wanyu/IteraTeR_v2 huggingface dataset:
Original:  We focus on four themes:  1) clinical melanoma research,  2) basic melanoma research,  3) clinical dermatology, and  4) basic pigment cell research, with the goal of outlining current highlights, challenges, and frontiers associated with pigmentation and melanocyte biology.
Revised:  We focus on four themes: 1) clinical melanoma research, 2) basic melanoma research, ( 3) clinical dermatology, and 4) basic pigment cell research, with the goal of outlining current highlights, challenges, and frontiers associated with pigmentation and melanocyte biology.
Edit intent: fluency
Original:  We focus on four themes:  1) clinical melanoma research,  2) basic melanoma research,  3) clinical dermatology, and  4) basic pigment cell research, with the goal of outlining current highlights, challenges, and frontiers associated with pigmentation and melanocyte biology.
Revised:  We focus on four themes: 1) clinical melanoma research, 2) basic melanoma research, 3) clinical dermatology, and ( 4) basic pigment cell research, with the goal of outlining current highlights, challenges, and frontiers associated with pigmentation and melanocyte biology.
Edit intent: fluency
Original:  This research encapsulates important advances in melanocyte and melanoma research including emerging frontiers in melanoma immunotherapy, medical and surgical oncology, dermatology, vitiligo, albinism, genomics and systems biology, epidemiology, pigment biophysics and chemistry, and evolution.
Revised:  Significantly, this document encapsulates important advances in melanocyte and melanoma research including emerging frontiers in melanoma immunotherapy, medical and surgical oncology, dermatology, vitiligo, albinism, genomics and systems biology, epidemiology, pigment biophysics and chemistry, and evolution.
Edit intent: clarity
Original:  Motivated by an application in energy systems, this paper presents OptStream , a novel algorithm for releasing differential private data streams .
Revised:  Motivated by an application in energy systems, this paper presents OPTSTREAM , a novel algorithm for releasing differential private data streams .
Edit intent: fluency
Original:  Motivated by an application in energy systems, this paper presents OptStream , a novel algorithm for releasing differential private data streams .
Revised:  Motivated by an application in energy systems, this paper presents OptStream , a novel algorithm for releasing differentially private data streams .
Edit intent: fluency
Original:  The sampling module selects a small set of points to access privately in each period of interest  ,
Revised:  First, the sampling module selects a small set of points to access privately in each period of interest ,
Edit intent: coherence
Original:  The sampling module selects a small set of points to access privately in each period of interest  ,
Revised:  The sampling module selects a small set of points to access in each period of interest ,
Edit intent: clarity
Original:  The sampling module selects a small set of points to access privately in each period of interest  ,
Revised:  The sampling module selects a small set of points to access privately in each period of interest . Then ,
Edit intent: coherence
Original:  the perturbation module adds noise to the sampled data points to guarantee privacy  , the reconstruction module re-assembles the non-sampling data points from the perturbed sampled points, and the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to ensure consistency of the data's salient features.
Revised:  the perturbation module adds noise to the sampled data points to guarantee privacy . Next , the reconstruction module re-assembles the non-sampling data points from the perturbed sampled points, and the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to ensure consistency of the data's salient features.
Edit intent: coherence
Original:  the perturbation module adds noise to the sampled data points to guarantee privacy  , the reconstruction module re-assembles the non-sampling data points from the perturbed sampled points, and the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to ensure consistency of the data's salient features.
Revised:  the perturbation module adds noise to the sampled data points to guarantee privacy , the reconstruction module reassembles non-sampled data points from the perturbed sampled points, and the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to ensure consistency of the data's salient features.
Edit intent: fluency
Original:  the perturbation module adds noise to the sampled data points to guarantee privacy  , the reconstruction module re-assembles the non-sampling data points from the perturbed sampled points, and the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to ensure consistency of the data's salient features.
Revised:  the perturbation module adds noise to the sampled data points to guarantee privacy , the reconstruction module re-assembles the non-sampling data points from the perturbed sample points. Finally, the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to ensure consistency of the data's salient features.
Edit intent: coherence
Original:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art  by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Revised:  OptStream is used to release a real data stream from the largest European transmission operator . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Edit intent: clarity
Original:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art  by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Revised:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OPTSTREAM may not only improve the accuracy of the state-of-the-art by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Edit intent: clarity
Original:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art  by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Revised:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of state-of-the-art by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Edit intent: fluency
Original:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art  by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Revised:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art methods by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Edit intent: clarity
Original:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art  by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Revised:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art by at least one order of magnitude but also supports accurate load forecasting based on the private data.
Edit intent: clarity
Original:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art  by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
Revised:  OptStream is used to release a real data stream from the largest transmission operator in Europe . Experimental results show that OptStream not only improves the accuracy of the state-of-the-art by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting on the private data.
Edit intent: clarity
Original:  In this paper, we consider a wireless powered mobile-edge computing (MEC ) network following the binary offloading policy, such that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server.
