wanyu/IteraTeR_v2 huggingface dataset:
Original:  Moreover, when n  UAVs are dispatched from different initial locations, we first prove that any two UAVs  will not fly across each other in the flight as long as they have the same initial energy storage , and then design a fully polynomial time approximation scheme (FPTAS) of time complexity O( n \log 1{\epsilon} ) to arbitrarily approach the optimum with relative error \epsilon.
Revised:  Moreover, when n UAVs are dispatched from different initial locations, we first prove that any two UAVs will not fly across each other in the flight as long as they have the same initial energy storage , and then design a fully polynomial time approximation scheme (FPTAS) of time complexity O( n \log 1{\epsilon} ) to arbitrarily approach the optimum with error \epsilon.
Edit intent: clarity
Original:  Further , we consider that UAVs may have different initial energy storages under the constraint of NFZs , and we prove this problem is NP-hard.
Revised:  Finally , we consider that UAVs may have different initial energy storages under the constraint of NFZs , and we prove this problem is NP-hard.
Edit intent: coherence
Original:  Further , we consider that UAVs may have different initial energy storages under the constraint of NFZs , and we prove this problem is NP-hard.
Revised:  Further , we consider that UAVs may have different initial energy storages , and we prove this problem is NP-hard.
Edit intent: clarity
Original:  The unsupervised BAE-NET is trained with all shapes in an input collection using a shape reconstruction loss, without  ground-truth segmentations . Specifically, the network takes an input shape and encodes it using a convolutional neural network, whereas the decoder concatenates the resulting feature code with a point coordinate and outputs a value indicating whether the point is inside/outside the shape.
Revised:  The unsupervised BAE-NET is trained with all shapes in an input collection using a shape reconstruction loss, without any ground-truth segmentations . Specifically, the network takes an input shape and encodes it using a convolutional neural network, whereas the decoder concatenates the resulting feature code with a point coordinate and outputs a value indicating whether the point is inside/outside the shape.
Edit intent: clarity
Original:  The unsupervised BAE-NET is trained with all shapes in an input collection using a shape reconstruction loss, without  ground-truth segmentations . Specifically, the network takes an input shape and encodes it using a convolutional neural network, whereas the decoder concatenates the resulting feature code with a point coordinate and outputs a value indicating whether the point is inside/outside the shape.
Revised:  The unsupervised BAE-NET is trained with all shapes in an input collection using a shape reconstruction loss, without ground-truth labels . Specifically, the network takes an input shape and encodes it using a convolutional neural network, whereas the decoder concatenates the resulting feature code with a point coordinate and outputs a value indicating whether the point is inside/outside the shape.
Edit intent: clarity
Original:  But, the biomarkers of Autism Spectrum Disorder (ASD) are still unknown, let alone automating its detection , due to intense connectivity of neurological pattern in brain . Studies from the neuroscience domain highlighted the fact that corpus callosum and intracranial brain volume holds significant information for detection of ASD.
Revised:  But, the biomarkers of Autism Spectrum Disorder (ASD) are still unknown, let alone automating its detection . Studies from the neuroscience domain highlighted the fact that corpus callosum and intracranial brain volume holds significant information for detection of ASD.
Edit intent: coherence
Original:  Thus, in this study we have applied machine learning algorithms on features extracted from corpus callosum and intracranial brain volume data . Corpus callosum and intracranial brain volume data is obtained from s-MRI (structural Magnetic Resonance Imaging) data-set known as ABIDE (Autism Brain Imaging Data Exchange) .
Revised:  Thus, in this study we have applied machine learning algorithms on features extracted from corpus callosum and intracranial brain volume from ABIDE dataset . Corpus callosum and intracranial brain volume data is obtained from s-MRI (structural Magnetic Resonance Imaging) data-set known as ABIDE (Autism Brain Imaging Data Exchange) .
