wanyu/IteraTeR_v2 huggingface dataset:
Original:  We show that the achievable precision and accuracy of this method of analysis is similar to conventional , algorithm-based signal analysis methods, by demonstrating that , the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cram\'er-Rao lower bound.
Revised:  We show that the achievable precision and accuracy of this method of analysis is similar to conventional algorithm-based signal analysis methods, by demonstrating that , the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cram\'er-Rao lower bound.
Edit intent: fluency
Original:  We show that the achievable precision and accuracy of this method of analysis is similar to conventional , algorithm-based signal analysis methods, by demonstrating that , the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cram\'er-Rao lower bound.
Revised:  We show that the achievable precision and accuracy of this method of analysis is similar to conventional , algorithm-based signal analysis methods, by demonstrating that the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cram\'er-Rao lower bound.
Edit intent: fluency
Original:  We show that the achievable precision and accuracy of this method of analysis is similar to conventional , algorithm-based signal analysis methods, by demonstrating that , the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cram\'er-Rao lower bound.
Revised:  We show that the achievable precision and accuracy of this method of analysis is similar to conventional , algorithm-based signal analysis methods, by demonstrating that , the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cram\'er-Rao bound.
Edit intent: clarity
Original:  Furthermore, we demonstrate that autoencoder networks are able to achieve signal analysis, and, hence, parameter extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional techniques with equal precision.
Revised:  Furthermore, we demonstrate that autoencoder networks are able to achieve signal analysis, and, hence, parameter extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional techniques with similar precision.
Edit intent: clarity
Original: Baker's technique is a powerful tool for designing polynomial-time approximation schemes, in particular for all optimization problems expressible in monotone first-order logic .
Revised: We give a number of approximation metatheorems for monotone maximization problems expressible in monotone first-order logic .
Edit intent: clarity
Original: Baker's technique is a powerful tool for designing polynomial-time approximation schemes, in particular for all optimization problems expressible in monotone first-order logic .
Revised: Baker's technique is a powerful tool for designing polynomial-time approximation schemes, in particular for all optimization problems expressible in the first-order logic .
Edit intent: clarity
Original: We introduce a new class G of plane bipartite graphs and prove that each graph in G admits a proper square contact representation.
Revised: We introduce a new class G of bipartite plane graphs and prove that each graph in G admits a proper square contact representation.
Edit intent: fluency
Original:  We implemented a bottom-left-fill greedy placement procedure, using an optimized ordering of the segment overlap tests.
Revised:  We implemented a bottom-left-fill greedy placement procedure, using an optimised ordering of the segment overlap tests.
Edit intent: fluency
Original:  We implemented a bottom-left-fill greedy placement procedure, using an optimized ordering of the segment overlap tests.
Revised:  We implemented a bottom-left-fill greedy placement procedure, using an optimized ordering of the segments overlap tests.
Edit intent: fluency
Original:  It executes the bottom-left-fill algorithm for typical ESICUPdata sets in a few milliseconds, even when the rotation of the pieces is considered, and thus provides a suitable basis for integration in metaheuristics.
Revised:  It executes the bottom-left-fill algorithm for typical ESICUP data sets in a few milliseconds, even when the rotation of the pieces is considered, and thus provides a suitable basis for integration in metaheuristics.
Edit intent: fluency
Original:  It executes the bottom-left-fill algorithm for typical ESICUPdata sets in a few milliseconds, even when the rotation of the pieces is considered, and thus provides a suitable basis for integration in metaheuristics.
Revised:  It executes the bottom-left-fill algorithm for typical ESICUPdata sets in a few milliseconds, even when rotation of the pieces is considered, and thus provides a suitable basis for integration in metaheuristics.
Edit intent: fluency
Original:  It executes the bottom-left-fill algorithm for typical ESICUPdata sets in a few milliseconds, even when the rotation of the pieces is considered, and thus provides a suitable basis for integration in metaheuristics.
Revised:  It executes the bottom-left-fill algorithm for typical ESICUPdata sets in a few milliseconds, even when the rotation of the pieces is considered, and thus provides a suitable `building block' for integration in metaheuristics.
Edit intent: clarity
Original:  This contrasts with checking a conventional matrix for total positivity, which may be done in cubic time .
