wanyu/IteraTeR_v2 huggingface dataset:
Original:  We select 11 representative graph embedding methods and conduct a systematic comparison on three important biomedical link prediction tasks: drug-disease association  prediction, drug-drug interaction  prediction, protein-protein interaction prediction, and two node classification tasks: medical term semantic type classification, protein function prediction.
Revised:  We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association prediction, drug-drug interaction prediction, protein-protein interaction prediction, and two node classification tasks: medical term semantic type classification, protein function prediction.
Edit intent: fluency
Original:  Our experimental results demonstrate that the recent graph embedding methods which have not been explored completely on biomedical tasks achieve promising results and deserve more attention in the future biomedical graph analysis.
Revised:  Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.
Edit intent: clarity
Original:  Besides, compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features  .
Revised:  Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features .
Edit intent: coherence
Original:  Our experiments on multiple datasets show that by considering spatio-temporal statistical distributions of tweets alone, this seemingly naive algorithm  achieves comparable results with  event detection methods that perform semantic analysis .
Revised:  Our experiments on multiple datasets show that by considering spatio-temporal statistical distributions of tweets alone, the seemingly naive algorithm achieves comparable results with event detection methods that perform semantic analysis .
Edit intent: fluency
Original: We present an artificial neural network ( ANN ) approach to value financial derivatives .
Revised: We present a neural network ( ANN ) approach to value financial derivatives .
Edit intent: clarity
Original:  Atypically to standard ANN applications, practitioners equally use option pricing models to validate market prices and to infer unobserved prices.
Revised:  Atypically to standard NN applications, financial industry practitioners use such models equally to replicate market prices and to infer unobserved prices.
Edit intent: clarity
Original:  Importantly, models need to generate realistic arbitrage-free  prices, meaning that no option portfolio can lead to risk-free profits.
Revised:  Importantly, models need to generate realistic arbitrage-free option prices, meaning that no option portfolio can lead to risk-free profits.
Edit intent: fluency
Original:  Importantly, models need to generate realistic arbitrage-free  prices, meaning that no option portfolio can lead to risk-free profits.
Revised:  Importantly, models need to generate realistic arbitrage-free prices, meaning that no portfolio can lead to risk-free profits.
Edit intent: clarity
Original:  The absence of arbitrage opportunities is guaranteed by penalizing the loss using soft constraints on an extended grid of input values.
Revised:  We propose an approach guaranteeing the absence of arbitrage opportunities is guaranteed by penalizing the loss using soft constraints on an extended grid of input values.
Edit intent: clarity
Original:  The absence of arbitrage opportunities is guaranteed by penalizing the loss using soft constraints on an extended grid of input values.
Revised:  The absence of arbitrage opportunities by penalizing the loss using soft constraints on an extended grid of input values.
Edit intent: clarity
Original:  We also  explore how deeper ANNs improve over shallower ones, as well as other properties of the network architecture.
Revised:  We also explore how deeper NNs improve over shallower ones, as well as other properties of the network architecture.
Edit intent: fluency
Original:  We validate our method both on training sets, and testing sets, namely, highlighting both their capacity to reproduce observed prices and predict new ones.
Revised:  We validate our method both on training sets, and testing sets, namely, we highlight both their capacity to reproduce observed prices and predict new ones.
Edit intent: fluency
Original: A broadcast on a graph G=(V,E) is a function f: V\longrightarrow \{0,\ldots,diam(G)\} such that f(v)\leq e _G (v) for every vertex v\in V, wherediam(G) denotes the diameter of G and e _G (v) the eccentricity of v in G. The cost of such a broadcast is then the value \sum _{v\in V  f(v).
Revised: A broadcast on a graph G=(V,E) is a function f: V\longrightarrow \{0,\ldots,diam(G)\} such that f(v)\leq e \_G (v) for every vertex v\in V, wherediam(G) denotes the diameter of G and e _G (v) the eccentricity of v in G. The cost of such a broadcast is then the value \sum _{v\in V f(v).