Edit intent: clarity
Original:  However it is still difficult to apply them to larger networks or in order to get robustness against larger perturbations.
Revised:  However it is still difficult to get guarantees for larger networks or in order to get robustness against larger perturbations.
Edit intent: clarity
Original:  However it is still difficult to apply them to larger networks or in order to get robustness against larger perturbations.
Revised:  However it is still difficult to apply them to larger networks or robustness against larger perturbations.
Edit intent: coherence
Original:  The policies obtained were found to be highly robust to model parameter changes and fluctuations not introduced during training .
Revised:  The policies obtained were found to be highly robust to variations in a key model parameter subject to significant uncertainty and unpredictable dynamical changes .
Edit intent: clarity
Original:  Following the existing literature, this survey will describe such generalization.
Revised:  Following the existing literature, this survey will revisit such generalization.
Edit intent: clarity
Original:  This paper targets all three communities, compiler engineers, software developers and hardware architects , by exposing missed optimization opportunities .
Revised:  This paper targets all three communities, compiler engineers, software developers and hardware architects .
Edit intent: clarity
Original:  The enhanced nightly regression system is capable of driving the improvement and tuning of the compiler's common optimizer .
Revised:  The enhanced nightly regression system is capable of driving the improvement and tuning of the compiler's common optimizer 
Edit intent: fluency
Original:  By exploiting the behavior of the standard optimization levels , such as the -O3, of the LLVM v6.0 compiler, we show how to reveal hidden cross-architecture and architecture-dependent potential optimizations on two popular processors: the Intel i5-6300U , widely used in portable PCs, and the ARM Cortex-A53-based Broadcom BCM2837 used in the Raspberry Pi 3B+.
Revised:  By exploiting the behavior of the standard optimization levels . Experimental evaluation using the LLVM v6.0 compiler, we show how to reveal hidden cross-architecture and architecture-dependent potential optimizations on two popular processors: the Intel i5-6300U , widely used in portable PCs, and the ARM Cortex-A53-based Broadcom BCM2837 used in the Raspberry Pi 3B+.
Edit intent: clarity
Original:  By exploiting the behavior of the standard optimization levels , such as the -O3, of the LLVM v6.0 compiler, we show how to reveal hidden cross-architecture and architecture-dependent potential optimizations on two popular processors: the Intel i5-6300U , widely used in portable PCs, and the ARM Cortex-A53-based Broadcom BCM2837 used in the Raspberry Pi 3B+.
Revised:  By exploiting the behavior of the standard optimization levels , such as the -O3, of the LLVM v6.0 compiler, we show how to reveal hidden cross-architecture and architecture-dependent potential optimizations on two popular processors: the Intel i5-6300U and the Arm Cortex-A53-based Broadcom BCM2837 used in the Raspberry Pi 3B+.
Edit intent: clarity
Original: We study three orientation-based shape descriptors on a set of continuously moving points P : the first principal component, the smallest oriented bounding box and the thinnest strip.
Revised: We study three orientation-based shape descriptors on a set of continuously moving points : the first principal component, the smallest oriented bounding box and the thinnest strip.
Edit intent: clarity
Original:  Each of these shape descriptors essentially defines a cost capturing the quality of the descriptor (sum of squared distances for principal component, area for oriented bounding box, and width for strip), and uses the orientation that minimizes the cost.
Revised:  Each of these shape descriptors essentially defines a cost capturing the quality of the descriptor and uses the orientation that minimizes the cost.
Edit intent: coherence
Original:  Alternatively, we can bound the speed with which the orientation of the descriptor may change .
Revised:  If we bound the speed with which the orientation of the descriptor may change .
Edit intent: clarity
Original:  However, this can increase the cost (and hence lower the quality ) of the resulting shape descriptor.
Revised:  However, this can increase the cost (and hence lower the quality of the resulting shape descriptor.
