wanyu/IteraTeR_v2 huggingface dataset:
Original:  The resulting residual bounds within both contractive and noncontractive settings clearly show the balance between the contributions from the higher and lower order terms, which are both dependent on the success of the optimization problem solved at each step of the algorithm.
Revised:  The resulting residual bounds within both contractive and noncontractive settings reveal the balance between the contributions from the higher and lower order terms, which are both dependent on the success of the optimization problem solved at each step of the algorithm.
Edit intent: clarity
Original:   In the contractive setting, the bounds sharpen previous convergence and acceleration results.
Revised:  In the contractive setting, these bounds sharpen previous convergence and acceleration results.
Edit intent: fluency
Original:  Several numerical tests illustrate the analysis primarily in the noncontractive setting, and demonstrate the use of the method on a nonlinear Helmholtz equation  and the steady Navier-Stokes equations with high Reynolds number in three spatial dimensions.
Revised:  Several numerical tests illustrate the analysis primarily in the noncontractive setting, and demonstrate the use of the method on a nonlinear Helmholtz equation , and the steady Navier-Stokes equations with high Reynolds number in three spatial dimensions.
Edit intent: fluency
Original:  We study this spurious shift , as a function of the integration method used, and also as a function of an additional interpolation of forces on a 2-dimensional lattice.
Revised:  We study this spurious shift , as a function of the integration method used, and then as a function of an additional interpolation of forces on a 2-dimensional lattice.
Edit intent: coherence
Original:  This is done for several choices of eccentricities , and semi-major axes.
Revised:  This is done for several choices of eccentricities and semi-major axes.
Edit intent: fluency
Original:  We show that any matrix in the semigroup is a product of matrices in \mathcal M whose length is at most 2^{n ( 2n + 3)} g(n)^{n+1} \in 2^{O(n^2 \log n)}, where g(n) is the maximum order of finite groups over rational n \times n-matrices  .
Revised:  We show that any matrix in the semigroup is a product of matrices in \mathcal M whose length is at most 2^{n ( 2 n + 3)} g(n)^{n+1} \in 2^{O(n^2 \log n)}, where g(n) is the maximum order of finite groups over rational n \times n-matrices .
Edit intent: fluency
Original:  In this work, we introduce a generalization of the fast Johnson-Lindenstrauss projection for embedding vectors with Kronecker product structure, the \emph{Kronecker fast Johnson-Lindenstrauss transform (KFJLT).
Revised:  In this work, we introduce a generalization of the fast Johnson-Lindenstrauss projection for embedding vectors with Kronecker product structure, the \emph{Kronecker fast Johnson-Lindenstrauss transform (KFJLT).
Edit intent: fluency
Original:  The KFJLT drastically reduces the embedding cost to an exponential factor of the standard fast Johnson-Lindenstrauss transform (FJLT)'s cost when applied to vectors with Kronecker structure, by avoiding explicitly forming the full Kronecker products.
Revised:  The KFJLT reduces the embedding cost to an exponential factor of the standard fast Johnson-Lindenstrauss transform (FJLT)'s cost when applied to vectors with Kronecker structure, by avoiding explicitly forming the full Kronecker products.
Edit intent: clarity
Original:  Most work in relation extraction detect a single entity pair mention on a short span of text, which is not ideal due to long sentences that appear in biomedical contexts.
Revised:  Most work only focus on relation extraction, and detect a single entity pair mention on a short span of text, which is not ideal due to long sentences that appear in biomedical contexts.
Edit intent: clarity
Original:  We propose an approach to both event and relation extraction, for simultaneously predicting relationships between all mention pairs in a text.
Revised:  We propose an approach to both relation and event extraction, for simultaneously predicting relationships between all mention pairs in a text.
Edit intent: fluency
Original:  Experiment results demonstrate that our approach outperforms the state-of-the-art on a set of benchmark biomedical corpora including BioNLP 2009, 2011, 2013 and BioCreative 2017 shared tasks.
