wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our experiments show that the residual (skip) connections in the proposed system are required for successful end-to-end signal enhancement , i.e., without filter mask estimation.
Revised:  Our experiments show that the residual (skip) connections in the proposed system are a prerequisite for successful spectral reconstruction , i.e., without filter mask estimation.
Edit intent: clarity
Original:  Further, they indicate a slight advantage of the  variational U-Net architecture over its  non-variational version in terms of signal enhancement performance under reverberant conditions . Specifically, PESQ scores show increases of 0.28 and 0.49 in reverberant and non-reverberant scenes , respectively.
Revised:  Results show, on average, an advantage of the variational U-Net architecture over its non-variational version in terms of signal enhancement performance under reverberant conditions . Specifically, PESQ scores show increases of 0.28 and 0.49 in reverberant and non-reverberant scenes , respectively.
Edit intent: clarity
Original:  Further, they indicate a slight advantage of the  variational U-Net architecture over its  non-variational version in terms of signal enhancement performance under reverberant conditions . Specifically, PESQ scores show increases of 0.28 and 0.49 in reverberant and non-reverberant scenes , respectively.
Revised:  Further, they indicate a slight advantage of the variational U-Net architecture over its non-variational version in signal enhancement performance under reverberant conditions . Specifically, PESQ scores show increases of 0.28 and 0.49 in reverberant and non-reverberant scenes , respectively.
Edit intent: clarity
Original:  Anecdotal evidence points to improved suppression of impulsive noise sources with the variational end-to-end U-Net compared to the recurrent mask estimation network baseline.
Revised:  Anecdotal evidence points to improved suppression of impulsive noise sources with the variational U-Net compared to the recurrent mask estimation network baseline.
Edit intent: clarity
Original: For graph classification tasks, many methods use a common strategy to aggregate information of vertex neighbors.
Revised: For graph learning tasks, many methods use a common strategy to aggregate information of vertex neighbors.
Edit intent: clarity
Original:  Furthermore,  we propose an LCS metric space and apply an adjacent point merge operation to reduce its computational costs.
Revised:  Furthermore, to reduce the computational cost, we propose an LCS metric space and apply an adjacent point merge operation to reduce its computational costs.
Edit intent: fluency
Original: In low-power wireless networks, Synchronous transmissions (ST)  protocols provide high reliability and energy-efficiency in the presence of little or no interference, while dependable ST protocols provide high reliability in harsher environments, through the use of custom rules, fixed configurations and higher retransmissions.
Revised: The last decade saw an emergence of Synchronous Transmissions (ST) protocols provide high reliability and energy-efficiency in the presence of little or no interference, while dependable ST protocols provide high reliability in harsher environments, through the use of custom rules, fixed configurations and higher retransmissions.
Edit intent: clarity
Original: In low-power wireless networks, Synchronous transmissions (ST)  protocols provide high reliability and energy-efficiency in the presence of little or no interference, while dependable ST protocols provide high reliability in harsher environments, through the use of custom rules, fixed configurations and higher retransmissions.
Revised: In low-power wireless networks, Synchronous transmissions (ST) as an effective communication paradigm in low-power wireless networks. Numerous ST protocols provide high reliability and energy-efficiency in the presence of little or no interference, while dependable ST protocols provide high reliability in harsher environments, through the use of custom rules, fixed configurations and higher retransmissions.
Edit intent: clarity
Original:  We introduce Dimmer as a self-adaptive , all-to-all communication primitive . Dimmer builds on LWB and uses Reinforcement Learning to tune its flooding parameters and match the current properties of the  medium.
Revised:  We introduce Dimmer as a self-adaptive ST protocol . Dimmer builds on LWB and uses Reinforcement Learning to tune its flooding parameters and match the current properties of the medium.
Edit intent: clarity
Original:  We introduce Dimmer as a self-adaptive , all-to-all communication primitive . Dimmer builds on LWB and uses Reinforcement Learning to tune its flooding parameters and match the current properties of the  medium.
Revised:  We introduce Dimmer as a self-adaptive , all-to-all communication primitive . Dimmer builds on LWB and uses Reinforcement Learning to tune its parameters and match the current properties of the medium.
