wanyu/IteraTeR_v2 huggingface dataset:
Original:  We compare the behavior of the membrane voltage and the potassium channel conductance in both the classical and quantum realms , subjected to AC sources . Numerical simulations show an expected increment and adaptation depending on the history of signals in all regimes.
Revised:  We compare the behavior of the membrane voltage and the potassium channel conductance in both the classical and quantum realms , subjected to AC sources . Numerical simulations show an expected increment and adaptation depending on the signal history in all regimes.
Edit intent: clarity
Original:  however, when computing higher moments of the voltage , we encounter purely quantum terms related to the  zero-point energy of the circuit.
Revised:  however, when computing higher moments of the voltage manifest purely quantum features related to the zero-point energy of the circuit.
Edit intent: clarity
Original:  however, when computing higher moments of the voltage , we encounter purely quantum terms related to the  zero-point energy of the circuit.
Revised:  however, when computing higher moments of the voltage , we encounter purely quantum terms related to the circuit zero-point energy of the circuit.
Edit intent: fluency
Original:  This could be considered a step forward towards the design of neuromorphic quantum architectures with direct applications in quantum machine learning.
Revised:  This could be considered a step forward towards the design of neuromorphic quantum architectures for quantum machine learning.
Edit intent: clarity
Original:  We simulate approximate sampling from the output of a circuit with 7 x 8 qubits and depth 1+40+1 by producing one million bitstring probabilitites with fidelity 0.5\%, at an estimated cost of %DIFDELCMD < \35184%%% . The simulation costs scale linearly with fidelity.
Revised:  We simulate approximate sampling from the output of a circuit with 7 x 8 qubits and depth 1+40+1 by producing one million bitstring probabilities with fidelity 0.5\%, at an estimated cost of %DIFDELCMD < \35184%%% . The simulation costs scale linearly with fidelity.
Edit intent: fluency
Original:  The leading candidate task for this quantum computational supremacy milestone entails sampling from the output distribution defined by a random quantum circuit.
Revised:  The leading candidate task for this milestone entails sampling from the output distribution defined by a random quantum circuit.
Edit intent: clarity
Original:  We perform this task on conventional computers for larger circuits than in previous results, by trading circuit fidelity for computational resources to match the fidelity of a given quantum computer .
Revised:  We perform this task on conventional computers for larger circuits than in previous results, by trading circuit fidelity for computational speedups, so as to match the fidelity of a given quantum computer .
Edit intent: clarity
Original:  By using publicly available Google Cloud Computing, we can price such simulations and enable comparisons by total cost across multiple hardware types .
Revised:  By using publicly available Google Cloud Computing, we price such simulations and enable comparisons by total cost across multiple hardware types .
Edit intent: coherence
Original:  By using publicly available Google Cloud Computing, we can price such simulations and enable comparisons by total cost across multiple hardware types .
Revised:  By using publicly available Google Cloud Computing, we can price such simulations and enable comparisons by total cost across hardware platforms .
Edit intent: clarity
Original:  We simulate approximate sampling from the output of a circuit with 7 x 8 qubits and depth 1+40+1 by producing one million bitstring probabilities with fidelity 0.5\%, at an estimated cost of 35184.
Revised:  We simulate approximate sampling from the output of a circuit with 7x8 qubits and depth 1+40+1 by producing one million bitstring probabilities with fidelity 0.5\%, at an estimated cost of 35184.
Edit intent: fluency
Original: Cell shape is an important biomarker . Previously extensive studies have established the relation between cell shape and cell function.
Revised: Cell shape is an important biomarker that is directly linked to cell function.
Edit intent: clarity
Original:  However, the morphodynamics, namely the temporal fluctuation of cell shape is much less understood.
Revised:  However, cell morphodynamics, namely the temporal fluctuation of cell shape is much less understood.
Edit intent: coherence
Original:  By employing machine learning techniques, we classify cell shape into five different morphological phenotypes corresponding to different migration modes.
Revised:  We employ machine learning to classify cell shape into five different morphological phenotypes corresponding to different migration modes.
Edit intent: clarity
Original:  We systematically characterize the phenotype dynamics including occurrence probability, dwell time, transition flux, and also obtain the invasion characteristicsof each phenotype . Using a tumor organoid model, we show that the distinct invasion potentials of each phenotype modulate the phenotype homeostasis.