Edit intent: fluency
Original:  \% We first show that there is no stateless algorithm, which depends only on the input points in one timestep and not on previous states , that both approximates the minimum cost of a shape descriptor and achieves bounded speed .
Revised:  \% We first show that there is no stateless algorithm, an algorithm that keeps no state over time , that both approximates the minimum cost of a shape descriptor and achieves bounded speed .
Edit intent: clarity
Original:  On the other hand, if we can use the previous state of the shape descriptor to compute the new state, then we can define "chasing" algorithms that attempt to follow the optimal orientation with bounded speed.
Revised:  On the other hand, if we can use the previous state of the shape descriptor to compute the new state, we can define "chasing" algorithms that attempt to follow the optimal orientation with bounded speed.
Edit intent: coherence
Original:  Under mild conditions, we show that chasing algorithms with sufficient bounded speed approximate the optimal cost at every time step for oriented bounding boxes and strips , but not for principal components .
Revised:  We show that, under mild conditions, we show that chasing algorithms with sufficient bounded speed approximate the optimal cost at every time step for oriented bounding boxes and strips , but not for principal components .
Edit intent: fluency
Original:  Under mild conditions, we show that chasing algorithms with sufficient bounded speed approximate the optimal cost at every time step for oriented bounding boxes and strips , but not for principal components .
Revised:  Under mild conditions, chasing algorithms with sufficient bounded speed approximate the optimal cost at every time step for oriented bounding boxes and strips , but not for principal components .
Edit intent: coherence
Original:  Under mild conditions, we show that chasing algorithms with sufficient bounded speed approximate the optimal cost at every time step for oriented bounding boxes and strips , but not for principal components .
Revised:  Under mild conditions, we show that chasing algorithms with sufficient bounded speed approximate the optimal cost at all times for oriented bounding boxes and strips , but not for principal components .
Edit intent: clarity
Original:  Under mild conditions, we show that chasing algorithms with sufficient bounded speed approximate the optimal cost at every time step for oriented bounding boxes and strips , but not for principal components .
Revised:  Under mild conditions, we show that chasing algorithms with sufficient bounded speed approximate the optimal cost at every time step for oriented bounding boxes and strips .
Edit intent: coherence
Original:  The analysis of such chasing algorithms is challenging and has received little attention in literature, hence we believe that our methods used to perform this analysis are of independent interest.
Revised:  The analysis of such chasing algorithms is challenging and has received little attention in literature, hence we believe that our methods used in this analysis are of independent interest.
Edit intent: clarity
Original:  Confirmation biasseems to account for user decisions about consuming and spreading content and, at the same time, aggregation of favored information within groups of like-minded people (echo chambers) reinforces selective exposure and group polarization .
Revised:  Primarily driven by confirmation bias, users tend to join homogeneous and polarized clusters (echo chambers) reinforces selective exposure and group polarization .
Edit intent: clarity
Original:  Confirmation biasseems to account for user decisions about consuming and spreading content and, at the same time, aggregation of favored information within groups of like-minded people (echo chambers) reinforces selective exposure and group polarization .
Revised:  Confirmation biasseems to account for user decisions about consuming and spreading content and, at the same time, aggregation of favored information within groups of like-minded people (echo chambers) where they cooperate to frame and reinforce a like-minded system of beliefs, thus facilitating misinformation cascades .
Edit intent: clarity
Original:  To gain a deeper understanding of the perspectives of these clustered communities , in this work we analyze the language of users engaged in the echo chambers emerging on Facebook around two distinct and conflicting narratives-- i.e., Science and Conspiracy news.
Revised:  To gain a deeper understanding of these phenomena , in this work we analyze the language of users engaged in the echo chambers emerging on Facebook around two distinct and conflicting narratives-- i.e., Science and Conspiracy news.
Edit intent: clarity
Original:  To gain a deeper understanding of the perspectives of these clustered communities , in this work we analyze the language of users engaged in the echo chambers emerging on Facebook around two distinct and conflicting narratives-- i.e., Science and Conspiracy news.
