wanyu/IteraTeR_v2 huggingface dataset:
Original:  With processing latency constraints at the edge cloud and backhaul , we aim to minimize the network energy consumption (the energy used for task offloading as well as computation) through jointly optimizing the cloud selection, the UE' s transmit power, the SBS' s receive beamformer , and the SBS' s transmit covariance matrix.
Revised:  With processing latency constraints at the central and edge networks , we aim to minimize the network energy consumption (the energy used for task offloading as well as computation) through jointly optimizing the cloud selection, the UE' s transmit power, the SBS' s receive beamformer , and the SBS' s transmit covariance matrix.
Edit intent: clarity
Original:  With processing latency constraints at the edge cloud and backhaul , we aim to minimize the network energy consumption (the energy used for task offloading as well as computation) through jointly optimizing the cloud selection, the UE' s transmit power, the SBS' s receive beamformer , and the SBS' s transmit covariance matrix.
Revised:  With processing latency constraints at the edge cloud and backhaul , we aim to minimize the system energy consumption used for task offloading as well as computation) through jointly optimizing the cloud selection, the UE' s transmit power, the SBS' s receive beamformer , and the SBS' s transmit covariance matrix.
Edit intent: clarity
Original:  With processing latency constraints at the edge cloud and backhaul , we aim to minimize the network energy consumption (the energy used for task offloading as well as computation) through jointly optimizing the cloud selection, the UE' s transmit power, the SBS' s receive beamformer , and the SBS' s transmit covariance matrix.
Revised:  With processing latency constraints at the edge cloud and backhaul , we aim to minimize the network energy consumption (the energy used for task offloading and computation. The problem is formulated by jointly optimizing the cloud selection, the UE' s transmit power, the SBS' s receive beamformer , and the SBS' s transmit covariance matrix.
Edit intent: clarity
Original:  With processing latency constraints at the edge cloud and backhaul , we aim to minimize the network energy consumption (the energy used for task offloading as well as computation) through jointly optimizing the cloud selection, the UE' s transmit power, the SBS' s receive beamformer , and the SBS' s transmit covariance matrix.
Revised:  With processing latency constraints at the edge cloud and backhaul , we aim to minimize the network energy consumption (the energy used for task offloading as well as computation) through jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers , and the SBS' s transmit covariance matrix.
Edit intent: fluency
Original:  With large-scale antennas at the MBS, the massive multiple-input multiple-output (MIMO ) backhaul can significantly reduce the complexity of our algorithm and obtain even better performance.
Revised:  With large-scale antennas at the MBS, the massive MIMO backhaul can significantly reduce the complexity of our algorithm and obtain even better performance.
Edit intent: clarity
Original:  With large-scale antennas at the MBS, the massive multiple-input multiple-output (MIMO ) backhaul can significantly reduce the complexity of our algorithm and obtain even better performance.
Revised:  With large-scale antennas at the MBS, the massive multiple-input multiple-output (MIMO ) backhaul can significantly reduce the complexity of the proposed algorithm and obtain even better performance.
Edit intent: clarity
Original: For the classic aesthetic interpolation problem, we propose a novel thoughtthat is applying the golden section principle .
Revised: For the classic aesthetic interpolation problem, we propose an entirely new thought: apply the golden section principle .
Edit intent: clarity
Original: For the classic aesthetic interpolation problem, we propose a novel thoughtthat is applying the golden section principle .
Revised: For the classic aesthetic interpolation problem, we propose a novel thoughtthat is applying the golden section .
Edit intent: clarity
Original:  We will show the optimality of a periodic barrier replenishment policy that replenishes any shortage below a certain barrier at each replenishment opportunity.
Revised:  We show the optimality of a periodic barrier replenishment policy that replenishes any shortage below a certain barrier at each replenishment opportunity.
Edit intent: coherence
Original:  We will show the optimality of a periodic barrier replenishment policy that replenishes any shortage below a certain barrier at each replenishment opportunity.
Revised:  We will show the optimality of a periodic barrier replenishment policy that restocks any shortage below a certain barrier at each replenishment opportunity.
Edit intent: clarity
Original:  We will show the optimality of a periodic barrier replenishment policy that replenishes any shortage below a certain barrier at each replenishment opportunity.
