers, temporally correlated stochastic events (dynamic facilitation), and, ergodicity breaking.
Revised:  I show using exact analytical calculations and numerical simulations that limiting dissipation qualitatively changes the kinetics in single cells marked by emergence of slow kinetics, large cell-to-cell variations of copy numbers, temporally correlated stochastic events (dynamic facilitation), and, ergodicity breaking.
Edit intent: fluency
Original:  Thus, constraints in energy dissipation, in addition to negatively affecting ligand discrimination in T cells,  create a fundamental difficulty in interpreting single cell kinetics from cell population level results found in the literature regarding the connection between high precision responses and dissipation .
Revised:  Thus, constraints in energy dissipation, in addition to negatively affecting ligand discrimination in T cells, can create a fundamental difficulty in interpreting single cell kinetics from cell population level results found in the literature regarding the connection between high precision responses and dissipation .
Edit intent: coherence
Original:  Thus, constraints in energy dissipation, in addition to negatively affecting ligand discrimination in T cells,  create a fundamental difficulty in interpreting single cell kinetics from cell population level results found in the literature regarding the connection between high precision responses and dissipation .
Revised:  Thus, constraints in energy dissipation, in addition to negatively affecting ligand discrimination in T cells, create a fundamental difficulty in interpreting single cell kinetics from cell population level results .
Edit intent: coherence
Original:  Cooperation , here, is a persistent behavioural pattern of individual entities pooling and sharing resources.
Revised:  Cooperation , here, is a persistent behavioral pattern of entities pooling and sharing resources.
Edit intent: clarity
Original:  Leveraging the multi-tenancy approach, radio resources are transferred in terms of physical radio Resource Blocks (RBs) among multiple heterogeneous base stations (i.e., tenants), interconnected via the X2 interface.
Revised:  Leveraging the multi-tenancy approach, radio resources are transferred in terms of physical radio Resource Blocks (RBs) among multiple heterogeneous base stations , interconnected via the X2 interface.
Edit intent: clarity
Original:  Both analytical studies and simulation experiments are conducted to evaluate RENEV in terms of network's throughput as well as its additional signaling overhead.
Revised:  Analytical studies and simulation experiments are conducted to evaluate RENEV in terms of network's throughput as well as its additional signaling overhead.
Edit intent: clarity
Original: The focus of this paper is the moral conflict between lying aversion and prosociality.
Revised: The opportunity to tell a white lie (i.e., a lie that benefits another person) generates a moral conflict between lying aversion and prosociality.
Edit intent: clarity
Original: The focus of this paper is the moral conflict between lying aversion and prosociality.
Revised: The focus of this paper is the moral conflict between two opposite moral dictates, one pushing towards telling always the truth and the other pushing towards helping others. Here we study how people resolve this moral conflict.
Edit intent: clarity
Original:  In the second stage , we used a sender-receiver game to measure aversion to telling a Pareto white lie (i.e., a lie that helps both the liar and the listener),  aversion to telling an altruistic white lie (i.e., a lie that helps the listener at the expense of the liar) , and skepticism towards believing a possibly untruthful message .
Revised:  In the second stage , we used a Deception Game to measure aversion to telling a Pareto white lie (i.e., a lie that helps both the liar and the listener), aversion to telling an altruistic white lie (i.e., a lie that helps the listener at the expense of the liar) , and skepticism towards believing a possibly untruthful message .
Edit intent: clarity
Original:  In the second stage , we used a sender-receiver game to measure aversion to telling a Pareto white lie (i.e., a lie that helps both the liar and the listener),  aversion to telling an altruistic white lie (i.e., a lie that helps the listener at the expense of the liar) , and skepticism towards believing a possibly untruthful message .
Revised:  In the second stage , we used a sender-receiver game to measure aversion to telling a Pareto white lie (i.e., a lie that helps both the liar and the listener), and aversion to telling an altruistic white lie (i.e., a lie that helps the listener at the expense of the liar) , and skepticism towards believing a possibly untruthful message .
Edit intent: coherence
Original:  Our results shed light on the relation between prosociality, truth-telling , and trust in others' tendencies to tell the truth .
Revised:  Our results shed light on the relation between prosociality, truth-telling .