Revised:  In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, such that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server.
Edit intent: clarity
Original:  In this paper, we consider a wireless powered mobile-edge computing (MEC ) network following the binary offloading policy, such that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server.
Revised:  In this paper, we consider a wireless powered mobile-edge computing (MEC ) network following the binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server.
Edit intent: coherence
Original:  Our goal is to acquire an online algorithm under time-varying wireless channels that jointly optimizes task offloading decisions and resource allocation to maximize the data processing capability of the network.
Revised:  Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and resource allocation to maximize the data processing capability of the network.
Edit intent: clarity
Original:  To tackle this problem, we propose in this paper a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network to generate offloading decisions .
Revised:  To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network to generate offloading decisions .
Edit intent: coherence
Original:  In particular, the proposed DROO framework does not require any manually labeled training data as the input, and thus completely removes the need of solving combinatorial optimization problems . Besides, it avoids the curse of dimensionality problem encountered by some existing reinforcement learning approaches and is computationally efficient in large-size networks.
Revised:  In particular, the proposed DROO framework does not require any manually labeled training data as the input, and thus completely removes the need of solving combinatorial optimization problems , and thus greatly reduces the computational complexity especially in large-size networks.
Edit intent: clarity
Original:  To further reduce the computational complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly.
Revised:  To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly.
Edit intent: clarity
Original:  Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing  methods.
Revised:  Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods.
Edit intent: clarity
Original:  For example, the complexity is reduced from several seconds to less than 0.1 second in a 30-user network, making real-time and optimal offloading design truly viable even in a fast fading environment.
Revised:  For example, the complexity is reduced from several seconds to less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment.
Edit intent: clarity
Original:  The two architectures have differing data flows dealing with a common component called the cell state  also referred to as the memory  .
Revised:  The two architectures have differing data flows dealing with a common component called the cell state ( also referred to as the memory .
Edit intent: fluency
Original:  The two architectures have differing data flows dealing with a common component called the cell state  also referred to as the memory  .
Revised:  The two architectures have differing data flows dealing with a common component called the cell state also referred to as the memory ) .
Edit intent: fluency
Original:  We attempt to enhance the memory by presenting a biologically inspired modification that we call the Mother Compact Recurrent Memory MCRM . MCRMs are a type of a nested LSTM-GRU architecture where the cell state is the GRU 's hidden state.
Revised:  We attempt to enhance the memory by presenting a modification that we call the Mother Compact Recurrent Memory MCRM . MCRMs are a type of a nested LSTM-GRU architecture where the cell state is the GRU 's hidden state.
Edit intent: clarity
Original:  We attempt to enhance the memory by presenting a biologically inspired modification that we call the Mother Compact Recurrent Memory MCRM . MCRMs are a type of a nested LSTM-GRU architecture where the cell state is the GRU 's hidden state.
Revised:  We attempt to enhance the memory by presenting a biologically inspired modification that we call the Mother Compact Recurrent Memory (MCRM) . MCRMs are a type of a nested LSTM-GRU architecture where the cell state is the GRU 's hidden state.
Edit intent: clarity
Original:  We attempt to enhance the memory by presenting a biologically inspired modification that we call the Mother Compact Recurrent Memory MCRM . MCRMs are a type of a nested LSTM-GRU architecture where the cell state is the GRU 's hidden state.
Revised:  We attempt to enhance the memory by presenting a biologically inspired modification that we call the Mother Compact Recurrent Memory MCRM . MCRMs are a type of a nested LSTM-GRU architecture where the cell state is the GRU hidden state.
Edit intent: fluency
Original:  The two arteries are considered as an input to the fetus which is analogous to the concatenation of the forget gate and input gate  from the LSTM . The vein is the output from the fetus which plays the role of the hidden state of the GRU . Because MCRMs has this type of nesting, MCRMs have a compact memory pattern consisting of neurons that act explicitly in both long-term and short-term fashions.
Revised:  The two arteries are considered as an input to the fetus which is analogous to the concatenation of the forget gate and input gate interactions from the LSTM . The vein is the output from the fetus which plays the role of the hidden state of the GRU . Because MCRMs has this type of nesting, MCRMs have a compact memory pattern consisting of neurons that act explicitly in both long-term and short-term fashions.
Edit intent: clarity
Original:  The two arteries are considered as an input to the fetus which is analogous to the concatenation of the forget gate and input gate  from the LSTM . The vein is the output from the fetus which plays the role of the hidden state of the GRU . Because MCRMs has this type of nesting, MCRMs have a compact memory pattern consisting of neurons that act explicitly in both long-term and short-term fashions.
Revised:  The two arteries are considered as an input to the fetus which is analogous to the concatenation of the forget gate and input gate from the LSTM are considered an input to the GRU cell . Because MCRMs has this type of nesting, MCRMs have a compact memory pattern consisting of neurons that act explicitly in both long-term and short-term fashions.
