wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our analysis has revealed traces of both positive (assimilative ) and negative ( repulsive) influence in how users decide whether to change their opinions , while magnitudes of changed opinions appear to obey a positive influence.
Revised:  Distinguishing between positive (toward opinions of friends ) and negative ( repulsive) influence in how users decide whether to change their opinions , while magnitudes of changed opinions appear to obey a positive influence.
Edit intent: meaning-changed
Original:  Our analysis has revealed traces of both positive (assimilative ) and negative ( repulsive) influence in how users decide whether to change their opinions , while magnitudes of changed opinions appear to obey a positive influence. We also found that individuals with networks that include many ideologically opposite users are less prone to radicalization than those with homogenous ones. Our results indicate that the opinion dynamics of online social network users cannot be described solely by any concrete theoretical opinion formation model; rather, they include patterns of multiple models simultaneously .
Revised:  Our analysis has revealed traces of both positive (assimilative ) and negative ( away from opinions of friends) opinion shifts, we find that the existence and magnitude of opinion shifts are positively related (largely through a linear form or an inverted U-shaped form) to the degree of divergence in opinions between user and their friends. Additionally, we moderate the balance between positive and negative shifts using opinion divergence: if the opinions of the focal user and their friends are too similar or dissimilar, there is a relatively low chance of a positive shift .
Edit intent: meaning-changed
Original:  Noteworthy, we illustrate how our safety mechanism mitigates triple-point faults, which are often beyond the focus of the safety community. Furthermore, on top of handling faults in the ADS, our design can effectively react to its own faults. To reduce specification errors, we developed an executable model of the safety concept in the formal specification language mCRL2.
Revised:  To verify our approach, we developed an executable model of the safety concept in the formal specification language mCRL2.
Edit intent: clarity
Original:  As most of the research focus on probing tasks or hidden states, previous works have found someprimitive patterns of attention head behavior by heuristic analytical methods, but a more system-atic analysis specific on the attention patterns still remains primitive.
Revised:  As most of the research focus on probing tasks or hidden states, previous works have found some primitive patterns of attention head behavior by heuristic analytical methods, but a more system-atic analysis specific on the attention patterns still remains primitive.
Edit intent: fluency
Original:  As most of the research focus on probing tasks or hidden states, previous works have found someprimitive patterns of attention head behavior by heuristic analytical methods, but a more system-atic analysis specific on the attention patterns still remains primitive.
Revised:  As most of the research focus on probing tasks or hidden states, previous works have found someprimitive patterns of attention head behavior by heuristic analytical methods, but a more systematic analysis specific on the attention patterns still remains primitive.
Edit intent: fluency
Original:  In this work, we clearlycluster the attention heatmaps into significantly different patterns through unsupervised cluster-ing on top of a set of proposed features, which corroborates with previous observations.
Revised:  In this work, we clearly cluster the attention heatmaps into significantly different patterns through unsupervised cluster-ing on top of a set of proposed features, which corroborates with previous observations.
Edit intent: fluency
Original:  In this work, we clearlycluster the attention heatmaps into significantly different patterns through unsupervised cluster-ing on top of a set of proposed features, which corroborates with previous observations.
Revised:  In this work, we clearlycluster the attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features, which corroborates with previous observations.
Edit intent: fluency
Original:  In this paper, we propose CABiNet (Context Aggregated Bi-lateral Network) , a dual branch convolutional neural network (CNN) , with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy.
Revised:  In this paper, we propose Context Aggregation Network , a dual branch convolutional neural network (CNN) , with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy.
Edit intent: coherence
Original:  In this paper, we propose CABiNet (Context Aggregated Bi-lateral Network) , a dual branch convolutional neural network (CNN) , with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy.
Revised:  In this paper, we propose CABiNet (Context Aggregated Bi-lateral Network) , a dual branch convolutional neural network , with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy.
Edit intent: clarity
Original:  Building upon the existing multi-branch architectures for high-speed semantic segmentation, we design a cheap high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads.
Revised:  Building upon the existing dual branch architectures for high-speed semantic segmentation, we design a cheap high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads.
Edit intent: meaning-changed
Original:  Specifically, we achieve 76.6\% and 75.9 \% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. Codes and training models will be made publicly available .
Revised:  We evaluate our method on two semantic segmentation datasets, namely Cityscapes dataset and UAVid dataset. For Cityscapes test set, our model achieves state-of-the-art results with mIOU of 75.9 \% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. Codes and training models will be made publicly available .