Revised:  To gain a deeper understanding of the perspectives of these clustered communities , in this work we analyze the lexicons used by the communities of users emerging on Facebook around two distinct and conflicting narratives-- i.e., Science and Conspiracy news.
Edit intent: clarity
Original:  Thus , the fact that even users with opposite views gradually coordinate their linguistic behavior when joining a discussion, suggests that users always aim to achieve communication when engaging in a conversation .
Revised:  Thus , the fact that even users with opposing views try to coordinate their lexical choices when joining a discussion, suggests that users always aim to achieve communication when engaging in a conversation .
Edit intent: clarity
Original:  Thus , the fact that even users with opposite views gradually coordinate their linguistic behavior when joining a discussion, suggests that users always aim to achieve communication when engaging in a conversation .
Revised:  Thus , the fact that even users with opposite views gradually coordinate their linguistic behavior when joining a discussion, suggests that a dialogue between competing parties is possible and indeed it should be stimulated in an attempt to smooth polarization and to reduce both the risk and the consequences of misinformation .
Edit intent: coherence
Original:  Primarily driven by confirmation bias, users tend to join homogeneous and polarized clusters (echo chambers) where they cooperate to frame and reinforce a like-minded system of beliefs, thus facilitating  misinformation cascades.
Revised:  Primarily driven by confirmation bias, users tend to join polarized clusters where they cooperate to frame and reinforce a like-minded system of beliefs, thus facilitating misinformation cascades.
Edit intent: clarity
Original:  Primarily driven by confirmation bias, users tend to join homogeneous and polarized clusters (echo chambers) where they cooperate to frame and reinforce a like-minded system of beliefs, thus facilitating  misinformation cascades.
Revised:  Primarily driven by confirmation bias, users tend to join homogeneous and polarized clusters (echo chambers) where they cooperate to reinforce a like-minded system of beliefs, thus facilitating misinformation cascades.
Edit intent: clarity
Original:  We show how the words exhibiting a significant differentiation in frequency from one community to another, provide important insights about the kind of information processed by the two groups of users and about the overall sentimentexpressed in their comments .
Revised:  We show how the lexical approach provides important insights about the kind of information processed by the two groups of users and about the overall sentimentexpressed in their comments .
Edit intent: clarity
Original:  We show how the words exhibiting a significant differentiation in frequency from one community to another, provide important insights about the kind of information processed by the two groups of users and about the overall sentimentexpressed in their comments .
Revised:  We show how the words exhibiting a significant differentiation in frequency from one community to another, provide important insights about the kind of information processed by the two communities of users and about the overall sentimentexpressed in their comments .
Edit intent: clarity
Original:  We show how the words exhibiting a significant differentiation in frequency from one community to another, provide important insights about the kind of information processed by the two groups of users and about the overall sentimentexpressed in their comments .
Revised:  We show how the words exhibiting a significant differentiation in frequency from one community to another, provide important insights about the kind of information processed by the two groups of users and about their overall sentiment .
Edit intent: clarity
Original:  Furthermore, by focusing on comment threads, a context of interaction mediated by the posts, we observe a strong positive correlation between the lexical convergence of co-commenters and their number of interactions, both in the case of co-commenters polarized towards the same content as well as in the case of co-commenters with opposing polarization.
Revised:  Furthermore, by focusing on comment threads, we observe a strong positive correlation between the lexical convergence of co-commenters and their number of interactions, both in the case of co-commenters polarized towards the same content as well as in the case of co-commenters with opposing polarization.
Edit intent: clarity
Original:  Nevertheless, the analysis of how lexical convergence evolves through time suggests that such a trend is a proxy of the emergence of collective identities in the case of co-commenters polarized toward the same content, whereas during cross interactions it is more likely due to the need for a common vocabulary to achieve communication than to real opinion convergence.
