wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Revised:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walks are random walks processes whose step length follows a long-tailed power law distribution. Due to their abundance as movement patterns of URLanisms, significant theoretical efforts have been devoted to identify the foraging circumstances that would make such patterns advantageous ] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Edit intent: meaning-changed
Original:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Revised:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an Viswanathan et al. Nature, 1999] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Edit intent: meaning-changed
Original:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Revised:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ]. Despite numerous attempts, there is currently no conclusive analytical evidence that L\'evy flights are preferable strategies in higher dimensions than one. Here we show that the optimality of inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Edit intent: meaning-changed
Original:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Revised:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square L\'evy walks in two-dimensions becomes striking when targets are sparse and unpredictable in size, and when detection is weak. Specifically, we prove that under the intermittent model, in which detection is not possible while moving ballistically ] sparse targets of any size and shape.
Edit intent: meaning-changed
Original:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Revised:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding B\'enichou et al. Reviews of Modern Physics, 2011] sparse targets of any size and shape.
Edit intent: meaning-changed
Original:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Revised:  We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding ], this strategy optimally finds sparse targets of any size and shape.
Edit intent: meaning-changed
Original:  Specifically , in a square torus of area  n, and assuming that the detection radius is normalized to 1, the strategy finds any connected set of diameter D in O(n/D) expected time, whereas \Omega(n/D) is an unconditional lower bound on the expected time, that holds even when assuming that the shape and size of the target are known.
Revised:  That is , in a square torus of area n, and assuming that the detection radius is normalized to 1, the strategy finds any connected set of diameter D in O(n/D) expected time, whereas \Omega(n/D) is an unconditional lower bound on the expected time, that holds even when assuming that the shape and size of the target are known.
Edit intent: coherence
Original:  Specifically , in a square torus of area  n, and assuming that the detection radius is normalized to 1, the strategy finds any connected set of diameter D in O(n/D) expected time, whereas \Omega(n/D) is an unconditional lower bound on the expected time, that holds even when assuming that the shape and size of the target are known.
Revised:  Specifically , in a square torus of area ~ n, and assuming that the detection radius is normalized to 1, the strategy finds any connected set of diameter D in O(n/D) expected time, whereas \Omega(n/D) is an unconditional lower bound on the expected time, that holds even when assuming that the shape and size of the target are known.
Edit intent: meaning-changed
Original:  Furthermore, this particular L\'evy process stands in stark contrast to many other basic intermittent processes, including all other L\'evy walks, which we prove to be extremely inefficient for wide ranges of target scales.
Revised:  Furthermore, this particular L\'evy process stands in stark contrast to many other basic intermittent processes, including all other L\'evy walks, whose competitive ratio is shown to be polynomial in n, for wide ranges of target scales.
Edit intent: meaning-changed
Original:  We express the multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in a quadratic form and fix their bandwidth parameter at a constant value.
Revised:  We formulate a multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in a quadratic form and fix their bandwidth parameter at a constant value.
Edit intent: clarity
Original:  We express the multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in a quadratic form and fix their bandwidth parameter at a constant value.
Revised:  We express the multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in quadratic form and fix their bandwidth parameter at a constant value.
Edit intent: fluency
Original:   Given an estimate for the noise level a common convex approach to recover the signal is basis pursuit denoising (BPDN) . If the measurement matrix has the robust null space property with respect to the \ell_2-norm, BPDN obeys stable and robust recovery guarantees.
Revised:  The recovery can be performed by multiple different decoders, however most of them rely on some tuning. Given an estimate for the noise level a common convex approach to recover the signal is basis pursuit denoising (BPDN) . If the measurement matrix has the robust null space property with respect to the \ell_2-norm, BPDN obeys stable and robust recovery guarantees.
Edit intent: meaning-changed
Original:   Given an estimate for the noise level a common convex approach to recover the signal is basis pursuit denoising (BPDN) . If the measurement matrix has the robust null space property with respect to the \ell_2-norm, BPDN obeys stable and robust recovery guarantees.
Revised:  Given an estimate for the noise level a common convex approach to recover the signal is basis pursuit denoising . If the measurement matrix has the robust null space property with respect to the \ell_2-norm, BPDN obeys stable and robust recovery guarantees.
Edit intent: clarity
Original:   Given an estimate for the noise level a common convex approach to recover the signal is basis pursuit denoising (BPDN) . If the measurement matrix has the robust null space property with respect to the \ell_2-norm, BPDN obeys stable and robust recovery guarantees.
Revised:  Given an estimate for the noise level a common convex approach to recover the signal is basis pursuit denoising (BPDN) . If the measurement matrix has the robust null space property with respect to the \ell_2-norm, basis pursuit denoising obeys stable and robust recovery guarantees.
Edit intent: coherence
Original:  Lastly, we explain how the NNLAD can be used for group testing in the recent COVID-19 crisis and why contamination of specimens may be modeled as peaky noise, which favors \ell_1 based data fidelity terms .
Revised:  Lastly, we explain how the NNLAD can be used for viral detection in the recent COVID-19 crisis and why contamination of specimens may be modeled as peaky noise, which favors \ell_1 based data fidelity terms .
Edit intent: meaning-changed
Original:  We prove that there exists a minimal splitting with respect to which a game is separable . Moreover we prove that in every strategic equivalence class, there is a game separable with respect to the minimal splitting in the class. This game is also graphical with respect to the smallest graph in the class, which represent a minimal complexity graphical description for the game.
Revised:  We prove that there exists a minimal splitting with respect to which a game is separable , providing a minimal complexity description for the game.
