wanyu/IteraTeR_v2 huggingface dataset:
Original:  Unlike prior deep quantization methods where the codewords for quantization are learned from data, we propose a novel scheme using predefined orthonormal vectors as codewords, which aims to enhance the quantization informativeness and reduce the codewords' redundancy.
Revised:  Unlike prior deep quantization methods where the codewords for quantization are learned from data, we propose a novel scheme that uses predefined orthonormal vectors as codewords, which aims to enhance the quantization informativeness and reduce the codewords' redundancy.
Edit intent: fluency
Original:  Unlike prior deep quantization methods where the codewords for quantization are learned from data, we propose a novel scheme using predefined orthonormal vectors as codewords, which aims to enhance the quantization informativeness and reduce the codewords' redundancy.
Revised:  Unlike prior deep quantization methods where the codewords for quantization are learned from data, we propose a novel scheme using predefined orthonormal vectors as codewords, to enhance the quantization informativeness and reduce the codewords' redundancy.
Edit intent: coherence
Original:  To make the most of the discriminative information, we design a tailored loss function that maximizes the identity discriminability in each quantization subspace for both the quantized and the original features.
Revised:  To make the most of the discriminative information, we design a tailored loss function maximizes discriminability among identities in each quantization subspace for both the quantized and the original features.
Edit intent: clarity
Original:  To make the most of the discriminative information, we design a tailored loss function that maximizes the identity discriminability in each quantization subspace for both the quantized and the original features.
Revised:  To make the most of the discriminative information, we design a tailored loss function that maximizes the identity discriminability in each quantization subspace for both the quantized and original features.
Edit intent: fluency
Original:  Furthermore, an entropy-based regularization term is imposed to reduce the quantization error.
Revised:  An entropy-based regularization term is imposed to reduce the quantization error.
Edit intent: coherence
Original:  We conduct experiments on three commonly-used datasets under the settings of both single-domain and cross-domain retrieval.
Revised:  Experiments were conducted on three commonly-used datasets under the settings of both single-domain and cross-domain retrieval.
Edit intent: clarity
Original:  We conduct experiments on three commonly-used datasets under the settings of both single-domain and cross-domain retrieval.
Revised:  We conduct experiments on three commonly-used datasets for both single- and cross-domain retrieval.
Edit intent: clarity
Original:  It shows that the proposed method outperforms all the compared deep hashing/quantization methods  under both settings with significant superiority.
Revised:  The proposed method outperformed all the deep hashing/quantization methods under both settings with significant superiority.
Edit intent: clarity
Original:  Besides, our model's better generalization ability than deep hashing models indicates that it is more suitable for scalable face image retrieval tasks .
Revised:  Besides, our model's better generalization ability than deep hashing models indicates that it is more suitable for scalable face image retrieval than deep hashing methods .
Edit intent: clarity
Original: The research of visual signal compression has a long history .
Revised: The visual signal compression has a long history .
Edit intent: clarity
Original: The research of visual signal compression has a long history .
Revised: The research of visual signal compression is a long-standing problem .
Edit intent: clarity
Original:  Fueled by  deep learning, exciting progress has been made recently.
Revised:  Fueled by the recent advances of deep learning, exciting progress has been made recently.
Edit intent: clarity
Original: Anti-unification in logic programming refers to the process of capturing common syntactic structure among given goals, computing as such a single new goal that is more general and hence called a generalization of the given goals.
Revised: Anti-unification in logic programming refers to the process of capturing common syntactic structure among given goals, computing a single new goal that is more general and hence called a generalization of the given goals.
Edit intent: clarity
Original: Anti-unification in logic programming refers to the process of capturing common syntactic structure among given goals, computing as such a single new goal that is more general and hence called a generalization of the given goals.
Revised: Anti-unification in logic programming refers to the process of capturing common syntactic structure among given goals, computing as such a single new goal that is more general called a generalization of the given goals.
Edit intent: coherence
Original:  Finding an arbitrary common generalization for two goals is trivial, but looking for those common generalizations that are either as large as possible (called largest common generalizations) or as specific as possible (called most specific generalizations) is a non-trivial optimization problem, in particular when goals are considered to be unordered sets of atoms.
Revised:  Finding an arbitrary common generalization for two goals is trivial, but looking for those common generalizations that are either as large as possible (called largest common generalizations) or as specific as possible (called most specific generalizations) is a non-trivial optimization problem, in particular when goals are considered to be unordered sets of atoms.
Edit intent: fluency
Original: Reconstructing the structure of the soil using non invasive techniques is a very relevant problem in many scientific fields, like geophysics and archaeology.
