wanyu/IteraTeR_v2 huggingface dataset:
Original:  We generate vectors through the state-of-the-art deep learning architecture GoogLeNet , for 20K images obtained from ImageNet.
Revised:  We generate vectors through the state-of-the-art deep learning architecture GoogLeNet for 20K images obtained from ImageNet.
Edit intent: fluency
Original:  We first evaluate the resultant vector-space  through its correlation with WordNet distances, and find vector distances to be strongly related with label semantics.
Revised:  We first evaluate the resultant vector-space semantics through its correlation with WordNet distances, and find vector distances to be strongly related with label semantics.
Edit intent: fluency
Original:  We first evaluate the resultant vector-space  through its correlation with WordNet distances, and find vector distances to be strongly related with label semantics.
Revised:  We first evaluate the resultant vector-space through its correlation with WordNet distances, and find vector distances to be strongly correlated with linguistic semantics.
Edit intent: clarity
Original:  We then explore the location of images within the vector space, finding semantically close elements to be clustered together, regardless of significant visual variances (e.g., 118 dogs types).
Revised:  We then explore the location of images within the vector space, finding semantically close elements to be clustered together, regardless of significant visual variances (e.g., 118 dog types).
Edit intent: fluency
Original:  More surprisingly, we find that the space unsupervisedly separates abstract classes without prior knowledge (e.g., living things).
Revised:  More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge (e.g., living things).
Edit intent: clarity
Original:  Finally, we consider vector arithmetics, and find them to be related with image concatenation (e.g., " horse cart - horse = rickshaw "), image overlap ("Panda - Brown bear \simeq Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger ").
Revised:  Finally, we consider vector arithmetics, and find them to be related with image concatenation (e.g., " Horse cart - horse = rickshaw "), image overlap ("Panda - Brown bear \simeq Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger ").
Edit intent: fluency
Original:  Finally, we consider vector arithmetics, and find them to be related with image concatenation (e.g., " horse cart - horse = rickshaw "), image overlap ("Panda - Brown bear \simeq Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger ").
Revised:  Finally, we consider vector arithmetics, and find them to be related with image concatenation (e.g., " horse cart - Horse = rickshaw "), image overlap ("Panda - Brown bear \simeq Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger ").
Edit intent: fluency
Original:  Finally, we consider vector arithmetics, and find them to be related with image concatenation (e.g., " horse cart - horse = rickshaw "), image overlap ("Panda - Brown bear \simeq Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger ").
Revised:  Finally, we consider vector arithmetics, and find them to be related with image concatenation (e.g., " horse cart - horse = Rickshaw "), image overlap ("Panda - Brown bear \simeq Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger ").
Edit intent: fluency
Original:  Finally, we consider vector arithmetics, and find them to be related with image concatenation (e.g., " horse cart - horse = rickshaw "), image overlap ("Panda - Brown bear \simeq Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger ").
Revised:  Finally, we consider vector arithmetics, and find them to be related with image concatenation (e.g., " horse cart - horse = rickshaw "), image overlap ("Panda - Brown bear = Skunk") and regularities ("Panda is to Brown bear as Skunk is to Badger ").
Edit intent: coherence
Original:  These results indicate that image vector embeddings may contain diverse and rich visual semantics , usable for learning and reasoning purposes.
Revised:  These results indicate that image vector embeddings may contain diverse and rich visual semantics usable for learning and reasoning purposes.
Edit intent: fluency
Original:  We then explore the location of images within the vector space, finding elements close in WordNet to be clustered together, regardless of significant visual variances ( e.g., \eg 118 dog types).
Revised:  We then explore the location of images within the vector space, finding elements close in WordNet to be clustered together, regardless of significant visual variances ( \eg 118 dog types).
Edit intent: coherence
Original:  More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge ( e.g., \eg living things).
Revised:  More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge ( \eg living things).
Edit intent: clarity
Original:  Finally , we consider vector arithmetics.