Revised:  Experiment results demonstrate that our approach outperforms the state of the art on a set of benchmark biomedical corpora including BioNLP 2009, 2011, 2013 and BioCreative 2017 shared tasks.
Edit intent: fluency
Original: This paper presents Packet Chasing, an attack on the network that does not require access to the network, which works regardless of the privilege level of the process receiving the packets.
Revised: This paper presents Packet Chasing, an attack on the network that does not require access to the network, and works regardless of the privilege level of the process receiving the packets.
Edit intent: coherence
Original:  This then enables packet frequency and packet sizes to be monitored through existing cache side channels.
Revised:  This then enables packet frequency and packet sizes to be monitored through cache side channels.
Edit intent: clarity
Original:  This enables both covert channels between a sender and a remote spy with no access to the network, as well as direct attacks that can identify  the web page access patterns of a victim on the network  .
Revised:  This allows both covert channels between a sender and a remote spy with no access to the network, as well as direct attacks that can identify the web page access patterns of a victim on the network .
Edit intent: fluency
Original:  This enables both covert channels between a sender and a remote spy with no access to the network, as well as direct attacks that can identify  the web page access patterns of a victim on the network  .
Revised:  This enables both covert channels between a sender and a remote spy with no access to the network, as well as direct attacks that can identify , among other things, the web page access patterns of a victim on the network .
Edit intent: clarity
Original:  In this paper, we use algebraic data types to define a formal basis for the property graph data models supported by popular open source and commercial graph databases.
Revised:  In this paper, we observe that algebraic data types to define a formal basis for the property graph data models supported by popular open source and commercial graph databases.
Edit intent: coherence
Original:  Even today, this is a very demanding task which encounters problems such as hanging nodes if only quadrilateral/hexahedral elements are deployed  .
Revised:  Even today, this is a very demanding task especially if only quadrilateral/hexahedral elements are deployed .
Edit intent: clarity
Original:  Consequently , it is possible to combine independently refined h- and p-meshes.
Revised:  Thus , it is possible to combine independently refined h- and p-meshes.
Edit intent: fluency
Original:  By means of several numerical examples, the convergence behavior is investigated in detail  and the asymptotic rates of convergence are determined  .
Revised:  By means of several numerical examples, the convergence behavior is investigated in detail , and the asymptotic rates of convergence are determined .
Edit intent: fluency
Original:  By means of several numerical examples, the convergence behavior is investigated in detail  and the asymptotic rates of convergence are determined  .
Revised:  By means of several numerical examples, the convergence behavior is investigated in detail and the asymptotic rates of convergence are determined numerically .
Edit intent: clarity
Original:  Two regularization parameters for fields and couplings are tuned to yield equal values for both the sample average and the ensemble average of evolutionary energies of natural proteins.
Revised:  Two regularization parameters are tuned to yield equal values for both the sample average and the ensemble average of evolutionary energies of natural proteins.
Edit intent: clarity
Original:  Both the averages along a learning process smoothly change and converge, but their  profiles are very different between the learning methods.
Revised:  Both the averages along a learning process smoothly change and converge, but their learning profiles are very different between the learning methods.
Edit intent: fluency
Original:  Both the averages along a learning process smoothly change and converge, but their  profiles are very different between the learning methods.
Revised:  Both the averages along a learning process smoothly change and converge, but their profiles are very different between learning methods.
Edit intent: fluency
Original:  Given the input to the decoder is distributed like a power-law with exponent k>0, our algorithm yields a runtime R^{n f(R,k)}, where f%DIFDELCMD < \le %%%  1/2, and f\rightarrow 0 exponentially quickly for growing k.
Revised:  When the input to the decoder is distributed like a power-law with exponent k>0, our algorithm yields a runtime R^{n f(R,k)}, where f%DIFDELCMD < \le %%% 1/2, and f\rightarrow 0 exponentially quickly for growing k.
