wanyu/IteraTeR_v2 huggingface dataset:
Original:  Furthermore, many platforms lack the ability to flexibly configure the simulation, hence turning the simulation environment into a black-box from the perspective of the learning system.
Revised:  Furthermore, many platforms lack the ability to flexibly configure the simulation, making the simulated environment a black-box from the perspective of the learning system.
Edit intent: coherence
Original:  By taking advantage of Unity as a simulation platform, the toolkit enables the development of learning environments which are rich in sensory and physicalcomplexity, provide compelling cognitive challenges, and support dynamic multi-agent interaction.
Revised:  By taking advantage of Unity as a simulation platform, the toolkit enables the development of learning environments that are rich in sensory and physicalcomplexity, provide compelling cognitive challenges, and support dynamic multi-agent interaction.
Edit intent: fluency
Original:  We formalize this problem and present a novel algorithm for it based on the beautiful theory of locality sensitive hashing for Maximum Inner Product Search (MIPS).
Revised:  We formalize this problem and present a novel algorithm for it based on the theory of locality sensitive hashing for Maximum Inner Product Search (MIPS).
Edit intent: clarity
Original:  The proposed algorithm can find k (can be practically a constant for ride sharing platforms) potential matches for a given ride from a pool of n rides in sub-linear time O(n ^\rho (k + \log n) \log k) for \rho < 1, which is significant saving compared to an exhaustive search in the pool requiring O(n) time.
Revised:  The proposed algorithm can find k (can be practically a constant for ride sharing platforms) potential matches for every ride from a pool of n rides in sub-linear time O(n ^\rho (k + \log n) \log k) for \rho < 1, which is significant saving compared to an exhaustive search in the pool requiring O(n) time.
Edit intent: clarity
Original:  The proposed algorithm can find k (can be practically a constant for ride sharing platforms) potential matches for a given ride from a pool of n rides in sub-linear time O(n ^\rho (k + \log n) \log k) for \rho < 1, which is significant saving compared to an exhaustive search in the pool requiring O(n) time.
Revised:  The proposed algorithm can find k (can be practically a constant for ride sharing platforms) potential matches for a given ride from a pool of n rides in time O(n ^\rho (k + \log n) \log k) for \rho < 1, which is significant saving compared to an exhaustive search in the pool requiring O(n) time.
Edit intent: clarity
Original:  There is typically a tradeoff between these two objectives , for example, utilization can be increased by shifting away resources from idle users to "scavenger" workload, but with the risk of the former then becoming active again.
Revised:  There is typically a tradeoff between these two objectives ; for example, utilization can be increased by shifting away resources from idle users to "scavenger" workload, but with the risk of the former then becoming active again.
Edit intent: fluency
Original:  The main algorithmic tool is a multiplicative weight update algorithm, and a duality argument to obtain its guarantees.
Revised:  The main algorithmic tool is a multiplicative weight update algorithm, and a primal-dual argument to obtain its guarantees.
Edit intent: clarity
Original:  Experiments on both synthetic and real production traces demonstrate the merits of our algorithm in practical settings .
Revised:  We also provide numerical validation on real data to demonstrate the performance of our algorithm in practical settings .
Edit intent: clarity
Original:  Experiments on both synthetic and real production traces demonstrate the merits of our algorithm in practical settings .
Revised:  Experiments on both synthetic and real production traces demonstrate the merits of our algorithm in practical applications .
Edit intent: clarity
Original:  Moreover, the edge features are adjusted by the attention function and fed to the next layer, which means our edge features are adaptive across network layers.
Revised:  Moreover, the edge features are adaptive across network layers.
Edit intent: coherence
Original:  As a result, our proposed adaptive edge features guided graph attention model can consolidate a rich source of graph information that current state-of-the-art graph learning methods cannot .
Revised:  As a result, our proposed adaptive edge features guided graph attention model can consolidate a rich source of graph information .
