wanyu/IteraTeR_v2 huggingface dataset:
Original:  notably, our approach shows how the environment plays a key role in determining the phenotypic behaviorof one of the two genotypes.
Revised:  notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the two genotypes.
Edit intent: clarity
Original: A significant amount of research in recent years has been dedicated towards single agent deep reinforcement learning.
Edit intent: clarity
Original:  Much of the success of  deep reinforcement learning  can be attributed towards the use of experience replay memories within which state transitions are stored.
Revised:  Much of the success of deep reinforcement learning can be attributed to the use of experience replay memories within which state transitions are stored.
Edit intent: fluency
Original:  However, considerations are required when using experience replay memories within multi-agent systems , as stored transitions can become outdated due to agents updating their respective policies in parallel [ 1 ].
Revised:  However, care is required when using experience replay memories within multi-agent systems , as stored transitions can become outdated due to agents updating their respective policies in parallel [ 1 ].
Edit intent: clarity
Original:  However, considerations are required when using experience replay memories within multi-agent systems , as stored transitions can become outdated due to agents updating their respective policies in parallel [ 1 ].
Revised:  However, considerations are required when using ERMs for multi-agent systems , as stored transitions can become outdated due to agents updating their respective policies in parallel [ 1 ].
Edit intent: clarity
Original:  However, considerations are required when using experience replay memories within multi-agent systems , as stored transitions can become outdated due to agents updating their respective policies in parallel [ 1 ].
Revised:  However, considerations are required when using experience replay memories within multi-agent deep reinforcement learning (MA-DRL) , as stored transitions can become outdated due to agents updating their respective policies in parallel [ 1 ].
Edit intent: clarity
Original:  However, considerations are required when using experience replay memories within multi-agent systems , as stored transitions can become outdated due to agents updating their respective policies in parallel [ 1 ].
Revised:  However, considerations are required when using experience replay memories within multi-agent systems , as stored transitions can become outdated because agents update their policies in parallel [ 1 ].
Edit intent: clarity
Original:  In this work we apply leniency [ 2 ] to multi-agent deep reinforcement learning ( MA-DRL ), acting as a control mechanism to determine which state-transitions sampled are allowed to update the DQN.
Revised:  In this work we apply leniency [ 2 ] to MA-DRL ), acting as a control mechanism to determine which state-transitions sampled are allowed to update the DQN.
Edit intent: clarity
Original:  Our resulting Lenient-DQN (LDQN) is evaluated using ]  variations of the Coordinated Multi-Agent Object Transportation Problem (CMOTP) outlined by Busoniu et al.
Revised:  Our resulting Lenient-DQN (LDQN) is evaluated using ] variations of the Coordinated Multi-Agent Object Transportation Problem (CMOTP) 
Edit intent: clarity
Original:  [ 3 ] . The LDQN significantly outperforms the existing hysteretic DQN (HDQN) 4%DIFDELCMD < ] %%% within environments that yield stochastic rewards.
Revised:  [ 3 ] 4%DIFDELCMD < ] %%% within environments that yield stochastic rewards.
Edit intent: coherence
Original:  [ 3 ] . The LDQN significantly outperforms the existing hysteretic DQN (HDQN) 4%DIFDELCMD < ] %%% within environments that yield stochastic rewards.
Revised:  [ 3 ] . The LDQN significantly outperforms the existing hysteretic DQN (HDQN) %DIFDELCMD < ] %%% within environments that yield stochastic rewards.
Edit intent: clarity
Original: ~[IPL, 1998] gave an algorithm that, given the trie recognizing a finite antifactorial language M, computes a DFA of the language having M as set of minimal forbidden factors.
Revised: ~[IPL, 1998] gave an algorithm that, given the trie recognizing a finite antifactorial language M, computes a DFA recognizing the language having M as set of minimal forbidden factors.
Edit intent: clarity
Original:  ~ [IPL, 1998] gave an algorithm that, given the trie recognizing a finite antifactorial language M, computes a DFA recognizing the language having M as set of minimal forbidden factors  .