Edit intent: fluency
Original:  Given the input to the decoder is distributed like a power-law with exponent k>0, our algorithm yields a runtime R^{n f(R,k)}, where f%DIFDELCMD < \le %%%  1/2, and f\rightarrow 0 exponentially quickly for growing k.
Revised:  Given the input to the decoder is distributed as a power-law with exponent k>0, our algorithm yields a runtime R^{n f(R,k)}, where f%DIFDELCMD < \le %%% 1/2, and f\rightarrow 0 exponentially quickly for growing k.
Edit intent: fluency
Original:  Given the input to the decoder is distributed like a power-law with exponent k>0, our algorithm yields a runtime R^{n f(R,k)}, where f%DIFDELCMD < \le %%%  1/2, and f\rightarrow 0 exponentially quickly for growing k.
Revised:  Given the input to the decoder is distributed like a power-law with exponent k>0, our algorithm has runtime R^{n f(R,k)}, where f%DIFDELCMD < \le %%% 1/2, and f\rightarrow 0 exponentially quickly for growing k.
Edit intent: clarity
Original:  Given the input to the decoder is distributed like a power-law with exponent k>0, our algorithm yields a runtime R^{n f(R,k)}, where f%DIFDELCMD < \le %%%  1/2, and f\rightarrow 0 exponentially quickly for growing k.
Revised:  Given the input to the decoder is distributed like a power-law with exponent k>0, our algorithm yields a runtime R^{n f(R,k)}, where %DIFDELCMD < \le %%% 1/2, and f\rightarrow 0 exponentially quickly for growing k.
Edit intent: fluency
Original:  This implies that our algorithm always yields a super-Grover type speedup, i.e. it is more than quadratically faster than its classical counterpart.
Revised:  This implies that our algorithm always more than quadratically faster than its classical counterpart.
Edit intent: clarity
Original:  Finally, we apply this quantum beam search decoder to Mozilla's implementation of Baidu's DeepSpeech neural net, which we show to exhibit such a power law word rank frequency , underpinning the applicability of our model .
Revised:  Finally, we apply this quantum beam search decoder to Mozilla's implementation of Baidu's DeepSpeech neural net, which we show to exhibit such a power law word rank frequency .
Edit intent: clarity
Original:  While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they entirely overlook the genetic profile and properties of the target disease.
Revised:  While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the genetic profile and properties of the target disease.
Edit intent: clarity
Original:  In the case of cancer, this is problematic since it is a highly genetic disease in which the biomolecular profile of target cells determines the response to therapy.
Edit intent: coherence
Original:  Here, we introduce the first deep generative model capable of generating anticancer compounds given a target biomolecular profile.
Revised:  Here, we introduce the first generative model capable of generating anticancer compounds given a target biomolecular profile.
Edit intent: clarity
Original:  Here, we introduce the first deep generative model capable of generating anticancer compounds given a target biomolecular profile.
Revised:  Here, we introduce the first deep generative model capable of tailoring anticancer compounds for a specific biomolecular profile.
Edit intent: clarity
Original:  Using a reinforcement learning framework, the transcriptomic profile of cancer cells is used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile .
Revised:  Using a RL framework, the transcriptomic profile of cancer cells is used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile .
Edit intent: clarity
Original:  Using a reinforcement learning framework, the transcriptomic profile of cancer cells is used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile .
Revised:  Using a reinforcement learning framework, the transcriptomic profiles of cancer cells is used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile .
Edit intent: fluency
Original:  Using a reinforcement learning framework, the transcriptomic profile of cancer cells is used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile .
Revised:  Using a reinforcement learning framework, the transcriptomic profile of cancer cells are used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile .
Edit intent: fluency
Original:  Using a reinforcement learning framework, the transcriptomic profile of cancer cells is used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile .
Revised:  Using a reinforcement learning framework, the transcriptomic profile of cancer cells is used as a context for the generation of candidate molecules .