Edit intent: clarity
Original:  We apply our proposed model to graph node classification , and experimental results on three citaion network datasetsand a biological network dataset show that out method outperforms the current state-of-the-art methods, testifying to the discriminative capability of edge features and the effectiveness of our adaptive edge features guided attention model.
Revised:  We apply our new models to graph node classification , and experimental results on three citaion network datasetsand a biological network dataset show that out method outperforms the current state-of-the-art methods, testifying to the discriminative capability of edge features and the effectiveness of our adaptive edge features guided attention model.
Edit intent: clarity
Original: Indexing highly repetitive texts --- such as genomic databases, software repositories and versioned text collections --- has become an important problem since the turn of the millennium.
Revised: Indexing highly repetitive texts - such as genomic databases, software repositories and versioned text collections --- has become an important problem since the turn of the millennium.
Edit intent: fluency
Original: Indexing highly repetitive texts --- such as genomic databases, software repositories and versioned text collections --- has become an important problem since the turn of the millennium.
Revised: Indexing highly repetitive texts --- such as genomic databases, software repositories and versioned text collections - has become an important problem since the turn of the millennium.
Edit intent: fluency
Original:  In this paper we close this long-standing problem, showing how to extend the Run-Length FM-index so that it can locate the occ occurrences efficiently within O(r) space (in loglogarithmic time each), and reaching optimal time  O(m + occ)  within O(r \log( }  n/r)) space,   on a RAM machine with words of w = \Omega(\log  n) bits.
Revised:  In this paper we close this long-standing problem, showing how to extend the Run-Length FM-index so that it can locate the occ occurrences efficiently within O(r) space (in loglogarithmic time each), and reaching optimal time , O(m + occ) within O(r \log( } n/r)) space, on a RAM machine with words of w = \Omega(\log n) bits.
Edit intent: fluency
Original:  In this paper we close this long-standing problem, showing how to extend the Run-Length FM-index so that it can locate the occ occurrences efficiently within O(r) space (in loglogarithmic time each), and reaching optimal time  O(m + occ)  within O(r \log( }  n/r)) space,   on a RAM machine with words of w = \Omega(\log  n) bits.
Revised:  In this paper we close this long-standing problem, showing how to extend the Run-Length FM-index so that it can locate the occ occurrences efficiently within O(r) space (in loglogarithmic time each), and reaching optimal time O(m + occ) , within O(r \log( } n/r)) space, on a RAM machine with words of w = \Omega(\log n) bits.
Edit intent: fluency
Original:  In this paper we close this long-standing problem, showing how to extend the Run-Length FM-index so that it can locate the occ occurrences efficiently within O(r) space (in loglogarithmic time each), and reaching optimal time  O(m + occ)  within O(r \log( }  n/r)) space,   on a RAM machine with words of w = \Omega(\log  n) bits.
Revised:  In this paper we close this long-standing problem, showing how to extend the Run-Length FM-index so that it can locate the occ occurrences efficiently within O(r) space (in loglogarithmic time each), and reaching optimal time O(m + occ) within O(r \log( } + n/r)) space, on a RAM machine with words of w = \Omega(\log n) bits.
Edit intent: fluency
Original:  Within O(r\log (n/r)) space, our index can also count in optimal time  O(m).
Revised:  Within that space, our index can also count in optimal time O(m).
Edit intent: clarity
Original:  Within O(r\log (n/r)) space, our index can also count in optimal time  O(m).
Revised:  Within O(r\log (n/r)) space, our index can also count in optimal time , O(m).
