wanyu/IteraTeR_v2 huggingface dataset:
Original: While students may find spline interpolation easily digestible, based on their familiarity with continuity of a function and its derivatives, some of its inherent value may be missed when students only see it applied to standard data interpolation exercises.
Revised: While students may find spline interpolation quite digestible, based on their familiarity with continuity of a function and its derivatives, some of its inherent value may be missed when students only see it applied to standard data interpolation exercises.
Edit intent: clarity
Original:  Moreover, students can define their own interpolation functions and visualize the dynamics  unfold.
Revised:  Moreover, students can define their own interpolation functions and visualize the dynamics that unfold.
Edit intent: clarity
Original: Despite many successes, machine learning (ML) methods such as neural nets often struggle to learn given small training sets.
Revised: Machine learning (ML) methods such as neural nets often struggle to learn given small training sets.
Edit intent: coherence
Original:  We can thus look to BNNs for  tools to improve performance of ML methods in this low-data regime.
Revised:  We can thus look to BNNs for possible tools to improve performance of ML methods in this low-data regime.
Edit intent: clarity
Original:  The insect olfactory network, though simple, can learn new odors very rapidly .
Revised:  The insect olfactory network, though simple, learns new odors very rapidly .
Edit intent: clarity
Original:  Its two key structures are a layer with competitive inhibition (the Antennal Lobe, AL) , followed by a high dimensional sparse plastic layer ( the Mushroom Body, MB) .
Revised:  Its two key structures are a layer with competitive inhibition (the Antennal Lobe, AL) ; a high-dimensional sparse plastic layer ( the Mushroom Body, MB) .
Edit intent: coherence
Original:  Its two key structures are a layer with competitive inhibition (the Antennal Lobe, AL) , followed by a high dimensional sparse plastic layer ( the Mushroom Body, MB) .
Revised:  Its two key structures are a layer with competitive inhibition (the Antennal Lobe, AL) , followed by a high dimensional sparse plastic layer ( Mushroom Body, MB) .
Edit intent: fluency
Original:  In this work, we deploy the AL-MB network as an automatic feature generator, using its Readout Neurons as additional features for standard ML classifiers.
Revised:  In this work, we deploy the AL-MB as an automatic feature generator, using its Readout Neurons as additional features for standard ML classifiers.
Edit intent: clarity
Original:  We find that these "insect cyborgs", ie classifiers that are part-moth and part-ML method, deliver significantly better performance than baseline ML methods alone on a generic (non-spatial) 85-feature, 10-class task derived from the MNIST dataset.
Revised:  We find that these ``insect cyborgs'', i.e. classifiers that are part-moth and part-ML method, deliver significantly better performance than baseline ML methods alone on a generic (non-spatial) 85-feature, 10-class task derived from the MNIST dataset.
Edit intent: coherence
Original:  We find that these "insect cyborgs", ie classifiers that are part-moth and part-ML method, deliver significantly better performance than baseline ML methods alone on a generic (non-spatial) 85-feature, 10-class task derived from the MNIST dataset.
Revised:  We find that these "insect cyborgs", ie classifiers that are part-insect model and part-ML method, deliver significantly better performance than baseline ML methods alone on a generic (non-spatial) 85-feature, 10-class task derived from the MNIST dataset.
Edit intent: clarity
Original:  We find that these "insect cyborgs", ie classifiers that are part-moth and part-ML method, deliver significantly better performance than baseline ML methods alone on a generic (non-spatial) 85-feature, 10-class task derived from the MNIST dataset.
Revised:  We find that these "insect cyborgs", ie classifiers that are part-moth and part-ML method, have significantly better performance than baseline ML methods alone on a generic (non-spatial) 85-feature, 10-class task derived from the MNIST dataset.
Edit intent: clarity
Original:  The two  structures in the AL-MB, a competitive inhibition layer and a high-dimensional sparse layer  with Hebbian plasticity, are novel in the context of artificial NNs but endemic in BNNs. We believe they can be deployed either prepended as feature generators or inserted as layers into deep NNs, to potentially improve ML performance .