Edit intent: clarity
Original:  The efficacy predictor is used to optimize the generated molecules through a reward determined by the predicted IC50 drug sensitivity for the generated molecule and the target profile .
Revised:  The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i.e., transcriptomic profile) .
Edit intent: clarity
Original:  We envision our approach to transform in silico anticancer drug design by increasing success rates in lead compound discovery via leveraging the biomolecular characteristics of the disease  .
Revised:  We envision our approach to transform in silico anticancer drug design by leveraging the biomolecular characteristics of the disease .
Edit intent: clarity
Original:  We envision our approach to transform in silico anticancer drug design by increasing success rates in lead compound discovery via leveraging the biomolecular characteristics of the disease  .
Revised:  We envision our approach to transform in silico anticancer drug design by increasing success rates in lead compound discovery via leveraging the biomolecular characteristics of the disease in order to increase success rates in lead compound discovery .
Edit intent: coherence
Original: It has long been suggested that living systems, in particular the brain, may operate near some critical point .
Revised: It has long been suggested that the biological brain operates at some critical point .
Edit intent: clarity
Original:  However, in this work, for the first time, we propose a novel Targeted Bit Trojan(TBT) , which eliminates the need for model re-training to insert the targeted Trojan .
Revised:  In this work, for the first time, we propose a novel Targeted Bit Trojan(TBT) , which eliminates the need for model re-training to insert the targeted Trojan .
Edit intent: coherence
Original:  However, in this work, for the first time, we propose a novel Targeted Bit Trojan(TBT) , which eliminates the need for model re-training to insert the targeted Trojan .
Revised:  However, in this work, for the first time, we propose a novel Targeted Bit Trojan(TBT) method, which can insert a targeted neural Trojan into a DNN through the bit-flip attack .
Edit intent: clarity
Original:  However, when the attacker activates the trigger embedded with input images , the network classifies all the inputs to a certain target class.
Revised:  However, when the attacker activates the trigger by embedding it with any input , the network classifies all the inputs to a certain target class.
Edit intent: clarity
Original:  However, when the attacker activates the trigger embedded with input images , the network classifies all the inputs to a certain target class.
Revised:  However, when the attacker activates the trigger embedded with input images , the network is forced to classify all inputs to a certain target class.
Edit intent: clarity
Original:  We demonstrate that flipping only several vulnerable bits founded by our method, using available bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN model into a Trojan infected model.
Revised:  We demonstrate that flipping only several vulnerable bits identified by our method, using available bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN model into a Trojan infected model.
Edit intent: fluency
Original:  We demonstrate that flipping only several vulnerable bits founded by our method, using available bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN model into a Trojan infected model.
Revised:  We demonstrate that flipping only several vulnerable bits founded by our method, using available bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN model into a Trojan-infected model.
Edit intent: fluency
Original:  Our proposed TBT could classify 93\% of the test images to a target class with as little as 82 bit-flips out of 88 million weight bits on Resnet-18 for CIFAR10 dataset.
Revised:  Our proposed TBT could classify 92 \% of test images to a target class with as little as 82 bit-flips out of 88 million weight bits on Resnet-18 for CIFAR10 dataset.
Edit intent: fluency
Original: Given a large and a small quantum circuit, we are interested in finding all maximal matches of the small circuit, called template , in the large circuit under consideration of pairwise commutation relations between quantum gates.
Revised: Given a large and a small quantum circuit, we are interested in finding all maximal matches of the small circuit, called pattern , in the large circuit under consideration of pairwise commutation relations between quantum gates.
Edit intent: clarity
Original: Given a large and a small quantum circuit, we are interested in finding all maximal matches of the small circuit, called template , in the large circuit under consideration of pairwise commutation relations between quantum gates.
Revised: Given a large and a small quantum circuit, we are interested in finding all maximal matches of the small circuit, called template , in the large circuit , considering pairwise commutation of quantum gates.
