wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We further explore the effect of script vs. morphological typology in cross-lingual transfer by fine-tuning multi-lingual models on languages which are a) morphologically distinct, but use the same script, b) morphologically similar, but use a distinct script, or c) are morphologically similar and use the same script.
Revised:  We further explore the effect of script vs. morphological typology in cross-lingual transfer by fine-tuning multi-lingual models on languages which are a) typologically distinct, but use the same script, b) morphologically similar, but use a distinct script, or c) are morphologically similar and use the same script.
Edit intent: fluency
Original:  We further explore the effect of script vs. morphological typology in cross-lingual transfer by fine-tuning multi-lingual models on languages which are a) morphologically distinct, but use the same script, b) morphologically similar, but use a distinct script, or c) are morphologically similar and use the same script.
Revised:  We further explore the effect of script vs. morphological typology in cross-lingual transfer by fine-tuning multi-lingual models on languages which are a) morphologically distinct, but use the same script, b) typologically similar, but use a distinct script, or c) are morphologically similar and use the same script.
Edit intent: clarity
Original:  We further explore the effect of script vs. morphological typology in cross-lingual transfer by fine-tuning multi-lingual models on languages which are a) morphologically distinct, but use the same script, b) morphologically similar, but use a distinct script, or c) are morphologically similar and use the same script.
Revised:  We further explore the effect of script vs. morphological typology in cross-lingual transfer by fine-tuning multi-lingual models on languages which are a) morphologically distinct, but use the same script, b) morphologically similar, but use a distinct script, or c) are typologically similar and use the same script.
Edit intent: fluency
Original:  Our learning rates demonstrate consistency of the sample estimator under drastically weaker assumptions than the state-of-the art, allowing the much broader application of conditional mean embeddings to more complex ML/RL settings involving infinite dimensional RKHS and continuous state spaces.
Revised:  We derive explicit, adaptive convergence rates for the sample estimator under drastically weaker assumptions than the state-of-the art, allowing the much broader application of conditional mean embeddings to more complex ML/RL settings involving infinite dimensional RKHS and continuous state spaces.
Edit intent: clarity
Original:  Our learning rates demonstrate consistency of the sample estimator under drastically weaker assumptions than the state-of-the art, allowing the much broader application of conditional mean embeddings to more complex ML/RL settings involving infinite dimensional RKHS and continuous state spaces.
Revised:  Our learning rates demonstrate consistency of the sample estimator under the misspecifed setting, where the target operator is not Hilbert-Schmidt or bounded with respect to the input/output RKHSs. We demonstrate that in certain parameter regimes, we can achieve uniform convergence rates in the output RKHS. We hope our analyses will allow the much broader application of conditional mean embeddings to more complex ML/RL settings involving infinite dimensional RKHS and continuous state spaces.
Edit intent: meaning-changed
Original:  Our learning rates demonstrate consistency of the sample estimator under drastically weaker assumptions than the state-of-the art, allowing the much broader application of conditional mean embeddings to more complex ML/RL settings involving infinite dimensional RKHS and continuous state spaces.
Revised:  Our learning rates demonstrate consistency of the sample estimator under drastically weaker assumptions than the state-of-the art, allowing the much broader application of conditional mean embeddings to more complex ML/RL settings involving infinite dimensional RKHSs and continuous state spaces.
Edit intent: fluency
Original:  NEW FINDINGS: We introduce a new deep neural network architecture, the Focus U-Net , which achieves state-of-the-art performance for polyp segmentationacross five public datasets containing images of polyps obtained during colonoscopy.
Revised:  However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity to leverage computer-aided systems to support clinicians and reduce the number of polyps missed. Method: In this work we introduce the Focus U-Net, a novel dual attention-gated deep neural network architecture, the Focus U-Net , which achieves state-of-the-art performance for polyp segmentationacross five public datasets containing images of polyps obtained during colonoscopy.
Edit intent: meaning-changed
Original:  NEW FINDINGS: We introduce a new deep neural network architecture, the Focus U-Net , which achieves state-of-the-art performance for polyp segmentationacross five public datasets containing images of polyps obtained during colonoscopy.