Edit intent: fluency
Original:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Revised:  Multiplying the space by O(w / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Edit intent: clarity
Original:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Revised:  Raising the space to O(r w\log_\sigma(n / log } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Edit intent: clarity
Original:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Revised:  Raising the space to O(r w\log_\sigma(n / r)) }) , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Edit intent: fluency
Original:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Revised:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma )/ %DIFDELCMD < \rceil%%% ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Edit intent: fluency
Original:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Revised:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% we ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Edit intent: fluency
Original:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Revised:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma )/ %DIFDELCMD < \rceil%%% + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Edit intent: fluency
Original:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma  )/ w%DIFDELCMD < \rceil%%%  + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Revised:  Raising the space to O(r w\log_\sigma(n / r)) } , we support count and locate in O( %DIFDELCMD < \lceil %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% ) and O( %DIFDELCMD < \lceil %DIFDELCMD < %%% m\log(\sigma )/ w%DIFDELCMD < \rceil%%% we + occ) time, which is optimal in the packed setting and had not been obtained before in compressed space.
Edit intent: fluency
Original:  This type of classification is useful for situations where the opinions are analyzed from the perspective of the reader . Recently, Deep Learning has been registering the state of the art in several single-label problems .
Revised:  This type of classification is useful for situations where the opinions are analyzed from the perspective of the reader , this happens because each person can have different interpretations and opinions on the same subject .
Edit intent: coherence
Original:  This paper discuss the efficiency of the Long Short-Term Memory compared to traditional multi-label classification approaches .
Revised:  This paper discuss the efficiency of problem transformation methods combined with different classification algorithms for the task of multi-label classification approaches .
Edit intent: clarity
Original:  This paper discuss the efficiency of the Long Short-Term Memory compared to traditional multi-label classification approaches .
Revised:  This paper discuss the efficiency of the Long Short-Term Memory compared to traditional multi-label classification of reactions in news texts .
Edit intent: clarity
Original:  However, it remains unclear how static structural connections guide the temporal progression of large-scale brain activity patterns in different cogni- tive states.
Revised:  However, it remains unclear how static structural connections guide the temporal progression of large-scale brain activity patterns in different cognitive states.
Edit intent: fluency
Original:  As the effectiveness of the proposed algorithms shown in simulation results, this paper indicates a novel localization method that is promising to further tackle the simultaneous localization and mapping (SLAM) problem .
Revised:  As the effectiveness of the proposed algorithms is substantiated not only by strong mathematical foundation but also by the comparison against other common localization methods .
Edit intent: coherence
Original:  For the competitive scenario, we formulate the MNOs' competitive interactions as a pricing game, and we show that MNO competition leads to equalized prices for users and an equilibrium may not exist .
Revised:  For the competitive scenario, we formulate the MNOs' competitive interactions as a pricing game, and we show that MNO competition leads to equalized prices for users if an equilibrium exists but does not guarantee its existence .
Edit intent: clarity
Original:  We find that CA with simpler rule tables produce trained networks with hierarchical structure and layer specialization, while more complex CA tend to produce shallower representations---illustrating how the underlying complexity of the CA's rules influences the specificity of these internal representations.
Revised:  We find that CA with simpler rule tables produce trained networks with hierarchical structure and layer specialization, while more complex CA produce shallower representations---illustrating how the underlying complexity of the CA's rules influences the specificity of these internal representations.
Edit intent: clarity
Original:  While in practice a more efficient algorithm would be preferable, replacing quadratic programming is challenging even for the special case of points in one dimension.
Revised:  While a more efficient algorithm would be preferable, replacing quadratic programming is challenging even for the special case of points in one dimension.
Edit intent: clarity
Original:  More recently , these methods have been applied to the numerical solution of high dimensional PDEs with great success.
Revised:  In particular , these methods have been applied to the numerical solution of high dimensional PDEs with great success.
Edit intent: coherence
Original:  More recently , these methods have been applied to the numerical solution of high dimensional PDEs with great success.
Revised:  More recently , these methods have been applied to the numerical solution of high-dimensional partial differential equations with great success.
Edit intent: clarity
Original:  In particular, recent simulations indicate that deep learning based algorithms are capable of overcoming the curse of dimensionality for the numerical solution of linear Kolmogorov PDEs.
Revised:  Recent simulations indicate that deep learning based algorithms are capable of overcoming the curse of dimensionality for the numerical solution of linear Kolmogorov PDEs.