Revised:  [IPL, 1998] gave an algorithm that, given the trie recognizing a finite antifactorial language M, computes a DFA recognizing the language having M as set of minimal forbidden factors .
Edit intent: fluency
Original:  ~ [IPL, 1998] gave an algorithm that, given the trie recognizing a finite antifactorial language M, computes a DFA recognizing the language having M as set of minimal forbidden factors  .
Revised:  ~ [IPL, 1998] gave an algorithm that, given the trie recognizing a finite antifactorial language M, computes a DFA recognizing the language whose set of minimal forbidden factors .
Edit intent: clarity
Original:  We also discuss combinatorial properties of the minimal forbidden factors of a circular word.
Revised:  We discuss several combinatorial properties of the minimal forbidden factors of a circular word.
Edit intent: clarity
Original:   Finally, we characterize the minimal forbidden factors of the circular Fibonacci words.
Revised:  Finally, we investigate the case of minimal forbidden factors of the circular Fibonacci words.
Edit intent: clarity
Original: A nonholonomic system is a mechanical system with velocity constraints not originating from position constraints : the typical example is rolling motion on a surface without slipping .
Revised: A nonholonomic system is a mechanical system with velocity constraints not originating from position constraints ; rolling without slipping is the typical example .
Edit intent: clarity
Original:  Such integrators are often based on discrete versions of the Lagrange--d'Alembert principle.
Edit intent: coherence
Original:  It has been observed numerically that many nonholonomic integrators exhibit excellent long-time behaviour when applied to standard test problems.
Revised:  It has been observed numerically that many nonholonomic integrators exhibit excellent long-time behaviour when applied to various test problems.
Edit intent: clarity
Original:  We therefore provide perturbed, unbiased test problems that are integrable but no longer reversible  with respect to the standard reversibility map .
Revised:  We therefore provide perturbed, unbiased test problems that are integrable but no longer reversible ( with respect to the standard reversibility map .
Edit intent: fluency
Original:  Theoretical network models that feature heterogeneous cell populations or random connectivity also generate diverse tuning behaviors across cells .
Revised:  Theoretical network models that feature heterogeneous cell populations or random connectivity also give rise to diverse tuning curves .
Edit intent: clarity
Original:  A major obstacle for fitting such models is that , in most cases of interest, an explicit form for the likelihood function , i.e. the distribution of generated tuning curves, is not available .
Revised:  A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable to compute .
Edit intent: clarity
Original:  Generative Adversarial Networks (GAN) provide one such framework , and have been successful in traditional machine learning tasks.
Revised:  Generative Adversarial Networks (GAN) provide one such framework which has been successful in traditional machine learning tasks.
Edit intent: coherence
Original:  Another strength of this approach is that it fits the entire, joint distribution of experimental tuning curves, instead of matching a few summary statistics picked  by the user.
Revised:  Another strength of this approach is that it fits the entire, joint distribution of experimental tuning curves, instead of matching a few summary statistics picked a priori by the user.
Edit intent: clarity
Original:  Our evaluation shows that (1) once a transformation is written, relatively little work is required to configure it for a new language (2) transformations built this way output readable code which preserve the structure of the original, according to participants in our human study, and (3) despite dealing with many languages, our transformations can still handle language corner-cases, and pass 90\% of compiler test suites.
Revised:  Our evaluation shows that (1) once a transformation is written, little work is required to configure it for a new language (2) transformations built this way output readable code which preserve the structure of the original, according to participants in our human study, and (3) despite dealing with many languages, our transformations can still handle language corner-cases, and pass 90\% of compiler test suites.
Edit intent: clarity
Original:  Our evaluation shows that (1) once a transformation is written, relatively little work is required to configure it for a new language (2) transformations built this way output readable code which preserve the structure of the original, according to participants in our human study, and (3) despite dealing with many languages, our transformations can still handle language corner-cases, and pass 90\% of compiler test suites.