Revised:  NEW FINDINGS: We introduce a new deep neural network , which achieves state-of-the-art performance for polyp segmentationacross five public datasets containing images of polyps obtained during colonoscopy.
Edit intent: clarity
Original:  NEW FINDINGS: We introduce a new deep neural network architecture, the Focus U-Net , which achieves state-of-the-art performance for polyp segmentationacross five public datasets containing images of polyps obtained during colonoscopy.
Revised:  NEW FINDINGS: We introduce a new deep neural network architecture, the Focus U-Net , which combines efficient spatial and channel-based attention into a single Focus Gate module to encourage selective learning of polyp features. The Focus U-Net further incorporates short-range skip connections and deep supervision. Furthermore, we introduce the Hybrid Focal loss, a new compound loss function based on the Focal loss and Focal Tversky loss, to handle class-imbalanced image segmentation. For our experiments, we selected five public datasets containing images of polyps obtained during colonoscopy.
Edit intent: meaning-changed
Original:  NEW FINDINGS: We introduce a new deep neural network architecture, the Focus U-Net , which achieves state-of-the-art performance for polyp segmentationacross five public datasets containing images of polyps obtained during colonoscopy. LIMITATIONS: The model has been validated on images taken during colonoscopy but requires validation on live video data to ensure generalisability. IMPACT: Once validated on live video data, our polyp segmentation algorithm could be integrated into colonoscopypractice and assist gastroenterologists by reducing the number of polyps missed
Revised:  NEW FINDINGS: We introduce a new deep neural network architecture, the Focus U-Net , which achieves state-of-the-art performance for polyp segmentationacross five public datasets containing images of polyps obtained during optical colonoscopy: CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB, ETIS-Larib PolypDB and EndoScene test set. To evaluate model performance, we use the Dice similarity coefficient (DSC) and Intersection over Union (IoU) metrics. Results: Our model achieves state-of-the-art results for both CVC-ClinicDB and Kvasir-SEG, with a mean DSC of 0.941 and 0.910, respectively. When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0.878 and mean IoU of 0.809, a 14\% and 15\% improvement over the previous state-of-the-art results of 0.768 and 0.702, respectively. Conclusions: This study shows the potential for deep learning to provide fast and accurate polyp segmentation results for use during colonoscopy. The Focus U-Net may be adapted for future use in newer non-invasive screening and more broadly to other biomedical image segmentation tasks involving class imbalance and requiring efficiency.
Edit intent: meaning-changed
Original:  The approximation of the state-transition operators  determined primarily by matrices of parameters to be identified based on data measured from a given system, is approached by proving the existence of low-rank approximations of submatrices of the trajectory matrices corresponding to the measured data, that can be used to compute approximate sparse representations of the matrices of parameters.
Revised:  The approximation of the state-transition operators that are determined primarily by matrices of parameters to be identified based on data measured from a given system, is approached by proving the existence of low-rank approximations of submatrices of the trajectory matrices corresponding to the measured data, that can be used to compute approximate sparse representations of the matrices of parameters.
Edit intent: fluency
Original:  The approximation of the state-transition operators  determined primarily by matrices of parameters to be identified based on data measured from a given system, is approached by proving the existence of low-rank approximations of submatrices of the trajectory matrices corresponding to the measured data, that can be used to compute approximate sparse representations of the matrices of parameters.
Revised:  The approximation of the state-transition operators determined primarily by matrices of parameters to be identified based on data measured from a given system, is approached by identifying conditions for the existence of low-rank approximations of submatrices of the trajectory matrices corresponding to the measured data, that can be used to compute approximate sparse representations of the matrices of parameters.
Edit intent: clarity
Original:  Prototypical algorithms and numerical implementations of the aforementioned techniques to the approximate identification and predictive simulation of time series models with symmetries and nonlinear structured dynamical systems in theoretical physics, fluid dynamics and weather forecasting are presented.
Revised:  Prototypical algorithms based on the aforementioned techniques to the approximate identification and predictive simulation of time series models with symmetries and nonlinear structured dynamical systems in theoretical physics, fluid dynamics and weather forecasting are presented.