Revised:  Afterwards , we consider vector arithmetics.
Edit intent: fluency
Original:  Finally, we provide the conditions for perfect recovery of a useful signal corrupted by sparse noise, showing that this problem is also intrinsically related to vertex-frequency localization properties.
Revised:  Finally, we provide the conditions for perfect recovery of a useful signal corrupted by sparse noise, showing that this problem is also intrinsically related to graph-frequency localization properties.
Edit intent: clarity
Original: In many applications of current interest , the observations are represented as a signal defined over  a graph.
Revised: In many applications , the observations are represented as a signal defined over a graph.
Edit intent: clarity
Original: In many applications of current interest , the observations are represented as a signal defined over  a graph.
Revised: In many applications of current interest , the observations can be represented as a signal defined over a graph.
Edit intent: clarity
Original:  In this work we start providing a class of graph signals maximally concentrated in the graph domain and  its dual.
Revised:  In this work , first, we provide a class of graph signals maximally concentrated in the graph domain and its dual.
Edit intent: coherence
Original:  In this work we start providing a class of graph signals maximally concentrated in the graph domain and  its dual.
Revised:  In this work we start providing a class of graph signals that are maximally concentrated on the graph domain and its dual.
Edit intent: fluency
Original:  In this work we start providing a class of graph signals maximally concentrated in the graph domain and  its dual.
Revised:  In this work we start providing a class of graph signals maximally concentrated in the graph domain and on its dual.
Edit intent: clarity
Original:  Then  building on this framework, we derive an uncertainty principle for graph signals and illustrate the conditions for the recovery of band-limited signals from a subset of their values .
Revised:  Then , building on this framework, we derive an uncertainty principle for graph signals and illustrate the conditions for the recovery of band-limited signals from a subset of their values .
Edit intent: fluency
Original:  Then  building on this framework, we derive an uncertainty principle for graph signals and illustrate the conditions for the recovery of band-limited signals from a subset of their values .
Revised:  Then building on this framework, we derive an uncertainty principle for graph signals and illustrate the conditions for the recovery of band-limited signals from a subset of samples .
Edit intent: clarity
Original:  Finally, we provide the conditions for perfect recovery of a useful signal corrupted by sparse noise, showing that this problem is also intrinsically related to graph-frequency localization properties.
Revised:  Finally, we provide the conditions for perfect recovery of a useful signal corrupted by sparse noise, showing that this problem is also intrinsically related to vertex-frequency localization properties.
Edit intent: clarity
Original:  Here we introduce a biologically plausible reward-modulated Hebbian learning algorithm, that can train recurrent networks based solely on delayed, phasic reward signals at the end of each trial.
Revised:  Here we show that a biologically plausible reward-modulated Hebbian learning algorithm, that can train recurrent networks based solely on delayed, phasic reward signals at the end of each trial.
Edit intent: coherence
Original:  The method requires no dedicated feedback or readout network : the whole network connectivity is subject to learning, and the network 's output is read from one arbitrarily chosen network cell.
Revised:  The method requires no dedicated feedback or readout networks : the whole network connectivity is subject to learning, and the network 's output is read from one arbitrarily chosen network cell.
Edit intent: fluency
Original:  The method requires no dedicated feedback or readout network : the whole network connectivity is subject to learning, and the network 's output is read from one arbitrarily chosen network cell.
Revised:  The method requires no dedicated feedback or readout network : the whole network connectivity is subject to learning, and the network output is read from one arbitrarily chosen network cell.
Edit intent: clarity
Original:  Furthermore, network activity moves from a stimulus-specific representation to a response-specific representation during response time, in accordance with neural recordings  for similar tasks.
Revised:  Furthermore, network activity moves from a stimulus-specific representation to a response-specific representation during response time, in accordance with neural recordings in behaving animals for similar tasks.
Edit intent: clarity
Original:  We conclude that recurrent neural networks, trained with reward-modulated Hebbian learning, offer a plausible model of cortical dynamics during both learning and performance of flexible association.