Revised:  We systematically characterize the phenotype evolutions including occurrence probability, dwell time, transition flux, and also obtain the invasion characteristicsof each phenotype . Using a tumor organoid model, we show that the distinct invasion potentials of each phenotype modulate the phenotype homeostasis.
Edit intent: clarity
Original:  Our results pave the way to  systematic characterization and functional understanding of cell morphodynamics  .
Revised:  Our results pave the way to the systematic characterization and functional understanding of cell morphodynamics .
Edit intent: fluency
Original:  In this paper, we prove that \theta\leq 2\pi-2\pi{r.m} such that m and r are the number of edges and internal points of CH , respectively.
Revised:  In this paper, we prove that \theta\leq 2\pi-2\pi{r.m} where m and r are the number of edges and internal points of CH , respectively.
Edit intent: clarity
Original:  We also introduce an innovative polynomial time algorithm to construct a polygon with the said upper bound on its angles.
Revised:  We also propose an algorithm to construct a polygon with the said upper bound on its angles.
Edit intent: clarity
Original:  Constructing a simple polygon with angular constraint on a given set of points in the plane is highly applicable to the fields of robotics, path planning , image processing, GIS, etc .
Revised:  Constructing a simple polygon with angular constraint on a given set of points in the plane can be used for path planning in robotics .
Edit intent: clarity
Original: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" ) domains .
Revised: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and cholesterol affects organization of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" ) domains .
Edit intent: clarity
Original:  Cholesterol was highly enriched in the annulus around the TMD, but this was a short range effect that extended over (at most) 5-10\AA.
Revised:  Cholesterol was highly enriched in the annulus around the TMD, but this effect extended over (at most) 5-10\AA.
Edit intent: clarity
Original:  Shorter n-6 chains (Linoleic acid) were far less effective at displacing cholesterol from non-annular sites.
Revised:  Shorter n - 6 chains were far less effective at displacing cholesterol from non-annular sites.
Edit intent: coherence
Original: Weight pruning methods of deep neural networks (DNNs) have been demonstrated to achieve a good model pruning ratio without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration.
Revised: Weight pruning methods of deep neural networks have been demonstrated to achieve a good model pruning ratio without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration.
Edit intent: clarity
Original:  However, the pruning ratio (degree of sparsity) and GPU acceleration are limited (to less than 50\%) when accuracy needs to be maintained.
Revised:  However, the pruning ratio and GPU acceleration are limited (to less than 50\%) when accuracy needs to be maintained.
Edit intent: clarity
Original:  However, the pruning ratio (degree of sparsity) and GPU acceleration are limited (to less than 50\%) when accuracy needs to be maintained.
Revised:  However, the pruning ratio (degree of sparsity) and GPU acceleration are limited when accuracy needs to be maintained.
Edit intent: clarity
Original:  In this work, we overcome pruning ratio and GPU acceleration limitations by proposing a unified, systematic framework of structured weight pruning for DNNs, named ADAM-ADMM (Adaptive Moment Estimation-Alternating Direction Method of Multipliers) .
Revised:  In this work, we overcome pruning ratio and GPU acceleration limitations by proposing a unified, systematic framework of structured weight pruning for DNNs, named ADAM-ADMM .
Edit intent: coherence
Original:  The average speedups reach 2.77 times and 7.5 times when allowing a moderate accuracy loss of 2\%.
Revised:  The average speedups reach 2.77 x and 7.5 times when allowing a moderate accuracy loss of 2\%.
Edit intent: fluency
Original:  The average speedups reach 2.77 times and 7.5 times when allowing a moderate accuracy loss of 2\%.
Revised:  The average speedups reach 2.77 times and 7.5 x when allowing a moderate accuracy loss of 2\%.
Edit intent: clarity
Original: Weight pruning methods of deep neural networks have been demonstrated to achieve a good model pruning ratio without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration.
Revised: Weight pruning methods of DNNs have been demonstrated to achieve a good model pruning ratio without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration.
Edit intent: clarity
Original: Weight pruning methods of deep neural networks have been demonstrated to achieve a good model pruning ratio without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration.
Revised: Weight pruning methods of deep neural networks have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration.
Edit intent: clarity
Original:  In this work,we overcome pruning ratio and GPU acceleration limitations by proposing a unified, systematic framework of structured weight pruning for DNNs , named ADAM-ADMM . It is a framework that can be used to induce different types of structured sparsity, such as filter-wise, channel-wise, and shape-wise sparsity, as well non-structured sparsity.