Edit intent: clarity
Original:  Prototypical algorithms and numerical implementations of the aforementioned techniques to the approximate identification and predictive simulation of time series models with symmetries and nonlinear structured dynamical systems in theoretical physics, fluid dynamics and weather forecasting are presented.
Revised:  Prototypical algorithms and numerical implementations of the aforementioned techniques together with some applications to approximate identification and predictive simulation of time series models with symmetries and nonlinear structured dynamical systems in theoretical physics, fluid dynamics and weather forecasting are presented.
Edit intent: meaning-changed
Original:  Our implementations show that EasyFL requires only three lines of code  to build a vanilla FL application , at least 10x lesser than other platforms . Besides, our evaluations demonstrate that EasyFL expedites  training by 1.5x.
Revised:  Compared with other platforms, EasyFL not only requires just three lines of code to build a vanilla FL application , at least 10x lesser than other platforms . Besides, our evaluations demonstrate that EasyFL expedites training by 1.5x.
Edit intent: coherence
Original:  Our implementations show that EasyFL requires only three lines of code  to build a vanilla FL application , at least 10x lesser than other platforms . Besides, our evaluations demonstrate that EasyFL expedites  training by 1.5x.
Revised:  Our implementations show that EasyFL requires only three lines of code (at least 10x lesser) to build a vanilla FL application , at least 10x lesser than other platforms . Besides, our evaluations demonstrate that EasyFL expedites training by 1.5x.
Edit intent: meaning-changed
Original:  Our implementations show that EasyFL requires only three lines of code  to build a vanilla FL application , at least 10x lesser than other platforms . Besides, our evaluations demonstrate that EasyFL expedites  training by 1.5x.
Revised:  Our implementations show that EasyFL requires only three lines of code to build a vanilla FL application but also incurs lower training overhead . Besides, our evaluations demonstrate that EasyFL expedites training by 1.5x.
Edit intent: meaning-changed
Original:  Our implementations show that EasyFL requires only three lines of code  to build a vanilla FL application , at least 10x lesser than other platforms . Besides, our evaluations demonstrate that EasyFL expedites  training by 1.5x.
Revised:  Our implementations show that EasyFL requires only three lines of code to build a vanilla FL application , at least 10x lesser than other platforms . Besides, our evaluations demonstrate that EasyFL expedites distributed training by 1.5x.
Edit intent: meaning-changed
Original:  As a  well-known problem in NPC, the Hamiltonian Cycle Problem can be now resolved practically in deterministic polynomial time , so this problem is in P and we prove that the conjecture of P=NP holds .
Revised:  As a result, the well-known problem in NPC, the Hamiltonian Cycle Problem can be now resolved practically in deterministic polynomial time , so this problem is in P and we prove that the conjecture of P=NP holds .
Edit intent: coherence
Original:  As a  well-known problem in NPC, the Hamiltonian Cycle Problem can be now resolved practically in deterministic polynomial time , so this problem is in P and we prove that the conjecture of P=NP holds .
Revised:  As a well-known problem HCP in NPC can be now resolved practically in deterministic polynomial time , so this problem is in P and we prove that the conjecture of P=NP holds .
Edit intent: clarity
Original:  As a  well-known problem in NPC, the Hamiltonian Cycle Problem can be now resolved practically in deterministic polynomial time , so this problem is in P and we prove that the conjecture of P=NP holds .
Revised:  As a well-known problem in NPC, the Hamiltonian Cycle Problem can be now resolved practically in deterministic polynomial time for general graphs in the worst case .
Edit intent: meaning-changed
Original: This paper introduces a general framework for explicitly constructing universal deep neural models with inputs from a complete, separable, and locally-compact metric space \mathcal{X outputs in the Wasserstein-1  P_1( \mathcal{Y .
Revised: We introduce a general framework for explicitly constructing universal deep neural models with inputs from a complete, separable, and locally-compact metric space \mathcal{X outputs in the Wasserstein-1 P_1( \mathcal{Y .
Edit intent: clarity
Original: This paper introduces a general framework for explicitly constructing universal deep neural models with inputs from a complete, separable, and locally-compact metric space \mathcal{X outputs in the Wasserstein-1  P_1( \mathcal{Y .