Revised:  This contrasts with checking a conventional matrix for total positivity, for which we provide a cubic time algorithm .
Edit intent: clarity
Original:  Prior to this work , parallel repetition from fortification was only known for projection games , and seemed hopeless for unique games .
Revised:  Prior to this work fortification was known for projection games , and seemed hopeless for unique games .
Edit intent: clarity
Original:  Prior to this work , parallel repetition from fortification was only known for projection games , and seemed hopeless for unique games .
Revised:  Prior to this work , parallel repetition from fortification was only known for projection games but seemed hopeless for unique games .
Edit intent: coherence
Original:  This relaxed-EQ method has been applied to several widely-used phase field models to highlight its effectiveness.
Revised:  Then we apply the relaxed-EQ method has been applied to several widely-used phase field models to highlight its effectiveness.
Edit intent: coherence
Original:  This relaxed-EQ method has been applied to several widely-used phase field models to highlight its effectiveness.
Revised:  This relaxed-EQ method to several widely-used phase field models to highlight its effectiveness.
Edit intent: clarity
Original: Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the training of physical networks, in particular thanks to the local updates of weights given by the internal dynamics of the system.
Revised: Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the training of physical networks, thanks to the local updates of weights given by the internal dynamics of the system.
Edit intent: coherence
Original:  However, the construction of such a hardware requires to make the algorithm compatible with the existing neuromorphic CMOS technology , which generally exploits digital communication between neurons and offers a limited amount of local memory.
Revised:  However, the construction of such a hardware requires to make the algorithm compatible with existing neuromorphic CMOS technology , which generally exploits digital communication between neurons and offers a limited amount of local memory.
Edit intent: fluency
Original:  However, the construction of such a hardware requires to make the algorithm compatible with the existing neuromorphic CMOS technology , which generally exploits digital communication between neurons and offers a limited amount of local memory.
Revised:  However, the construction of such a hardware requires to make the algorithm compatible with the existing neuromorphic CMOS technologies , which generally exploits digital communication between neurons and offers a limited amount of local memory.
Edit intent: fluency
Original:  However, the construction of such a hardware requires to make the algorithm compatible with the existing neuromorphic CMOS technology , which generally exploits digital communication between neurons and offers a limited amount of local memory.
Revised:  However, the construction of such a hardware requires to make the algorithm compatible with the existing neuromorphic CMOS technology , which generally exploit digital communication between neurons and offers a limited amount of local memory.
Edit intent: fluency
Original:  However, the construction of such a hardware requires to make the algorithm compatible with the existing neuromorphic CMOS technology , which generally exploits digital communication between neurons and offers a limited amount of local memory.
Revised:  However, the construction of such a hardware requires to make the algorithm compatible with the existing neuromorphic CMOS technology , which generally exploits digital communication between neurons and offer a limited amount of local memory.
Edit intent: fluency
Original:  We then extend our method to the training of models with binary activations and weights on MNIST, achieving an accuracy within 1\% of the full-precision reference for fully connected architectures and reaching the full-precision reference accuracy for the convolutional architecture .
Revised:  We then extend our method to the training of models with binary activations and weights on MNIST, achieving an accuracy within 1\% of the full-precision reference for fully connected architectures and reaching the full-precision accuracy for convolutional architectures .
Edit intent: fluency
Original:  Our extension of EP training to binary networks is consistent with the requirements of today's dynamic, brain-inspired hardware platforms and paves the way for very low-power end-to-end learning .
Revised:  Our extension of EP to binary networks is consistent with the requirements of today's dynamic, brain-inspired hardware platforms and paves the way for very low-power end-to-end learning .
Edit intent: clarity
Original:  Our extension of EP training to binary networks is consistent with the requirements of today's dynamic, brain-inspired hardware platforms and paves the way for very low-power end-to-end learning .
Revised:  Our extension of EP training to binary networks opens new solutions for on-chip learning and provides a compact framework for training BNNs end-to-end learning .
Edit intent: clarity
Original: We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semisupervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully connected layer (the classification layer) is  using Stochastic Gradient Descent (SGD).
Revised: We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semi-supervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully connected layer (the classification layer) is using Stochastic Gradient Descent (SGD).