Revised:  In this work,we overcome these limitations by proposing a unified, systematic framework of structured weight pruning for DNNs , named ADAM-ADMM . It is a framework that can be used to induce different types of structured sparsity, such as filter-wise, channel-wise, and shape-wise sparsity, as well non-structured sparsity.
Edit intent: clarity
Original:  In this work,we overcome pruning ratio and GPU acceleration limitations by proposing a unified, systematic framework of structured weight pruning for DNNs , named ADAM-ADMM . It is a framework that can be used to induce different types of structured sparsity, such as filter-wise, channel-wise, and shape-wise sparsity, as well non-structured sparsity.
Revised:  In this work,we overcome pruning ratio and GPU acceleration limitations by proposing a unified, systematic framework of structured weight pruning for DNNs . It is a framework that can be used to induce different types of structured sparsity, such as filter-wise, channel-wise, and shape-wise sparsity, as well non-structured sparsity.
Edit intent: clarity
Original:  Without loss of accuracy on the AlexNet model, we achieve 2.58 x and 3.65 x average measured speedup on two GPUs, clearly outperforming the prior work.
Revised:  Without loss of accuracy on the AlexNet model, we achieve 2.58 X and 3.65 x average measured speedup on two GPUs, clearly outperforming the prior work.
Edit intent: fluency
Original:  Without loss of accuracy on the AlexNet model, we achieve 2.58 x and 3.65 x average measured speedup on two GPUs, clearly outperforming the prior work.
Revised:  Without loss of accuracy on the AlexNet model, we achieve 2.58 x and 3.65 X average measured speedup on two GPUs, clearly outperforming the prior work.
Edit intent: clarity
Original:  The average speedups reach 2.77x and 7.5x when allowing a moderate accuracy loss of 2\%.
Revised:  The average speedups reach 2.77x and 7.5x when allowing a moderate ac-curacy loss of 2\%.
Edit intent: fluency
Original:  Our experiments on ResNet model and on other datasets like UCF101 and CIFAR-10 demonstrate the consistently higher performance of our framework.
Revised:  Our experiments on ResNet model and on other data sets like UCF101 and CIFAR-10 demonstrate the consistently higher performance of our framework.
Edit intent: clarity
Original: We consider an electorate composed of both honest and fake voters, aka sybils , and investigate the resilience of its democratic decision making against two types of sybil attacks: Sybils enforcing decisions on the honest voters, and sybils blocking decisions by the honest voters.
Revised: We consider an electorate composed of both genuine and fake voters, aka sybils , and investigate the resilience of its democratic decision making against two types of sybil attacks: Sybils enforcing decisions on the honest voters, and sybils blocking decisions by the honest voters.
Edit intent: clarity
Original: We consider an electorate composed of both honest and fake voters, aka sybils , and investigate the resilience of its democratic decision making against two types of sybil attacks: Sybils enforcing decisions on the honest voters, and sybils blocking decisions by the honest voters.
Revised: We consider an electorate composed of both honest and fake voters, aka sybils , and investigate the resilience of its democratic decision making against two types of sybil attacks: Sybils enforcing decisions on the honest voters, and sybils blocking decisions by the honest voters.
Edit intent: fluency
Original: We consider an electorate composed of both honest and fake voters, aka sybils , and investigate the resilience of its democratic decision making against two types of sybil attacks: Sybils enforcing decisions on the honest voters, and sybils blocking decisions by the honest voters.
Revised: We consider an electorate composed of both honest and fake voters, aka sybils , and investigate the resilience of its decision making against two types of sybil attacks: Sybils enforcing decisions on the honest voters, and sybils blocking decisions by the honest voters.
Edit intent: clarity
Original: We consider an electorate composed of both honest and fake voters, aka sybils , and investigate the resilience of its democratic decision making against two types of sybil attacks: Sybils enforcing decisions on the honest voters, and sybils blocking decisions by the honest voters.
Revised: We consider an electorate composed of both honest and fake voters, aka sybils , and investigate the resilience of its democratic decision making against two types of sybil attacks: Sybils enforcing decisions on the genuine voters, and sybils blocking decisions by the honest voters.
Edit intent: clarity
Original: We consider an electorate composed of both honest and fake voters, aka sybils , and investigate the resilience of its democratic decision making against two types of sybil attacks: Sybils enforcing decisions on the honest voters, and sybils blocking decisions by the honest voters.