Revised:  We conclude that recurrent neural networks, trained with reward-modulated Hebbian learning, offer a plausible model of cortical dynamics during learning and performance of flexible association.
Edit intent: clarity
Original: The forthcoming 5G cellular network is expected to overlay millimeter-wave (mmW) transmissions with the incumbent micro-wave ( %DIFDELCMD < \muW%%%   ) architecture.
Revised: The forthcoming 5G cellular network is expected to overlay millimeter-wave (mmW) transmissions with the incumbent micro-wave ( %DIFDELCMD < \muW%%% W ) architecture.
Edit intent: clarity
Original:  This paper aims at maximizing the overall downlink (DL) rate with a minimum uplink (UL) rate constraint, and concludes: mmW tends to more focus on DL transmissions while %DIFDELCMD < \muW %%%   has high priority for complementing UL, under time-division duplex (TDD) mmW operations.
Revised:  This paper aims at maximizing the overall downlink (DL) rate with a minimum uplink (UL) rate constraint, and concludes: mmW tends to focus more on DL transmissions while %DIFDELCMD < \muW %%% has high priority for complementing UL, under time-division duplex (TDD) mmW operations.
Edit intent: fluency
Original:  To further relieve this UL bottleneck, we propose mmW UL decoupling that allows a%DIFDELCMD < \muW %%%  } base station (BS) to receive mmW signals.
Revised:  To further relieve this UL bottleneck, we propose mmW UL decoupling that allows %DIFDELCMD < \muW %%% } base station (BS) to receive mmW signals.
Edit intent: fluency
Original:  A double covering technique is used to turn a simply-connected open surface into a genus-0 closed surface, and then a linear algorithm for parameterization of genus-0 closed surfaces can be applied.
Revised:  A double covering technique is used to turn a simply-connected open surface into a genus-0 closed surface, and then a fast algorithm for parameterization of genus-0 closed surfaces can be applied.
Edit intent: clarity
Original:  Applications for texture mapping are considered for illustrating the effectiveness of our proposed algorithm.
Revised:  Applications to texture mapping are considered for illustrating the effectiveness of our proposed algorithm.
Edit intent: clarity
Original:  Applications for texture mapping are considered for illustrating the effectiveness of our proposed algorithm.
Revised:  Applications for texture mapping are presented for illustrating the effectiveness of our proposed algorithm.
Edit intent: clarity
Original:  The violation is significant and , amazingly enough, numerically almost equal to the violation of the CHSH inequality  in coincidence spin measurements on a compound system of two quantum particles in an entangled spin state.
Revised:  The violation is significant and numerically almost equal to the violation of the CHSH inequality in coincidence spin measurements on a compound system of two quantum particles in an entangled spin state.
Edit intent: coherence
Original:  The violation is significant and , amazingly enough, numerically almost equal to the violation of the CHSH inequality  in coincidence spin measurements on a compound system of two quantum particles in an entangled spin state.
Revised:  The violation is significant and , amazingly enough, numerically almost equal to the violation of the CHSH inequality in coincidence spin measurements on pairs of quantum particles in an entangled spin state.
Edit intent: clarity
Original:  Hence, it seems as if the human mind selects probabilistically on wind directions and their difference in the same way as Stern-Gerlach apparata select probabilistically on spin directions and their difference.
Revised:  Hence, it seems as if the human mind selects probabilistically on wind directions and their difference in the same way as Stern-Gerlach apparatuses select probabilistically on spin directions and their difference.
Edit intent: clarity
Original:  As in the quantum case , our violation reveals that entanglement systematically occurs in human cognition, and that  non-classical probabilistic structures of a quantum type are needed in the modeling of cognitive processes.
Revised:  As in the quantum violation , our violation reveals that entanglement systematically occurs in human cognition, and that non-classical probabilistic structures of a quantum type are needed in the modeling of cognitive processes.