Revised:  The two structures in the AL-MB, a competitive inhibition layer and a high-dimensional sparse layer with Hebbian plasticity, act as effective, automated feature generators that substantially improve ML classification in the test case we examine .
Edit intent: clarity
Original:  We can thus look to BNNs for possible tools to improve performance of ML methods in this low-data regime.
Revised:  We can thus look to BNNs for tools to improve performance of ML methods in this low-data regime.
Edit intent: clarity
Original:  We can thus look to BNNs for possible tools to improve performance of ML methods in this low-data regime.
Revised:  We can thus look to BNNs for possible tools to improve ML performance in this low-data regime.
Edit intent: clarity
Original:  The insect olfactory network , though simple, learns new odors very rapidly, by means of three key elements: A competitive inhibition layer (Antennal Lobe, AL) ;
Revised:  The insect olfactory network learns new odors very rapidly, by means of three key elements: A competitive inhibition layer (Antennal Lobe, AL) ;
Edit intent: coherence
Original:  The insect olfactory network , though simple, learns new odors very rapidly, by means of three key elements: A competitive inhibition layer (Antennal Lobe, AL) ;
Revised:  The insect olfactory network , though simple, learns new odors very rapidly, by means of three key elements: A competitive inhibition layer ;
Edit intent: coherence
Original:  a high-dimensional sparse plastic layer (Mushroom Body, MB) ;
Revised:  a high-dimensional sparse plastic layer ;
Edit intent: coherence
Original:  In this work, we deploy a computational model of the insect AL-MB as an automatic feature generator , attached as a front-end pre-processor so that its Readout Neurons provide new features, derived from the original features, for use by standard ML classifiers.
Revised:  In this work, we deploy a computational model of the insect AL-MB as an automatic feature generator : Attached as a front-end pre-processor so that its Readout Neurons provide new features, derived from the original features, for use by standard ML classifiers.
Edit intent: fluency
Original:  In this work, we deploy a computational model of the insect AL-MB as an automatic feature generator , attached as a front-end pre-processor so that its Readout Neurons provide new features, derived from the original features, for use by standard ML classifiers.
Revised:  In this work, we deploy a computational model of the insect AL-MB as an automatic feature generator , attached as a front-end pre-processor , its Readout Neurons provide new features, derived from the original features, for use by standard ML classifiers.
Edit intent: coherence
Original:  In this work, we deploy a computational model of the insect AL-MB as an automatic feature generator , attached as a front-end pre-processor so that its Readout Neurons provide new features, derived from the original features, for use by standard ML classifiers.
Revised:  In this work, we deploy a computational model of the insect AL-MB as an automatic feature generator , attached as a front-end pre-processor so that its Readout Neurons provided new features, derived from the original features, for use by standard ML classifiers.
Edit intent: fluency
Original:  We find that these ``insect cyborgs'' , i.e. classifiers that are part-insect model and part-ML method, have significantly better performance than baseline ML methods alone on a vectorized MNIST dataset.
Revised:  We found that these "insect cyborgs" , i.e. classifiers that are part-insect model and part-ML method, have significantly better performance than baseline ML methods alone on a vectorized MNIST dataset.
Edit intent: fluency
Original:  We find that these ``insect cyborgs'' , i.e. classifiers that are part-insect model and part-ML method, have significantly better performance than baseline ML methods alone on a vectorized MNIST dataset.
Revised:  We find that these ``insect cyborgs'' , i.e. classifiers that are part-insect model and part-ML method, had significantly better performance than baseline ML methods alone on a vectorized MNIST dataset.
Edit intent: fluency
Original:  Relative Test set accuracy improves by an average of 6\% to 33\% depending on baseline ML accuracy, while relative reduction in Test set error reaches more than 50\% for higher baseline accuracy ML models.