Edit intent: meaning-changed
Original:  Specifically, we achieve 76.6\% and 75.9 \% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. Codes and training models will be made publicly available .
Revised:  Specifically, we achieve 76.6\% and 75.9 \%, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. Codes and training models will be made publicly available .
Edit intent: clarity
Original:  Specifically, we achieve 76.6\% and 75.9 \% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. Codes and training models will be made publicly available .
Revised:  Specifically, we achieve 76.6\% and 75.9 \% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. With regards to UAVid dataset, our proposed network achieves mIOU score of 63.5\% with high execution speed (15 FPS) .
Edit intent: meaning-changed
Original: Multiplexed immunofluorescence tissue imagingenables precise spatial assessment of protein expression in medical resection specimens. However, tissue sections are stained with a mixture of antibodies , DNA and RNA markers, the detection of weak or broken edges due to fluorescent membrane staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis.
Revised: Multiplexed immuno-fluorescence tissue imaging, allowing simultaneous detection of molecular properties of cells, is an essential tool for characterizing the complex cellular mechanisms in translational research and clinical practice. New image analysis approaches are needed because tissue section stained with a mixture of antibodies , DNA and RNA markers, the detection of weak or broken edges due to fluorescent membrane staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis.
Edit intent: meaning-changed
Original:  However, tissue sections are stained with a mixture of antibodies , DNA and RNA markers, the detection of weak or broken edges due to fluorescent membrane staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis.
Revised:  However, tissue sections are stained with a mixture of protein , DNA and RNA markers, the detection of weak or broken edges due to fluorescent membrane staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis.
Edit intent: meaning-changed
Original:  However, tissue sections are stained with a mixture of antibodies , DNA and RNA markers, the detection of weak or broken edges due to fluorescent membrane staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis.
Revised:  However, tissue sections are stained with a mixture of antibodies , DNA and RNA biomarkers are introducing various complexities, including spurious edges due to fluorescent membrane staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis.
Edit intent: meaning-changed
Original:  However, tissue sections are stained with a mixture of antibodies , DNA and RNA markers, the detection of weak or broken edges due to fluorescent membrane staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis.
Revised:  However, tissue sections are stained with a mixture of antibodies , DNA and RNA markers, the detection of weak or broken edges due to fluorescent staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis.
Edit intent: clarity
Original:  However, tissue sections are stained with a mixture of antibodies , DNA and RNA markers, the detection of weak or broken edges due to fluorescent membrane staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis. Sometimes detecting these kinds of edges which are even lacking discrimination or judgment by human visual intelligence. We have built a GPU client-server and have developed a hybrid system combining the stochastic random-reaction-seed (RRS) method and deep neural learning U-net to identify cell-membrane accurately and automatically.
Revised:  However, tissue sections are stained with a mixture of antibodies , DNA and RNA markers, the detection of weak or broken edges due to fluorescent membrane staining artifacts between touching or overlapping cells . We have developed the RRScell method harnessing the stochastic random-reaction-seed (RRS) method and deep neural learning U-net to identify cell-membrane accurately and automatically.
Edit intent: coherence
Original:  We have built a GPU client-server and have developed a hybrid system combining the stochastic random-reaction-seed (RRS) method and deep neural learning U-net to identify cell-membrane accurately and automatically.
Revised:  We have built a GPU client-server and have developed a hybrid system combining the stochastic random-reaction-seed (RRS) algorithm and deep neural learning U-net to identify cell-membrane accurately and automatically.
Edit intent: clarity
Original:  We have built a GPU client-server and have developed a hybrid system combining the stochastic random-reaction-seed (RRS) method and deep neural learning U-net to identify cell-membrane accurately and automatically.
Revised:  We have built a GPU client-server and have developed a hybrid system combining the stochastic random-reaction-seed (RRS) method and deep neural learning U-net to extract single-cell resolution profiling-map of gene expression over a million cells tissue section accurately and automatically.
Edit intent: meaning-changed
Original:  Furthermore, we have designed a high performance machine-learning AI-pipeline in quantifying spatial distribution of cell phenotypes from tissue images with various complexities, and extract single-cell or even subcellular resolution profiling-map of protein and RNA expression over a million cells tissue section .
Revised:  Furthermore, with the use of manifold learning technique UMAP for cell phenotype cluster analysis, the AI-driven RRScell has equipped with a marker-based image cytometry analysis tool (markerUMAP) in quantifying spatial distribution of cell phenotypes from tissue images with various complexities, and extract single-cell or even subcellular resolution profiling-map of protein and RNA expression over a million cells tissue section .