Edit intent: fluency
Original:  We introduce Dimmer as a self-adaptive , all-to-all communication primitive . Dimmer builds on LWB and uses Reinforcement Learning to tune its flooding parameters and match the current properties of the  medium.
Revised:  We introduce Dimmer as a self-adaptive , all-to-all communication primitive . Dimmer builds on LWB and uses Reinforcement Learning to tune its flooding parameters and match the current properties of the wireless medium.
Edit intent: clarity
Original:  By learning how to behave from unlabeled traces , Dimmer adapts to different interference types and patterns, and is even able to tackle previously unseen interference.
Revised:  By learning how to behave from an unlabeled dataset , Dimmer adapts to different interference types and patterns, and is even able to tackle previously unseen interference.
Edit intent: clarity
Original:  By learning how to behave from unlabeled traces , Dimmer adapts to different interference types and patterns, and is even able to tackle previously unseen interference.
Revised:  By learning how to behave from unlabeled traces , Dimmer adapts to different interference types and patterns, and is able to tackle previously unseen interference.
Edit intent: coherence
Original:  In addition, we share through Dimmer insights on how to efficiently design AI-based systems for constrained devices, and  evaluate our protocol on two deployments of 18 and 48 resource-constrained sensor nodes (4 MHz CPU, 10 kB RAM), showing it improves reliability under WiFi interferenceand IEEE 802.15.4 jamming, while turning superfluous transmitters off in the absence of disturbances .
Revised:  With Dimmer, we explore how to efficiently design AI-based systems for constrained devices, and evaluate our protocol on two deployments of 18 and 48 resource-constrained sensor nodes (4 MHz CPU, 10 kB RAM), showing it improves reliability under WiFi interferenceand IEEE 802.15.4 jamming, while turning superfluous transmitters off in the absence of disturbances .
Edit intent: clarity
Original:  In this article, we use machine learning and apply drift detection methods in a novel way to measure the effectiveness of policy interventions : We analyze the effect of NPIs on the development of daily case numbers of COVID-19 across 9 European countries and 28 US states.
Revised:  In this article, we use machine learning and apply drift detection methods in a novel way to measure the effectiveness of policy interventions with respect to the development of daily case numbers of COVID-19 across 9 European countries and 28 US states.
Edit intent: clarity
Original:  Our analysis shows that it takes more than two weeks on average until NPIs show a significant effect on the number of new cases.
Revised:  Our analysis shows that there are, on average, more than two weeks on average until NPIs show a significant effect on the number of new cases.
Edit intent: clarity
Original:  Its goal is to output an approximation of the average 1{n ^{(i) of vectors }%DIFDELCMD < x%%% ^{(i) \in }%DIFDELCMD < [%%% 0,1 ^k, while preserving the privacy with respect to any%DIFDELCMD < x%%% ^{(i) We present an  (\epsilon,\delta) -private mechanism with optimal \ell_\infty error for most values of \delta.
Revised:  Its goal is to output an approximation of the average 1{n of vectors }%DIFDELCMD < x%%% ^{(i) \in }%DIFDELCMD < [%%% 0,1 ^k, while preserving the privacy with respect to any%DIFDELCMD < x%%% ^{(i) We present an (\epsilon,\delta) -private mechanism with optimal \ell_\infty error for most values of \delta.
Edit intent: fluency
Original:  Its goal is to output an approximation of the average 1{n ^{(i) of vectors }%DIFDELCMD < x%%% ^{(i) \in }%DIFDELCMD < [%%% 0,1 ^k, while preserving the privacy with respect to any%DIFDELCMD < x%%% ^{(i) We present an  (\epsilon,\delta) -private mechanism with optimal \ell_\infty error for most values of \delta.
Revised:  Its goal is to output an approximation of the average 1{n ^{(i) of vectors }%DIFDELCMD < x%%% \in }%DIFDELCMD < [%%% 0,1 ^k, while preserving the privacy with respect to any%DIFDELCMD < x%%% ^{(i) We present an (\epsilon,\delta) -private mechanism with optimal \ell_\infty error for most values of \delta.
