wanyu/IteraTeR_v2 huggingface dataset:
Original:  We measure Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal to Interference and Noise Ratio (SINR), and the downlink throughput  .
Revised:  We measure Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal to Interference and Noise Ratio (SINR), the downlink throughput .
Edit intent: coherence
Original:  Build a compact data structure that, on receiving a pair of vertices s,t\in V and any edge (x,y) as query, can efficiently report the value of the mincut between s and t upon failure of the edge (x,y ).
Revised:  Build a compact data structure that, on receiving any pair of vertices s,t\in V and any edge (x,y) as query, can efficiently report the value of the mincut between s and t upon failure of the edge (x,y ).
Edit intent: fluency
Original:  Build a compact data structure that, on receiving a pair of vertices s,t\in V and any edge (x,y) as query, can efficiently report the value of the mincut between s and t upon failure of the edge (x,y ).
Revised:  Build a compact data structure that, on receiving a pair of vertices s,t\in V and any edge (x,y) as query, can efficiently report the mincut between s and t upon failure of the edge (x,y ).
Edit intent: clarity
Original:  The query time is {\cal O}(\min(m,n c_{s,t})) where c_{s,t} is the value of the mincut between s and t in G. The query time guaranteed by our data structure is faster by a factor of \Omega( \sqrt{n,\sqrt{n})} ) compared to the best known  algorithm to compute a (s,t)-mincut {\cal   .
Revised:  The query time is {\cal O}(\min(m,n c_{s,t})) where c_{s,t} is the value of the mincut between s and t in G. This query time is faster by a factor of \Omega( \sqrt{n,\sqrt{n})} ) compared to the best known algorithm to compute a (s,t)-mincut {\cal .
Edit intent: clarity
Original:  The query time is {\cal O}(\min(m,n c_{s,t})) where c_{s,t} is the value of the mincut between s and t in G. The query time guaranteed by our data structure is faster by a factor of \Omega( \sqrt{n,\sqrt{n})} ) compared to the best known  algorithm to compute a (s,t)-mincut {\cal   .
Revised:  The query time is {\cal O}(\min(m,n c_{s,t})) where c_{s,t} is the value of the mincut between s and t in G. The query time guaranteed by our data structure is faster by a factor of \Omega( \sqrt{n,\sqrt{n})} ) compared to the best known algorithm to compute a (s,t)-mincut {\cal O .
Edit intent: fluency
Original:  For  supervised cross-modal hashing methods, how to make the learned hash codes preserve semantic structure information sufficiently is a key point to further enhance the retrieval performance.
Revised:  For the supervised cross-modal hashing methods, how to make the learned hash codes preserve semantic structure information sufficiently is a key point to further enhance the retrieval performance.
Edit intent: fluency
Original:  As far as we know , almost all supervised cross-modal hashing methods preserve semantic structure information depending on at-least-one similarity definition fully or partly , i. e., it defines two datapoints as similar ones if they share at least one common category otherwise they are dissimilar.
Revised:  Hence , almost all supervised cross-modal hashing methods preserve semantic structure information depending on at-least-one similarity definition fully or partly , i. e., it defines two datapoints as similar ones if they share at least one common category otherwise they are dissimilar.
Edit intent: coherence
Original:  To tackle this problem , in this paper,  we propose a novel  Deep Cross-modal Proxy Hashing , called DCPH  . Specifically, DCPH first learns a proxy hashing network to generate a discriminative proxy hash code for each category.
Revised:  To tackle this problem , in this paper, we propose a novel Deep Cross-modal Hashing via . Specifically, DCPH first learns a proxy hashing network to generate a discriminative proxy hash code for each category.
Edit intent: clarity
Original:  Then, by utilizing the learned proxy hash code as supervised information , a novel Margin-SoftMax-like\ loss is proposed without defining the at-least-one similarity between datapoints .
Revised:  Then, by utilizing the learned proxy hash code as supervised information , a novel Margin-SoftMax-like\ loss is proposed without defining the similarity between datapoints .
Edit intent: clarity
Original:  By  minimizing the novel Margin-SoftMax-like\ loss, the learned hash codes will simultaneously preserve the cross-modal similarity and abundant semantic structure information well.
Revised:  By minimizing the novel margin-dynamic-softmax loss simultaneously preserve the cross-modal similarity and abundant semantic structure information well.
Edit intent: clarity
Original:  However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions , disjunctions,\land\lor and existential quantifiers  , while accounting for missing edges.
Revised:  However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions (\land\lor and existential quantifiers , while accounting for missing edges.
Edit intent: fluency
Original:  However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions , disjunctions,\land\lor and existential quantifiers  , while accounting for missing edges.