Edit intent: meaning-changed
Original:  Furthermore, we have designed a high performance machine-learning AI-pipeline in quantifying spatial distribution of cell phenotypes from tissue images with various complexities, and extract single-cell or even subcellular resolution profiling-map of protein and RNA expression over a million cells tissue section .
Revised:  Furthermore, we have designed a high performance machine-learning AI-pipeline in quantifying spatial distribution of cell phenotypes from tissue images with a mixture of biomarkers. The results achieved in this study suggest that RRScell provides a robust enough way for extracting cytometric single cell morphology as well as biomarker content in various tissue types, while the build-in markerUMAP tool secures the efficiency of dimension reduction, making it viable as a general tool in the spatial analysis of high dimensional tissue image .
Edit intent: meaning-changed
Original:  In this paper, we propose  a safe trajectory optimization and control approach (Safe-CDDP) for systems under additive uncertainties and non-linear safety constraints based on constrained differential dynamic programming (DDP).
Revised:  In this paper, we propose Safe-CDDP, a safe trajectory optimization and control approach (Safe-CDDP) for systems under additive uncertainties and non-linear safety constraints based on constrained differential dynamic programming (DDP).
Edit intent: fluency
Original:  We go on to use the general method to derive a novel, mathematical robust, finite difference method for solving the 2D diffusion equation within a 1D reduced-dimension model  .
Revised:  We go on to use the general method to derive a novel, mathematical robust, finite difference method for solving the 2D diffusion equation within a 1D reduced-dimension model . The methods described here can be used to improve the accuracy of many reduced-dimension models while retaining benefits of system simplification .
Edit intent: meaning-changed
Original:  Combined with cross-entropy, the SCL loss we propose obtains improvements over a strong RoBERTa-Large baseline on multiple datasets of the GLUE benchmark in both the high-data and low-data regimes, and it does not require any specialized architecture, data augmentation of any kind , memory banks, or additional unsupervised data.
Revised:  Combined with cross-entropy, our proposed SCL loss obtains significant improvements over a strong RoBERTa-Large baseline on multiple datasets of the GLUE benchmark in both the high-data and low-data regimes, and it does not require any specialized architecture, data augmentation of any kind , memory banks, or additional unsupervised data.
Edit intent: clarity
Original:  Combined with cross-entropy, the SCL loss we propose obtains improvements over a strong RoBERTa-Large baseline on multiple datasets of the GLUE benchmark in both the high-data and low-data regimes, and it does not require any specialized architecture, data augmentation of any kind , memory banks, or additional unsupervised data.
Revised:  Combined with cross-entropy, the SCL loss we propose obtains improvements over a strong RoBERTa-Large baseline on multiple datasets of the GLUE benchmark in few-shot learning settings, without requiring specialized architecture, data augmentation of any kind , memory banks, or additional unsupervised data.
Edit intent: clarity
Original:  Combined with cross-entropy, the SCL loss we propose obtains improvements over a strong RoBERTa-Large baseline on multiple datasets of the GLUE benchmark in both the high-data and low-data regimes, and it does not require any specialized architecture, data augmentation of any kind , memory banks, or additional unsupervised data.
Revised:  Combined with cross-entropy, the SCL loss we propose obtains improvements over a strong RoBERTa-Large baseline on multiple datasets of the GLUE benchmark in both the high-data and low-data regimes, and it does not require any specialized architecture, data augmentations , memory banks, or additional unsupervised data.
Edit intent: clarity
Original: Energy preserving reduced-order models are developed for the rotating thermal shallow water (RTSW) equation in the non-canonical Hamiltonian /Poisson form . The RTSW equation is discretized in space by the skew-symmetric finite-difference operators to preserve the Hamiltonian structure.
Revised: Energy preserving reduced-order models are developed for the rotating thermal shallow water (RTSW) equation in the non-canonical Hamiltonian form with the state-dependent Poisson matrix. The high fidelity full solutions are obtained by discretizing the RTSWE in space with skew-symmetric finite-difference operators to preserve the Hamiltonian structure.
Edit intent: meaning-changed
Original:  An energy preserving, computationally efficient reduced-order model (ROM) is constructed by proper orthogonal decomposition (POD) with the Galerkin projection  .
