wanyu/IteraTeR_full_sent huggingface dataset:
Original: URLanized bistability  is the counterpart of URLanized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Revised: URLanized bistability is the counterpart of URLanized criticality ' (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Edit intent: fluency
Original: URLanized bistability  is the counterpart of URLanized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Revised: URLanized bistability is the counterpart of URLanized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous phase transition, rather than to the critical point of a continuous /second order one.
Edit intent: clarity
Original: URLanized bistability  is the counterpart of URLanized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Revised: URLanized bistability is the counterpart of URLanized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous one.
Edit intent: clarity
Original:  The equations defining the  theory of SOB turn out to be very similar to a mesoscopic (Landau-Ginzburg) theory recently proposed to describe the dynamics in the cerebral cortex.
Revised:  The equations defining the mathematical theory of SOB turn out to be very similar to a mesoscopic (Landau-Ginzburg) theory recently proposed to describe the dynamics in the cerebral cortex.
Edit intent: meaning-changed
Original:  However, the theory for cortex dynamics entails  significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former  there is a parameter that controls the overall system state (in blatant contrast with the very idea of URLanization).
Revised:  The theory for cortex dynamics entails significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former there is a parameter that controls the overall system state (in blatant contrast with the very idea of URLanization).
Edit intent: coherence
Original:  However, the theory for cortex dynamics entails  significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former  there is a parameter that controls the overall system state (in blatant contrast with the very idea of URLanization).
Revised:  However, the theory for cortex dynamics entails , however, some significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former there is a parameter that controls the overall system state (in blatant contrast with the very idea of URLanization).
Edit intent: coherence
Original:  However, the theory for cortex dynamics entails  significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former  there is a parameter that controls the overall system state (in blatant contrast with the very idea of URLanization).
Revised:  However, the theory for cortex dynamics entails significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales and , the fact that in the former there is a parameter that controls the overall system state (in blatant contrast with the very idea of URLanization).
Edit intent: clarity
Original:  However, the theory for cortex dynamics entails  significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former  there is a parameter that controls the overall system state (in blatant contrast with the very idea of URLanization).
Revised:  However, the theory for cortex dynamics entails significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former , but not in the second, there is a parameter that controls the overall system state (in blatant contrast with the very idea of URLanization).
Edit intent: meaning-changed
Original:  Here, we scrutinize --by employing a combination of analytical and computational tools-- the analogies and differences between both theories and explore whether in some limit SOB could play an important role to explain the emergence of scale-invariant neuronal avalanches observed empirically in the cortex.
Revised:  Here, we scrutinize --by employing a combination of analytical and computational tools-- the analogies and differences between both theories and explore whether in some limit SOB can play an important role to explain the emergence of scale-invariant neuronal avalanches observed empirically in the cortex.
Edit intent: fluency
Original:  We conclude that  actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the URLanization mechanism to be effective does not seem to be biologically plausible.
Revised:  We conclude that , actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the URLanization mechanism to be effective does not seem to be biologically plausible.
Edit intent: fluency
Original:  We conclude that  actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the URLanization mechanism to be effective does not seem to be biologically plausible.
Revised:  We conclude that actually, in the limit of infinitely slow synaptic-dynamics, the two theories become identical , but the separation of timescales required for the URLanization mechanism to be effective does not seem to be biologically plausible.
Edit intent: clarity
Original:  We conclude that  actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the URLanization mechanism to be effective does not seem to be biologically plausible.
Revised:  We conclude that actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the timescales required for the URLanization mechanism to be effective does not seem to be biologically plausible.
Edit intent: clarity
Original:  We conclude that  actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the URLanization mechanism to be effective does not seem to be biologically plausible.
Revised:  We conclude that actually, in the limit of infinitely slow synaptic-dynamics, the two theories behave identically , but the separation of timescales required for the URLanization mechanism to be effective do not seem to be biologically plausible.
Edit intent: fluency
Original:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we  scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance  in cortical dynamics.
Revised:  We discuss the key differences between URLanization mechanisms with / without conservation and separated timescales, and in particular, we scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance in cortical dynamics.
Edit intent: fluency
Original:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we  scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance  in cortical dynamics.
Revised:  We discuss the key differences between URLanization mechanisms with or without conservation and with/without infinitely separated timescales. In particular, we scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance in cortical dynamics.
Edit intent: meaning-changed
Original:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we  scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance  in cortical dynamics.
Revised:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we introduce the concept of URLanized collective oscillations' and scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance in cortical dynamics.
Edit intent: meaning-changed
Original:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we  scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance  in cortical dynamics.
Revised:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we scrutinize the implications of our findings in neuroscience, shedding new light into the problem of scale invariance in cortical dynamics.