Edit intent: fluency
Original:  Its goal is to output an approximation of the average 1{n ^{(i) of vectors }%DIFDELCMD < x%%% ^{(i) \in }%DIFDELCMD < [%%% 0,1 ^k, while preserving the privacy with respect to any%DIFDELCMD < x%%% ^{(i) We present an  (\epsilon,\delta) -private mechanism with optimal \ell_\infty error for most values of \delta.
Revised:  Its goal is to output an approximation of the average 1{n ^{(i) of vectors }%DIFDELCMD < x%%% ^{(i) \in }%DIFDELCMD < [%%% 0,1 %DIFDELCMD < x%%% ^{(i) We present an (\epsilon,\delta) -private mechanism with optimal \ell_\infty error for most values of \delta.
Edit intent: coherence
Original:  Its goal is to output an approximation of the average 1{n ^{(i) of vectors }%DIFDELCMD < x%%% ^{(i) \in }%DIFDELCMD < [%%% 0,1 ^k, while preserving the privacy with respect to any%DIFDELCMD < x%%% ^{(i) We present an  (\epsilon,\delta) -private mechanism with optimal \ell_\infty error for most values of \delta.
Revised:  Its goal is to output an approximation of the average 1{n ^{(i) of vectors }%DIFDELCMD < x%%% ^{(i) \in }%DIFDELCMD < [%%% 0,1 ^k, while preserving the privacy with respect to any%DIFDELCMD < x%%% We present an (\epsilon,\delta) -private mechanism with optimal \ell_\infty error for most values of \delta.
Edit intent: fluency
Original:  This result settles the conjecture of Steinke and Ullman [2020] for the these values of \delta .
Revised:  This result settles the conjecture of Steinke and Ullman [2020] .
Edit intent: clarity
Original:  We uncover blocking challenges in known approaches and introduce a new method for quantum derivative pricing - the re-parameterization method - that avoids them.
Revised:  We uncover blocking challenges in known approaches and introduce a new method for quantum derivative pricing - the re-parameterization method - that avoids them.
Edit intent: fluency
Original: Countries around the world implemented non-pharmaceutical interventions (NPIs) to flatten the curve of COVID-19 cases but failed to eradicate the disease .
Revised: Countries around the world implement nonpharmaceutical interventions (NPIs) to flatten the curve of COVID-19 cases but failed to eradicate the disease .
Edit intent: fluency
Original: Countries around the world implemented non-pharmaceutical interventions (NPIs) to flatten the curve of COVID-19 cases but failed to eradicate the disease .
Revised: Countries around the world implemented non-pharmaceutical interventions (NPIs) to mitigate the spread of COVID-19 cases but failed to eradicate the disease .
Edit intent: clarity
Original: Countries around the world implemented non-pharmaceutical interventions (NPIs) to flatten the curve of COVID-19 cases but failed to eradicate the disease .
Revised: Countries around the world implemented non-pharmaceutical interventions (NPIs) to flatten the curve of COVID-19 .
Edit intent: coherence
Original:  Design of efficient NPIs requires identification of the structure of the underlying transmission network.
Revised:  Design of efficient NPIs requires identification of the structure of the disease transmission network.
Edit intent: clarity
Original:  Prior work proposes Markov Chain Monte Carlo (MCMC) methods that simultaneously sample and generate the graph , eliminating the need for storage .
Revised:  Prior work proposes Markov chain Monte Carlo (MCMC) methods that simultaneously sample and generate the graph , eliminating the need for storage .
Edit intent: fluency
Original:  Unfortunately, these existing methods are not scalable to massive real-world graphs.
Revised:  Unfortunately, these existing estimators are not scalable to massive real-world graphs.
Edit intent: clarity
Original:  Even challenging non-Lambertian reflectances, complex geometry, and unknown illumination can be decomposed to high-quality models.
Revised:  Even challenging non-Lambertian reflectances, complex geometry, and unknown illumination can be decomposed into high-quality models.
Edit intent: fluency
Original:  The datasets and code is available at the project page: URL
Revised:  The datasets and code is available on the project page: URL
Edit intent: fluency
Original: Decomposing a scene into its shape, reflectance, and illumination is a challenging but essential problem in computer vision and graphics.
