wanyu/IteraTeR_v2 huggingface dataset:
Original:  Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal on a user's preference , such as views, clicks, adding a product to shop carts and so on.
Revised:  Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal on a user's preference , such as views, clicks, and so on.
Edit intent: clarity
Original:  They should be taken into account properly to provide quality recommendation for users.
Edit intent: coherence
Original:  In this work, we contribute a novel solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from multiple types of user behaviors.
Revised:  In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from multiple types of user behaviors.
Edit intent: clarity
Original:  To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
Revised:  We perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
Edit intent: coherence
Original:  Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data .
Revised:  Extensive experiments on the real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data .
Edit intent: clarity
Original:  Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data .
Revised:  Extensive experiments on two real-world dataset demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data .
Edit intent: fluency
Original: Most existing recommender systems leverage user behavior data of one type  , such as the purchase behavior data in E-commerce .
Revised: Most existing recommender systems leverage user behavior data of one type only , such as the purchase behavior data in E-commerce .
Edit intent: clarity
Original: Most existing recommender systems leverage user behavior data of one type  , such as the purchase behavior data in E-commerce .
Revised: Most existing recommender systems leverage user behavior data of one type , such as the purchase behavior in E-commerce .
Edit intent: fluency
Original:  We argue that other types of user behavior data also provide valuable signal, such as views, clicks,  and so on.
Revised:  We argue that other forms of user behaviors also provide valuable signal, such as views, clicks, and so on.
Edit intent: clarity
Original:  Extensive experiments on the real-world dataset demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data  .
Revised:  Extensive experiments on two real-world dataset demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data .
Edit intent: clarity
Original:  Extensive experiments on the real-world dataset demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data  .
Revised:  Extensive experiments on the real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data .
Edit intent: fluency
Original:  Our algorithms use polar coordinates and implement a Chebychev collocation scheme for the radial dependence and a Fourier spectral method for the azimuthal dependence.
Revised:  Our algorithms use polar coordinates and implement a Chebychev spectral scheme for the radial dependence and a Fourier spectral method for the azimuthal dependence.
Edit intent: clarity
Original:  The focus is placed on the construction of complex geometric optics (CGO)  which are needed in the context of electrical impedance tomography .
Revised:  The focus is placed on the construction of complex geometric optics (CGO) solutions which are needed in the context of electrical impedance tomography .
Edit intent: clarity
Original:  Commonly, link prediction is performed by means of a similarity metric, with the idea that a pair of similar nodes are likely to be connected .
Revised:  Commonly, link prediction is performed by means of a similarity metric, with the idea that a pair of similar nodes are likely to be linked .
Edit intent: fluency
Original: We present a stable mergesort\Adaptive  , called~%DIFDELCMD < \ASS%%% , that exploits the existence of monotonic runs for sorting efficiently partially sorted data.
Revised: We present one stable mergesort algorithm, called\Adaptive , called~%DIFDELCMD < \ASS%%% , that exploits the existence of monotonic runs for sorting efficiently partially sorted data.
Edit intent: clarity
Original: We present a stable mergesort\Adaptive  , called~%DIFDELCMD < \ASS%%% , that exploits the existence of monotonic runs for sorting efficiently partially sorted data.
Revised: We present a stable mergesort\Adaptive , %DIFDELCMD < \ASS%%% , that exploits the existence of monotonic runs for sorting efficiently partially sorted data.
Edit intent: coherence
Original: We present a stable mergesort\Adaptive  , called~%DIFDELCMD < \ASS%%% , that exploits the existence of monotonic runs for sorting efficiently partially sorted data.
Revised: We present a stable mergesort\Adaptive , called~%DIFDELCMD < \ASS%%% that exploits the existence of monotonic runs for sorting efficiently partially sorted data.
Edit intent: fluency
Original: Gale and Shapley's college admission problem and concept of stability (Gale and Shapley 1962) have been extensively studied, applied, and extended.
Revised: Gale and Shapley's stable assignment problem has been extensively studied, applied, and extended.
Edit intent: clarity
Original:  In school choice problems , mechanisms often aim to obtain an assignment that is more favorable to students.