Edit intent: clarity
Original:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we  scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance  in cortical dynamics.
Revised:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problems of scale invariance in cortical dynamics.
Edit intent: fluency
Original:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we  scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance  in cortical dynamics.
Revised:  We discuss the key differences between URLanization mechanisms with or without conservation and separated timescales, and in particular, we scrutinize the implications of our findings in neuroscience, hopefully shedding new light into the problem of scale invariance and oscillations in cortical dynamics.
Edit intent: meaning-changed
Original:  Since miRNAs have different roles in diseases, the miRNA-disease associations are categorized into multiple types according to their roles. Predicting miRNA-disease associations and types is critical to understand the underlying pathogenesis of human diseases from the molecular level . Results: In this paper, we formulate the problem as a link prediction in knowledge graphs.
Revised:  Since miRNAs have different roles in diseases, the miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miRNA-disease associations and types is critical to understand the underlying pathogenesis of human diseases from the molecular level . Results: In this paper, we formulate the problem as a link prediction in knowledge graphs.
Edit intent: meaning-changed
Original:  Predicting miRNA-disease associations and types is critical to understand the underlying pathogenesis of human diseases from the molecular level . Results: In this paper, we formulate the problem as a link prediction in knowledge graphs.
Revised:  Predicting miRNA-disease association exists or not. However, the roles of miRNAs in diseases are prominently diverged, for instance, Genetic variants of microRNA (mir-15) may affect expression level of miRNAs leading to B cell chronic lymphocytic leukemia, while circulating miRNAs (including mir-1246, mir-1307-3p, etc.) have potentials to detecting breast cancer in the early stage. In this paper, we formulate the problem as a link prediction in knowledge graphs.
Edit intent: meaning-changed
Original:  Predicting miRNA-disease associations and types is critical to understand the underlying pathogenesis of human diseases from the molecular level . Results: In this paper, we formulate the problem as a link prediction in knowledge graphs. We use biomedical knowledge bases to build a knowledge graph of entities representing miRNAs and disease and multi-relations, and we propose a tensor decomposition-based model named TDRC to predict  miRNA-disease associations and their types from the knowledge graph.
Revised:  Predicting miRNA-disease associations and types is critical to understand the underlying pathogenesis of human diseases from the molecular level . Results: In this paper, we aim to predict miRNA-disease associations and their types from the knowledge graph.
Edit intent: clarity
Original:  We use biomedical knowledge bases to build a knowledge graph of entities representing miRNAs and disease and multi-relations, and we propose a tensor decomposition-based model named TDRC to predict  miRNA-disease associations and their types from the knowledge graph.
Revised:  We use biomedical knowledge bases to build a knowledge graph of entities representing miRNAs and disease and multi-relations, and we propose a tensor decomposition-based model named TDRC to predict multi-type miRNA-disease associations and their types from the knowledge graph.
Edit intent: clarity
Original:  We use biomedical knowledge bases to build a knowledge graph of entities representing miRNAs and disease and multi-relations, and we propose a tensor decomposition-based model named TDRC to predict  miRNA-disease associations and their types from the knowledge graph. We have experimentally evaluated our methodand compared it to several baseline methods. The results demonstrate that the proposed method has high-accuracy and high-efficiency performances .
Revised:  We use biomedical knowledge bases to build a knowledge graph of entities representing miRNAs and disease and multi-relations, and we propose a tensor decomposition-based model named TDRC to predict miRNA-disease associations instead of taking them as binary. To this end, we innovatively represent miRNA-disease-type triplets as a tensor and introduce Tensor Decomposition methods to solve the prediction task. Experimental results on two widely-adopted miRNA-disease datasets: HMDD v2.0 and HMDD v3.2 show that tensor decomposition methods improve a recent baseline in a large scale (up to 38\% in top-1 F1). We further propose a novel method, Tensor Decomposition with Relational Constraints (TDRC), which incorporates biological features as relational constraints to further the existing tensor decomposition methods. Compared with two existing tensor decomposition methods, TDRC can produce better performance while being more efficient .
Edit intent: meaning-changed
Original:  In a minimal model of pleiotropic receptor cross-talk, we show that a single pleiotropic receptor is sufficient to simultaneously identify the ligand and accurately sense the concentration of a presented ligand, enabling specific and accurate signaling .
Revised:  In a minimal model we show that a single pleiotropic receptor is sufficient to simultaneously identify the ligand and accurately sense the concentration of a presented ligand, enabling specific and accurate signaling .
Edit intent: clarity
Original:  In a minimal model of pleiotropic receptor cross-talk, we show that a single pleiotropic receptor is sufficient to simultaneously identify the ligand and accurately sense the concentration of a presented ligand, enabling specific and accurate signaling .
