wanyu/IteraTeR_v2 huggingface dataset:
Original:  We study queueing networks that operate under the Max-Weight scheduling policy, for the case where some queues receive heavy-tailed traffic, while some other queues receive light-tailed traffic.
Revised:  We study queueing networks that operate under the Max-Weight scheduling policy, for the case where some queues receive heavy-tailed and some receive light-tailed traffic.
Edit intent: clarity
Original:  Within this context, and for any given "tail exponents" of the input traffic, we develop tight ( necessary and sufficient ) conditions under which a queue with light-tailed arrivals is robustly delay stable, in terms of jumping fluid (JF) models-an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows.
Revised:  Within this context, and for any given "tail exponents" of the input traffic, we develop a necessary and sufficient ) conditions under which a queue with light-tailed arrivals is robustly delay stable, in terms of jumping fluid (JF) models-an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows.
Edit intent: clarity
Original:  Within this context, and for any given "tail exponents" of the input traffic, we develop tight ( necessary and sufficient ) conditions under which a queue with light-tailed arrivals is robustly delay stable, in terms of jumping fluid (JF) models-an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows.
Revised:  Within this context, and for any given "tail exponents" of the input traffic, we develop tight ( necessary and sufficient condition under which a queue with light-tailed arrivals is robustly delay stable, in terms of jumping fluid (JF) models-an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows.
Edit intent: fluency
Original:  Within this context, and for any given "tail exponents" of the input traffic, we develop tight ( necessary and sufficient ) conditions under which a queue with light-tailed arrivals is robustly delay stable, in terms of jumping fluid (JF) models-an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows.
Revised:  Within this context, and for any given "tail exponents" of the input traffic, we develop tight ( necessary and sufficient ) conditions under which a queue is robustly delay stable, in terms of jumping fluid (JF) models-an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows.
Edit intent: clarity
Original:  Within this context, and for any given "tail exponents" of the input traffic, we develop tight ( necessary and sufficient ) conditions under which a queue with light-tailed arrivals is robustly delay stable, in terms of jumping fluid (JF) models-an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows.
Revised:  Within this context, and for any given "tail exponents" of the input traffic, we develop tight ( necessary and sufficient ) conditions under which a queue with light-tailed arrivals is robustly delay stable, in terms of jumping fluid models - an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows.
Edit intent: clarity
Original:  We also explore the power of Lyapunov functions in the study of robust delay stability.
Revised:  Finally, we explore the power of Lyapunov functions in the study of robust delay stability.
Edit intent: coherence
Original:  We also explore the power of Lyapunov functions in the study of robust delay stability.
Revised:  We also explore the power of Lyapunov functions in the study of delay stability.
Edit intent: clarity
Original:  However, 3D CNNs require a square-shaped video  whose spatial dimension is smaller than the original one.
Revised:  However, 3D CNNs require a square-shaped video , whose spatial dimension is smaller than the original one.
Edit intent: fluency
Original:  Random or center-cropping techniques in use may leave out the video's subject altogether.
Revised:  Random or center-cropping may leave out the video's subject altogether.
Edit intent: clarity
Original:  The synthesized video maintains  1:1 aspect ratio,  smaller in size and is targeted at the video-subject throughout the whole duration.
Revised:  The synthesized video maintains a 1:1 aspect ratio, smaller in size and is targeted at the video-subject throughout the whole duration.
Edit intent: fluency
Original:  The synthesized video maintains  1:1 aspect ratio,  smaller in size and is targeted at the video-subject throughout the whole duration.
Revised:  The synthesized video maintains 1:1 aspect ratio, is smaller in size and is targeted at the video-subject throughout the whole duration.
Edit intent: fluency
Original:  The synthesized video maintains  1:1 aspect ratio,  smaller in size and is targeted at the video-subject throughout the whole duration.
Revised:  The synthesized video maintains 1:1 aspect ratio, smaller in size and is targeted at video-subject throughout the whole duration.
Edit intent: fluency
Original:  The synthesized video maintains  1:1 aspect ratio,  smaller in size and is targeted at the video-subject throughout the whole duration.
Revised:  The synthesized video maintains 1:1 aspect ratio, smaller in size and is targeted at the video-subject (s) throughout the entire duration.
Edit intent: clarity
Original:  First, action localization on the individual frames is performed by identifying patches with homogeneous motion patterns and a single salient patch is pin-pointed.