Edit intent: fluency
Original: We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semisupervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully connected layer (the classification layer) is  using Stochastic Gradient Descent (SGD).
Revised: We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semisupervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully connected layer (the classification layer) is trained using Stochastic Gradient Descent (SGD).
Edit intent: fluency
Original:  In fact, as Hebbian learning is an unsupervised learning method, its potential lies in the possibility of training the internal layers of a DCNN without labeled examples .
Revised:  In fact, as Hebbian learning is an unsupervised learning method, its potential lies in the possibility of training the internal layers of a DCNN without labels .
Edit intent: clarity
Original:  We performed experiments on various object recognition datasets, in different regimes of sample efficiency, comparing our semi-supervised (Hebbian for internal layers + SGD for the final fully  layer) approach with end-to-end supervised backpropagation training .
Revised:  We performed experiments on various object recognition datasets, in different regimes of sample efficiency, comparing our semi-supervised (Hebbian for internal layers + SGD for the final fully connected layer) approach with end-to-end supervised backpropagation training .
Edit intent: clarity
Original:  The results show that, in regimes where the number of available labeled samples is low, our semi-supervised approach outperforms full backpropagation in almost all the cases.
Revised:  The results show that, in regimes where the number of available labeled samples is low, our semi-supervised approach outperforms the other approaches in almost all the cases.
Edit intent: clarity
Original: This paper develops a practical numerical method for locating the zeros and poles of a meromorphic function , based upon the generalized argument principle.
Revised: On the basis of the generalized argument principle, here we develop a numerical scheme for locating zeros and poles of a meromorphic function , based upon the generalized argument principle.
Edit intent: clarity
Original:  A novel approach to the error estimation is also proposed . Numerical examples are given to illustrate the effectiveness and practicability, with emphasis on the potential applications in plasma physics.
Revised:  A novel feature of this algorithm is the ability to estimate the error level automatically . Numerical examples are given to illustrate the effectiveness and practicability, with emphasis on the potential applications in plasma physics.
Edit intent: clarity
Original:  A novel approach to the error estimation is also proposed . Numerical examples are given to illustrate the effectiveness and practicability, with emphasis on the potential applications in plasma physics.
Revised:  A novel approach to the error estimation is also proposed . Numerical examples are also presented, with an emphasis on potential applications to plasma physics.
Edit intent: clarity
Original:  The challenge features two tracks, one focuses on using cough sounds, and the other on using a collection of breath, sustained vowel phonation, and number counting speech recordings.
Revised:  The challenge features two tracks, one focusing on cough sounds, and the other on using a collection of breath, sustained vowel phonation, and number counting speech recordings.
Edit intent: fluency
Original:  In this paper, we introduce the challenge and provide a detailed description of the dataset, task, and present a baseline system for the task.
Revised:  In this paper, we introduce the challenge and provide a detailed description of the task, and present a baseline system for the task.
Edit intent: coherence
Original: Inside a cell, heterotypic proteins assemble in an inhomogeneous, crowded systems where the abundance of these proteins numbers vary with cell types.
Revised: Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins numbers vary with cell types.
Edit intent: fluency
Original: Inside a cell, heterotypic proteins assemble in an inhomogeneous, crowded systems where the abundance of these proteins numbers vary with cell types.
Revised: Inside a cell, heterotypic proteins assemble in an inhomogeneous, crowded systems where the abundance of these proteins vary with cell types.
Edit intent: clarity
Original:  While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to  solved because of their dynamic associations with one another.
Revised:  While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another.
Edit intent: fluency
Original:  Yet, it is possible to infer these protein complexes through  physical model.
Revised:  Yet, it is possible to infer these protein complexes through a physical model.
Edit intent: fluency
Original:  However, it is often not clear to physicists what kind of data from biology is necessary for such  modeling endeavor.
Revised:  However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor.
Edit intent: fluency
Original:  We inferred the chemical potential, that dictate the particle number distribution of each protein subunit, from the knowledge of protein abundance from experimental data.
Revised:  We inferred the chemical potential, that dictates the particle number distribution of each protein subunit, from the knowledge of protein abundance from experimental data.