Edit intent: clarity
Original:  In particular, the first two findings suggest that a significant proportion of people have non-distributional notions of what is the right thing to do  : irrespective of the economic consequences, they tell the truth, they cooperate, they share their money .
Revised:  In particular, the first finding suggests that a significant proportion of people have non-distributional notions of what is the right thing to do : irrespective of the economic consequences, they tell the truth, they cooperate, they share their money .
Edit intent: clarity
Original:  In particular, the first two findings suggest that a significant proportion of people have non-distributional notions of what is the right thing to do  : irrespective of the economic consequences, they tell the truth, they cooperate, they share their money .
Revised:  In particular, the first two findings suggest that a significant proportion of people have non-distributional notions of what the right thing to do : irrespective of the economic consequences, they tell the truth, they cooperate, they share their money .
Edit intent: clarity
Original:  In particular, the first two findings suggest that a significant proportion of people have non-distributional notions of what is the right thing to do  : irrespective of the economic consequences, they tell the truth, they cooperate, they share their money .
Revised:  In particular, the first two findings suggest that a significant proportion of people have non-distributional notions of what is the right thing to do is : irrespective of the economic consequences, they tell the truth, they cooperate, they share their money .
Edit intent: clarity
Original:  In particular, the first two findings suggest that a significant proportion of people have non-distributional notions of what is the right thing to do  : irrespective of the economic consequences, they tell the truth, they cooperate, they share their money .
Revised:  In particular, the first two findings suggest that a significant proportion of people have non-distributional notions of what is the right thing to do : irrespective of their economic consequences, they tell the truth, they cooperate, they share their money .
Edit intent: clarity
Original:  This result has recently been lifted to FO definable string-to-string functions, with equivalent representations by means of aperiodic deterministic two-way transducers and aperiodic ( 1-bounded ) streaming string transducers (SST) . In this paper, we study transformationsbetween these two models of transducers, and prove that they preserve the aperiodicity of transducers.
Revised:  This result has recently been lifted to FO definable functions, with equivalent representations by means of aperiodic deterministic two-way transducers and aperiodic ( 1-bounded ) streaming string transducers (SST) . In this paper, we study transformationsbetween these two models of transducers, and prove that they preserve the aperiodicity of transducers.
Edit intent: clarity
Original:  This result has recently been lifted to FO definable string-to-string functions, with equivalent representations by means of aperiodic deterministic two-way transducers and aperiodic ( 1-bounded ) streaming string transducers (SST) . In this paper, we study transformationsbetween these two models of transducers, and prove that they preserve the aperiodicity of transducers.
Revised:  This result has recently been lifted to FO definable string-to-string functions, with equivalent representations by means of aperiodic 2DFT and aperiodic 1-bounded ) streaming string transducers (SST) . In this paper, we study transformationsbetween these two models of transducers, and prove that they preserve the aperiodicity of transducers.
Edit intent: clarity
Original:  Our model is an alignment-based  long short-term memory recurrent neural network ( attention-based LSTM-RNN ) that encodes the free-form navigational instruction sentence and the corresponding representation of the environment state.
Revised:  Our model is an alignment-based , long short-term memory recurrent neural network ( attention-based LSTM-RNN ) that encodes the free-form navigational instruction sentence and the corresponding representation of the environment state.
Edit intent: fluency
Original:  Our model is an alignment-based  long short-term memory recurrent neural network ( attention-based LSTM-RNN ) that encodes the free-form navigational instruction sentence and the corresponding representation of the environment state.
Revised:  Our model is an alignment-based long short-term memory recurrent neural network ( LSTM RNN ) that encodes the free-form navigational instruction sentence and the corresponding representation of the environment state.
Edit intent: clarity
Original:  We integrate alignment as part of our network , thereby empowering the model to focus on important sentence "regions . " The alignment-based LSTM then decodes the learned representation to obtain the inferred action sequence.
Revised:  We integrate alignment as part of our network that empowers the model to focus on important sentence "regions . " The alignment-based LSTM then decodes the learned representation to obtain the inferred action sequence.
Edit intent: coherence
Original:  We integrate alignment as part of our network , thereby empowering the model to focus on important sentence "regions . " The alignment-based LSTM then decodes the learned representation to obtain the inferred action sequence.