Edit intent: clarity
Original:  The two arteries are considered as an input to the fetus which is analogous to the concatenation of the forget gate and input gate  from the LSTM . The vein is the output from the fetus which plays the role of the hidden state of the GRU . Because MCRMs has this type of nesting, MCRMs have a compact memory pattern consisting of neurons that act explicitly in both long-term and short-term fashions.
Revised:  The two arteries are considered as an input to the fetus which is analogous to the concatenation of the forget gate and input gate from the LSTM . The vein is the output from the fetus which plays the role of the hidden state of the GRU . Because MCRMs has this type of nesting, MCRMs have a compact memory pattern consisting of neurons that acts explicitly in both long-term and short-term fashions.
Edit intent: fluency
Original:  This can be inhomogeneity in the distribution of salinity, temperature or terrain relief, e. t.c. Our specific goal is to examine the effect of shortwave inhomogeneity.
Revised:  This can be inhomogeneity in the distribution of salinity, temperature or terrain relief, etc. Our goal is to examine the effect of shortwave inhomogeneity.
Edit intent: clarity
Original:  We employ a kind of homogenization in order to separate the shortwave component of the system response from the smooth component and to derive a slow system governing the smooth component.
Revised:  We employ a kind of homogenization in order to separate the shortwave component of the system response and derive a slow system governing the smooth component.
Edit intent: coherence
Original:  We show how to stochastically optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning.
Revised:  We show how to optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning.
Edit intent: clarity
Original:  A simple effective approach is the size-change termination (SCT) method, which operates in two-phases : (1) abstract programs into "size-change graphs," and (2) check these graphs for the size-change property: the existence of paths that lead to infinitely decreasing value sequences.
Revised:  One such method is the size-change termination (SCT) method, which operates in two-phases : (1) abstract programs into "size-change graphs," and (2) check these graphs for the size-change property: the existence of paths that lead to infinitely decreasing value sequences.
Edit intent: clarity
Original:  A simple effective approach is the size-change termination (SCT) method, which operates in two-phases : (1) abstract programs into "size-change graphs," and (2) check these graphs for the size-change property: the existence of paths that lead to infinitely decreasing value sequences.
Revised:  A simple effective approach is the size-change termination (SCT) method, which operates in two phases : (1) abstract programs into "size-change graphs," and (2) check these graphs for the size-change property: the existence of paths that lead to infinitely decreasing value sequences.
Edit intent: fluency
Original:  This paper explores the termination problem starting from a different vantage point: we propose transposing the two phases of the SCT analysis by developing an operational semantics that accounts for the run time checking of the size-change property, postponing program abstraction or avoiding it entirely .
Revised:  This paper explores the termination problem starting from a different vantage point: we propose transposing the two phases of the SCT analysis by developing an operational semantics that accounts for the run-time enforcement of the size-change property, postponing program abstraction or avoiding it entirely .
Edit intent: clarity
Original:  This paper explores the termination problem starting from a different vantage point: we propose transposing the two phases of the SCT analysis by developing an operational semantics that accounts for the run time checking of the size-change property, postponing program abstraction or avoiding it entirely .
Revised:  This paper explores the termination problem starting from a different vantage point: we propose transposing the two phases of the SCT analysis by developing an operational semantics that accounts for the run time checking of the size-change property, postponing (or entirely avoiding) program abstraction .
Edit intent: clarity
Original:  This choice has two important consequences: SCT can be monitored and enforced at run-time and termination analysis can be rephrased as a traditional safety property and computed using existing abstract interpretation methods .
Revised:  This choice has two key consequences: (1) size-change termination can be checked at run-time and termination analysis can be rephrased as a traditional safety property and computed using existing abstract interpretation methods .
Edit intent: clarity
Original:  This choice has two important consequences: SCT can be monitored and enforced at run-time and termination analysis can be rephrased as a traditional safety property and computed using existing abstract interpretation methods .
Revised:  This choice has two important consequences: SCT can be monitored and enforced at run-time and (2) termination can be rephrased as a traditional safety property and computed using existing abstract interpretation methods .
Edit intent: clarity
Original:  This choice has two important consequences: SCT can be monitored and enforced at run-time and termination analysis can be rephrased as a traditional safety property and computed using existing abstract interpretation methods .
Revised:  This choice has two important consequences: SCT can be monitored and enforced at run-time and termination analysis can be rephrased as a safety property analyzed using existing methods for systematic abstraction .
Edit intent: clarity
Original:  We formulate this run-time size-change check as a contract.
Revised:  We formulate run-time size-change check as a contract.
Edit intent: clarity
Original:  The result nicely compliments existing contracts that enforce partial correctness to obtain the first contracts for total correctness.
Revised:  The result compliments existing contracts that enforce partial correctness to obtain the first contracts for total correctness.
Edit intent: coherence