Revised:  Nevertheless, the analysis of how lexical convergence evolves through time suggests that such a trend could be a proxy for the emergence of collective identities in the case of co-commenters polarized toward the same content, whereas during cross interactions it is more likely due to the need for a common vocabulary to achieve communication than to real opinion convergence.
Edit intent: clarity
Original: Increased data gathering capacity, together with the spreading of data analytics techniques, has allowed an unprecedented concentration of information related to the individuals' preferences in the hands of a few gatekeepers.
Revised: Increased data gathering capacity, together with the spread of data analytics techniques, has allowed an unprecedented concentration of information related to the individuals' preferences in the hands of a few gatekeepers.
Edit intent: fluency
Original: Increased data gathering capacity, together with the spreading of data analytics techniques, has allowed an unprecedented concentration of information related to the individuals' preferences in the hands of a few gatekeepers.
Revised: Increased data gathering capacity, together with the spreading of data analytics techniques, has prompted an unprecedented concentration of information related to the individuals' preferences in the hands of a few gatekeepers.
Edit intent: clarity
Original: Reinforcement learning (RL) is a widely used approach for controlling systems with unknown or time-varying dynamics.
Revised: Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) is a widely used approach for controlling systems with unknown or time-varying dynamics.
Edit intent: coherence
Original:  We propose to employ symbolic regression (SR) to construct parsimonious process models described by analytic equations for real-time RL control .
Revised:  We propose to employ symbolic regression (SR) to construct parsimonious process models described by analytic equations .
Edit intent: clarity
Original:  We have tested our method with two different state-of-the-art SR algorithms which automatically search for equations that fit the measured data  .
Revised:  We have equipped our method with two different state-of-the-art SR algorithms which automatically search for equations that fit the measured data .
Edit intent: clarity
Original:  We present the approach on three simulated examples with up to 14-dimensional state space: an inverted pendulum, a mobile robot, and a biped walking robot.
Revised:  We present the approach on three simulated examples with up to 14-dimensional state space: an inverted pendulum, a mobile robot, and a bipedal walking robot.
Edit intent: fluency
Original:  We demonstrate on a real pendulum system that the analytic model found enables RL to successfully perform the swing-up task, based on a model constructed from only 100 data samples.
Revised:  We demonstrate on a real pendulum system that the analytic model found enables a RL controller to successfully perform the swing-up task, based on a model constructed from only 100 data samples.
Edit intent: clarity
Original:  In this paper, an important advancement is achieved: we define the " scalp--price " {\cal P} as the sum of only those price moves that are relevant to market dynamics;
Revised:  In this paper, an important advancement is achieved: we define the " scalp-price " {\cal P} as the sum of only those price moves that are relevant to market dynamics;
Edit intent: fluency
Original:  Changes in the scalp--price defined this way indicate a market trend change --- not a bear market rally or a bull market sell--off .
Revised:  Changes in the scalp-price defined this way indicate a market trend change --- not a bear market rally or a bull market sell--off .
Edit intent: fluency
Original:  Changes in the scalp--price defined this way indicate a market trend change --- not a bear market rally or a bull market sell--off .
Revised:  Changes in the scalp--price defined this way indicate a market trend change - not a bear market rally or a bull market sell--off .
Edit intent: fluency
Original:  The nematode Caenorhabditis elegans , where genetically identical individuals possess the same complement of neurons located at invariant positions, provides us with a model organism for studying this question.
Revised:  The nematode Caenorhabditis elegans provides us with a model organism for studying this question.
Edit intent: clarity
Original:  In this paper, we focus on the  connection structure and spatial arrangement of the neurons comprising the somatic neuronal network of this organism to understand the key developmental constraints underlying the designof a simple nervous system .
Revised:  In this paper, we focus on the invariant connection structure and spatial arrangement of the neurons comprising the somatic neuronal network of this organism to understand the key developmental constraints underlying the designof a simple nervous system .
Edit intent: clarity