Edit intent: fluency
Original: A broadcast on a graph G=(V,E) is a function f: V\longrightarrow \{0,\ldots,diam(G)\} such that f(v)\leq e _G (v) for every vertex v\in V, wherediam(G) denotes the diameter of G and e _G (v) the eccentricity of v in G. The cost of such a broadcast is then the value \sum _{v\in V  f(v).
Revised: A broadcast on a graph G=(V,E) is a function f: V\longrightarrow \{0,\ldots,diam(G)\} such that f(v)\leq e _G (v) for every vertex v\in V, wherediam(G) denotes the diameter of G and e \_G (v) the eccentricity of v in G. The cost of such a broadcast is then the value \sum _{v\in V f(v).
Edit intent: fluency
Original: A broadcast on a graph G=(V,E) is a function f: V\longrightarrow \{0,\ldots,diam(G)\} such that f(v)\leq e _G (v) for every vertex v\in V, wherediam(G) denotes the diameter of G and e _G (v) the eccentricity of v in G. The cost of such a broadcast is then the value \sum _{v\in V  f(v).
Revised: A broadcast on a graph G=(V,E) is a function f: V\longrightarrow \{0,\ldots,diam(G)\} such that f(v)\leq e _G (v) for every vertex v\in V, wherediam(G) denotes the diameter of G and e _G (v) the eccentricity of v in G. The cost of such a broadcast is then the value \sum _{v\in V v\in V f(v).
Edit intent: fluency
Original: Various types of broadcast functions on graphs have been considered in the literature, in relation with domination, irredundence, independence or packing, leading to the introduction of several broadcast numbers on graphs.
Revised: Various types of broadcast functions on graphs have been considered in the literature, in relation with domination, irredundence, independenceor packing, leading to the introduction of several broadcast numbers on graphs.
Edit intent: fluency
Original: In this paper, we determine these broadcast numbers for all paths and cycles, thus answering a question raised in [D.
Revised: In this paper, we determine these broadcast numbers for all paths and cycles, thus answering a questionraised in [D.
Edit intent: fluency
Original: Mobile phones and the Internet of Things provide unprecedented opportunities for transportation researchers and computational social scientists to observe city-scale human dynamics in terms of millions of vehicles or people moving around.
Revised: The capability of traffic-information systems to sense the movement of millions of vehicles or people moving around.
Edit intent: clarity
Original:  Evaluation with one synthesized dataset (SynthTown) , one partly real and partly synthesized dataset (Berlin) , and three real world datasets (Santiago de Chile, Dakar, and NYC) show that the discrete-event decision process gives an accurate estimation of complex system dynamics due to its better integration of high-fidelity dynamics and human mobility data .
Revised:  Evaluation with one synthesized dataset (SynthTown) , one partly synthesized scenario , and three real world datasets (Santiago de Chile, Dakar, and NYC) show that the discrete-event decision process gives an accurate estimation of complex system dynamics due to its better integration of high-fidelity dynamics and human mobility data .
Edit intent: clarity
Original:  Evaluation with one synthesized dataset (SynthTown) , one partly real and partly synthesized dataset (Berlin) , and three real world datasets (Santiago de Chile, Dakar, and NYC) show that the discrete-event decision process gives an accurate estimation of complex system dynamics due to its better integration of high-fidelity dynamics and human mobility data .
Revised:  Evaluation with one synthesized dataset (SynthTown) , one partly real and partly synthesized dataset (Berlin) , and three real-world scenarios. This study points to the potential of a "living lab" approach where we learn, predict, and optimize behaviors in the real world .
Edit intent: clarity
Original: This article generalizes the Near-Shortest Paths Problem introduced by Byers and Waterman in 1984 using concepts of the Universal Shortest Path Problem established by Turner and Hamacher in 2011.