Revised: This paper introduces a general framework for approximating regular conditional distributions (RCDs). Our approximations of these RCDs are implemented by a new class of geometric deep learning models with inputs from a complete, separable, and locally-compact metric space \mathcal{X outputs in the Wasserstein-1 P_1( \mathcal{Y .
Edit intent: meaning-changed
Original: This paper introduces a general framework for explicitly constructing universal deep neural models with inputs from a complete, separable, and locally-compact metric space \mathcal{X outputs in the Wasserstein-1  P_1( \mathcal{Y .
Revised: This paper introduces a general framework for explicitly constructing universal deep neural models with inputs in \mathbb{R outputs in the Wasserstein-1 P_1( \mathcal{Y .
Edit intent: meaning-changed
Original: This paper introduces a general framework for explicitly constructing universal deep neural models with inputs from a complete, separable, and locally-compact metric space \mathcal{X outputs in the Wasserstein-1  P_1( \mathcal{Y .
Revised: This paper introduces a general framework for explicitly constructing universal deep neural models with inputs from a complete, separable, and locally-compact metric space \mathcal{X outputs in the Wasserstein-1 space P_1( \mathcal{Y .
Edit intent: meaning-changed
Original: This paper introduces a general framework for explicitly constructing universal deep neural models with inputs from a complete, separable, and locally-compact metric space \mathcal{X outputs in the Wasserstein-1  P_1( \mathcal{Y .
Revised: This paper introduces a general framework for explicitly constructing universal deep neural models with inputs from a complete, separable, and locally-compact metric space \mathcal{X outputs in the Wasserstein-1 P_1( \mathbb{R .
Edit intent: meaning-changed
Original:  We find that any model built using the proposed framework is dense in the space C(\mathcal{X continuous functions from \mathcal{X to \mathcal{P}_1( \mathcal{Y .
Revised:  We find that any model built using the proposed framework is dense in the space C(\mathcal{X continuous functions from \mathbb{R to \mathcal{P}_1( \mathcal{Y .
Edit intent: meaning-changed
Original:  We find that any model built using the proposed framework is dense in the space C(\mathcal{X continuous functions from \mathcal{X to \mathcal{P}_1( \mathcal{Y .
Revised:  We find that any model built using the proposed framework is dense in the space C(\mathcal{X continuous functions from \mathcal{X to \mathcal{P}_1( \mathbb{R .
Edit intent: meaning-changed
Original:  We identify two methods in which the curse of dimensionality can be broken.The first approach constructs subsets of C(\mathcal{X ,\mathcal{P}_1( \mathcal{Y can be efficiently approximated .
Revised:  We identify two methods in which the curse of dimensionality "; i.e.: the number of parameters determining the approximating neural network depends only polynomially on the involved dimension and the approximation error. The first solution describes functions in C(\mathbb{R ,\mathcal{P}_1( \mathcal{Y can be efficiently approximated .
Edit intent: meaning-changed
Original: The first approach constructs subsets of C(\mathcal{X ,\mathcal{P}_1( \mathcal{Y can be efficiently approximated . In the second approach ^d, on which any function in C(\mathbb{R}^d} , given any fixed f \in C(\mathcal{X \mathcal{P}_1( \mathcal{Y)) , we build non-trivial subsets of \mathcal{X} on which f }  can be efficiently approximated.
Revised: The first approach constructs subsets of C(\mathcal{X ,\mathcal{P}_1( \mathcal{Y can be efficiently approximated on any compact subset of \mathbb{R the second approach ^d, on which any function in C(\mathbb{R}^d} , given any fixed f \in C(\mathcal{X \mathcal{P}_1( \mathcal{Y)) , we build non-trivial subsets of \mathcal{X} on which f } can be efficiently approximated.
Edit intent: meaning-changed
Original:  In the second approach ^d, on which any function in C(\mathbb{R}^d} , given any fixed f \in C(\mathcal{X \mathcal{P}_1( \mathcal{Y)) , we build non-trivial subsets of \mathcal{X} on which f }  can be efficiently approximated.
Revised:  In the second approach describes sets in \mathbb{R^d, on which any function in C(\mathbb{R}^d} , given any fixed f \in C(\mathcal{X \mathcal{P}_1( \mathcal{Y)) , we build non-trivial subsets of \mathcal{X} on which f } can be efficiently approximated.