Revised:  Our evaluation shows that (1) once a transformation is written, relatively little work is required to configure it for a new language (2) transformations built this way output readable code which preserve the structure of the original, according to participants in our human study, and (3) our transformations can still handle language corner-cases, and pass 90\% of compiler test suites.
Edit intent: coherence
Original:  Our evaluation shows that (1) once a transformation is written, relatively little work is required to configure it for a new language (2) transformations built this way output readable code which preserve the structure of the original, according to participants in our human study, and (3) despite dealing with many languages, our transformations can still handle language corner-cases, and pass 90\% of compiler test suites.
Revised:  Our evaluation shows that (1) once a transformation is written, relatively little work is required to configure it for a new language (2) transformations built this way output readable code which preserve the structure of the original, according to participants in our human study, and (3) despite dealing with many languages, our transformations can still handle language corner-cases, as validated on compiler test suites.
Edit intent: clarity
Original: The main problems in fine-grained  complexity are CNF-SAT, the Orthogonal Vectors problem, 3SUM, and the Negative-Weight Triangle problem (which is closely related to All-Pairs Shortest Path).
Revised: In this paper, we introduce a general framework for fine-grained complexity are CNF-SAT, the Orthogonal Vectors problem, 3SUM, and the Negative-Weight Triangle problem (which is closely related to All-Pairs Shortest Path).
Edit intent: clarity
Original: The main problems in fine-grained  complexity are CNF-SAT, the Orthogonal Vectors problem, 3SUM, and the Negative-Weight Triangle problem (which is closely related to All-Pairs Shortest Path).
Revised: The main problems in fine-grained complexity : the Orthogonal Vectors problem, 3SUM, and the Negative-Weight Triangle problem (which is closely related to All-Pairs Shortest Path).
Edit intent: clarity
Original:  For example, if there is an O(c^n)-time algorithm that solves k-SAT  for all k, then we prove there is an O((c+o(1))^n)-time algorithm to approximately count the satisfying assignments.
Revised:  For example, if there is an O(c^n)-time algorithm for k-SAT for all k, then we prove there is an O((c+o(1))^n)-time algorithm to approximately count the satisfying assignments.
Edit intent: clarity
Original:  For example, if there is an O(c^n)-time algorithm that solves k-SAT  for all k, then we prove there is an O((c+o(1))^n)-time algorithm to approximately count the satisfying assignments.
Revised:  For example, if there is an O(c^n)-time algorithm that solves k-SAT . Then we prove that for all k, then we prove there is an O((c+o(1))^n)-time algorithm to approximately count the satisfying assignments.
Edit intent: coherence
Original:  For example, if there is an O(c^n)-time algorithm that solves k-SAT  for all k, then we prove there is an O((c+o(1))^n)-time algorithm to approximately count the satisfying assignments.
Revised:  For example, if there is an O(c^n)-time algorithm that solves k-SAT for all k, there is an O((c+o(1))^n)-time algorithm to approximately count the satisfying assignments.
Edit intent: coherence
Original:  Because of model computation complexity and increased model sizes, those trained models are usually hosted in the cloud.
Revised:  Because of computational complexity and size constraints, these trained models are usually hosted in the cloud.
Edit intent: clarity
Original:  Because of model computation complexity and increased model sizes, those trained models are usually hosted in the cloud.
Revised:  Because of model computation complexity and increased model sizes, those trained models are often hosted in the cloud.
Edit intent: clarity
Original:  When mobile apps need to utilize those models, they will have to send input data over the network.
Revised:  To utilize these cloud-based models, mobile apps will have to send input data over the network.
Edit intent: clarity
Original:  While cloud-based deep learning can provide reasonable response time for mobile apps, it also restricts the use case scenarios, e.g. mobile apps need to have access to network .
Revised:  While cloud-based deep learning can provide reasonable response time for mobile apps, it restricts the use case scenarios, e.g. mobile apps need to have access to network .
Edit intent: clarity
Original:  While cloud-based deep learning can provide reasonable response time for mobile apps, it also restricts the use case scenarios, e.g. mobile apps need to have access to network .
