wanyu/IteraTeR_v2 huggingface dataset:
Original:  fast and requires significantly less processing time than fusion methods using multi-object filtering densities, and ii) achieves better tracking accuracy by considering tracking errors measured by the Optimal Sub-Pattern Assignment (OSPA) metric over several scans rather than a single scan . Numerical experiments demonstrate the real-time capability of our proposed solution , in computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios  .
Revised:  fast and requires significantly less processing time than fusion methods using multi-object filtering densities, and ii) achieves better tracking accuracy by considering tracking errors measured by the Optimal Sub-Pattern Assignment (OSPA) tracking errors over several scans rather than a single scan . Numerical experiments demonstrate the real-time capability of our proposed solution , in computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios .
Edit intent: clarity
Original:  fast and requires significantly less processing time than fusion methods using multi-object filtering densities, and ii) achieves better tracking accuracy by considering tracking errors measured by the Optimal Sub-Pattern Assignment (OSPA) metric over several scans rather than a single scan . Numerical experiments demonstrate the real-time capability of our proposed solution , in computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios  .
Revised:  fast and requires significantly less processing time than fusion methods using multi-object filtering densities, and ii) achieves better tracking accuracy by considering tracking errors measured by the Optimal Sub-Pattern Assignment (OSPA) metric over several scans rather than a single scan . Numerical experiments demonstrate our proposed solution , in computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios .
Edit intent: clarity
Original:  fast and requires significantly less processing time than fusion methods using multi-object filtering densities, and ii) achieves better tracking accuracy by considering tracking errors measured by the Optimal Sub-Pattern Assignment (OSPA) metric over several scans rather than a single scan . Numerical experiments demonstrate the real-time capability of our proposed solution , in computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios  .
Revised:  fast and requires significantly less processing time than fusion methods using multi-object filtering densities, and ii) achieves better tracking accuracy by considering tracking errors measured by the Optimal Sub-Pattern Assignment (OSPA) metric over several scans rather than a single scan . Numerical experiments demonstrate the real-time capability of our proposed solution 's real-time computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios .
Edit intent: clarity
Original:  We often  need to compare different models.
Revised:  We often , therefore, need to compare different models.
Edit intent: coherence
Original:  But there is a lack of rigorous methods to compare different models a priori.
Revised:  There is, however, a lack of rigorous methods to compare different models a priori.
Edit intent: coherence
Original:  Using well-developed and understood concepts from simplicial geometry we are able to define a distance based on the persistent homology applied to the simplicial complexes that captures the model structure .
Revised:  Using well-developed concepts from simplicial geometry we are able to define a distance based on the persistent homology applied to the simplicial complexes that captures the model structure .
Edit intent: coherence
Original:  Using well-developed and understood concepts from simplicial geometry we are able to define a distance based on the persistent homology applied to the simplicial complexes that captures the model structure .
Revised:  Using well-developed and understood concepts from simplicial geometry we are able to define a distance based on the persistent homology of the simplicial complexes that captures the model structure .
Edit intent: clarity
Original:  We then expand this, and move from a distance between simplicial complexes to studying equivalences between models in order to determine their functional relatedness .
Revised:  We then expand on this measure of distance between simplicial complexes to studying equivalences between models in order to determine their functional relatedness .
Edit intent: clarity
Original:  We then expand this, and move from a distance between simplicial complexes to studying equivalences between models in order to determine their functional relatedness .
Revised:  We then expand this, and move from a distance between simplicial complexes to study the concept of equivalence between models in order to determine their functional relatedness .
Edit intent: clarity
Original:  Using well-developed concepts from simplicial algebraic topology, we are able to define a distance based on the persistent homology of the simplicial complexes.
Revised:  Using well-developed concepts from simplicial algebraic topology, we define a distance based on the persistent homology of the simplicial complexes.
Edit intent: coherence
Original:  We then expand on this measure of distance between simplicial complexes to study the concept of equivalence between models in order to determine their conceptual similarity .
Revised:  We then expand on this measure of model distance to study the concept of equivalence between models in order to determine their conceptual similarity .
Edit intent: clarity
Original:  We then expand on this measure of distance between simplicial complexes to study the concept of equivalence between models in order to determine their conceptual similarity .
Revised:  We then expand on this measure of distance between simplicial complexes to study the concept of model equivalence in order to determine their conceptual similarity .
Edit intent: fluency
Original:  We then expand on this measure of distance between simplicial complexes to study the concept of equivalence between models in order to determine their conceptual similarity .