Revised:  We integrate alignment as part of our network , thereby empowering the model to focus on salient sentence "regions . " The alignment-based LSTM then decodes the learned representation to obtain the inferred action sequence.
Edit intent: clarity
Original:  We integrate alignment as part of our network , thereby empowering the model to focus on important sentence "regions . " The alignment-based LSTM then decodes the learned representation to obtain the inferred action sequence.
Revised:  We integrate alignment as part of our network , thereby empowering the model to focus on important sentence "regions " The alignment-based LSTM then decodes the learned representation to obtain the inferred action sequence.
Edit intent: fluency
Original:  In contrast with existing methods, our model uses no additional information or resources about the task or language at all (e.g., parsers or seed lexicons) and still achieves state-of-the-art on a single-sentence benchmark dataset and strong results in the limited-training multi-sentence setting .
Revised:  In contrast to existing methods, our model uses no additional information or resources about the task or language at all (e.g., parsers or seed lexicons) and still achieves state-of-the-art on a single-sentence benchmark dataset and strong results in the limited-training multi-sentence setting .
Edit intent: fluency
Original:  In contrast with existing methods, our model uses no additional information or resources about the task or language at all (e.g., parsers or seed lexicons) and still achieves state-of-the-art on a single-sentence benchmark dataset and strong results in the limited-training multi-sentence setting .
Revised:  In contrast with existing methods, our model uses no additional information or resources about the task or language (e.g., parsers or seed lexicons) and still achieves state-of-the-art on a single-sentence benchmark dataset and strong results in the limited-training multi-sentence setting .
Edit intent: clarity
Original:  In contrast with existing methods, our model uses no additional information or resources about the task or language at all (e.g., parsers or seed lexicons) and still achieves state-of-the-art on a single-sentence benchmark dataset and strong results in the limited-training multi-sentence setting .
Revised:  In contrast with existing methods, our model uses no additional information or resources about the task or language at all (e.g., parsers or seed lexicons) and still achieves the best results reported to-date on a benchmark single-sentence dataset and gives competitive results for the limited-training multi-sentence setting .
Edit intent: clarity
Original:  Our model is a bidirectional, alignment-based ,  long short-term memory recurrent neural network (LSTM RNN) that encodes the free-form navigational instruction sentence and the corresponding representation of the environment state.
Revised:  Our model is a bidirectional, alignment-based , long short-term memory recurrent neural networks (LSTM-RNN) translates natural language instructions to action sequences based upon a representation of the environment state.
Edit intent: clarity
Original:  Our model is a bidirectional, alignment-based ,  long short-term memory recurrent neural network (LSTM RNN) that encodes the free-form navigational instruction sentence and the corresponding representation of the environment state.
Revised:  Our model is a bidirectional, alignment-based , long short-term memory recurrent neural network (LSTM RNN) that encodes the free-form navigational instruction sentence and the corresponding representation of the observable world state.
Edit intent: clarity
Original:  We propose a multi-level aligner as part of our network that empowers the model to focus on salient sentence "regions" , using both high- and low-level input representations.
Revised:  We introduce a multi-level aligner as part of our network that empowers the model to focus on salient sentence "regions" , using both high- and low-level input representations.
Edit intent: clarity
Original:  We propose a multi-level aligner as part of our network that empowers the model to focus on salient sentence "regions" , using both high- and low-level input representations.
Revised:  We propose a multi-level aligner that empowers our model to focus on salient sentence "regions" , using both high- and low-level input representations.
Edit intent: clarity
Original:  We propose a multi-level aligner as part of our network that empowers the model to focus on salient sentence "regions" , using both high- and low-level input representations.
Revised:  We propose a multi-level aligner as part of our network that empowers the model to focus on sentence "regions" , using both high- and low-level input representations.
Edit intent: clarity
Original:  In contrast to existing methods, our model uses no additional information or resources about the task or language (e.g., parsers or seed lexicons) and still achieves the best results reported to-date on a benchmark single-sentence dataset and gives competitive results for the limited-training multi-sentence setting.
Revised:  In contrast to existing methods, our model uses no specialized linguistic resources (e.g., parsers or seed lexicons) and still achieves the best results reported to-date on a benchmark single-sentence dataset and gives competitive results for the limited-training multi-sentence setting.
Edit intent: clarity