Revised: Reconstructing the structure of the soil using non-invasive techniques is a very relevant problem in many scientific fields, like geophysics and archaeology.
Edit intent: fluency
Original:  Regularization methods aim at reducing this sensitivity .
Revised:  Regularization methods substitute the original ill-posed problem with a well-posed one whose solution is an accurate approximation of the desired one .
Edit intent: clarity
Original:  The minimized functional is made up by the sum of two terms, the data fitting term ensures that the recovered solution fits the measured data, while the regularization term enforces sparsity on the Laplacian of the solution.
Revised:  The minimized functional is made up by the sum of two term: the data fitting term ensures that the recovered solution fits the measured data, while the regularization term enforces sparsity on the Laplacian of the solution.
Edit intent: fluency
Original:  This is achieved by minimizing an \ell_2 - \ell_q functional with 0 < q \leq 2. Since the functional we wish to minimize is nonconvex , we show that the variational problem admits a solution.
Revised:  This is achieved by minimizing an \ell_2 - \ell_q functional with 0 < q \leq 2. Since the functional we wish to minimize is non-convex , we show that the variational problem admits a solution.
Edit intent: fluency
Original: 6M dataset for both single and interacting hands across all metrics .
Revised: 6M dataset .
Edit intent: clarity
Original: The BLUES function method is  applied to the epidemiological SIRS model with vaccination , described by coupled nonlinear ordinary differential equations .
Revised: The BLUES function method is applied to the epidemiological SIRS model with vaccination .
Edit intent: coherence
Original:  In contrast with these methods, the BLUES iteration converges rapidly, globally, and captures the exact asymptotic behaviour for long times.
Revised:  In contrast with these methods, the BLUES iteration converges rapidly, globally, and captures the exact asymptotic behavior for long times.
Edit intent: fluency
Original:  The new approach is based on expanding a generic spatial discretization, which is not necessarily positivity preserving, by introducing a space-time Lagrange multiplier coupled with Karush-Kuhn-Tucker (KKT) conditions to preserve positivity.
Revised:  The new approach introduces a space-time Lagrange multiplier coupled with Karush-Kuhn-Tucker (KKT) conditions to preserve positivity.
Edit intent: coherence
Original:  The new approach is based on expanding a generic spatial discretization, which is not necessarily positivity preserving, by introducing a space-time Lagrange multiplier coupled with Karush-Kuhn-Tucker (KKT) conditions to preserve positivity.
Revised:  The new approach is based on expanding a generic spatial discretization, which is not necessarily positivity preserving, by introducing a space-time Lagrange multiplier to enforce the positivity with the Karush-Kuhn-Tucker (KKT) conditions to preserve positivity.
Edit intent: clarity
Original:  We establish some stability results under a general setting, and carry out error estimates for first-order versions of our approach and linear parabolic equation.
Revised:  We establish stability results for our first- and second-order schemes under a general setting, and carry out error estimates for first-order versions of our approach and linear parabolic equation.
Edit intent: clarity
Original:  In this work, we draw on insights from the social sciences and humanistic studies brought into the realm of causal modeling and constrained optimization, and develop a novel algorithmic framework for tackling pre-existing real-world disparities.
Revised:  In this work, we draw on insights from the social sciences brought into the realm of causal modeling and constrained optimization, and develop a novel algorithmic framework for tackling pre-existing real-world disparities.
Edit intent: coherence
Original:  We demonstrate impact remediation with a real-world case study and compare our disaggregated approach to an existing state-of-the-art approach, comparing its structure and resulting policy recommendations.
Revised:  We demonstrate impact remediation with a hypothetical case study and compare our disaggregated approach to an existing state-of-the-art approach, comparing its structure and resulting policy recommendations.
Edit intent: clarity
Original:  In various application domains, including computer vision, natural language processing and audio/speech signal processing, a wide range of features where engineered through decades of research efforts.
Revised:  In audio/speech signal processing, a wide range of features where engineered through decades of research efforts.
Edit intent: clarity
Original:  As it turns out, learning to predict such features  has proven to be a particularly relevant pretext task leading to building useful self-supervised representations that prove to be effective for downstream tasks.
Revised:  As it turns out, learning to predict such features has proven to be a particularly relevant pretext task , leading to useful self-supervised representations that prove to be effective for downstream tasks.
Edit intent: clarity
Original:  As it turns out, learning to predict such features  has proven to be a particularly relevant pretext task leading to building useful self-supervised representations that prove to be effective for downstream tasks.