Revised:  Relative Test set accuracy improves by an average of 6\% to 33\% depending on baseline ML accuracy, while relative reduction in test set error exceeded 50\% for higher baseline accuracy ML models.
Edit intent: clarity
Original:  The two basic structures in the AL-MB, a competitive inhibition layer and a high-dimensional sparse layer, coupled with Hebbian plasticity, act as effective, automated feature generators that substantially improve ML classification in the test case we examine .
Revised:  These results indicate the potential value of BNN-inspired feature generators in the ML context .
Edit intent: clarity
Original:  The method is based on MCMC sampling of trajectories of the GPDM and estimating the hyperparameters of the covariance function of the Gaussian process.
Revised:  BINGO is based on MCMC sampling of trajectories of the GPDM and estimating the hyperparameters of the covariance function of the Gaussian process.
Edit intent: clarity
Original:  Using benchmark data examples, we show that our method is computationally feasible and superior in dealing with poor time resolution  .
Revised:  Using benchmark data examples, we show that BINGO is superior in dealing with poor time resolution .
Edit intent: clarity
Original:  This includes invocations on free objects and constraints over object types, while taking arbitrary class hierarchies of a Java-based language into account.
Revised:  This includes invocations on free objects and constraints over object types, while taking arbitrary class hierarchies of programs written in object-oriented languages into account.
Edit intent: clarity
Original:  This work discusses interactions between a constraint-logic object-oriented language and reference type logic variables, particularly invocations on and access to logic variables, typecasts , and equality , and proposes approaches as to how these interactions can be handled  .
Revised:  The concept discusses interactions between a constraint-logic object-oriented language and reference type logic variables, particularly invocations on and access to logic variables, typecasts , and equality , and proposes approaches as to how these interactions can be handled .
Edit intent: clarity
Original:  This work discusses interactions between a constraint-logic object-oriented language and reference type logic variables, particularly invocations on and access to logic variables, typecasts , and equality , and proposes approaches as to how these interactions can be handled  .
Revised:  This work discusses interactions between constraint-logic object-oriented language and reference type logic variables, particularly invocations on and access to logic variables, typecasts , and equality , and proposes approaches as to how these interactions can be handled .
Edit intent: fluency
Original:  This work discusses interactions between a constraint-logic object-oriented language and reference type logic variables, particularly invocations on and access to logic variables, typecasts , and equality , and proposes approaches as to how these interactions can be handled  .
Revised:  This work discusses interactions between a constraint-logic object-oriented programs and reference type logic variables, particularly invocations on and access to logic variables, typecasts , and equality , and proposes approaches as to how these interactions can be handled .
Edit intent: clarity
Original:  This work discusses interactions between a constraint-logic object-oriented language and reference type logic variables, particularly invocations on and access to logic variables, typecasts , and equality , and proposes approaches as to how these interactions can be handled  .
Revised:  This work discusses interactions between a constraint-logic object-oriented language and reference type logic variables, particularly invocations on and access to logic variables, type operations , and equality , and proposes approaches as to how these interactions can be handled .
Edit intent: clarity
Original:  This work discusses interactions between a constraint-logic object-oriented language and reference type logic variables, particularly invocations on and access to logic variables, typecasts , and equality , and proposes approaches as to how these interactions can be handled  .
Revised:  This work discusses interactions between a constraint-logic object-oriented language and reference type logic variables, particularly invocations on and access to logic variables, typecasts , and equality . Furthermore, it proposes approaches as to how these interactions can be handled .
Edit intent: coherence
Original:  This classification gives a new perspective on the structure-function relation for membrane proteins, and simplifies the task of translating between molecular sensing mechanisms and  large-scale cellular consequences.
Revised:  This classification gives a new perspective on the structure-function relation for membrane proteins, and simplifies the task of translating between molecular sensing mechanisms and their large-scale cellular consequences.
Edit intent: clarity
Original:  Despite being a small cohort lacking external validation, these promising preliminary results warrant larger prospective validation studies.