Revised: Decomposing a scene into its shape, reflectance, and illumination is a challenging but important problem in computer vision and graphics.
Edit intent: clarity
Original:  Though recent work has shown that implicit representations can be used to model the radiance field of an object,  these techniques only enable view synthesis and not relighting.
Revised:  Though recent work has shown that implicit representations can be used to model the radiance field of an object, most of these techniques only enable view synthesis and not relighting.
Edit intent: coherence
Original: We study the computational complexity of computing allocations that are both fair and maximize the utilitarian social welfare, i.e., the sum of utilities reported by the agents .
Revised: We study the computational complexity of computing allocations that are both fair and maximize the utilitarian social welfare, i.e., the sum of agents' utilities .
Edit intent: clarity
Original:  We show that both problem (1) and problem (2) are strongly NP-hard when the number of agents is variable, and remain NP-hard for a fixed number of agents greater than two.
Revised:  We show that both problems are strongly NP-hard when the number of agents is variable, and remain NP-hard for a fixed number of agents greater than two.
Edit intent: clarity
Original:  Focusing on the special case of two agents, we find that problem (1) is polynomial-time solvable, whereas problem (2) remains NP-hard.
Revised:  For the special case of two agents, we find that problem (1) is polynomial-time solvable, whereas problem (2) remains NP-hard.
Edit intent: clarity
Original:  Focusing on the special case of two agents, we find that problem (1) is polynomial-time solvable, whereas problem (2) remains NP-hard.
Revised:  Focusing on the special case of two agents, we find that problem (1) is polynomial-time solvable, while problem (2) remains NP-hard.
Edit intent: fluency
Original:  Finally, for the case of fixed number of agents, we design pseudopolynomial-time algorithms for both problems.
Revised:  Finally, with a fixed number of agents, we design pseudopolynomial-time algorithms for both problems.
Edit intent: clarity
Original: Computational methods that operate directly on three-dimensional molecular structure hold large potential to solve important questions in biology and chemistry.
Revised: Computational methods that operate on three-dimensional molecular structure hold large potential to solve important questions in biology and chemistry.
Edit intent: clarity
Original: Computational methods that operate directly on three-dimensional molecular structure hold large potential to solve important questions in biology and chemistry.
Revised: Computational methods that operate directly on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry.
Edit intent: clarity
Original:  In particular  deep neural networks have recently gained significant attention .
Revised:  In particular , deep neural networks have recently gained significant attention .
Edit intent: fluency
Original:  In particular  deep neural networks have recently gained significant attention .
Revised:  In particular deep neural networks have gained significant attention .
Edit intent: clarity
Original:  We develop three-dimensional molecular learning networks for each of these tasks , finding that they consistently improve performance relative to  one- and two-dimensional methods .
Revised:  We develop three-dimensional molecular learning methods for each of these tasks , finding that they consistently improve performance relative to one- and two-dimensional methods .
Edit intent: clarity
Original:  We develop three-dimensional molecular learning networks for each of these tasks , finding that they consistently improve performance relative to  one- and two-dimensional methods .
Revised:  We develop three-dimensional molecular learning networks for each of these tasks and show that they consistently improve performance relative to one- and two-dimensional methods .
Edit intent: clarity
Original:  We develop three-dimensional molecular learning networks for each of these tasks , finding that they consistently improve performance relative to  one- and two-dimensional methods .
Revised:  We develop three-dimensional molecular learning networks for each of these tasks , finding that they consistently improve performance relative to methods based on one- and two-dimensional methods .
Edit intent: clarity
Original:  We develop three-dimensional molecular learning networks for each of these tasks , finding that they consistently improve performance relative to  one- and two-dimensional methods .
Revised:  We develop three-dimensional molecular learning networks for each of these tasks , finding that they consistently improve performance relative to one- and two-dimensional representations .
Edit intent: clarity
Original:  The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, while graph networks perform well on systems requiring detailed positional information .
Revised:  The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, graph networks performing well on systems requiring detailed positional information .
Edit intent: coherence
Original:  Our results indicate  many molecular problems stand to gain from three-dimensional molecular learning .
Revised:  Our results indicate that many molecular problems stand to gain from three-dimensional molecular learning .