Revised:  As it turns out, learning to predict such features has proven to be a particularly relevant pretext task leading to building useful self-supervised representations which prove to be effective for downstream tasks.
Edit intent: fluency
Original:  However, methods and common practices for combining such pretext tasks , where each task targets a different group of features for better performance on the downstream task have not been explored and understood properly.
Revised:  However, methods and common practices for combining such pretext tasks for better performance on the downstream task have not been explored and understood properly.
Edit intent: clarity
Original:  The method we propose estimates properly calibrated weights for the partial losses corresponding to the considered pretext tasks during the self-supervised training process.
Revised:  The method we propose estimates calibrated weights for the partial losses corresponding to the considered pretext tasks during the self-supervised training process.
Edit intent: clarity
Original:  We show that these modifications consistently improve sound event classification in all cases considered , without adding any (or adding very few) trainable parameters, which makes them an appealing alternative to conventional pooling layers.
Revised:  We show that these modifications consistently improve sound event classification in all cases considered , without adding any (or adding very few) trainable parameters, which makes these methods an appealing alternative to conventional pooling layers.
Edit intent: clarity
Original:  We illustrate that this semi-sparsity can be identified into a generalized L_0-norm minimization in higher-order gradient domains, thereby giving rise to a new `` feature-aware '' filtering method with a powerful simultaneous-fitting ability in both sparse features (singularities and sharpening edges) and non-sparse regions (polynomial-smoothing surfaces).
Revised:  We illustrate that this semi-sparsity can be identified into a generalized L_0-norm minimization in higher-order gradient domains, thereby giving rise to a new " feature-aware '' filtering method with a powerful simultaneous-fitting ability in both sparse features (singularities and sharpening edges) and non-sparse regions (polynomial-smoothing surfaces).
Edit intent: fluency
Original:  We illustrate that this semi-sparsity can be identified into a generalized L_0-norm minimization in higher-order gradient domains, thereby giving rise to a new `` feature-aware '' filtering method with a powerful simultaneous-fitting ability in both sparse features (singularities and sharpening edges) and non-sparse regions (polynomial-smoothing surfaces).
Revised:  We illustrate that this semi-sparsity can be identified into a generalized L_0-norm minimization in higher-order gradient domains, thereby giving rise to a new `` feature-aware " filtering method with a powerful simultaneous-fitting ability in both sparse features (singularities and sharpening edges) and non-sparse regions (polynomial-smoothing surfaces).
Edit intent: fluency
Original:  The proposed system attempts label-to-label matching, not image-to-image matching  between aerial images and a map database.
Revised:  The proposed system attempts label-to-label matching, not image-to-image matching , between aerial images and a map database.
Edit intent: fluency
Original: In real life, people communicate using both speech and non-verbal signals such as gestures, face expression or body pose.
Revised: People communicate using both speech and non-verbal signals such as gestures, face expression or body pose.
Edit intent: clarity
Original:  We introduce an ensemble of deep learning models (EnsembleDLM) for robust DTI prediction.
Revised:  We introduce an ensemble of deep learning models (EnsembleDLM) for DTI prediction.
Edit intent: clarity
Original:  This approach reduces the chance of overfitting, yields an unbiased prediction, and achieves state-of-the-art performance in Davis and KIBA datasets . EnsembleDLM also reaches state-of-the-art performance in cross-domain applications and decent cross-domain performance (Pearson correlation coefficient and concordance index > 0.8) with transfer learning using approximately twice the amount of test data in the new domain .
Revised:  This approach not only achieves state-of-the-art performance in Davis and KIBA datasets . EnsembleDLM also reaches state-of-the-art performance in cross-domain applications and decent cross-domain performance (Pearson correlation coefficient and concordance index > 0.8) with transfer learning using approximately twice the amount of test data in the new domain .
Edit intent: coherence
Original:  This approach reduces the chance of overfitting, yields an unbiased prediction, and achieves state-of-the-art performance in Davis and KIBA datasets . EnsembleDLM also reaches state-of-the-art performance in cross-domain applications and decent cross-domain performance (Pearson correlation coefficient and concordance index > 0.8) with transfer learning using approximately twice the amount of test data in the new domain .
Revised:  This approach reduces the chance of overfitting, yields an unbiased prediction, and achieves state-of-the-art performance in Davis and KIBA datasets but also reaches cutting-edge performance in the cross-domain applications and decent cross-domain performance (Pearson correlation coefficient and concordance index > 0.8) with transfer learning using approximately twice the amount of test data in the new domain .