Revised: This article generalizes the Near Shortest Paths Problem introduced by Byers and Waterman in 1984 using concepts of the Universal Shortest Path Problem established by Turner and Hamacher in 2011.
Edit intent: fluency
Original:  We apply this concept to the Near-Shortest Paths Problem in a way that we are able to enumerate all universal near-shortest simple paths.
Revised:  We apply this concept to the Near Shortest Paths Problem in a way that we are able to enumerate all universal near-shortest simple paths.
Edit intent: fluency
Original:  We apply this concept to the Near-Shortest Paths Problem in a way that we are able to enumerate all universal near-shortest simple paths.
Revised:  We apply this concept to the Near-Shortest Paths Problem in a way that we are able to enumerate all universal near shortest simple paths.
Edit intent: fluency
Original:  Therefore, we present a recursive algorithm to compute the set of universal near-shortest simple paths between two prespecified nodes and evaluate the running time complexity per path enumerated with respect to different values of the universal weight vector.
Revised:  Therefore, we present a recursive algorithm to compute the set of universal near shortest simple paths between two prespecified nodes and evaluate the running time complexity per path enumerated with respect to different values of the universal weight vector.
Edit intent: fluency
Original:  Therefore, we present a recursive algorithm to compute the set of universal near-shortest simple paths between two prespecified nodes and evaluate the running time complexity per path enumerated with respect to different values of the universal weight vector.
Revised:  Therefore, we present a recursive algorithm to compute the set of universal near-shortest simple paths between two prespecified vertices and evaluate the running time complexity per path enumerated with respect to different values of the universal weight vector.
Edit intent: clarity
Original: A series of recent works established a rigorous correspondence between very wide deep neural networks (DNNs) , trained in a particular manner, and noiseless Bayesian Inference with a certain Gaussian Process (GP) known as the Neural Tangent Kernel (NTK).
Revised: In the past decade, deep neural networks (DNNs) , trained in a particular manner, and noiseless Bayesian Inference with a certain Gaussian Process (GP) known as the Neural Tangent Kernel (NTK).
Edit intent: clarity
Original:  While still far behind the application forefront, the theoretical study of DNNs has recently made important advancements in analyzing the highly over-parametrized regime where some exact results have been obtained.
Revised:  While still far behind the application forefront, the theoretical study of DNNs has recently made important advancements in analyzing the highly over-parameterized regime where some exact results have been obtained.
Edit intent: fluency
Original:  Leveraging these ideas and adopting a more physics-like approach, here we construct a versatile field-theory formalism for supervised deep learning, involving renormalization group, Feynmann diagrams , and replicas.
Revised:  Leveraging these ideas and adopting a more physics-like approach, here we construct a versatile field-theory formalism for supervised deep learning, involving renormalization group, Feynman diagrams and replicas.
Edit intent: fluency
Original:  In particular , we show that our approach leads to highly accurate predictions of learning curves of truly deep DNNs trained on polynomial regression problems.
Revised:  In particular we show that our approach leads to highly accurate predictions of learning curves of truly deep DNNs trained on polynomial regression problems.
Edit intent: fluency
Original:  It also explains in a concrete manner why DNNs generalize well despite being highly over-parametrized , this due to an entropic bias to simple functions -- low order polynomials in the input vector , for the case of fully-connected DNNs  . Being a complex interacting system of artificial neurons, we believe that such tools and methodologies borrowed from condensed matter physics would prove essential for obtaining an accurate quantitative understanding of deep learning.
Revised:  It also explains in a concrete manner why DNNs generalize well despite being highly over-parameterized , this due to an entropic bias to simple functions -- low order polynomials in the input vector , for the case of fully-connected DNNs . Being a complex interacting system of artificial neurons, we believe that such tools and methodologies borrowed from condensed matter physics would prove essential for obtaining an accurate quantitative understanding of deep learning.