Revised:  While cloud-based deep learning can provide reasonable response time for mobile apps, it also restricts the use case scenarios, e.g. mobile apps need to have network access .
Edit intent: fluency
Original:  With mobile specific deep learning optimizations, it is now possible to employ device-based inference.
Revised:  With mobile specific deep learning optimizations, it is now possible to employ on-device inference.
Edit intent: clarity
Original:  In this paper, we empirically evaluate the inference efficiency of three Convolutional Neural Networks  using a benchmark Android application we developed.
Revised:  In this paper, we empirically evaluate the inference performance of three Convolutional Neural Networks using a benchmark Android application we developed.
Edit intent: clarity
Original:  Based on our application-driven analysis , we have identified several performance bottlenecks for mobile applications powered by on-device deep learning inference .
Revised:  Based on our application-driven analysis , we have identified several performance bottlenecks for on-device deep learning inference .
Edit intent: clarity
Original:  Based on our application-driven analysis , we have identified several performance bottlenecks for mobile applications powered by on-device deep learning inference .
Revised:  Based on our application-driven analysis , we have identified several performance bottlenecks for mobile applications powered by on-device deep inferences .
Edit intent: clarity
Original:  To address this , I revisit a minimal model of logistic growth coupled to density-independent catastrophes and derive exact expressions for its stationary moments, neglecting the possibility of extinction.
Revised:  To address this , I revisit a minimal model of logistic growth coupled to density-independent catastrophes and derive exact expressions for the model's stationary moments, neglecting the possibility of extinction.
Edit intent: clarity
Original:  Together, these findings enable succinct and predicative characterizations of an important class of stochastic dynamical systems  .
Revised:  Together, these results provide insight into a wide range of stochastic dynamical systems .
Edit intent: clarity
Original: We  improve our earlier lower bounds on the number of electrical transformations required to reduce an n-vertex plane graph in the worst case [SOCG 2016] in two different directions.
Revised: We improve our earlier lower bound on the number of electrical transformations required to reduce an n-vertex plane graph in the worst case [SOCG 2016] in two different directions.
Edit intent: fluency
Original:  As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behaviour of neurons in cortical microcircuits of the human brain.
Revised:  As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain.
Edit intent: fluency
Original:  Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) Artificial Neural Networks (ANN) , too, should not have fully-connected layers.
Revised:  Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks , too, should not have fully-connected layers.
Edit intent: clarity
Original:  Following a Darwinian evolutionary approach, we propose a novel algorithm which evolves an initial random sparse topology ( i.e. an Erdos-R\'enyi random graph) of two consecutive layers of neurons into a scale-free topology, during the ANN training process.
Revised:  Following a Darwinian evolutionary approach, we propose a novel algorithm which evolves an initial sparse topology ( i.e. an Erdos-R\'enyi random graph) of two consecutive layers of neurons into a scale-free topology, during the ANN training process.
Edit intent: clarity
Original:  Following a Darwinian evolutionary approach, we propose a novel algorithm which evolves an initial random sparse topology ( i.e. an Erdos-R\'enyi random graph) of two consecutive layers of neurons into a scale-free topology, during the ANN training process.
Revised:  Following a Darwinian evolutionary approach, we propose a novel algorithm which evolves an initial random sparse topology ( Erdos-R\'enyi random graph) of two consecutive layers of neurons into a scale-free topology, during the ANN training process.
Edit intent: coherence
Original:  Our method allows to quadratically reduce the number of parameters in the fully conencted layers of ANNs, yielding quadratically faster computational times in both phases (i.e. training and inference), at no decrease in accuracy.
Revised:  Our method allows to quadratically reduce the number of parameters , with no decrease in accuracy.
Edit intent: clarity
Original:  We demonstrate our claims on two popular ANN types (restricted Boltzmann machine and multi-layer perceptron), on two types of tasks (supervised and unsupervised learning), and on 14 benchmark datasets.