Revised:  We will show the optimality of a periodic barrier replenishment policy that replenishes any shortage below a certain threshold at each replenishment opportunity.
Edit intent: clarity
Original:  The key ingredient is to find out the continuity of exit operator under Skorokhod topology, which reveals the intrinsic connection of overfitting Dirichlet boundary with fine topology.
Revised:  The key ingredient is to find out the continuity of exit operator under Skorokhod topology, which reveals the intrinsic connection between overfitting Dirichlet boundary with fine topology.
Edit intent: clarity
Original:  The key ingredient is to find out the continuity of exit operator under Skorokhod topology, which reveals the intrinsic connection of overfitting Dirichlet boundary with fine topology.
Revised:  The key ingredient is to find out the continuity of exit operator under Skorokhod topology, which reveals the intrinsic connection of overfitting Dirichlet boundary and fine topology.
Edit intent: fluency
Original:  The recognition accuracy and it's processing time depends on task properties such as viewing condition, level of noise and etc.
Revised:  The recognition accuracy and its processing time depend on task properties such as viewing condition, level of noise and etc.
Edit intent: fluency
Original:  Results also provide enough evidence that temporal representation of features are informative since they can improve the accuracy over time which is in line with the well-known idea of speed accuracy trade-off in decision making studies.
Revised:  Results provide enough evidence that temporal representation of features are informative since they can improve the accuracy over time which is in line with the well-known idea of speed accuracy trade-off in decision making studies.
Edit intent: clarity
Original: Resolution over linear equations (introduced in RT08%DIFDELCMD < ]%%% ) emerged recently as an important object of study.
Revised: Resolution over linear equations RT08%DIFDELCMD < ]%%% ) emerged recently as an important object of study.
Edit intent: clarity
Original:  This refutation system, denoted Res(lin_R), operates with disjunction of linear equations  over a ring R . On the one hand, the system captures a natural "minimal" extension of resolution in which efficient counting can be achieved;
Revised:  This refutation system, denoted Res(lin_R), this refutation system operates with disjunctions of linear equations over a ring R . On the one hand, the system captures a natural "minimal" extension of resolution in which efficient counting can be achieved;
Edit intent: coherence
Original:  while on the other hand, as observed by, e. g., Krajicek [ Kra16 [  (cf.[  IS14, KO18, GK17 ]) , when considered over prime fields, and specifically F_2, super-polynomial lower bounds on Res(lin _{\mathbf{F with counting gates (AC^0[2]-Frege) lower bounds .
Revised:  while on the other hand, as observed by, e. g., Krajicek [ Kra16 [IS14 (cf.[ IS14, KO18, GK17 ]) , when considered over prime fields, and specifically F_2, super-polynomial lower bounds on Res(lin _{\mathbf{F with counting gates (AC^0[2]-Frege) lower bounds .
Edit intent: fluency
Original:  while on the other hand, as observed by, e. g., Krajicek [ Kra16 [  (cf.[  IS14, KO18, GK17 ]) , when considered over prime fields, and specifically F_2, super-polynomial lower bounds on Res(lin _{\mathbf{F with counting gates (AC^0[2]-Frege) lower bounds .
Revised:  while on the other hand, as observed by, e. g., Krajicek [ Kra16 [ (cf.[ Kra17, IS14, KO18, GK17 ]) , when considered over prime fields, and specifically F_2, super-polynomial lower bounds on Res(lin _{\mathbf{F with counting gates (AC^0[2]-Frege) lower bounds .
Edit intent: fluency
Original:  Moreover, for instances with small coefficients, we separate the tree and dag-like versions of Res(lin_{\mathbf{F}), when \mathbf{F} is of characteristic zero, by employing the notion of immunity from Alekhnovich-Razborov }%DIFDELCMD < [%%% AR01 , among other techniques . (Abstract continued in the full paper.)
Revised:  Moreover, for instances with small coefficients, we separate the tree and dag-like }), when \mathbf{F} is of characteristic zero, by employing the notion of immunity from Alekhnovich-Razborov }%DIFDELCMD < [%%% AR01 , among other techniques . (Abstract continued in the full paper.)