Revised:  Despite being a small cohort lacking external validation, these preliminary results warrant larger prospective validation studies.
Edit intent: clarity
Original: We present a short statement and proof of the following result by S. Parsa.
Revised: We present a short exposition of the following result by S. Parsa.
Edit intent: clarity
Original: We present a short statement and proof of the following result by S. Parsa.
Revised: We present a short statement and proof of the following results by S. Parsa.
Edit intent: fluency
Original:  We also present corrected statement and proof of the analogue of the second result for intrinsic linking.
Revised:  We also present the analogue of the second result for intrinsic linking.
Edit intent: coherence
Original:  Unfortu- nately , the classical Schr\"oder tree model is not adapted to take into account the chronology between the branching nodes.
Revised:  Unfortunately , the classical Schr\"oder tree model is not adapted to take into account the chronology between the branching nodes.
Edit intent: fluency
Original:  For that purpose, we first develop a model of (strongly) increasing Schr\"oder trees, symbolically de- scribed in the classical context of increasing labeling.
Revised:  For that purpose, we first develop a model of (strongly) increasing Schr\"oder trees, symbolically described in the classical context of increasing labeling.
Edit intent: fluency
Original:  This algorithm uses a memory of O(n^{\alpha}) per machine  and a total memory of O(m+ n^{1+\varepsilon), for any desirable constants \alpha,\varepsilon>0}  , where m is the size of the graph.
Revised:  This algorithm uses a memory of O(n^{\alpha}) per machine , for any desirable constant \alpha>0, and a total memory of O(m+ n^{1+\varepsilon), for any desirable constants \alpha,\varepsilon>0} , where m is the size of the graph.
Edit intent: fluency
Original:  This algorithm uses a memory of O(n^{\alpha}) per machine  and a total memory of O(m+ n^{1+\varepsilon), for any desirable constants \alpha,\varepsilon>0}  , where m is the size of the graph.
Revised:  This algorithm uses a memory of O(n^{\alpha}) per machine and a total memory of O(m+ n^{1+\varepsilon), for any desirable constants \alpha,\varepsilon>0} (m) , where m is the size of the graph.
Edit intent: clarity
Original:  This matches the bound of Parter and Su [DISC'18] , which improved on the O( \log\log \Delta \log^* \Delta)-round  algorithm of Parter [ICALP'18].
Revised:  This matches the bound of Parter and Su [DISC'18] and O(( \log\log \Delta \log^* \Delta)-round algorithm of Parter [ICALP'18].
Edit intent: coherence
Original:  Moreover, the same random partitioning leads to a (\Delta+1)  coloring algorithm with round complexity O( \log^* \Delta+ \log\log n) in the Massively Parallel Computation (MPC) model with strongly sublinear memory , which is the first sublogarithmic-time algorithm in this regime .
Revised:  Massively Parallel Computation: We present a (\Delta+1) coloring algorithm with round complexity O( \log^* \Delta+ \log\log n) in the Massively Parallel Computation (MPC) model with strongly sublinear memory , which is the first sublogarithmic-time algorithm in this regime .
Edit intent: clarity
Original:  Moreover, the same random partitioning leads to a (\Delta+1)  coloring algorithm with round complexity O( \log^* \Delta+ \log\log n) in the Massively Parallel Computation (MPC) model with strongly sublinear memory , which is the first sublogarithmic-time algorithm in this regime .
Revised:  Moreover, the same random partitioning leads to a (\Delta+1) coloring algorithm with round complexity O( \log\log n) in the Massively Parallel Computation (MPC) model with strongly sublinear memory , which is the first sublogarithmic-time algorithm in this regime .
Edit intent: clarity
Original:  Moreover, the same random partitioning leads to a (\Delta+1)  coloring algorithm with round complexity O( \log^* \Delta+ \log\log n) in the Massively Parallel Computation (MPC) model with strongly sublinear memory , which is the first sublogarithmic-time algorithm in this regime .