Edit intent: fluency
Original:  It also explains in a concrete manner why DNNs generalize well despite being highly over-parametrized , this due to an entropic bias to simple functions -- low order polynomials in the input vector , for the case of fully-connected DNNs  . Being a complex interacting system of artificial neurons, we believe that such tools and methodologies borrowed from condensed matter physics would prove essential for obtaining an accurate quantitative understanding of deep learning.
Revised:  It also explains in a concrete manner why DNNs generalize well despite being highly over-parametrized , this due to an entropic bias to simple functions which , for the case of fully-connected DNNs . Being a complex interacting system of artificial neurons, we believe that such tools and methodologies borrowed from condensed matter physics would prove essential for obtaining an accurate quantitative understanding of deep learning.
Edit intent: clarity
Original: Variational inference for Bayesian deep neural networks (DNNs ) requires specifying priors and approximate posterior distributions for neural network weights.
Revised: Variational inference for Bayesian deep neural network (DNN ) requires specifying priors and approximate posterior distributions for neural network weights.
Edit intent: clarity
Original: Variational inference for Bayesian deep neural networks (DNNs ) requires specifying priors and approximate posterior distributions for neural network weights.
Revised: Variational inference for Bayesian deep neural networks (DNNs ) requires specifying priors and approximate posterior distributions over neural network weights.
Edit intent: clarity
Original:  We evaluate the proposed approach on multiple datasets and real-world application domains with a range of varying complex model architecturesto demonstrate MOPED enables scalable variational inference for Bayesian DNNs.
Revised:  We evaluate the proposed approach on real-world application domains with a range of varying complex model architecturesto demonstrate MOPED enables scalable variational inference for Bayesian DNNs.
Edit intent: clarity
Original:  We propose MOdel Priors from Empirical Bayes using DNN (MOPED) method to choose informed prior distributions for Bayesian neural network weights .
Revised:  We propose MOdel Priors with Empirical Bayes using DNN (MOPED) method to choose informed prior distributions for Bayesian neural network weights .
Edit intent: coherence
Original:  We propose MOdel Priors from Empirical Bayes using DNN (MOPED) method to choose informed prior distributions for Bayesian neural network weights .
Revised:  We propose MOdel Priors from Empirical Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian neural networks .
Edit intent: clarity
Original: Geospatial Linked Data is an emerging domain , with growing interest in research and industry.
Revised: Geospatial Linked Data is an emerging domain with growing interest in research and industry.
Edit intent: fluency
Original:  The growing sources of open-source geospatial Linked Data  need to be interlinked and easily integrated with private and industrial linked data clouds, e.g., in the agricultural domain.
Revised:  The growing sources of open-source geospatial Linked Data resources and they need to be interlinked and easily integrated with private and industrial linked data clouds, e.g., in the agricultural domain.