Revised:  In the context of school choice , mechanisms often aim to obtain an assignment that is more favorable to students.
Edit intent: clarity
Original:  In school choice problems , mechanisms often aim to obtain an assignment that is more favorable to students.
Revised:  In school choice problems , mechanisms often aim at finding an assignment that is more favorable to students.
Edit intent: clarity
Original:  We investigate two extensions introduced in this context -- legal assignments (Morrill 2016) and the EADAM algorithm (Kesten 2010) -- through the lens of classical theory of stable matchings.
Revised:  We investigate two extensions introduced in this framework -- legal assignments (Morrill 2016) and the EADAM algorithm (Kesten 2010) -- through the lens of classical theory of stable matchings.
Edit intent: clarity
Original:  We investigate two extensions introduced in this context -- legal assignments (Morrill 2016) and the EADAM algorithm (Kesten 2010) -- through the lens of classical theory of stable matchings.
Revised:  We investigate two extensions introduced in this context -- legal assignments and the EADAM algorithm (Kesten 2010) -- through the lens of classical theory of stable matchings.
Edit intent: coherence
Original:  We investigate two extensions introduced in this context -- legal assignments (Morrill 2016) and the EADAM algorithm (Kesten 2010) -- through the lens of classical theory of stable matchings.
Revised:  We investigate two extensions introduced in this context -- legal assignments (Morrill 2016) and the EADAM algorithm -- through the lens of classical theory of stable matchings.
Edit intent: coherence
Original:  We prove that {\cal L} is exactly the set of stable assignments in another instance , and that essentially any optimization problem over {\cal L} can be solved within the same time bound needed for solving them over the set of stable assignments.
Revised:  We prove that {\cal L} is exactly the set of stable assignments in another instance . Moreover, we show that essentially all optimization problems over {\cal L} can be solved within the same time bound needed for solving them over the set of stable assignments.
Edit intent: clarity
Original:  We prove that {\cal L} is exactly the set of stable assignments in another instance , and that essentially any optimization problem over {\cal L} can be solved within the same time bound needed for solving them over the set of stable assignments.
Revised:  We prove that {\cal L} is exactly the set of stable assignments in another instance , and that essentially any optimization problem over {\cal L} can be solved within the same time bound needed for solving it over the set of stable assignments.
Edit intent: clarity
Original:  A key tool for these results is an algorithm that finds the student-optimal legal assignment.
Revised:  A key tool for this latter result is an algorithm that finds the student-optimal legal assignment.
Edit intent: clarity
Original:  We then generalize our algorithm to obtain the  output of EADAM with any given set of consenting students without sacrificing the running time, hence improving over known algorithms in both theory and practice  . Lastly, we investigate how larger the set {\cal L} can be compared to the set of stable matchings in the one-to-one case, and connect legal matchings with certain concepts and open problems in the literature.
Revised:  We then generalize our algorithm to obtain the output of EADAM with any given set of consenting students without sacrificing the running time, hence largely improving in both theory and practice . Lastly, we investigate how larger the set {\cal L} can be compared to the set of stable matchings in the one-to-one case, and connect legal matchings with certain concepts and open problems in the literature.
Edit intent: clarity
Original:  We then generalize our algorithm to obtain the  output of EADAM with any given set of consenting students without sacrificing the running time, hence improving over known algorithms in both theory and practice  . Lastly, we investigate how larger the set {\cal L} can be compared to the set of stable matchings in the one-to-one case, and connect legal matchings with certain concepts and open problems in the literature.
Revised:  We then generalize our algorithm to obtain the output of EADAM with any given set of consenting students without sacrificing the running time, hence improving over known algorithms in both theory and practice . Lastly, we show that the set {\cal L} can be compared to the set of stable matchings in the one-to-one case, and connect legal matchings with certain concepts and open problems in the literature.
Edit intent: coherence
Original:  We then generalize our algorithm to obtain the  output of EADAM with any given set of consenting students without sacrificing the running time, hence improving over known algorithms in both theory and practice  . Lastly, we investigate how larger the set {\cal L} can be compared to the set of stable matchings in the one-to-one case, and connect legal matchings with certain concepts and open problems in the literature.