Revised:  However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions , disjunctions,\land), disjunctions (\lor and existential quantifiers , while accounting for missing edges.
Edit intent: fluency
Original:  However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions , disjunctions,\land\lor and existential quantifiers  , while accounting for missing edges.
Revised:  However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions , disjunctions,\land\lor) and existential quantifiers , while accounting for missing edges.
Edit intent: fluency
Original:  We also use Monae to revisit the lifting theorems of modular monad transformers by providing equational proofs and explaining how to patch a known bug with a non-standard use of Coq that combines impredicative polymorphism and parametricity.
Revised:  We also use Monae to revisit the lifting theorems of modular monad transformers by providing equational proofs and explaining how to patch a known bug using a non-standard use of Coq that combines impredicative polymorphism and parametricity.
Edit intent: fluency
Original:  Our most efficient methods are deterministic and use optimal, O(n), space, improving upon the space complexity of the previous state-of-the-art O(\epsilon^{-1})-pass algorithm of Ahn and Gupta .
Revised:  Our most efficient methods are deterministic and use optimal, O(n), space, improving upon the space complexity of the previous state-of-the-art O(\epsilon^{-1})-pass algorithm of Ahn and Guha .
Edit intent: clarity
Original: Many modern virtual machines, such as JVMs, .NET Framework  and V8, employ a just-in-time (JIT) compiler to achieve their high-performance.
Revised: Many modern virtual machines, such as JVMs, .NET Framework , and V8, employ a just-in-time (JIT) compiler to achieve their high-performance.
Edit intent: fluency
Original:  We also present the proof-of-concept implementation BacCaml .
Revised:  We implemented the BacCaml framework for proof-of-concept implementation BacCaml .
Edit intent: clarity
Original:  We also present the proof-of-concept implementation BacCaml .
Revised:  We also present the proof-of-concept .
Edit intent: clarity
Original: We describe a sequence-to-sequence neural network which can directly generate speech waveforms from text inputs.
Revised: We describe a sequence-to-sequence neural network which directly generates speech waveforms from text inputs.
Edit intent: clarity
Original:  Output waveforms are modeled as a sequence of non-overlapping fixed-length frames , each one containing hundreds of samples.
Revised:  Output waveforms are modeled as a sequence of non-overlapping fixed-length blocks , each one containing hundreds of samples.
Edit intent: clarity
Original:  The interdependencies of waveform samples within each frame are modeled using the normalizing flow, enabling parallel training and synthesis.
Revised:  The interdependencies of waveform samples within each block are modeled using the normalizing flow, enabling parallel training and synthesis.
Edit intent: clarity
Original:  Longer-term dependencies are handled autoregressively by conditioning each flow on preceding frames .
Revised:  Longer-term dependencies are handled autoregressively by conditioning each flow on preceding blocks .
Edit intent: clarity
Original: This model can be optimized directly with maximum likelihood, without using intermediate, hand-designed features nor additional loss terms.
Revised: This model can be optimized directly with maximum likelihood, with-out using intermediate, hand-designed features nor additional loss terms.
Edit intent: fluency
Original: We present  a learning-based method (LCollision) that synthesizes collision-free 3D human poses.
Revised: We present LCollision, a learning-based method (LCollision) that synthesizes collision-free 3D human poses.
Edit intent: fluency
Original: We present  a learning-based method (LCollision) that synthesizes collision-free 3D human poses.
Revised: We present a learning-based method that synthesizes collision-free 3D human poses.
Edit intent: clarity
Original:  At the crux of our approach is a novel deep architecture that simultaneously decodes new human poses from the latent space and classifies the collision status .
Revised:  At the crux of our approach is a novel deep architecture that simultaneously decodes new human poses from the latent space and predicts colliding body parts .
Edit intent: clarity
Original:  These two components of our architecture are used as the objective function and surrogate hard-constraints in a constrained-optimization algorithm for collision-free human pose generation.
Revised:  These two components of our architecture are used as the objective function and surrogate hard constraints in a constrained optimization for collision-free human pose generation.
Edit intent: clarity
Original:  We show that solving our constrained optimization formulation can resolve significantly more collision artifacts than prior learning algorithms .
Revised:  By solving the constrained optimizations, we show that a significant amount of collision artifacts can be resolved .
Edit intent: clarity
Original: 1\\%DIF < with 80\times speedup over exact collision detection algorithms. %DIF > with 80\times speedup over exact collision detection  To the best of our knowledge, LCollision is the first approach that can obtain high accuracy in terms of handling non-penetration and collision constraints in a learning framework .