Edit intent: clarity
Original: In this work, we initiate a systematic study of parameterized streaming complexity of graph deletion problems :%DIFDELCMD < {\cal %%% F -Subgraph Deletion,%DIFDELCMD < {\cal %DIFDELCMD < %%% F -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Revised: The study of parameterized streaming complexity of graph deletion problems :%DIFDELCMD < {\cal %%% F -Subgraph Deletion,%DIFDELCMD < {\cal %DIFDELCMD < %%% F -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Edit intent: clarity
Original: In this work, we initiate a systematic study of parameterized streaming complexity of graph deletion problems :%DIFDELCMD < {\cal %%% F -Subgraph Deletion,%DIFDELCMD < {\cal %DIFDELCMD < %%% F -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Revised: In this work, we initiate a systematic study of parameterized streaming complexity %DIFDELCMD < {\cal %%% F -Subgraph Deletion,%DIFDELCMD < {\cal %DIFDELCMD < %%% F -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Edit intent: clarity
Original: In this work, we initiate a systematic study of parameterized streaming complexity of graph deletion problems :%DIFDELCMD < {\cal %%% F -Subgraph Deletion,%DIFDELCMD < {\cal %DIFDELCMD < %%% F -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Revised: In this work, we initiate a systematic study of parameterized streaming complexity of graph deletion problems :%DIFDELCMD < {\cal %%% -Subgraph Deletion,%DIFDELCMD < {\cal %DIFDELCMD < %%% F -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Edit intent: fluency
Original: In this work, we initiate a systematic study of parameterized streaming complexity of graph deletion problems :%DIFDELCMD < {\cal %%% F -Subgraph Deletion,%DIFDELCMD < {\cal %DIFDELCMD < %%% F -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Revised: In this work, we initiate a systematic study of parameterized streaming complexity of graph deletion problems :%DIFDELCMD < {\cal %%% F %DIFDELCMD < {\cal %DIFDELCMD < %%% F -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Edit intent: coherence
Original: In this work, we initiate a systematic study of parameterized streaming complexity of graph deletion problems :%DIFDELCMD < {\cal %%% F -Subgraph Deletion,%DIFDELCMD < {\cal %DIFDELCMD < %%% F -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Revised: In this work, we initiate a systematic study of parameterized streaming complexity of graph deletion problems :%DIFDELCMD < {\cal %%% F -Subgraph Deletion,%DIFDELCMD < {\cal %DIFDELCMD < %%% -Minor Deletion and Cluster Vertex Deletion in the four most well-studied streaming models: the EA (edge arrival) , DEA (dynamic edge arrival), VA (vertex arrival) and AL (adjacency list)models.
Edit intent: fluency
Original: We investigate a population-genetic model in a specific fluctuating environment .
Revised: We investigate a population-genetic model with a temporally-fluctuating sawtooth fitness landscape .
Edit intent: clarity
Original:  We numerically show that a counter-intuitive behavior occurs where fitness of the system decreases as selection pressure increases from zero.
Revised:  We numerically show that a counter-intuitive behavior occurs where the rate of evolution of the system decreases as selection pressure increases from zero.
Edit intent: clarity
Original:  Finally, performance analysis of the novel algorithms is derived using two real-world engineering applications in Electroencephalogram feature learning and Distributed Generator parameter tuning, each of which having nonlinearity and complex multi-modal peaks distributions as its characteristics.
Revised:  Finally, performance analysis of the proposed algorithms is sought in two real-world engineering applications in Electroencephalogram feature learning and Distributed Generator parameter tuning, each of which having nonlinearity and complex multi-modal peaks distributions as its characteristics.
Edit intent: clarity
Original:  Finally, performance analysis of the novel algorithms is derived using two real-world engineering applications in Electroencephalogram feature learning and Distributed Generator parameter tuning, each of which having nonlinearity and complex multi-modal peaks distributions as its characteristics.
Revised:  Finally, performance analysis of the novel algorithms is derived using two real-world engineering applications , i.e., Electroencephalogram feature learning and Distributed Generator parameter tuning, each of which having nonlinearity and complex multi-modal peaks distributions as its characteristics.
Edit intent: clarity
Original:  The performance analysis among other optimizers in the same context suggests that the proposed algorithm is useful both solely and in hybrid Gradient Descent settings where problem's search space is nonhomogeneous in terms of local peaks density.
Revised:  The performance comparison with other optimizers in the same context suggests that the proposed algorithm is useful both solely and in hybrid Gradient Descent settings where problem's search space is nonhomogeneous in terms of local peaks density.
Edit intent: clarity
Original:  The performance analysis among other optimizers in the same context suggests that the proposed algorithm is useful both solely and in hybrid Gradient Descent settings where problem's search space is nonhomogeneous in terms of local peaks density.
Revised:  The performance analysis among other optimizers in the same context suggests that proposed algorithm is useful both solely and in hybrid Gradient Descent settings where problem's search space is nonhomogeneous in terms of local peaks density.
Edit intent: fluency