Revised:  First, action localization is performed on each frame by identifying patches with homogeneous motion patterns and a single salient patch is pin-pointed.
Edit intent: clarity
Original:  First, action localization on the individual frames is performed by identifying patches with homogeneous motion patterns and a single salient patch is pin-pointed.
Revised:  First, action localization on the individual frames is performed by identifying patches with homogeneous motion patterns . Thus, a single salient patch is pin-pointed.
Edit intent: coherence
Original:  To avoid viewpoint jitters and flickering artifacts , any inter-frame scale or position changes among the patches is performed gradually over time.
Revised:  To avoid viewpoint jitters and flickering , any inter-frame scale or position changes among the patches is performed gradually over time.
Edit intent: clarity
Original:  To avoid viewpoint jitters and flickering artifacts , any inter-frame scale or position changes among the patches is performed gradually over time.
Revised:  To avoid viewpoint jitters and flickering artifacts , any inter-frame scale or position changes among the patches should be performed gradually over time.
Edit intent: clarity
Original:  This issue is addressed with a poly-Bezier fitting in 3D space that passes through some chosen pivot timestamps and its shape is influenced by  in-between control timestamps.
Revised:  This issue is addressed with a polyBezier fitting in 3D space that passes through some chosen pivot timestamps and its shape is influenced by in-between control timestamps.
Edit intent: fluency
Original:  This issue is addressed with a poly-Bezier fitting in 3D space that passes through some chosen pivot timestamps and its shape is influenced by  in-between control timestamps.
Revised:  This issue is addressed with a poly-Bezier fitting in 3D space that passes through some chosen pivot timestamps and whose shape is influenced by in-between control timestamps.
Edit intent: clarity
Original:  This issue is addressed with a poly-Bezier fitting in 3D space that passes through some chosen pivot timestamps and its shape is influenced by  in-between control timestamps.
Revised:  This issue is addressed with a poly-Bezier fitting in 3D space that passes through some chosen pivot timestamps and its shape is influenced by the in-between control timestamps.
Edit intent: fluency
Original:  To corroborate the effectiveness of the proposed method, we evaluate the video classification task by comparing our dynamic cropping with static random on three benchmark datasets : UCF-101, HMDB-51 and ActivityNet v1.3.
Revised:  To corroborate the effectiveness of the proposed method, we evaluate the video classification task by comparing our dynamic cropping technique with random cropping on three benchmark datasets : UCF-101, HMDB-51 and ActivityNet v1.3.
Edit intent: clarity
Original:  To corroborate the effectiveness of the proposed method, we evaluate the video classification task by comparing our dynamic cropping with static random on three benchmark datasets : UCF-101, HMDB-51 and ActivityNet v1.3.
Revised:  To corroborate the effectiveness of the proposed method, we evaluate the video classification task by comparing our dynamic cropping with static random on three benchmark datasets , viz. UCF-101, HMDB-51 and ActivityNet v1.3.
Edit intent: clarity
Original:  The clip accuracy and top-1 accuracy for video classification after our cropping, outperform 3D CNN performances for same-sized inputs with random crop;
Revised:  The clip and top-1 accuracy for video classification after our cropping, outperform 3D CNN performances for same-sized inputs with random crop;
Edit intent: fluency
Original:  In this work, we show that there is a deterministic algorithm for multivariate multipoint evaluation over a field q of characteristic p which evaluates an n-variate polynomial of degree less than d in each variable on N inputs in time \left((N + d^n)^{1 + o(1)} %DIFDELCMD < \poly%%%  (\log q, d, p, n)\right) \, , provided that p is at most d^{o(1)}, and q is at most (\exp(\exp(\exp(\cdots (\exp(d))))), where the height of this tower of exponentials is fixed.
Revised:  In this work, we show that there is a deterministic algorithm for multivariate multipoint evaluation over a field q of characteristic p which evaluates an n-variate polynomial of degree less than d in each variable on N inputs in time \left((N + d^n)^{1 + o(1)} %DIFDELCMD < \poly%%% (\log q, d, p, n)\right) provided that p is at most d^{o(1)}, and q is at most (\exp(\exp(\exp(\cdots (\exp(d))))), where the height of this tower of exponentials is fixed.