Revised:  We then expand on this measure of distance between simplicial complexes to study the concept of equivalence between models in order to determine the conceptual similarity of models .
Edit intent: clarity
Original: In this paper, we propose a fully local index named "sensitivity" for each neuron to control chaoticity or gradient globally in a neural network (NN) , and also propose a learning method to adjust it named "sensitivity adjustment learning (SAL)".
Revised: Here, we introduce a fully local index named "sensitivity" for each neuron to control chaoticity or gradient globally in a neural network (NN) , and also propose a learning method to adjust it named "sensitivity adjustment learning (SAL)".
Edit intent: clarity
Original: In this paper, we propose a fully local index named "sensitivity" for each neuron to control chaoticity or gradient globally in a neural network (NN) , and also propose a learning method to adjust it named "sensitivity adjustment learning (SAL)".
Revised: In this paper, we propose a fully local index named "sensitivity" for each neuron to control chaoticity or gradient globally in a neural network (NN) . We also propose a learning method to adjust it named "sensitivity adjustment learning (SAL)".
Edit intent: coherence
Original:  By adjusting it around 1.0  , information transmission in the neuron changes to  moderate without shrinking or expanding for both forward and backward computations , and the information transmission through a layer of neurons also moderate when the weights and inputs are random.
Revised:  By adjusting it around 1.0 , information transmission in the neuron changes to be moderate without shrinking or expanding for both forward and backward computations , and the information transmission through a layer of neurons also moderate when the weights and inputs are random.
Edit intent: clarity
Original:  By adjusting it around 1.0  , information transmission in the neuron changes to  moderate without shrinking or expanding for both forward and backward computations , and the information transmission through a layer of neurons also moderate when the weights and inputs are random.
Revised:  By adjusting it around 1.0 , information transmission in the neuron changes to moderate without shrinking or expanding for both forward and backward computations . That results in moderate information transmission through a layer of neurons also moderate when the weights and inputs are random.
Edit intent: coherence
Original:  By adjusting it around 1.0  , information transmission in the neuron changes to  moderate without shrinking or expanding for both forward and backward computations , and the information transmission through a layer of neurons also moderate when the weights and inputs are random.
Revised:  By adjusting it around 1.0 , information transmission in the neuron changes to moderate without shrinking or expanding for both forward and backward computations , and the information transmission through a layer of neurons when the weights and inputs are random.
Edit intent: clarity
Original:  Therefore, it can be used in a recurrent NN (RNN) to control chaoticity of its global network dynamics, and also can be used to solve the vanishing gradient problem in error back propagation (BP) learning in a deep feedforward NN (DFNN) or an RNN with long-term dependency.
Revised:  Therefore, SAL can control the chaoticity of the network dynamics in a recurrent NN (RNN) to control chaoticity of its global network dynamics, and also can be used to solve the vanishing gradient problem in error back propagation (BP) learning in a deep feedforward NN (DFNN) or an RNN with long-term dependency.
Edit intent: coherence
Original:  Therefore, it can be used in a recurrent NN (RNN) to control chaoticity of its global network dynamics, and also can be used to solve the vanishing gradient problem in error back propagation (BP) learning in a deep feedforward NN (DFNN) or an RNN with long-term dependency.
Revised:  Therefore, it can be used in a recurrent NN (RNN) . It can also solve the vanishing gradient problem in error back propagation (BP) learning in a deep feedforward NN (DFNN) or an RNN with long-term dependency.
Edit intent: coherence
Original:  Therefore, it can be used in a recurrent NN (RNN) to control chaoticity of its global network dynamics, and also can be used to solve the vanishing gradient problem in error back propagation (BP) learning in a deep feedforward NN (DFNN) or an RNN with long-term dependency.
Revised:  Therefore, it can be used in a recurrent NN (RNN) to control chaoticity of its global network dynamics, and also can be used to solve the vanishing gradient problem in error backpropagation (BP) learning in a deep feedforward NN (DFNN) or an RNN with long-term dependency.
Edit intent: fluency
Original:  Therefore, it can be used in a recurrent NN (RNN) to control chaoticity of its global network dynamics, and also can be used to solve the vanishing gradient problem in error back propagation (BP) learning in a deep feedforward NN (DFNN) or an RNN with long-term dependency.
Revised:  Therefore, it can be used in a recurrent NN (RNN) to control chaoticity of its global network dynamics, and also can be used to solve the vanishing gradient problem in error back propagation (BP) learning in a deep feedforward NN or an RNN with long-term dependency.