Edit intent: clarity
Original:  Moreover, for instances with small coefficients, we separate the tree and dag-like versions of Res(lin_{\mathbf{F}), when \mathbf{F} is of characteristic zero, by employing the notion of immunity from Alekhnovich-Razborov }%DIFDELCMD < [%%% AR01 , among other techniques . (Abstract continued in the full paper.)
Revised:  Moreover, for instances with small coefficients, we separate the tree and dag-like versions of Res(lin_{\mathbf{F}), when \mathbf{F} is of characteristic zero, by employing the notion of immunity from Alekhnovich-Razborov }%DIFDELCMD < [%%% AR01 refutations over the rationals . (Abstract continued in the full paper.)
Edit intent: clarity
Original:  Our approach is compared to an state-of-the-art adaptive technique along with non-adaptive techniques.
Revised:  Our approach is compared to state-of-the-art adaptive technique along with non-adaptive techniques.
Edit intent: fluency
Original:  Our approach is compared to an state-of-the-art adaptive technique along with non-adaptive techniques.
Revised:  Our approach is compared to an state-of-the-art adaptive and non-adaptive techniques.
Edit intent: clarity
Original:  Two accepted bench mark suites are used to compare performance , in all cases we show that the improvement resulting from our approach is statistically significant.
Revised:  Two accepted bench mark suites are used to compare performance and in all cases we show that the improvement resulting from our approach is statistically significant.
Edit intent: coherence
Original:  The majority of the computational effort of this methodology is performed off-line, however even  taking into account the additional on-line cost our approach outperforms other adaptive techniques.
Revised:  The majority of the computational effort of this methodology is performed off-line, however even when taking into account the additional on-line cost our approach outperforms other adaptive techniques.
Edit intent: coherence
Original:  We also study the key tuning parameters of our methodology,  which further support the finding that the simple features selected are predictors of effective control parameters.
Revised:  We also investigate the key tuning parameters of our methodology, which further support the finding that the simple features selected are predictors of effective control parameters.
Edit intent: clarity
Original:  The findings presented in this paper are significant because they show that simple to calculate features of objective functions can help  select control parameters for optimisation algorithms.
Revised:  The findings presented in this paper are significant because they show that simple to calculate features of objective functions can help to select control parameters for optimisation algorithms.
Edit intent: fluency
Original:  This can have an immediate positive impact  the application of these optimisation algorithms on real world problems  where it is often difficult to select effective control parameters.
Revised:  This can have an immediate positive impact on the application of these optimisation algorithms on real world problems where it is often difficult to select effective control parameters.
Edit intent: fluency
Original:  This can have an immediate positive impact  the application of these optimisation algorithms on real world problems  where it is often difficult to select effective control parameters.
Revised:  This can have an immediate positive impact the application of these optimisation algorithms on real world problems , where it is often difficult to select effective control parameters.
Edit intent: fluency
Original:  The state-of-the- art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step thanks to exploiting the properties of the extracted n-gram strings.
Revised:  The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step thanks to exploiting the properties of the extracted n-gram strings.
Edit intent: fluency
Original:  Decoding on the intracranial data without pre-transfer reaches median accuracies of (76.94 \pm 2.17 )\,\\%DIF < and (81.50 %DIF > . \\%DIF > of the data without pre-training reaches performances  \pm 9.49 )\,\\%DIF < , respectively.abstract %DIF > , compared to (64.95 \pm 0.79)\,\\% with 
Revised:  Classification on the intracranial data without pre-transfer reaches median accuracies of (76.94 \pm 2.17 )\,\\%DIF < and (81.50 %DIF > . \\%DIF > of the data without pre-training reaches performances \pm 9.49 )\,\\%DIF < , respectively.abstract %DIF > , compared to (64.95 \pm 0.79)\,\\% with 
Edit intent: fluency