Edit intent: clarity
Original:  As in the quantum case , our violation reveals that entanglement systematically occurs in human cognition, and that  non-classical probabilistic structures of a quantum type are needed in the modeling of cognitive processes.
Revised:  As in the quantum case , our violation indicates that entanglement systematically occurs in human cognition, and that non-classical probabilistic structures of a quantum type are needed in the modeling of cognitive processes.
Edit intent: fluency
Original:  As in the quantum case , our violation reveals that entanglement systematically occurs in human cognition, and that  non-classical probabilistic structures of a quantum type are needed in the modeling of cognitive processes.
Revised:  As in the quantum case , our violation reveals that entanglement systematically occurs in human cognition, and that accordingly non-classical probabilistic structures of a quantum type are needed in the modeling of cognitive processes.
Edit intent: coherence
Original:  We support this result by presenting a quantum theoretical model in Hilbert space, which exactly reproduces the collected data and further illustrates the analogies between quantum and cognitive entities.
Revised:  We support this result by presenting a quantum theoretical model in Hilbert space, which exactly reproduces the collected data and enlightens the deep analogies existing between quantum and cognitive entities.
Edit intent: clarity
Original:  We support this result by presenting a quantum theoretical model in Hilbert space, which exactly reproduces the collected data and enlightens the deep analogies existing between quantum and cognitive entities .
Revised:  We support this result by presenting a quantum theoretical model in Hilbert space, which exactly reproduces the collected data in Hilbert space .
Edit intent: coherence
Original:  We develop several general design techniques for programming these molecules.
Revised:  We introduce general design techniques for programming these molecules.
Edit intent: clarity
Original: We evaluate complex time series  of online user communication in Twitter social network.
Revised: We study complex time series of online user communication in Twitter social network.
Edit intent: clarity
Original:  By applying the local variation originally established for neuron spike trains , we quantify the temporal behavior of active and passive but popular usersseparately .
Revised:  By applying the local variation originally established for neuron spike trains , we quantify the temporal behavior of active and passive users, as a function of their activity and popularity .
Edit intent: clarity
Original:  We show that the local variation of active users give bursts independent of the activation frequency.
Revised:  We show that the active spike trains are bursty, independently of their activation frequency.
Edit intent: clarity
Original:  To understand the coincidence in the temporal patterns of two distinct interactions, we propose linear correlations of the local variation of the filtered spikes based on concerned interactions.
Revised:  To understand the coincidence in the temporal patterns of two distinct interactions, we propose linear correlations of the local variation in different interactions.
Edit intent: clarity
Original:  We conclude that the local variations of the retweet and mention spike trains provide a good agreement only for most popular users, which suggests that the dynamics of mention a user together with that of retweet is a better identity of popular users instead of only paying attention of the dynamics of retweet, a conventional measure of user popularity .
Revised:  We obtain high values of correlation, and thus consistent temporal behavior, between retweets and mentions, but only for popular users, which suggests that the dynamics of mention a user together with that of retweet is a better identity of popular users instead of only paying attention of the dynamics of retweet, a conventional measure of user popularity .
Edit intent: clarity
Original:  We conclude that the local variations of the retweet and mention spike trains provide a good agreement only for most popular users, which suggests that the dynamics of mention a user together with that of retweet is a better identity of popular users instead of only paying attention of the dynamics of retweet, a conventional measure of user popularity .
Revised:  We conclude that the local variations of the retweet and mention spike trains provide a good agreement only for most popular users, indicating that creating online attention suggests an alignment in the dynamics of retweet, a conventional measure of user popularity .
Edit intent: clarity
Original:  We conclude that the local variations of the retweet and mention spike trains provide a good agreement only for most popular users, which suggests that the dynamics of mention a user together with that of retweet is a better identity of popular users instead of only paying attention of the dynamics of retweet, a conventional measure of user popularity .