Revised: 1\\%DIF < with 80\times speedup over exact collision detection algorithms. %DIF > with 80\times speedup over exact collision detection algorithms. To the best of our knowledge, LCollision is the first approach that can obtain high accuracy in terms of handling non-penetration and collision constraints in a learning framework .
Edit intent: fluency
Original: 1\\%DIF < with 80\times speedup over exact collision detection algorithms. %DIF > with 80\times speedup over exact collision detection  To the best of our knowledge, LCollision is the first approach that can obtain high accuracy in terms of handling non-penetration and collision constraints in a learning framework .
Revised: 1\\%DIF < with 80\times speedup over exact collision detection algorithms. %DIF > with 80\times speedup over exact collision detection To the best of our knowledge, LCollision is the first approach that accelerates collision detection and resolves penetrations using a neural network .
Edit intent: clarity
Original: This paper is concerned with a new Timoshenko beam model with thermal, mass diffusion and viscoelastic effects .
Revised: Bresse-Timoshenko beam model with thermal, mass diffusion and viscoelastic effects .
Edit intent: clarity
Original:  It provides an acceptable ranging accuracy near the service requirements in favorable environments when a line-of-sight (LOS) path exists.
Revised:  It provides an acceptable ranging accuracy in favorable environments when a line-of-sight (LOS) path exists.
Edit intent: clarity
Original:  To address it, we aim at leveraging the history of user mobility detected by a smartphone's inertial measurement units, called pedestrian dead reckoning (PDR).
Revised:  To address it, we aim at leveraging the user mobility trajectory detected by a smartphone's inertial measurement units, called pedestrian dead reckoning (PDR).
Edit intent: clarity
Original:  It provides a user's relative trajectory defined on the  local coordinate system of the concerned AP . Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned , called trajectory alignment .
Revised:  It enables us to construct a user's relative trajectory defined on the local coordinate system of the concerned AP . Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned , called trajectory alignment .
Edit intent: clarity
Original:  It provides a user's relative trajectory defined on the  local coordinate system of the concerned AP . Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned , called trajectory alignment .
Revised:  It provides a user's relative trajectory defined on the concerned AP's local coordinate system of the concerned AP . Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned , called trajectory alignment .
Edit intent: coherence
Original:  It provides a user's relative trajectory defined on the  local coordinate system of the concerned AP . Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned , called trajectory alignment .
Revised:  It provides a user's relative trajectory defined on the local coordinate system . Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned , called trajectory alignment .
Edit intent: clarity
Original:  It provides a user's relative trajectory defined on the  local coordinate system of the concerned AP . Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned , called trajectory alignment .
Revised:  It provides a user's relative trajectory defined on the local coordinate system of the concerned AP . Second, we align every AP's relative trajectory into a single one , called trajectory alignment .
Edit intent: clarity
Original:  Such a graph is temporally connected if each ordered pair of vertices is connected by a path which traverses edges in chronological order (i.e.  a temporal path).
Revised:  Such a graph is temporally connected if each ordered pair of vertices is connected by a path which traverses edges in chronological order (i.e. , a temporal path).
Edit intent: fluency
Original:  In this paper, we consider a simple model of random temporal graph, obtained by assigning to every edge of an Erd \H{o}%DIFDELCMD < }%%% s-R \'e%DIFDELCMD < }%%% nyis-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Revised:  In this paper, we consider a simple model of random temporal graph, obtained \H{o}%DIFDELCMD < }%%% s-R \'e%DIFDELCMD < }%%% nyis-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Edit intent: clarity
Original:  In this paper, we consider a simple model of random temporal graph, obtained by assigning to every edge of an Erd \H{o}%DIFDELCMD < }%%% s-R \'e%DIFDELCMD < }%%% nyis-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Revised:  In this paper, we consider a simple model of random temporal graph, obtained by assigning to every edge of an Erd }%DIFDELCMD < }%%% s-R \'e%DIFDELCMD < }%%% nyis-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Edit intent: coherence
Original:  In this paper, we consider a simple model of random temporal graph, obtained by assigning to every edge of an Erd \H{o}%DIFDELCMD < }%%% s-R \'e%DIFDELCMD < }%%% nyis-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Revised:  In this paper, we consider a simple model of random temporal graph, obtained by assigning to every edge of an Erd \H{o}%DIFDELCMD < }%%% \'e%DIFDELCMD < }%%% nyis-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Edit intent: clarity
Original:  In this paper, we consider a simple model of random temporal graph, obtained by assigning to every edge of an Erd \H{o}%DIFDELCMD < }%%% s-R \'e%DIFDELCMD < }%%% nyis-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Revised:  In this paper, we consider a simple model of random temporal graph, obtained by assigning to every edge of an Erd \H{o}%DIFDELCMD < }%%% s-R %DIFDELCMD < }%%% nyis-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Edit intent: clarity
Original:  In this paper, we consider a simple model of random temporal graph, obtained by assigning to every edge of an Erd \H{o}%DIFDELCMD < }%%% s-R \'e%DIFDELCMD < }%%% nyis-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Revised:  In this paper, we consider a simple model of random temporal graph, obtained by assigning to every edge of an Erd \H{o}%DIFDELCMD < }%%% s-R \'e%DIFDELCMD < }%%% from an Erd\H{os-R\'enyi } random graph G _{n,p 0, 1%DIFDELCMD < ]%%% . It turns out that by considering a random permutation \pi of the edges and interpreting the ranks in \pi as presence times.