Revised:  We then generalize our algorithm to obtain the output of EADAM with any given set of consenting students without sacrificing the running time, hence improving over known algorithms in both theory and practice . Lastly, we investigate how larger the set {\cal L} can be compared to the set of stable matchings , connecting legal matchings with certain concepts and open problems in the literature.
Edit intent: coherence
Original:  This data-dependent activation function remarkably improves both classification accuracy and stability to adversarial perturbations.
Revised:  This data-dependent activation remarkably improves both classification accuracy and stability to adversarial perturbations.
Edit intent: clarity
Original:  Together with the\\%DIF > to \sim 69\\%  total variation minimization of adversarial images and augmented training , under the strongest attack, we achieve up to 20.6 \\%DIF < , 50.7\\%, and 68.7\\% accuracy improvement w.r.t. the fast gradient sign method, iterative fast gradient sign method, and Carlini-Wagner L_2 attacks, respectively.
Revised:  Together with the\\%DIF > to \sim 69\\% total variation minimization on adversarial images and augmented training , under the strongest attack, we achieve up to 20.6 \\%DIF < , 50.7\\%, and 68.7\\% accuracy improvement w.r.t. the fast gradient sign method, iterative fast gradient sign method, and Carlini-Wagner L_2 attacks, respectively.
Edit intent: fluency
Original:  We give %DIF > for ResNet56 under the strongest  an intuitive explanation of our defense strategy via analyzing the geometry of the feature space.
Revised:  We give %DIF > for ResNet56 under the strongest an intuitive explanation of our defense by analyzing the geometry of the feature space.
Edit intent: clarity
Original:  For reproducibility, the code is made available at : URL
Revised:  For reproducibility, the code is made available at URL
Edit intent: fluency
Original:  This data-dependent activation remarkably improves both  generalization and robustness towards adversarial attacks .
Revised:  This data-dependent activation remarkably improves both the generalization and robustness towards adversarial attacks .
Edit intent: fluency
Original:  In the CIFAR10 benchmark, we raise the  accuracy of the Projected Gradient Descent adversarial training from \sim 46\\%DIF < to \sim 69\\% for ResNet20 %DIF > to \sim 69\\% under the state-of-the-art  .
Revised:  In the CIFAR10 benchmark, we raise the robust accuracy of the Projected Gradient Descent adversarial training from \sim 46\\%DIF < to \sim 69\\% for ResNet20 %DIF > to \sim 69\\% under the state-of-the-art .
Edit intent: clarity
Original:  In the CIFAR10 benchmark, we raise the  accuracy of the Projected Gradient Descent adversarial training from \sim 46\\%DIF < to \sim 69\\% for ResNet20 %DIF > to \sim 69\\% under the state-of-the-art  .
Revised:  In the CIFAR10 benchmark, we raise the accuracy of the adversarially trained ResNet20 from \sim 46\\%DIF < to \sim 69\\% for ResNet20 %DIF > to \sim 69\\% under the state-of-the-art .
Edit intent: clarity
Original:  In the CIFAR10 benchmark, we raise the  accuracy of the Projected Gradient Descent adversarial training from \sim 46\\%DIF < to \sim 69\\% for ResNet20 %DIF > to \sim 69\\% under the state-of-the-art  .
Revised:  In the CIFAR10 benchmark, we raise the accuracy of the Projected Gradient Descent adversarial training from \sim 46\\%DIF < to \sim 69\\% %DIF > to \sim 69\\% under the state-of-the-art .
Edit intent: coherence
Original:  We further %DIF > for ResNet56 under the strongest IFGSM attack.  provide an intuitive explanation of our defense by analyzing the geometry of the feature space.
Revised:  We further %DIF > for ResNet56 under the strongest IFGSM attack. Furthermore, We provide an intuitive explanation of our defense by analyzing the geometry of the feature space.
Edit intent: coherence
Original:  The Greedy Algorithm repeatedly merges two strings with the largest intersection into one, until only one string remains.
Edit intent: coherence
Original:  We support the conjecture by showing that the Greedy Hierarchical Algorithm gives a 2-approximation in the case when all input strings have length at most 3 (which until recently had been the only case where the Greedy Conjecture was proven).