Revised:  We conclude that the local variations of the retweet and mention spike trains provide a good agreement only for most popular users, which suggests that the dynamics of mention a user together with that of retweet is a better identity of popular users instead of only paying attention of the dynamics of the two interactions .
Edit intent: clarity
Original: We show how to price and replicate a variety of barrier-style claims written on the \log price X and quadratic variation \<X \> \langle \rangle of a risky asset.
Revised: We show how to price and replicate a variety of barrier-style claims written on the \log price X and quadratic variation \langle \rangle of a risky asset.
Edit intent: clarity
Original: We show how to price and replicate a variety of barrier-style claims written on the \log price X and quadratic variation \<X \> \langle \rangle of a risky asset.
Revised: We show how to price and replicate a variety of barrier-style claims written on the \log price X and quadratic variation \<X \> \langle X\rangle of a risky asset.
Edit intent: fluency
Original:  As hedging instruments, we use only the underlying risky asset, a zero-coupon bond , and European calls and puts with the same maturity as the barrier-style claim.
Revised:  As hedging instruments, we use only the underlying risky asset, zero-coupon bond , and European calls and puts with the same maturity as the barrier-style claim.
Edit intent: fluency
Original:  As hedging instruments, we use only the underlying risky asset, a zero-coupon bond , and European calls and puts with the same maturity as the barrier-style claim.
Revised:  As hedging instruments, we use only the underlying risky asset, a zero-coupon bonds , and European calls and puts with the same maturity as the barrier-style claim.
Edit intent: fluency
Original:  We consider both single-barrier and double barrier claims in three varieties: knock-in, knock-out and rebate  .
Revised:  We consider knock-in, knock-out and rebate .
Edit intent: clarity
Original:  We consider both single-barrier and double barrier claims in three varieties: knock-in, knock-out and rebate  .
Revised:  We consider both single-barrier and double barrier claims in three varieties: knock-in, knock-out and rebate claims in single and double barrier varieties .
Edit intent: clarity
Original: Temporal networks come with a wide variety of heterogeneities, from burstiness of event sequences to correlations between timingsof node and link activations.
Revised: Temporal networks come with a wide variety of heterogeneities, from burstiness of event sequences to correlations between timings of node and link activations.
Edit intent: fluency
Original:  In this work, we study the problem of learning social knowledge graphs , which aims to accurately connect social network vertices to knowledge concepts .
Revised:  In this work, we study the problem of learning social knowledge graphs .
Edit intent: clarity
Original:  We propose a multi-source Bayesian embedding model, GenVector, to jointly incorporate topic models and word / network embeddings.
Revised:  We propose a multi-modal Bayesian embedding model, GenVector, to jointly incorporate topic models and word / network embeddings.
Edit intent: clarity
Original:  We propose a multi-source Bayesian embedding model, GenVector, to jointly incorporate topic models and word / network embeddings.
Revised:  We propose a multi-source Bayesian embedding model, GenVector, to learn latent topics that generate word and network embeddings.
Edit intent: clarity
Original:  The model leverages large-scale unlabeled data by incorporating the embeddings , and co-represents social network vertices and knowledge concepts in a shared latent topic space.
Revised:  GenVector leverages large-scale unlabeled data by incorporating the embeddings , and co-represents social network vertices and knowledge concepts in a shared latent topic space.
Edit intent: clarity
Original:  The model leverages large-scale unlabeled data by incorporating the embeddings , and co-represents social network vertices and knowledge concepts in a shared latent topic space.
Revised:  The model leverages large-scale unlabeled data with embeddings and represents data of two modalities---i.e., social network users and knowledge concepts---in a shared latent topic space.
Edit intent: clarity
Original: Compared to traditional online maps, crowdsourced maps such as Waze are unique in providing real-time updates on traffic, congestion, accidents and points of interest.
Revised: Real-time crowdsourced maps such as Waze are unique in providing real-time updates on traffic, congestion, accidents and points of interest.
Edit intent: clarity