Edit intent: clarity
Original:  Temporal reachability in } this model exhibits a surprisingly regular sequence of thresholds related to temporal reachability .
Revised:  Temporal reachability in } this model exhibits a surprisingly regular sequence of thresholds .
Edit intent: clarity
Original:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other , at 2 log n/n at least one vertex (and in fact, any fixed node ) can a.a.s. reach all others , and at 3 log n/n all the vertices can a.a.s. reach each other ( i.e.  the graph is temporally connected ).
Revised:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other ; at 2 log n/n at least one vertex (and in fact, any fixed node ) can a.a.s. reach all others , and at 3 log n/n all the vertices can a.a.s. reach each other ( i.e. the graph is temporally connected ).
Edit intent: fluency
Original:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other , at 2 log n/n at least one vertex (and in fact, any fixed node ) can a.a.s. reach all others , and at 3 log n/n all the vertices can a.a.s. reach each other ( i.e.  the graph is temporally connected ).
Revised:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other , at 2 log n/n at least one vertex (and in fact, any fixed vertex ) can a.a.s. reach all others , and at 3 log n/n all the vertices can a.a.s. reach each other ( i.e. the graph is temporally connected ).
Edit intent: clarity
Original:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other , at 2 log n/n at least one vertex (and in fact, any fixed node ) can a.a.s. reach all others , and at 3 log n/n all the vertices can a.a.s. reach each other ( i.e.  the graph is temporally connected ).
Revised:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other , at 2 log n/n at least one vertex (and in fact, any fixed node ) can a.a.s. reach all others ; and at 3 log n/n all the vertices can a.a.s. reach each other ( i.e. the graph is temporally connected ).
Edit intent: fluency
Original:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other , at 2 log n/n at least one vertex (and in fact, any fixed node ) can a.a.s. reach all others , and at 3 log n/n all the vertices can a.a.s. reach each other ( i.e.  the graph is temporally connected ).
Revised:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other , at 2 log n/n at least one vertex (and in fact, any fixed node ) can a.a.s. reach all others , and at 3 log n/n all the vertices can a.a.s. reach each other , i.e. the graph is temporally connected ).
Edit intent: fluency
Original:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other , at 2 log n/n at least one vertex (and in fact, any fixed node ) can a.a.s. reach all others , and at 3 log n/n all the vertices can a.a.s. reach each other ( i.e.  the graph is temporally connected ).
Revised:  In particular, we show that at p= log n/n any fixed pair of vertices can a.a.s. reach each other , at 2 log n/n at least one vertex (and in fact, any fixed node ) can a.a.s. reach all others , and at 3 log n/n all the vertices can a.a.s. reach each other ( i.e. , the graph is temporally connected ).
Edit intent: fluency
Original:  Our results indicate that the 4-states Non-Homogeneous Hidden Markov model has the best one-step-ahead forecasting performance among all the competing models for all three series.
Revised:  Our results indicate that the Non-Homogeneous Hidden Markov model has the best one-step-ahead forecasting performance among all the competing models for all three series.
Edit intent: clarity
Original:  Our results indicate that the 4-states Non-Homogeneous Hidden Markov model has the best one-step-ahead forecasting performance among all the competing models for all three series.
Revised:  Our results indicate that the 4-states Non-Homogeneous Hidden Markov model has the best one-step-ahead forecasting performance among all competing models for all three series.
Edit intent: fluency
Original:  The superiority of the predictive densities , over the single regime random walk model , relies on the fact that the states capture alternating periods with distinct returns' characteristics.
Revised:  The dominance of the predictive densities , over the single regime random walk model , relies on the fact that the states capture alternating periods with distinct returns' characteristics.
Edit intent: clarity
Original:  The superiority of the predictive densities , over the single regime random walk model , relies on the fact that the states capture alternating periods with distinct returns' characteristics.
Revised:  The superiority of the predictive densities over the single regime random walk model , relies on the fact that the states capture alternating periods with distinct returns' characteristics.
Edit intent: fluency