Edit intent: fluency
Original:  We focus on generalization error upper bounds and bounds , and introduce a categorisation of bounds depending on assumptions on the algorithm and data.
Revised:  We focus on generalization error upper bounds , and introduce a categorisation of bounds depending on assumptions on the algorithm and data.
Edit intent: coherence
Original:  The results for the 6 and 7 are promising, but not yet fully conclusive, while only desideratum 5 is currently beyond the scope of our bound.
Revised:  The results for 6 and 7 are promising, but not yet fully conclusive, while only desideratum 5 is currently beyond the scope of our bound.
Edit intent: fluency
Original:  To investigate the effect of active fluctuations on single kinesin molecules , we used an in vitro measurement system to apply actively fluctuating external forces (i.e. noise), artificially mimicking intracellular active fluctuations.
Revised:  To investigate the effect of active fluctuations on single kinesin molecules was observed while applying noisy external forces that mimic intracellular active fluctuations.
Edit intent: clarity
Original:  Here we show that kinesin accelerates in response to the applied noise, especially under the application of a large average hindering force (load ).
Revised:  We found kinesin accelerates under noise, especially under the application of a large average hindering force (load ).
Edit intent: clarity
Original:  As examples, we can mention the occurrence of explosive user dynamics triggered by social polarization, echo chamber phenomena, fake news, etc. Explosive user dynamics collectively refer to the phenomenon of flaming.
Revised:  As examples, we can mention explosive user dynamics triggered by social polarization, echo chamber phenomena, fake news, etc. Explosive user dynamics collectively refer to the phenomenon of flaming.
Edit intent: clarity
Original:  As examples, we can mention the occurrence of explosive user dynamics triggered by social polarization, echo chamber phenomena, fake news, etc. Explosive user dynamics collectively refer to the phenomenon of flaming.
Revised:  As examples, we can mention the occurrence of explosive user dynamics triggered by social polarization, echo chamber phenomena, fake news, etc. Explosive user dynamics are frequently called online flaming.
Edit intent: clarity
Original:  This model can be used to understand the relationship between the occurrence of explosive user dynamics such as flaming and the structure of social networks.
Revised:  This model can be used to understand the relationship between explosive user dynamics such as flaming and the structure of social networks.
Edit intent: clarity
Original:  This model can be used to understand the relationship between the occurrence of explosive user dynamics such as flaming and the structure of social networks.
Revised:  This model can be used to understand the relationship between the occurrence of explosive user dynamics and the structure of social networks.
Edit intent: clarity
Original:  However, since the oscillation model was introduced as a purely theoretical construct with regard to social networks, it is necessary to confirm whether the model describes real phenomena correctly or not.
Revised:  However, since the oscillation model was introduced as a purely theoretical model of social networks, it is necessary to confirm whether the model describes real phenomena correctly or not.
Edit intent: clarity
Original:  In this paper, we first show a prediction from the oscillation model in which the low-frequency oscillation mode of user dynamics will be dominant when user activity in online social networks is increased .
Revised:  In this paper, we first show a prediction from the oscillation model ; the low-frequency oscillation mode of user dynamics will be dominant when user activity in online social networks is increased .
Edit intent: coherence
Original:  In this paper, we first show a prediction from the oscillation model in which the low-frequency oscillation mode of user dynamics will be dominant when user activity in online social networks is increased .
Revised:  In this paper, we first show a prediction from the oscillation model in which the low-frequency oscillation mode of user dynamics will be dominant when the structure of online social networks is increased .
Edit intent: clarity
Original:  The results support well and thus validate the predictions of the theory .
Revised:  The results support the predictions from the theoretical model .
Edit intent: clarity
Original: We consider (i) the problem of finding a (possibly mixed) Nash equilibrium in congestion game , and (ii) the problem of finding an (exponential precision) fixed point of the gradient descent dynamics of a smooth function f:[0,1]^n \rightarrow R.
Revised: We consider (i) the problem of finding a (possibly mixed) Nash equilibrium in congestion games , and (ii) the problem of finding an (exponential precision) fixed point of the gradient descent dynamics of a smooth function f:[0,1]^n \rightarrow R.
Edit intent: fluency