Edit intent: coherence
Original: High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes , e. g., the venation network of leaves.
Revised: High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes .
Edit intent: coherence
Original:  However, the acquisition of high-resolution (HR) imagery of roots in situ remains a challenge. We apply super-resolution (SR)  convolutional neural networks (CNNs) to boost the resolution capability of a backscatter X-ray system designed to image buried roots.
Revised:  However, the acquisition of high-resolution (HR) imagery of plant roots is more challenging than above-ground data collection. Thus, an effective super-resolution (SR) convolutional neural networks (CNNs) to boost the resolution capability of a backscatter X-ray system designed to image buried roots.
Edit intent: meaning-changed
Original:  We apply super-resolution (SR)  convolutional neural networks (CNNs) to boost the resolution capability of a backscatter X-ray system designed to image buried roots.
Revised:  We apply super-resolution (SR) algorithm is desired for overcoming resolution limitations of sensors, reducing storage space requirements, and boosting the performance of later analysis, such as automatic segmentation. We propose a SR framework for enhancing images of plant roots by using convolutional neural networks (CNNs) to boost the resolution capability of a backscatter X-ray system designed to image buried roots.
Edit intent: meaning-changed
Original:  We apply super-resolution (SR)  convolutional neural networks (CNNs) to boost the resolution capability of a backscatter X-ray system designed to image buried roots. To overcome limited available backscatter X-ray data for training, we compare three alternatives for training  : i)  non-plant-root images, ii)  plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network).
Revised:  We apply super-resolution (SR) convolutional neural networks (CNNs) . We compare three alternatives for training : i) non-plant-root images, ii) plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network).
Edit intent: clarity
Original:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training  : i)  non-plant-root images, ii)  plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network).
Revised:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training the SR model : i) non-plant-root images, ii) plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network).
Edit intent: meaning-changed
Original:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training  : i)  non-plant-root images, ii)  plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network).
Revised:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training : i) training with non-plant-root images, ii) plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network).
Edit intent: fluency
Original:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training  : i)  non-plant-root images, ii)  plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network).
Revised:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training : i) non-plant-root images, ii) training with plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network).
Edit intent: clarity
Original:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training  : i)  non-plant-root images, ii)  plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images and two deep learning approaches i) Fast Super Resolution Convolutional Neural Network and ii) Super Resolution Generative Adversarial Network). We evaluate SR performance using signal to noise ratio (SNR) and intersection over union (IoU) metrics when segmenting the SR images. In our experiments, we observe that the studied SR models improve the quality of the low-resolution images (LR) of plant roots of an unseen dataset in terms of SNR. Likewise, we demonstrate that SR pre-processing boosts the performance of a machine learning system trained to separate plant roots from their background.
Revised:  To overcome limited available backscatter X-ray data for training, we compare three alternatives for training : i) non-plant-root images, ii) plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images . We demonstrate on a collection of publicly available datasets that the SR pre-processing boosts the performance of a machine learning system trained to separate plant roots from their background.
Edit intent: clarity
Original:  In our experiments, we observe that the studied SR models improve the quality of the low-resolution images (LR) of plant roots of an unseen dataset in terms of SNR. Likewise, we demonstrate that SR pre-processing boosts the performance of a machine learning system trained to separate plant roots from their background. In addition, we show examples of backscatter X-ray images upscaled by using the SR model. The current technology for non-intrusive root imaging acquires noisy and LR images. In this study, we show that this issue can be tackled by the incorporation of a deep-learning based SR model in the image formation process .
Revised:  In our experiments, we observe that the studied SR models improve the quality of the low-resolution images (LR) of plant roots of an unseen dataset in terms of SNR. Likewise, we demonstrate that SR models outperform the basic bicubic interpolation even when trained with non-root datasets. Also, our segmentation experiments show that high performance on this task can be achieved independently of the SNR. Therefore, we conclude that the quality of the image enhancement depends on the application .
Edit intent: meaning-changed
Original:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Revised:  This dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Edit intent: coherence
Original:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Revised:  We propose this dynamic equation also describes the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Edit intent: fluency
Original:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Revised:  We propose this dynamic equation also for the describing the evolution of the number of infected people for 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Edit intent: clarity
Original:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Revised:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses ( including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Edit intent: fluency
Original:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Revised:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 ). We validate our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Edit intent: coherence
Original:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Revised:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with experimental data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Edit intent: clarity
Original:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Revised:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium , and compare them with the predictions provided by using the logistic model.
Edit intent: clarity
Original:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions provided by using the logistic model.
Revised:  We propose this dynamic equation also for the describing the evolution of the number of infected people by 13 common respiratory viruses , including the COVID-19 . We have compared our theoretical predictions with the real data for Italy and Belgium and, as a benchmarking operation, also with the predictions of the logistic model.
Edit intent: clarity
Original:  We find that are predictions are in good agreement with the real data from the beginning of the appearance of the COVID-19 , which is not the case for the logistic model that applies only to the few last days.
Revised:  We find that our predictions are in good agreement with the real data from the beginning of the appearance of the COVID-19 , which is not the case for the logistic model that applies only to the few last days.
Edit intent: fluency
Original:  We find that are predictions are in good agreement with the real data from the beginning of the appearance of the COVID-19 , which is not the case for the logistic model that applies only to the few last days.
Revised:  We find that are predictions are in good agreement with the real world since the beginning of the appearance of the COVID-19 , which is not the case for the logistic model that applies only to the few last days.
Edit intent: clarity