Edit intent: fluency
Original:  Mathematical analyses demonstrate that a combination of the gradient descent algorithm and the selection and crossover algorithm--with a biased crossover weight--maximises the search efficacy . Remarkably, its computational cost is of the square root order in contrast to that of the conventional gradient descent algorithms, indicating a quadratic speedup of global search.
Revised:  Mathematical analyses demonstrate that a combination of the gradient descent algorithm and the selection and crossover algorithm--with a biased crossover weight--maximises the search efficiency . Remarkably, its computational cost is of the square root order in contrast to that of the conventional gradient descent algorithms, indicating a quadratic speedup of global search.
Edit intent: fluency
Original: We introduce a new algorithm to efficiently compute the functions belonging to a suitable set \mathcal F defined as follows: f\in \mathcal F means that f(s,x), s\in A\subset \mathbb R being fixed and x>0, has a power series expansion centred at x_0=1 with convergence radius greater or equal than 1;
Revised: We introduce a new algorithm to efficiently compute the functions belonging to a suitable set \mathcal F defined as follows: f\in \mathcal F means that f(s,x), s\in A\subset \mathbb R being fixed and x>0, has a power series expansion centred at x_0=1 with convergence radius greater or equal than 1;
Edit intent: fluency
Original:  moreover, it satisfies a difference equation of step 1 and the Euler-Maclaurin summation formula can be applied to f. Denoting Euler's function as \Gamma, we will show, for x>0, that \log \Gamma(x), the digamma function \psi(x), the polygamma functions \psi^{(w)}(x), w\in \mathbb N , w\ge1, and, for s>1 being fixed, the Hurwitz \zeta(s,x)-function and its first partial derivative \partial\zeta{\partial s}(s,x)
Revised:  moreover, it satisfies a difference equation of step 1 and the Euler-Maclaurin summation formula can be applied to f. Denoting Euler's function as \Gamma, we will show, for x>0, that \log \Gamma(x), the digamma function \psi(x), the polygamma functions \psi^{(w)}(x), w\in \mathbb N , w\ge1, and, for s>1 being fixed, the Hurwitz \zeta(s,x)-function and its first partial derivative \partial\zeta{\partial s}(s,x)
Edit intent: fluency
Original:  are in \mathcal F . In all these cases the power series involved will depend on the values of \zeta(u), u>1, where \zeta is Riemann's function.
Revised:  are in \mathcal F . In all these cases the power series involved will depend on the values of \zeta(u), u>1, where \zeta is Riemann's function.
Edit intent: fluency
Original:  are in \mathcal F . In all these cases the power series involved will depend on the values of \zeta(u), u>1, where \zeta is Riemann's function.
Revised:  are in \mathcal F . In all these cases the coefficients of the involved power series will depend on the values of \zeta(u), u>1, where \zeta is Riemann's function.
Edit intent: clarity
Original:  As a by-product, we will also show how  compute efficiently the Dirichlet L-functions L(s,\chi) and L^\prime(s,\chi), s> 1, \chi being a primitive Dirichlet character, by inserting the reflection formulae of \zeta(s,x) and \partial\zeta{\partial s}(s,x) into the first step of the Fast Fourier Transform algorithm.
Revised:  As a by-product, we will also show how to compute efficiently the Dirichlet L-functions L(s,\chi) and L^\prime(s,\chi), s> 1, \chi being a primitive Dirichlet character, by inserting the reflection formulae of \zeta(s,x) and \partial\zeta{\partial s}(s,x) into the first step of the Fast Fourier Transform algorithm.
Edit intent: fluency
Original:  In the last section we will also describe some tests that show an important performance gain with respect to a standard multiprecision implementation of \zeta(s,x) and \frac{\partial\zeta}{\partial s}(s,x), s>1, x>0.
Revised:  In the last section we will also describe some tests that show a performance gain with respect to a standard multiprecision implementation of \zeta(s,x) and \frac{\partial\zeta}{\partial s}(s,x), s>1, x>0.
Edit intent: clarity
Original: Self-supervised speech model is a rapid progressing research topic, and many pre-trained models have been released and used in various down-stream tasks.
Revised: Self-supervised speech model is a rapid progressing research topic, and many pre-trained models have been released and used in various down stream tasks.
Edit intent: fluency
Original:  For speech anti-spoofing, most countermeasures (CMs) are using signal processing algorithms to extract acoustic features for classification.