Edit intent: clarity
Original:  We demonstrated that when SAL is applied to an RNN with small random weights, the sum of log-sensitivities is almost equivalent to the maximum Lyapunov exponent until it reaches 0.0 regardless of the network architecture .
Revised:  We demonstrated that when SAL is applied to an RNN with small random weights, the sum of log-sensitivities is almost equivalent to the maximum Lyapunov exponent until it reaches 0.0 .
Edit intent: clarity
Original:  We also show that SAL works with BP or BPTT  to avoid the vanishing gradient problem in a 300-layer DFNN or an RNN solving a problem with 300-step lag between the first input and the output.
Revised:  We also show that SAL works with BP or BPTT to avoid the vanishing gradient problem in a 300-layer DFNN or an RNN that learns a problem with 300-step lag between the first input and the output.
Edit intent: clarity
Original:  We also show that SAL works with BP or BPTT  to avoid the vanishing gradient problem in a 300-layer DFNN or an RNN solving a problem with 300-step lag between the first input and the output.
Revised:  We also show that SAL works with BP or BPTT to avoid the vanishing gradient problem in a 300-layer DFNN or an RNN solving a problem with a lag of 300 steps between the first input and the output.
Edit intent: clarity
Original:  Compared with the fine manual tuning of the spectral radius of  weight matrix before learning, the learning performance was quite better due to the continuous nonlinear learning nature of SAL, which prevented the loss of sensitivity .
Revised:  Compared with manually fine-tuning the spectral radius of weight matrix before learning, the learning performance was quite better due to the continuous nonlinear learning nature of SAL, which prevented the loss of sensitivity .
Edit intent: fluency
Original:  Compared with the fine manual tuning of the spectral radius of  weight matrix before learning, the learning performance was quite better due to the continuous nonlinear learning nature of SAL, which prevented the loss of sensitivity .
Revised:  Compared with the fine manual tuning of the spectral radius of the weight matrix before learning, the learning performance was quite better due to the continuous nonlinear learning nature of SAL, which prevented the loss of sensitivity .
Edit intent: fluency
Original:  Compared with the fine manual tuning of the spectral radius of  weight matrix before learning, the learning performance was quite better due to the continuous nonlinear learning nature of SAL, which prevented the loss of sensitivity .
Revised:  Compared with the fine manual tuning of the spectral radius of weight matrix before learning, SAL's continuous nonlinear learning nature of SAL, which prevented the loss of sensitivity .
Edit intent: clarity
Original:  Compared with the fine manual tuning of the spectral radius of  weight matrix before learning, the learning performance was quite better due to the continuous nonlinear learning nature of SAL, which prevented the loss of sensitivity .
Revised:  Compared with the fine manual tuning of the spectral radius of weight matrix before learning, the learning performance was quite better due to the continuous nonlinear learning nature prevents loss of sensitivities during learning, resulting in a significant improvement in learning performance .
Edit intent: clarity
Original:  We propose a new approach which unveils potential effects of data sharing in impactful real-life situations.
Revised:  In order to unveil such usages, we propose an approach which focuses on the effects of data sharing in impactful real-life situations.
Edit intent: clarity
Original:  The approach relies on three components: (1) a set of concepts with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors used to analyze users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated for each situation.
Revised:  The approach relies on three components: (1) a set of visual objects with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors used to analyze users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated for each situation.
Edit intent: clarity
Original:  The approach relies on three components: (1) a set of concepts with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors used to analyze users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated for each situation.
Revised:  The approach relies on three components: (1) a set of concepts with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors for mining users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated for each situation.
Edit intent: clarity
Original:  The approach relies on three components: (1) a set of concepts with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors used to analyze users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated for each situation.
Revised:  The approach relies on three components: (1) a set of concepts with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors used to analyze users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated per situation.
Edit intent: fluency
Original:  LERVUP exploits a new image descriptor which aggregates concept ratings and object detections at user level .
Revised:  LERVUP exploits a new image descriptor which aggregates object ratings and object detections at user level .
Edit intent: clarity
Original:  It also uses an attention mechanism to boost the detections of highly-rated concepts to prevent them from being overwhelmed by low-rated ones.
Revised:  It also uses an attention mechanism which boosts highly-rated concepts to prevent them from being overwhelmed by low-rated ones.
Edit intent: clarity
Original:  It also uses an attention mechanism to boost the detections of highly-rated concepts to prevent them from being overwhelmed by low-rated ones.