Revised:  Moreover, the same random partitioning leads to a (\Delta+1) coloring algorithm with round complexity O( \log^* \Delta+ \log\log n) in the Massively Parallel Computation (MPC) model with strongly sublinear memory per machine .
Edit intent: clarity
Original: Existing fuzzy neural networks (FNNs ) are mostly developed under a shallow network configuration having lower generalization power than those of deep structures.
Revised: Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures.
Edit intent: clarity
Original:  This paper proposes a novel self-organizing deep fuzzy neural network, namely deep evolving fuzzy neural networks (DEVFNN) . Fuzzy rules can be automatically extracted from data streams or removed if they play little role during their lifespan.
Revised:  This paper proposes a novel self-organizing deep FNN, namely DEVFNN . Fuzzy rules can be automatically extracted from data streams or removed if they play little role during their lifespan.
Edit intent: clarity
Original:  This paper proposes a novel self-organizing deep fuzzy neural network, namely deep evolving fuzzy neural networks (DEVFNN) . Fuzzy rules can be automatically extracted from data streams or removed if they play little role during their lifespan.
Revised:  This paper proposes a novel self-organizing deep fuzzy neural network, namely deep evolving fuzzy neural networks (DEVFNN) . Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan.
Edit intent: clarity
Original:  DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely Generic Classifier (gClass) , drives the hidden layer.
Revised:  DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass , drives the hidden layer.
Edit intent: clarity
Original:  A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of input space dimension due to the nature of feature augmentation approach in building a deep network structure.
Revised:  A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure.
Edit intent: clarity
Original: Online social media provide users with unprecedented opportunities to engage with diverse opinions.
Revised: Online social networks provide users with unprecedented opportunities to engage with diverse opinions.
Edit intent: clarity
Original:  Simultaneously, they allow the spread of misinformation by empowering individuals to self-select the narratives they want to be exposed to , both through active (confirmation bias) and passive (personalized news algorithms) self-reinforcing mechanisms . A precise theoretical understanding of such trade-offs is still largely missing.
Revised:  Simultaneously, they allow the spread of misinformation by empowering individuals to self-select narratives they want to be exposed to , both through active (confirmation bias) and passive (personalized news algorithms) self-reinforcing mechanisms . A precise theoretical understanding of such trade-offs is still largely missing.
Edit intent: fluency
Original:  Simultaneously, they allow the spread of misinformation by empowering individuals to self-select the narratives they want to be exposed to , both through active (confirmation bias) and passive (personalized news algorithms) self-reinforcing mechanisms . A precise theoretical understanding of such trade-offs is still largely missing.
Revised:  Simultaneously, they allow the spread of misinformation by empowering individuals to self-select the narratives they want to be exposed to . A precise theoretical understanding of such trade-offs is still largely missing.
Edit intent: coherence
Original:  Simultaneously, they allow the spread of misinformation by empowering individuals to self-select the narratives they want to be exposed to , both through active (confirmation bias) and passive (personalized news algorithms) self-reinforcing mechanisms . A precise theoretical understanding of such trade-offs is still largely missing.
Revised:  Simultaneously, they allow the spread of misinformation by empowering individuals to self-select the narratives they want to be exposed to , both through active (confirmation bias) and passive (personalized news algorithms) self-reinforcing mechanisms . A precise understanding of such trade-offs is still largely missing.
Edit intent: clarity
Original:  Simultaneously, they allow the spread of misinformation by empowering individuals to self-select the narratives they want to be exposed to , both through active (confirmation bias) and passive (personalized news algorithms) self-reinforcing mechanisms . A precise theoretical understanding of such trade-offs is still largely missing.
Revised:  Simultaneously, they allow the spread of misinformation by empowering individuals to self-select the narratives they want to be exposed to , both through active (confirmation bias) and passive (personalized news algorithms) self-reinforcing mechanisms . A precise theoretical understanding of such tradeoffs is still largely missing.
Edit intent: fluency