Edit intent: meaning-changed
Original:  In the second approach ^d, on which any function in C(\mathbb{R}^d} , given any fixed f \in C(\mathcal{X \mathcal{P}_1( \mathcal{Y)) , we build non-trivial subsets of \mathcal{X} on which f }  can be efficiently approximated.
Revised:  In the second approach ^d, on which any function in C(\mathbb{R}^d} , \mathcal{P}_1( \mathcal{Y)) , we build non-trivial subsets of \mathcal{X} on which f } can be efficiently approximated.
Edit intent: coherence
Original:  In the second approach ^d, on which any function in C(\mathbb{R}^d} , given any fixed f \in C(\mathcal{X \mathcal{P}_1( \mathcal{Y)) , we build non-trivial subsets of \mathcal{X} on which f }  can be efficiently approximated.
Revised:  In the second approach ^d, on which any function in C(\mathbb{R}^d} , given any fixed f \in C(\mathcal{X \mathcal{P}_1( )) , we build non-trivial subsets of \mathcal{X} on which f } can be efficiently approximated.
Edit intent: clarity
Original:  In the second approach ^d, on which any function in C(\mathbb{R}^d} , given any fixed f \in C(\mathcal{X \mathcal{P}_1( \mathcal{Y)) , we build non-trivial subsets of \mathcal{X} on which f }  can be efficiently approximated.
Revised:  In the second approach ^d, on which any function in C(\mathbb{R}^d} , given any fixed f \in C(\mathcal{X \mathcal{P}_1( \mathcal{Y)) , we build non-trivial subsets of \mathcal{X} on which f } \mathbb{R can be efficiently approximated.
Edit intent: meaning-changed
Original:  The results are applied to three open problems lying at the interface of applied probability and computational learning theory. We find that the proposed models can approximate any regular conditional distribution of a \mathcal{Y-valued random element Y depending on an \mathcal{X}-valued random element X, with arbitrarily high probability.
Revised:  -valued random element Y depending on an \mathcal{X}-valued random element X, with arbitrarily high probability.
Edit intent: clarity
Original: Most of existing gradient-based meta-learning approaches to few-shot learning assume that all tasks have the same input feature space. However, in the real world scenarios, there are many cases that the input structures of tasks can be different, that is , different tasks may vary in the number of input modalities or the data structure of each modality. Existing gradient-based approaches cannot handle such heterogeneous task distribution (HTD) as different types of tasks only share partial meta-parameters.
Revised: In this paper, we study the problem of how to learn a model-agnostic meta-learner that simultaneously learning from different feature spaces. The reason that most of model-agnostic meta-learner methods cannot handle multiple task spaces is due to less common knowledge for the task instances. The reduction of shared knowledge is because different tasks with different example-level manifolds cannot entirely share the same model architecture. Actually, various tasks only share partial meta-parameters.
Edit intent: coherence
Original:  In this paper, we propose HetMAML, a task-heterogeneous meta-agnostic meta-learning framework that can generalize not only common meta-parameters shared across different types of tasks but also type-specific meta-parameters. Specifically, we design a multi-channel backbone module that encodes the input of each type of tasks into the samelength sequence of modality-specific embeddings. Then, we propose a task-aware multimodal encoder which can automatically take into account the context of task-specific input structures and adaptively project the heterogeneous input spaces to the same lower-dimensional concept space. The extensive experiments on five task-heterogeneous datasets demonstrate that our HetMAML successfully captures both type-specific and shared meta-parameters across heterogeneous tasks which fast adapt to all types of new tasks.
Revised:  For example, for two multi-feature tasks whose example-level manifolds contain a same subspace but their remaining subspaces are not the same, one can imagine that the common knowledge can be the feature extractor for that common subspace, but other subspaces' feature extractors cannot be used between the two tasks.
Edit intent: meaning-changed
Original:  This paper introduces a novel analytical approach that poses the problem of safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader) , and an initially hidden traffic participant (pursuer).
Revised:  This paper introduces a novel analytical approach that poses safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader) , and an initially hidden traffic participant (pursuer).
Edit intent: clarity