Original:  Decoding on the intracranial data without pre-transfer reaches median accuracies of (76.94 \pm 2.17 )\,\\%DIF < and (81.50 %DIF > . \\%DIF > of the data without pre-training reaches performances  \pm 9.49 )\,\\%DIF < , respectively.abstract %DIF > , compared to (64.95 \pm 0.79)\,\\% with 
Revised:  Decoding on the intracranial data yields in median accuracies up to (81.50 \pm 2.17 )\,\\%DIF < and (81.50 %DIF > . \\%DIF > of the data without pre-training reaches performances \pm 9.49 )\,\\%DIF < , respectively.abstract %DIF > , compared to (64.95 \pm 0.79)\,\\% with 
Edit intent: clarity
Original:  Decoding on the intracranial data without pre-transfer reaches median accuracies of (76.94 \pm 2.17 )\,\\%DIF < and (81.50 %DIF > . \\%DIF > of the data without pre-training reaches performances  \pm 9.49 )\,\\%DIF < , respectively.abstract %DIF > , compared to (64.95 \pm 0.79)\,\\% with 
Revised:  Decoding on the intracranial data without pre-transfer reaches median accuracies of (76.94 \pm 2.17 )\,\\%DIF < %DIF > . \\%DIF > of the data without pre-training reaches performances \pm 9.49 )\,\\%DIF < , respectively.abstract %DIF > , compared to (64.95 \pm 0.79)\,\\% with 
Edit intent: coherence
Original:  Decoding on the intracranial data without pre-transfer reaches median accuracies of (76.94 \pm 2.17 )\,\\%DIF < and (81.50 %DIF > . \\%DIF > of the data without pre-training reaches performances  \pm 9.49 )\,\\%DIF < , respectively.abstract %DIF > , compared to (64.95 \pm 0.79)\,\\% with 
Revised:  Decoding on the intracranial data without pre-transfer reaches median accuracies of (76.94 \pm 2.17 )\,\\%DIF < and (81.50 %DIF > . \\%DIF > of the data without pre-training reaches performances \pm 9.49 )\,\\%DIF < , respectively.abstract %DIF > , compared to (64.95 \pm 0.79)\,\\% with pre-training.
Edit intent: fluency
Original: We establish several recurrence relations and an explicit formula for the number V(n)  of factorizations of the length-n prefix of the Fibonacci word into a (not  strictly) decreasing sequence of standard Fibonacci words (OEIS sequence A300066) .
Revised: We establish several recurrence relations and an explicit formula for V(n) of factorizations of the length-n prefix of the Fibonacci word into a (not strictly) decreasing sequence of standard Fibonacci words (OEIS sequence A300066) .
Edit intent: clarity
Original: We establish several recurrence relations and an explicit formula for the number V(n)  of factorizations of the length-n prefix of the Fibonacci word into a (not  strictly) decreasing sequence of standard Fibonacci words (OEIS sequence A300066) .
Revised: We establish several recurrence relations and an explicit formula for the number V(n) , the number of factorizations of the length-n prefix of the Fibonacci word into a (not strictly) decreasing sequence of standard Fibonacci words (OEIS sequence A300066) .
Edit intent: fluency
Original: We establish several recurrence relations and an explicit formula for the number V(n)  of factorizations of the length-n prefix of the Fibonacci word into a (not  strictly) decreasing sequence of standard Fibonacci words (OEIS sequence A300066) .
Revised: We establish several recurrence relations and an explicit formula for the number V(n) of factorizations of the length-n prefix of the Fibonacci word into a (not necessarily strictly) decreasing sequence of standard Fibonacci words (OEIS sequence A300066) .
Edit intent: coherence
Original:  In particular, we show that the sequence V(n) is just a shuffle of upper integer parts of two linear functions  .
Revised:  In particular, we show that the sequence V(n) is the shuffle of the ceilings of two linear functions .
Edit intent: clarity
Original:  In particular, we show that the sequence V(n) is just a shuffle of upper integer parts of two linear functions  .
Revised:  In particular, we show that the sequence V(n) is just a shuffle of upper integer parts of two linear functions of n .
Edit intent: clarity
Original: We study a problem that models safely routing a convoy through a transportation network, where any vertex adjacent to the travel path of the convoy requires additional precaution: Given a graph G=(V,E), two vertices s,t\in V, and two integers k,\ell, we search for a simple s-t-path with at most k vertices and at most \ell neighbors.
Revised:  Given a graph G=(V,E), two vertices s,t\in V, and two integers k,\ell, we search for a simple s-t-path with at most k vertices and at most \ell neighbors.
Edit intent: coherence
Original:  We study the problem in two types of transportation networks: graphs with small crossing number, as formed by road networks, and tree-like graphs, as formed by waterways.