Edit intent: coherence
Original:  In particular we show that for Kolmogorov PDEs with affine drift and diffusion coefficients and a given accuracy \varepsilon>0, ERM over deep neural network hypothesis classes of size scaling polynomially in the dimension d and \varepsilon^{-1 number of training samples scaling polynomially in the dimension d and \varepsilon^{-1} approximates the solution of the Kolmogorov PDE to within accuracy \varepsilon with high probability.
Revised:  In particular we show that for Kolmogorov PDEs with affine drift and diffusion coefficients and a given accuracy \varepsilon>0, ERM over deep neural network hypothesis classes of size scaling polynomially in the dimension d and \varepsilon^{-1 number of training samples scaling only polynomially in d and \varepsilon^{-1} approximates the solution of the Kolmogorov PDE to within accuracy \varepsilon with high probability.
Edit intent: clarity
Original: The development of new classification and regression algorithms based on empirical risk minimization (ERM) over deep neural network hypothesis classes, coined Deep Learning , revolutionized the area of artificial intelligence, machine learning, and data analysis.
Revised: The development of new classification and regression algorithms based on empirical risk minimization (ERM) over deep neural network hypothesis classes, coined deep learning , revolutionized the area of artificial intelligence, machine learning, and data analysis.
Edit intent: fluency
Original:  Recent simulations indicate that deep learning based algorithms are capable of overcoming the curse of dimensionality for the numerical solution of Kolmogorov equations, which are widely used in models from engineering, finance, and the natural sciences.
Revised:  Recent simulations indicate that deep learning-based algorithms are capable of overcoming the curse of dimensionality for the numerical solution of Kolmogorov equations, which are widely used in models from engineering, finance, and the natural sciences.
Edit intent: fluency
Original:  It can be concluded that ERM over deep neural network hypothesis classes breaks the curse of dimensionality for the numerical solution of linear Kolmogorov equations with affine coefficients.
Revised:  It can be concluded that ERM over deep neural network hypothesis classes overcomes the curse of dimensionality for the numerical solution of linear Kolmogorov equations with affine coefficients.
Edit intent: clarity
Original: Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances that can "evade" the classifier and get misclassified . Despite a massive amount of work focusing on making classifiers robust, the task seems quite challenging.
Revised: Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances . Despite a massive amount of work focusing on making classifiers robust, the task seems quite challenging.
Edit intent: coherence
Original:  In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of " concentration of measure " in metric measure spaces.
Revised:  In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of concentration of measure " in metric measure spaces.
Edit intent: fluency
Original:  In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of " concentration of measure " in metric measure spaces.
Revised:  In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of " concentration of measure in metric measure spaces.
Edit intent: fluency
Original:  However, many more Levy families are known for which we immediately obtain new attacks finding adversarial examples (e.g., product distribution under the Hamming distance)  .
Revised:  However, many more Levy families are known (e.g., product distribution under the Hamming distance) .
Edit intent: coherence
Original:  Finally, we show that concentration of measure for product spaces implies the existence of so called "poisoning" attacks in which the adversary tampers with the training data with the goal of increasing the error of the classifier.
Revised:  Finally, we show that concentration of measure for product spaces implies the existence of forms of "poisoning" attacks in which the adversary tampers with the training data with the goal of increasing the error of the classifier.
Edit intent: clarity
Original:  We show that for any deterministic learning algorithm that uses m training examples, there is an adversary who substitutes O(  \sqrt m) of the examples with other (still correctly labeled) ones and can almost fully degrade the confidence parameter of any PAC learning algorithm or alternatively increase the risk to almost 1 if the adversary also knows the final test example .
Revised:  We show that for any learning algorithm that uses m training examples, there is an adversary who substitutes O( \sqrt m) of the examples with other (still correctly labeled) ones and can almost fully degrade the confidence parameter of any PAC learning algorithm or alternatively increase the risk to almost 1 if the adversary also knows the final test example .
Edit intent: clarity