Revised:  We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptron), on two types of tasks (supervised and unsupervised learning), and on 14 benchmark datasets.
Edit intent: clarity
Original:  We focus on the scenario in which a resource-limited encoder needs to report a small subset S from a universe of N objects to a more powerful decoder  .
Revised:  We focus on the scenario in which a resource-limited encoder has to report a small subset S from a universe of N objects to a more powerful decoder .
Edit intent: fluency
Original:  We focus on the scenario in which a resource-limited encoder needs to report a small subset S from a universe of N objects to a more powerful decoder  .
Revised:  We focus on the scenario in which a resource-limited encoder needs to report a small set S from a universe of N objects to a more powerful decoder .
Edit intent: clarity
Original:  We focus on the scenario in which a resource-limited encoder needs to report a small subset S from a universe of N objects to a more powerful decoder  .
Revised:  We focus on the scenario in which a resource-limited encoder needs to report a small subset S from a universe of N items to a more powerful decoder .
Edit intent: clarity
Original:  The distinguishing feature of our model is asymmetry : the subset S is an i.i.d. sample from a prior distribution \mu, and \mu is only known to the decoder.
Revised:  The distinguishing feature is asymmetric information : the subset S is an i.i.d. sample from a prior distribution \mu, and \mu is only known to the decoder.
Edit intent: clarity
Original:  The distinguishing feature of our model is asymmetry : the subset S is an i.i.d. sample from a prior distribution \mu, and \mu is only known to the decoder.
Revised:  The distinguishing feature of our model is asymmetry : the subset S is comprised of i.i.d. sample from a prior distribution \mu, and \mu is only known to the decoder.
Edit intent: clarity
Original:  The distinguishing feature of our model is asymmetry : the subset S is an i.i.d. sample from a prior distribution \mu, and \mu is only known to the decoder.
Revised:  The distinguishing feature of our model is asymmetry : the subset S is an i.i.d. samples from a prior distribution \mu, and \mu is only known to the decoder.
Edit intent: fluency
Original:  This scenario implies that the encoder must use an oblivious compression scheme which can, nonetheless, achieve communication comparable to the entropy rate |S| \cdot H(\mu), the standard benchmark when both encoder and decoder have access to the prior \mu (as achieved by the Huffman code) .
Revised:  The goal for the encoder is to encode S obliviously, while achieving the information-theoretic bound of |S| \cdot H(\mu), the standard benchmark when both encoder and decoder have access to the prior \mu (as achieved by the Huffman code) .
Edit intent: clarity
Original:  We first show that in order to exploit the prior \mu in a non-trivial way in such asymmetric information scenario, the compression scheme must be randomized {\em   .
Revised:  We first show that any such compression scheme must be randomized {\em .
Edit intent: clarity
Original:  We first show that in order to exploit the prior \mu in a non-trivial way in such asymmetric information scenario, the compression scheme must be randomized {\em   .
Revised:  We first show that in order to exploit the prior \mu in a non-trivial way in such asymmetric information scenario, the compression scheme must be {\em .
Edit intent: clarity
Original:  On the other hand, a rather simple argument shows that, when |S|=k, a random linear code achieves essentially optimal communication rate of O( k\cdot H(\mu) ) bits, nearly-matching the communication benchmark in the symmetric case .
Revised:  On the other hand, a rather simple argument shows that, when |S|=k, a random linear code achieves near-optimal communication rate of O( k\cdot H(\mu) ) bits, nearly-matching the communication benchmark in the symmetric case .
Edit intent: clarity
Original:  On the other hand, a rather simple argument shows that, when |S|=k, a random linear code achieves essentially optimal communication rate of O( k\cdot H(\mu) ) bits, nearly-matching the communication benchmark in the symmetric case .
Revised:  On the other hand, a rather simple argument shows that, when |S|=k, a random linear code achieves essentially optimal communication rate of about k\cdot H(\mu) ) bits, nearly-matching the communication benchmark in the symmetric case .
Edit intent: clarity