Edit intent: coherence
Original:  For graphs with constant crossing number, we provide a subexponential 2^{O(\sqrt n)}-time algorithm and prove a matching lower bound .
Revised:  For graphs with constant crossing number, we provide a subexponential 2^{O(\sqrt n)}-time algorithm , prove a matching lower bound .
Edit intent: coherence
Original:  Regarding tree-like graphs, we obtain a 2^{O( \mathrm{tw )}\cdot \ell^2\cdot n-time algorithm for graphs of treewidth \mathrm{tw , show that there is no problem kernel with size polynomial in \mathrm{tw , yet show a problem kernel with size polynomial in the feedback edge number of the input graph  .
Revised:  We obtain a 2^{O( \mathrm{tw )}\cdot \ell^2\cdot n-time algorithm for graphs of treewidth \mathrm{tw , show that there is no problem kernel with size polynomial in \mathrm{tw , yet show a problem kernel with size polynomial in the feedback edge number of the input graph .
Edit intent: clarity
Original:  Regarding tree-like graphs, we obtain a 2^{O( \mathrm{tw )}\cdot \ell^2\cdot n-time algorithm for graphs of treewidth \mathrm{tw , show that there is no problem kernel with size polynomial in \mathrm{tw , yet show a problem kernel with size polynomial in the feedback edge number of the input graph  .
Revised:  Regarding tree-like graphs, we obtain a 2^{O( \mathrm{tw )}\cdot \ell^2\cdot n-time algorithm for graphs of treewidth \mathrm{tw , show that there is no problem kernel with size polynomial in \mathrm{tw , yet show a problem kernels with size polynomial in the feedback edge number of the input graph .
Edit intent: fluency
Original: Given a graph G=(V,E), two vertices s,t\in V, and two integers k,\ell, we search for a simple s-t-path with at most k vertices and at most \ell neighbors.
Revised: Given a graph G=(V,E), two vertices s,t\in V, and two integers k,\ell, we search for a simple s-t-path with at most k vertices and \ell neighbors.
Edit intent: fluency
Original:  We discuss the convergence properties of the algorithm, and demonstrate it on several machine learning applications.
Revised:  We discuss the convergence properties of the algorithm, and demonstrate it on several signal processing applications.
Edit intent: clarity
Original:  Moreover, we experimentally demonstrate that these data reduction rules provide significant speedups of the state-of-the art implementation for computing matchings in real-world graphs: the average speedup is 3800\% in the unweighted case and "just" 30\% in the weighted case.
Revised:  Moreover, we experimentally demonstrate that these data reduction rules provide significant speedups of the state-of-the art implementations for computing matchings in real-world graphs: the average speedup is 3800\% in the unweighted case and "just" 30\% in the weighted case.
Edit intent: fluency
Original:  The projection of wavelets defined in these coordinates therefore yields a sequence that is closed under projection and with analytically described functions .
Revised:  The projection of wavelets defined in these coordinates can be computed analytically, yielding a sequence of wavelets closed under projection and with analytically described functions .
Edit intent: clarity
Original:  The projection of wavelets defined in these coordinates therefore yields a sequence that is closed under projection and with analytically described functions .
Revised:  The projection of wavelets defined in these coordinates therefore yields a sequence that is closed under projection .
Edit intent: clarity
Original:  Our local analogue of the Fourier slice theorem then implements projection as reconstruction with "sliced" wavelets , and with computational costs that scale linearly in the complexity of the projected signal.
Revised:  Our local Fourier slice equation then realizes projection as reconstruction with "sliced" wavelets , and with computational costs that scale linearly in the complexity of the projected signal.
Edit intent: clarity
Original:  Our local analogue of the Fourier slice theorem then implements projection as reconstruction with "sliced" wavelets , and with computational costs that scale linearly in the complexity of the projected signal.
Revised:  Our local analogue of the Fourier slice theorem then implements projection as reconstruction with "sliced" wavelets with computational costs that scale linearly in the complexity of the projected signal.
Edit intent: coherence
Original:  In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigation .
Revised:  In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations .
Edit intent: fluency
Original:  We call a function f a \emph{linear k-junta if it is completely determined by some k-dimensional subspace of the input space.
Revised:  We call a function f a \emph{linear k-junta if it is completely determined by some k-dimensional subspace of the input space.
Edit intent: fluency