Revised:  An energy preserving, computationally efficient reduced-order model (ROM) is constructed in the same way as the full order model (FOM), preserving the reduced skew-symmetric structure and integrating in time with the AVF method. Relying on structure-preserving discretizations in space and time and applying proper orthogonal decomposition (POD) with the Galerkin projection .
Edit intent: meaning-changed
Original:  An energy preserving, computationally efficient reduced-order model (ROM) is constructed by proper orthogonal decomposition (POD) with the Galerkin projection  .
Revised:  An energy preserving, computationally efficient reduced-order model (ROM) is constructed by proper orthogonal decomposition (POD) with the Galerkin projection , an energy preserving reduced order model (ROM) is constructed .
Edit intent: coherence
Original:  The nonlinearities in the ROM are efficiently computed by discrete empirical interpolation method (DEIM) . Preservation of the energy (Hamiltonian), and other conserved quantities ;
Revised:  The nonlinearities in the ROM are efficiently computed by discrete empirical interpolation method (DEIM) method to reduce the computational cost. The computation of the reduced-order solutions is accelerated further by the use of tensor techniques. The overall procedure yields a clear separation of the offline and online computational cost of the reduced solutions. The accuracy and computational efficiency of the ROMs are demonstrated for a numerical test problem. Preservation of the energy (Hamiltonian), and other conserved quantities ;
Edit intent: meaning-changed
Original:  The nonlinearities in the ROM are efficiently computed by discrete empirical interpolation method (DEIM) . Preservation of the energy (Hamiltonian), and other conserved quantities ; total mass, total buoyancyand total potential vorticity , by the reduced-order solutions are demonstrated which ensures the long term stability of the reduced-order solutions .
Revised:  The nonlinearities in the ROM are efficiently computed by discrete empirical interpolation method (DEIM) . Preservation of the energy (Hamiltonian), and other conserved quantities , i.e. mass, buoyancy, and total vorticity show that the reduced-order solutions are demonstrated which ensures the long term stability of the reduced-order solutions .
Edit intent: clarity
Original:  total mass, total buoyancyand total potential vorticity , by the reduced-order solutions are demonstrated which ensures the long term stability of the reduced-order solutions . The accuracy and computational efficiency of the ROMs are shown by a numerical test problem .
Revised:  total mass, total buoyancyand total potential vorticity , by the reduced-order solutions are demonstrated which ensures the long term stability of the solutions while exhibiting several orders of magnitude computational speedup over the FOM .
Edit intent: meaning-changed
Original:  However, most existing algorithms do not take sliding contactsinto account or consider them only for specialized scenarios. We propose a method to extend constraint-based planning framework using contact transitions for sliding contacts.
Revised:  However, most existing algorithms concentrate on the control and learning aspect of sliding contacts, but do not embed the problem into a principled framework to provide guarantees on completeness or optimality. To address this problem, we propose a method to extend constraint-based planning framework using contact transitions for sliding contacts.
Edit intent: meaning-changed
Original:  Moreover, in addition to the robots' own imperfect models, day-to-day operations entail various sources of errors that , despite small, rapidly accumulate as tasks change and robots are re-programmed, often requiring time-consuming calibrations.
Revised:  Moreover, in addition to the robots' own imperfect models, day-to-day operations entail various sources of errors that despite being small rapidly accumulate. This happens as tasks change and robots are re-programmed, often requiring time-consuming calibrations.
Edit intent: coherence
Original:  The approach is validated in a needle threading experiment, with a 150 \mu m thread and a 300 \mu m needle  hole, and a USB insertion task using two 7-axis Panda robots.
Revised:  The approach is validated in a needle threading experiment, with a 150 \mu m thread and a 300 \mu m needle hole, and a USB insertion task using two 7-axis Panda robots.
Edit intent: fluency
Original:  The approach is validated in a needle threading experiment, with a 150 \mu m thread and a 300 \mu m needle  hole, and a USB insertion task using two 7-axis Panda robots.
Revised:  The approach is validated in a needle threading experiment, with a 150 \mu m thread and a 300 \mu hole, and a USB insertion task using two 7-axis Panda robots.
Edit intent: clarity
Original:  The approach is validated in a needle threading experiment, with a 150 \mu m thread and a 300 \mu m needle  hole, and a USB insertion task using two 7-axis Panda robots.
Revised:  The approach is validated in a needle threading experiment, with a 150 \mu m thread and a 300 \mu m needle m hole, and a USB insertion task using two 7-axis Panda robots.
Edit intent: fluency