Edit intent: clarity
Original:  This approach reduces the chance of overfitting, yields an unbiased prediction, and achieves state-of-the-art performance in Davis and KIBA datasets . EnsembleDLM also reaches state-of-the-art performance in cross-domain applications and decent cross-domain performance (Pearson correlation coefficient and concordance index > 0.8) with transfer learning using approximately twice the amount of test data in the new domain .
Revised:  This approach reduces the chance of overfitting, yields an unbiased prediction, and achieves state-of-the-art performance in Davis and KIBA datasets . EnsembleDLM also reaches state-of-the-art performance in cross-domain applications across different bio-activity types and different protein classes. We also demonstrate that EnsembleDLM achieves a good performance (Pearson correlation coefficient and concordance index > 0.8) with transfer learning using approximately twice the amount of test data in the new domain .
Edit intent: clarity
Original: A free-floating bike-sharing system (FFBSS) is a dockless rental system where an individual can borrow a bike and returns it everywhere , within the service area.
Revised: A free-floating bike-sharing system (FFBSS) is a dockless rental system where an individual can borrow a bike and returns it anywhere , within the service area.
Edit intent: clarity
Original:  We show that detecting these areas is NP-complete, but there exists a simple and efficient 1-1/e approximation algorithm;
Revised:  We show that detecting these zones is NP-complete, but there exists a simple and efficient 1-1/e approximation algorithm;
Edit intent: clarity
Original: The rapid growth in the number of devices and their connectivity has enlarged the attack surface and weakened cyber systems .
Revised: Digitization and remote connectivity have enlarged the attack surface and weakened cyber systems .
Edit intent: clarity
Original: The rapid growth in the number of devices and their connectivity has enlarged the attack surface and weakened cyber systems .
Revised: The rapid growth in the number of devices and their connectivity has enlarged the attack surface and made cyber systems more vulnerable .
Edit intent: clarity
Original:  As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure  cyber systems.
Revised:  As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure the cyber systems.
Edit intent: fluency
Original:  A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain the critical functions of the cyber systems  .
Revised:  A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain critical functions of the cyber systems .
Edit intent: fluency
Original:  Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation  of the CRM.
Revised:  Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation process of the CRM.
Edit intent: clarity
Original:  Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency, allowing the CRM to provide dynamic and sequential responses to attacks with limited  prior knowledge of the  attacker.
Revised:  Reinforcement Learning (RL) is an essential tool that epitomizes the feedback architectures for cyber resiliency, allowing the CRM to provide dynamic and sequential responses to attacks with limited prior knowledge of the attacker.
Edit intent: clarity
Original:  Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency, allowing the CRM to provide dynamic and sequential responses to attacks with limited  prior knowledge of the  attacker.
Revised:  Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resilience. It allows the CRM to provide dynamic and sequential responses to attacks with limited prior knowledge of the attacker.
Edit intent: coherence
Original:  Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency, allowing the CRM to provide dynamic and sequential responses to attacks with limited  prior knowledge of the  attacker.
Revised:  Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency, allowing the CRM to provide sequential responses to attacks with limited prior knowledge of the attacker.
Edit intent: clarity
Original:  Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency, allowing the CRM to provide dynamic and sequential responses to attacks with limited  prior knowledge of the  attacker.
Revised:  Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency, allowing the CRM to provide dynamic and sequential responses to attacks with limited or without prior knowledge of the attacker.
Edit intent: clarity
Original:  Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency, allowing the CRM to provide dynamic and sequential responses to attacks with limited  prior knowledge of the  attacker.
Revised:  Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency, allowing the CRM to provide dynamic and sequential responses to attacks with limited prior knowledge of the environment and the attacker.
Edit intent: clarity
Original:  In this work, we review the literature on RL for cyber resiliency and discuss the cyber-resilient defenses against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities.
Revised:  In this work, we review the literature on RL for cyber resilience and discuss cyber resilience against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities.
Edit intent: clarity
Original:  We introduce  moving target defense, defensive cyber deception, and assistive human security technologies as three application domains of CRMs to elaborate on their designs .
Revised:  We introduce three application domains of CRMs: moving target defense, defensive cyber deception, and assistive human security technologies as three application domains of CRMs to elaborate on their designs .
Edit intent: fluency
Original:  We introduce  moving target defense, defensive cyber deception, and assistive human security technologies as three application domains of CRMs to elaborate on their designs .
Revised:  We introduce moving target defense, defensive cyber deception, and assistive human security technologies .
Edit intent: coherence