Revised:  In a minimal model of pleiotropic receptor cross-talk, we show that a single pleiotropic receptor can simultaneously identify and accurately sense the concentration of a presented ligand, enabling specific and accurate signaling .
Edit intent: clarity
Original:  In a minimal model of pleiotropic receptor cross-talk, we show that a single pleiotropic receptor is sufficient to simultaneously identify the ligand and accurately sense the concentration of a presented ligand, enabling specific and accurate signaling .
Revised:  In a minimal model of pleiotropic receptor cross-talk, we show that a single pleiotropic receptor is sufficient to simultaneously identify the ligand and accurately sense the concentrations of arbitrary unknown ligands present individually or in a mixture .
Edit intent: clarity
Original:  We calculate the fundamental limits of the signaling specificity and accuracy of a pleiotropic receptor arising from the molecular noise both at the receptor level and downstream. We further investigate the effects of common signaling schemes such as Kinetic Proofreading (KPR) and show that KPR does not necessarily increase the signaling specificity once the effects of molecular noise are taken into account .
Revised:  We calculate the fundamental limits of the signaling specificity and accuracy of such signaling schemes .
Edit intent: clarity
Original:  The model serves as an elementary "building block" towards understanding more complex cross-wired receptor-ligand signaling networks , and suggests how cross-talk and pleiotropy might be inherent features of combinatorial signaling in cross-wired networks. The results of the paper suggest a novel outlook at a number of experimental systems .
Revised:  The model serves as an elementary "building block" towards understanding more complex cross-wired receptor-ligand signaling networks .
Edit intent: coherence
Original:  As a result, insurance cash flows and prospective reserves depend on each other in a circular way, and it is a non-trivial problem to solve that circularity and make cash flows and  reserves well-defined.
Revised:  As a result, insurance cash flows and prospective reserves depend on each other in a circular way, and it is a non-trivial problem to solve that circularity and make cash flows and prospective reserves well-defined.
Edit intent: fluency
Original:  Second, we account for cross-sectional heterogeneity and cluster impacts of monetary policy shocks to production industries via a sparse finite Gaussian mixture model. The results suggest substantial heterogeneities in the responses of industries to surprise monetary policy shocks. Moreover, we find that the role of network effects varies strongly over time . In particular, US recessions tend to coincide with periods where between 40 to 60 percent of the overall effects can be attributed to network effects; expansionary economic episodes show muted network effects with magnitudes of roughly 20 to 30 percent .
Revised:  Second, we allow for time-varying network structures and dependence. This feature captures both variation in the structure of the production network, but also differences in cross-industry demand elasticities. We find that impacts vary substantially over time and the cross-section. Higher-order effects appear to be particularly important in periods of economic and financial uncertainty, often coinciding with tight credit market conditions and financial stress. Differentials in industry-specific responses can be explained by how close the respective industries are to end-consumers .
Edit intent: clarity
Original: In this paper we introduce a biologically inspired CNN architecture that has a first convolutional layer  that mimics the role of the LGN . The first layer of the net shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian  .
Revised: In this paper we introduce a biologically inspired Convolutional Neural Network (CNN) architecture called LGN-CNN that has a first convolutional layer that mimics the role of the LGN . The first layer of the net shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian .
Edit intent: meaning-changed
Original: In this paper we introduce a biologically inspired CNN architecture that has a first convolutional layer  that mimics the role of the LGN . The first layer of the net shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian  .
Revised: In this paper we introduce a biologically inspired CNN architecture that has a first convolutional layer composed by a single filter that mimics the role of the LGN . The first layer of the net shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian .
Edit intent: meaning-changed
Original: In this paper we introduce a biologically inspired CNN architecture that has a first convolutional layer  that mimics the role of the LGN . The first layer of the net shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian  .
Revised: In this paper we introduce a biologically inspired CNN architecture that has a first convolutional layer that mimics the role of the Lateral Geniculate Nucleus (LGN) . The first layer of the net shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian .
Edit intent: meaning-changed
Original: In this paper we introduce a biologically inspired CNN architecture that has a first convolutional layer  that mimics the role of the LGN . The first layer of the net shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian  .
Revised: In this paper we introduce a biologically inspired CNN architecture that has a first convolutional layer that mimics the role of the LGN . The first layer of the neural network shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian .
Edit intent: clarity
Original: In this paper we introduce a biologically inspired CNN architecture that has a first convolutional layer  that mimics the role of the LGN . The first layer of the net shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian  .
Revised: In this paper we introduce a biologically inspired CNN architecture that has a first convolutional layer that mimics the role of the LGN . The first layer of the net shows a rotationally symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian (LoG) .
Edit intent: clarity