Edit intent: fluency
Original:  Guided by the maximum entropy principle, we formulate an inverse statistical mechanical method to infer the distribution  particle numbers from the data of protein abundance as chemical potentials for a grand canonical multi-component mixture.
Revised:  Guided by the maximum entropy principle, we formulate an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multi-component mixture.
Edit intent: fluency
Original:  The complexity of hierarchical clusters varies with the relative protein abundance of each subunits in distinctive cell types such as lung, heart, and brain.
Revised:  The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain.
Edit intent: fluency
Original:  When the crowding content increases, we observed that crowding stabilizes emergent clusters that does not exist in dilute conditions.
Revised:  When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions.
Edit intent: fluency
Original:  We therefore proposed a testable hypothesis that the hierarchical complexity of protein clusters in a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.
Revised:  We , therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters in a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.
Edit intent: fluency
Original:  We therefore proposed a testable hypothesis that the hierarchical complexity of protein clusters in a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.
Revised:  We therefore proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.
Edit intent: clarity
Original: Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse and uninformative rewards.
Revised: Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.
Edit intent: fluency
Original:  However, RS  relies on manually engineered shaping-reward functions whose construction is typically time-consuming and error-prone.
Revised:  However, RS typically relies on manually engineered shaping-reward functions whose construction is typically time-consuming and error-prone.
Edit intent: fluency
Original:  However, RS  relies on manually engineered shaping-reward functions whose construction is typically time-consuming and error-prone.
Revised:  However, RS relies on manually engineered shaping-reward functions whose construction is time-consuming and error-prone.
Edit intent: clarity
Original:  One agent learns both which states to add shaping rewards and their optimal magnitudes and the other agent  learns the optimal policy for the task using the shaped rewards.
Revised:  One agent learns both which states to add shaping rewards and their optimal values while the other agent learns the optimal policy for the task using the shaped rewards.
Edit intent: clarity
Original:  One agent learns both which states to add shaping rewards and their optimal magnitudes and the other agent  learns the optimal policy for the task using the shaped rewards.
Revised:  One agent learns both which states to add shaping rewards and their optimal magnitudes and the other agent learns the optimal policy for the task using these shaped rewards.
Edit intent: fluency
Original:  We introduce Reinforcement Learning Optimal Shaping Algorithm (ROSA), an automated RS framework in which the shaping-reward function is constructed in a novel Markov game between two agents.
Revised:  We introduce Reinforcement Learning Optimising Shaping Algorithm (ROSA), an automated RS framework in which the shaping-reward function is constructed in a novel Markov game between two agents.
Edit intent: clarity
Original:  In this paper, we introduce a new generation of MARL learners that can handle nonzero-sum payoff structures and continuous settings.
Revised:  In this paper, we introduce anew generation of MARL learners that can handle nonzero-sum payoff structures and continuous settings.
Edit intent: fluency
Original:  We prove theoretically our learning method, SPot-AC, enables independent agents to learn Nash equilibrium strategies in polynomial time  .
Revised:  We prove theoretically our learning method, SPot-AC, en-ables independent agents to learn Nash equilibrium strategies in polynomial time .
Edit intent: fluency
Original:  In this paper, we introduce anew generation of MARL learners that can handle nonzero-sum payoff structures and continuous settings.
Revised:  In this paper, we introduce a new generation of MARL learners that can handle nonzero-sum payoff structures and continuous settings.
Edit intent: fluency
Original:  We prove theoretically our learning method, SPot-AC, en-ables independent agents to learn Nash equilibrium strategies in polynomial time.
Revised:  We prove theoretically our learning method, SPot-AC, enables independent agents to learn Nash equilibrium strategies in polynomial time.
Edit intent: fluency
Original:  The access  the colors of the points (or even the points themselves) is provided indirectly via various queries (such as nearest neighbor, or separation queries).
Revised:  The access to the colors of the points (or even the points themselves) is provided indirectly via various queries (such as nearest neighbor, or separation queries).
Edit intent: fluency
Original:  On the other hand, we show that some computational assumption is likely needed to construct pseudorandom states, by proving that pseudorandom states do not exist if BQP = PP.
Revised:  On the other hand, we show that some computational complexity assumption is needed to construct pseudorandom states, by proving that pseudorandom states do not exist if BQP = PP.
Edit intent: clarity