Revised:  For speech anti-spoofing, most countermeasures (CMs) use signal processing algorithms to extract acoustic features for classification.
Edit intent: coherence
Original:  We investigated different back end architectures to be combined with the self-supervised front end, the effectiveness of fine tuning the front end, and the performance of using different pre-trained self-supervised models.
Revised:  We investigated different back end architectures to be combined with the self-supervised front end, the effectiveness of fine-tuning the front end, and the performance of using different pre-trained self-supervised models.
Edit intent: fluency
Original:  Our experiments found that, when a good pre-trained front end was fine tuned with either a shallow or a deep neural-network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on  ASVspoof 2015, 2021 LA, and 2021 deepfake test sets.
Revised:  Our findings showed that, when a good pre-trained front end was fine tuned with either a shallow or a deep neural-network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on ASVspoof 2015, 2021 LA, and 2021 deepfake test sets.
Edit intent: clarity
Original:  Our experiments found that, when a good pre-trained front end was fine tuned with either a shallow or a deep neural-network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on  ASVspoof 2015, 2021 LA, and 2021 deepfake test sets.
Revised:  Our experiments found that, when a good pre-trained front end was fine-tuned with either a shallow or a deep neural-network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on ASVspoof 2015, 2021 LA, and 2021 deepfake test sets.
Edit intent: fluency
Original:  Our experiments found that, when a good pre-trained front end was fine tuned with either a shallow or a deep neural-network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on  ASVspoof 2015, 2021 LA, and 2021 deepfake test sets.
Revised:  Our experiments found that, when a good pre-trained front end was fine tuned with either a shallow or a deep neural network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on ASVspoof 2015, 2021 LA, and 2021 deepfake test sets.
Edit intent: fluency
Original:  Our experiments found that, when a good pre-trained front end was fine tuned with either a shallow or a deep neural-network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on  ASVspoof 2015, 2021 LA, and 2021 deepfake test sets.
Revised:  Our experiments found that, when a good pre-trained front end was fine tuned with either a shallow or a deep neural-network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on the ASVspoof 2015, 2021 LA, and 2021 deepfake test sets.
Edit intent: fluency
Original: Faster-than-Nyquist (FTN) signaling aided non-orthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence of random link delays of the different users.
Revised: Faster-than-Nyquist (FTN) signaling aided non-orthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence of random link delays of the different users.
Edit intent: fluency
Original: Control  application 
Revised: Control , the application 
Edit intent: coherence
Original: and Proximal Policy Optimization on a mobile selfbalancing Extendible Wheeled Inverted Pendulum EWIP system Such RL models make the task of finding  satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control  In this article two RLbased controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory 
Revised: and Proximal Policy Optimization on a mobile self-balancing Extendible Wheeled Inverted Pendulum EWIP system Such RL models make the task of finding satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control In this article two RLbased controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory 
Edit intent: fluency
Original: and Proximal Policy Optimization on a mobile selfbalancing Extendible Wheeled Inverted Pendulum EWIP system Such RL models make the task of finding  satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control  In this article two RLbased controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory 
Revised: and Proximal Policy Optimization on a mobile selfbalancing Extendible Wheeled Inverted Pendulum (E-WIP) system. Such RL models make the task of finding satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control In this article two RLbased controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory 
Edit intent: clarity
Original: and Proximal Policy Optimization on a mobile selfbalancing Extendible Wheeled Inverted Pendulum EWIP system Such RL models make the task of finding  satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control  In this article two RLbased controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory 
Revised: and Proximal Policy Optimization on a mobile selfbalancing Extendible Wheeled Inverted Pendulum EWIP system Such RL models make the task of finding a satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control In this article two RLbased controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory 
Edit intent: fluency
Original: and Proximal Policy Optimization on a mobile selfbalancing Extendible Wheeled Inverted Pendulum EWIP system Such RL models make the task of finding  satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control  In this article two RLbased controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory 
Revised: and Proximal Policy Optimization on a mobile selfbalancing Extendible Wheeled Inverted Pendulum EWIP system Such RL models make the task of finding satisfactory control scheme easier and respond to the dynamics effectively while self-tuning the parameters to provide better control . In this article two RLbased controllers are pitted against an MPC controller to evaluate the performance on the basis of state variables of the EWIP system while following a specific desired trajectory 
Edit intent: fluency