Edit intent: clarity
Original:  In this work  we present a classical algorithm for this task that provably finds all maximal matches with a running time that is polynomial in the number of gates and the number of qubits of the circuit for a fixed template size.
Revised:  In this work , we present a classical algorithm for this task that provably finds all maximal matches with a running time that is polynomial in the number of gates and the number of qubits of the circuit for a fixed template size.
Edit intent: fluency
Original:  In this work  we present a classical algorithm for this task that provably finds all maximal matches with a running time that is polynomial in the number of gates and the number of qubits of the circuit for a fixed template size.
Revised:  In this work we present a classical algorithm for pattern matching that provably finds all maximal matches with a running time that is polynomial in the number of gates and the number of qubits of the circuit for a fixed template size.
Edit intent: clarity
Original:  In this work  we present a classical algorithm for this task that provably finds all maximal matches with a running time that is polynomial in the number of gates and the number of qubits of the circuit for a fixed template size.
Revised:  In this work we present a classical algorithm for this task that provably finds all maximal matches in time polynomial in the number of gates and the number of qubits of the circuit for a fixed template size.
Edit intent: clarity
Original:  In this work  we present a classical algorithm for this task that provably finds all maximal matches with a running time that is polynomial in the number of gates and the number of qubits of the circuit for a fixed template size.
Revised:  In this work we present a classical algorithm for this task that provably finds all maximal matches with a running time that is polynomial in the circuit size ( for a fixed template size.
Edit intent: clarity
Original:  We show here that even very simple nanodevices , modeled as chemical reaction networks, can by a different mechanism also sense their own population densities and change their behaviors dramatically at certain thresholds.
Revised:  We show here that even very simple nanodevices , modeled as chemical reaction networks, can have behaviors that exhibit dramatic phase transitions at certain population thresholds.
Edit intent: clarity
Original:  Moreover, the magnitudes of these thresholds can thwart attempts to use  model checking, simulation, or approximation by differential equations to formally verify the behaviors of such nanodevices at realistic population densities .
Revised:  Moreover, the magnitudes of these thresholds can thwart attempts to use model checking, simulation, or approximation by differential equations to formally verify the behaviors of such systems at realistic populations .
Edit intent: clarity
Original:  We show how formal theorem provers can successfully verify some such systems at sizes where other verification methods fail.
Revised:  We show how formal theorem provers can successfully verify some such systems at populations where other verification methods fail.
Edit intent: clarity
Original:  It approximates locally tree-like networks well  and can lead to surprisingly good performance on more dense networks, as we show using real world data , including a miRNA- miRNA probabilistic interaction network for gastrointestinal cancer.
Revised:  It approximates locally tree-like networks well and can lead to surprisingly good performance on more dense networks, as we also exemplify on real world data , including a miRNA- miRNA probabilistic interaction network for gastrointestinal cancer.
Edit intent: clarity
Original:  We prove that there exists an L(p,q)-labeling with span at most \max\{2(p+q-1)\Delta-4q+2, (2p-1)\mu+(2q-1)\Delta-2q+1\} for interval k-graphs, \max\{p,q\}\Delta for interval graphs,  \max\{p,q\}\Delta+p \omega for circular arc graphs, 2(p+q-1)\Delta-2q+1 for permutation graphs and (2p-1)\Delta+(2q-1)(\mu-1) for cointerval graphs.
Revised:  We prove that there exists an L(p,q)-labeling with span at most \max\{2(p+q-1)\Delta-4q+2, (2p-1)\mu+(2q-1)\Delta-2q+1\} for interval k-graphs, \max\{p,q\}\Delta for interval graphs, \max\{p,q\}\Delta+p for circular-arc graphs, 2(p+q-1)\Delta-2q+1 for permutation graphs and (2p-1)\Delta+(2q-1)(\mu-1) for cointerval graphs.
Edit intent: clarity