Revised:  It also uses an attention mechanism to boost the detections of highly-rated objects to prevent them from being overwhelmed by low-rated ones.
Edit intent: clarity
Original:  This finding indicates that providing meaningful automatic situation-related feedback about the effects of data sharing is feasible .
Revised:  A practical implementation of the approach in a mobile app which raises user awareness about shared data usage is also discussed .
Edit intent: clarity
Original: Tactile sensing has seen  rapid adoption with the advent of vision-based tactile sensors.
Revised: Tactile sensing has seen a rapid adoption with the advent of vision-based tactile sensors.
Edit intent: fluency
Original:  Vision-based tactile sensors provide high resolution, compact and inexpensive data to perform precise in-hand manipulation and robot-human interaction.
Revised:  Vision-based tactile sensors provide high resolution, compact and inexpensive data to perform precise in-hand manipulation and human-robot interaction.
Edit intent: fluency
Original:  In this paper, we built the first fully general optical tactile simulation system for GelSight using physics-based rendering techniques.
Revised:  In this paper, we built the first fully general optical tactile simulation system for a GelSight sensor using physics-based rendering techniques.
Edit intent: clarity
Original: A similar distinction exists in the United States, where Northeastern states have been the most successful in the country  .
Revised: A similar distinction exists in the United States, in which Northeastern states have been the most successful in the country .
Edit intent: clarity
Original:  Anderson-like accelerations are widely used in electronic structure calculations to improve a fixed point algorithm for finding Self Consistent Field (SCF) solutions of Hartree-Fock models.
Edit intent: coherence
Original:  In this paper, it is showed how to apply this type of acceleration to Sequential Residual Methods.
Revised:  In this paper, we suggest to employ a variation of the Sequential Secant Method in order to accelerate Sequential Residual Methods.
Edit intent: clarity
Original: Shared-task campaigns such as NIST TREC select documents to judge by pooling rankings from many participant systems.
Revised: Research community evaluations in information retrieval, such as NIST TREC select documents to judge by pooling rankings from many participant systems.
Edit intent: clarity
Original:  Therefore , the quality of the test collection greatly depends on the number of participants and the quality of  submitted runs.
Revised:  Therefore , the quality of the resulting test collection thus greatly depends on the number of participants and the quality of submitted runs.
Edit intent: clarity
Original:  Therefore , the quality of the test collection greatly depends on the number of participants and the quality of  submitted runs.
Revised:  Therefore , the quality of the test collection greatly depends on the number of participating teams and the quality of submitted runs.
Edit intent: clarity
Original:  Therefore , the quality of the test collection greatly depends on the number of participants and the quality of  submitted runs.
Revised:  Therefore , the quality of the test collection greatly depends on the number of participants and the quality of their submitted runs.
Edit intent: clarity
Original:  In this work, we investigate  i) how the number of participants, coupled with other factors, affects the quality of a test collection;
Revised:  In this work, we investigate : i) how the number of participants, coupled with other factors, affects the quality of a test collection;
Edit intent: fluency
Original:  and ii) whether the quality of a test collection can be inferred prior to collecting relevance judgments . Experiments on six TREC collections demonstrate that the required number of participants to construct a high-quality test collection varies significantly across different test collectionsdue to a variety of factors.
Revised:  and ii) whether the quality of a test collection can be inferred prior to collecting relevance judgments from human assessors. Experiments conducted on six TREC collections demonstrate that the required number of participants to construct a high-quality test collection varies significantly across different test collectionsdue to a variety of factors.
Edit intent: clarity
Original: Diseases spread over temporal networks of contacts between individuals.
Revised: Diseases spread over temporal networks of interaction events between individuals.
Edit intent: clarity
Original:  Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusions .
Revised:  Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusion .
Edit intent: fluency
Original:  We also provide a regularly updated project page on : URL
Revised:  We also provide a regularly updated project page : URL
Edit intent: fluency
Original: Many purely neural network based speech separation approaches have been proposed that greatly improve objective assessment scores, but they often introduce nonlinear distortions that are harmful to  automatic speech recognition (ASR)  . Minimum variance distortionless response (MVDR) filters strive to remove nonlinear distortions, however, these approaches either are not optimal for removing residual (linear) noise, or they are unstable when used jointly with neural networks.
Revised: Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are harmful to automatic speech recognition (ASR) . Minimum variance distortionless response (MVDR) filters strive to remove nonlinear distortions, however, these approaches either are not optimal for removing residual (linear) noise, or they are unstable when used jointly with neural networks.
Edit intent: coherence