Revised: We consider an electorate composed of both honest and fake voters, aka sybils , and investigate the resilience of its democratic decision making against two types of sybil attacks: Sybils enforcing decisions on the honest voters, and sybils blocking decisions by the genuine voters.
Edit intent: clarity
Original:  We follow Reality-aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by  -- the inability of sybils to change the status quo against the will of the majority of the honest voters, and  -- the ability of the honest voters to change the status quo against the will of the sybils.
Revised:  We follow Reality-Aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by -- the inability of sybils to change the status quo against the will of the majority of the honest voters, and -- the ability of the honest voters to change the status quo against the will of the sybils.
Edit intent: fluency
Original:  We follow Reality-aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by  -- the inability of sybils to change the status quo against the will of the majority of the honest voters, and  -- the ability of the honest voters to change the status quo against the will of the sybils.
Revised:  We follow Reality-aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by -- the inability of sybils to change the status quo against the will of the genuine voters, and -- the ability of the honest voters to change the status quo against the will of the sybils.
Edit intent: clarity
Original:  We follow Reality-aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by  -- the inability of sybils to change the status quo against the will of the majority of the honest voters, and  -- the ability of the honest voters to change the status quo against the will of the sybils.
Revised:  We follow Reality-aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by -- the inability of sybils to change the status quo against the will of the majority of the honest voters, and -- the ability of the genuine voters to change the status quo against the will of the sybils.
Edit intent: clarity
Original: We consider an electorate composed of both genuine and fake voters, aka sybils , and investigate the resilience of its decision making against two types of sybil attacks: Sybils enforcing decisions on the genuine voters, and sybils blocking decisions by the genuine voters.
Revised: Sybil attacks, in which fake or duplicate identities ( sybils , and investigate the resilience of its decision making against two types of sybil attacks: Sybils enforcing decisions on the genuine voters, and sybils blocking decisions by the genuine voters.
Edit intent: clarity
Original:  We follow Reality-Aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by sybil safety -- the inability of sybils to change the status quo against the will of the genuine voters , and sybil liveness -- the ability of the genuine voters to change the status quo against the will of the sybils.
Revised:  We follow Reality-Aware Social Choice , we use the status quo as the anchor of sybil resilience, which we characterize by sybil safety -- the inability of sybils to change the status quo against the will of the genuine voters , and sybil liveness -- the ability of the genuine voters to change the status quo against the will of the sybils.
Edit intent: coherence
Original:  We follow Reality-Aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by sybil safety -- the inability of sybils to change the status quo against the will of the genuine voters , and sybil liveness -- the ability of the genuine voters to change the status quo against the will of the sybils.
Revised:  We follow Reality-Aware Social Choice and use the status quo as the anchor of sybil resilience, characterized by sybil safety -- the inability of sybils to change the status quo against the will of the genuine voters , and sybil liveness -- the ability of the genuine voters to change the status quo against the will of the sybils.
Edit intent: coherence
Original:  We follow Reality-Aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by sybil safety -- the inability of sybils to change the status quo against the will of the genuine voters , and sybil liveness -- the ability of the genuine voters to change the status quo against the will of the sybils.
Revised:  We follow Reality-Aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by sybil safety -- the inability of sybils to change the status quo against the will of the genuine agents , and sybil liveness -- the ability of the genuine voters to change the status quo against the will of the sybils.
Edit intent: clarity
Original:  We follow Reality-Aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by sybil safety -- the inability of sybils to change the status quo against the will of the genuine voters , and sybil liveness -- the ability of the genuine voters to change the status quo against the will of the sybils.
Revised:  We follow Reality-Aware Social Choice and use the status quo as the anchor of sybil resilience, which we characterize by sybil safety -- the inability of sybils to change the status quo against the will of the genuine voters , and sybil liveness -- the ability of the genuine agents to change the status quo against the will of the sybils.
Edit intent: clarity
Original:  Thus, our goal here is to enhance social choice theory with effective group decision mechanisms for communities with bounded sybil penetration.
Revised:  Thus, our goal is to enhance social choice theory with effective group decision mechanisms for communities with bounded sybil penetration.
Edit intent: coherence
Original:  For two agents with arbitrary monotonic valuations , we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries.
Revised:  For two agents with arbitrary monotonic utilities , we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries.
Edit intent: clarity