Revised:  We support these equivalent conjectures by giving a proof for the special case where all input strings have length at most 3 (which until recently had been the only case where the Greedy Conjecture was proven).
Edit intent: clarity
Original:  We also tested our conjecture on tens of thousands of instances of SCS. Except for its conjectured good approximation ratio, the Greedy Hierarchical Algorithm finds exact solutions for the special cases where we know polynomial time (not greedy) exact algorithms: (1) when the input strings form a spectrum of a string (2) when all input strings have length at most 2.
Revised:  We also tested our conjecture on tens of thousands of instances of SCS. Except for its (conjectured) good approximation ratio, the Greedy Hierarchical Algorithm finds exact solutions for the special cases where we know polynomial time (not greedy) exact algorithms: (1) when the input strings form a spectrum of a string (2) when all input strings have length at most 2.
Edit intent: clarity
Original: As a key technique for enabling artificial intelligence, machine learning (ML) has been shown to be capable of solving complex problems without explicit programming.
Revised: As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming.
Edit intent: clarity
Original:  Motivated by its successful applications to many practical tasks like image recognition and recommendation systems , both industry and the research community have advocated the applications of ML in wireless communication.
Revised:  Motivated by its successful applications to many practical tasks like image recognition , both industry and the research community have advocated the applications of ML in wireless communication.
Edit intent: clarity
Original:  The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design , and computation resource management, while ML-based networking focuses on the applications in base station (BS) clustering, BS switching control, user association , and routing.
Revised:  The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML-based networking focuses on the applications in base station (BS) clustering, BS switching control, user association , and routing.
Edit intent: fluency
Original:  The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design , and computation resource management, while ML-based networking focuses on the applications in base station (BS) clustering, BS switching control, user association , and routing.
Revised:  The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design , and computation resource management, while ML based networking focuses on the applications in base station (BS) clustering, BS switching control, user association , and routing.
Edit intent: fluency
Original:  The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design , and computation resource management, while ML-based networking focuses on the applications in base station (BS) clustering, BS switching control, user association , and routing.
Revised:  The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design , and computation resource management, while ML-based networking focuses on the applications in clustering, base station switching control, user association , and routing.
Edit intent: clarity
Original:  The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design , and computation resource management, while ML-based networking focuses on the applications in base station (BS) clustering, BS switching control, user association , and routing.
Revised:  The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design , and computation resource management, while ML-based networking focuses on the applications in base station (BS) clustering, BS switching control, user association and routing.
Edit intent: fluency
Original:  Each aspect is further categorized according to the adopted ML techniques.
Revised:  Moreover, literatures in each aspect is organized according to the adopted ML techniques.
Edit intent: clarity
Original:  Additionally, given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where the topics of ML-based network slicing, infrastructure update to support ML-based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation , and so on are discussed.
Revised:  Additionally, given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML-based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation , and so on are discussed.
Edit intent: clarity
Original:  Additionally, given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where the topics of ML-based network slicing, infrastructure update to support ML-based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation , and so on are discussed.
Revised:  Additionally, given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where the topics of ML-based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation , and so on are discussed.
Edit intent: fluency
Original:  Additionally, given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where the topics of ML-based network slicing, infrastructure update to support ML-based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation , and so on are discussed.
Revised:  Additionally, given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where the topics of ML-based network slicing, infrastructure update to support ML-based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.
Edit intent: fluency
Original:  Experimental results demonstrate that the proposed DeepFD successfully detected 94.7\% fake images generated by several state-of-the-art GANs 
Revised:  Experimental results demonstrate that the proposed DeepFD successfully detected 94.7\% fake images generated by several state-of-the-art GANs .
Edit intent: fluency
Original:  Our bounds are derived from a general convergence rate result for non-linear inverse problems whose forward map satisfies a mild modulus of continuity condition, a result of independent interest that is applicable also to linear inverse problems, illustrated in an example with the Radon transform.
Revised:  Our bounds are derived from a general convergence rate result for non-linear inverse problems whose forward map satisfies a modulus of continuity condition, a result of independent interest that is applicable also to linear inverse problems, illustrated in an example with the Radon transform.
Edit intent: clarity