wanyu/IteraTeR_v2 huggingface dataset:
Original:  To overcome the aforementioned limitations, we propose an unbiased classification scheme taking advantage of collective knowledge , Wikipedia, an Internet encyclopedia edited by millions of users set the prompt classification in collective fashions .
Revised:  To overcome the aforementioned limitations, we propose an unbiased classification scheme that takes advantage of collective knowledge , Wikipedia, an Internet encyclopedia edited by millions of users set the prompt classification in collective fashions .
Edit intent: coherence
Original:  To overcome the aforementioned limitations, we propose an unbiased classification scheme taking advantage of collective knowledge , Wikipedia, an Internet encyclopedia edited by millions of users set the prompt classification in collective fashions .
Revised:  To overcome the aforementioned limitations, we propose an unbiased classification scheme taking advantage of collective knowledge ; Wikipedia, an Internet encyclopedia edited by millions of users set the prompt classification in collective fashions .
Edit intent: fluency
Original:  To overcome the aforementioned limitations, we propose an unbiased classification scheme taking advantage of collective knowledge , Wikipedia, an Internet encyclopedia edited by millions of users set the prompt classification in collective fashions .
Revised:  To overcome the aforementioned limitations, we propose an unbiased classification scheme taking advantage of collective knowledge , Wikipedia, an Internet encyclopedia edited by millions of users , sets a prompt classification in collective fashions .
Edit intent: fluency
Original:  To overcome the aforementioned limitations, we propose an unbiased classification scheme taking advantage of collective knowledge , Wikipedia, an Internet encyclopedia edited by millions of users set the prompt classification in collective fashions .
Revised:  To overcome the aforementioned limitations, we propose an unbiased classification scheme taking advantage of collective knowledge , Wikipedia, an Internet encyclopedia edited by millions of users set the prompt classification in a collective fashion .
Edit intent: fluency
Original:  We construct the Wikipedia network for scientific disciplines and extract the backbone of the network.
Revised:  We construct a Wikipedia network for scientific disciplines and extract the backbone of the network.
Edit intent: fluency
Original:  This structure displays the landscape of science and technology  based on a collective intelligence  that is more unbiased and adaptable than conventional classifications.
Revised:  This structure displays a landscape of science and technology based on a collective intelligence that is more unbiased and adaptable than conventional classifications.
Edit intent: fluency
Original:  This structure displays the landscape of science and technology  based on a collective intelligence  that is more unbiased and adaptable than conventional classifications.
Revised:  This structure displays the landscape of science and technology that is based on a collective intelligence that is more unbiased and adaptable than conventional classifications.
Edit intent: fluency
Original:  This structure displays the landscape of science and technology  based on a collective intelligence  that is more unbiased and adaptable than conventional classifications.
Revised:  This structure displays the landscape of science and technology based on a collective intelligence and that is more unbiased and adaptable than conventional classifications.
Edit intent: coherence
Original: Several neuroimaging markers have been established for the early diagnosis of Alzheimer's disease, among them amyloid-beta deposition, glucose metabolism, and gray matter volume.
Revised: A sequence of pathological changes takes place in Alzheimer's disease, among them amyloid-beta deposition, glucose metabolism, and gray matter volume.
Edit intent: clarity
Original:  Gaussian graphical models (GGMs) are able to estimate the conditional dependency between many individual random variables.
Revised:  Gaussian graphical models (GGMs) for analyzing the conditional dependency between many individual random variables.
Edit intent: clarity
Original:   GGMs were estimated using a Bayesian framework and for each individual diagnosis, graph-theoretical statistics were calculated to determine structural changes associated with disease severity.
Revised:  Separate GGMs were estimated using a Bayesian framework and for each individual diagnosis, graph-theoretical statistics were calculated to determine structural changes associated with disease severity.
Edit intent: fluency
Original:   GGMs were estimated using a Bayesian framework and for each individual diagnosis, graph-theoretical statistics were calculated to determine structural changes associated with disease severity.
Revised:  GGMs were estimated using a Bayesian framework for the combined multimodal data for each diagnostic category. Graph-theoretical statistics were calculated to determine structural changes associated with disease severity.
Edit intent: clarity
Original:   GGMs were estimated using a Bayesian framework and for each individual diagnosis, graph-theoretical statistics were calculated to determine structural changes associated with disease severity.
Revised:  GGMs were estimated using a Bayesian framework and for each individual diagnosis, graph-theoretical statistics were calculated to determine network alterations associated with disease severity.
Edit intent: clarity
Original:  Mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) was calculatedfor each brain region.
Revised:  The mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) was calculatedfor each brain region.
Edit intent: clarity
Original:  Following this, it had been an open question whether one can obtain an algorithm that is polynomial in \log d.
Edit intent: coherence
Original:  In a footnote in Harrow's thesis H05, a brief description of how to make the algorithm of BCH07 polynomial in \log d is given using the unitary group representation theory (however, this has not been explained in detail anywhere ) . In this article, we present a quantum algorithm for the Schur transform that is polynomial in n, \log d and \log\epsilon^{-1} using a different approach.
Revised:  In a footnote in Harrow's thesis H05, a brief description of how to make the algorithm of BCH07 polynomial in \log d is given using the unitary group representation theory (however, this has not been explained in detail anywhere . In this article, we present a quantum algorithm for the Schur transform that is polynomial in n, \log d and \log\epsilon^{-1} using a different approach.
Edit intent: fluency
Original:  We build this transform using the representation theory of the symmetric group and in this sense our technique can be considered a "dual" algorithm to BCH07.
Revised:  Specifically, we build this transform using the representation theory of the symmetric group and in this sense our technique can be considered a "dual" algorithm to BCH07.
Edit intent: coherence
Original:  A novel feature of our algorithm is that we construct the quantum Fourier transform over permutation modules that could have other applications.
Revised:  A novel feature of our algorithm is that we construct the quantum Fourier transform over the so called could have other applications.
Edit intent: clarity
Original:  A novel feature of our algorithm is that we construct the quantum Fourier transform over permutation modules that could have other applications.
Revised:  A novel feature of our algorithm is that we construct the quantum Fourier transform over permutation modules thatpermutation modules could have other applications.
Edit intent: fluency
Original:  A novel feature of our algorithm is that we construct the quantum Fourier transform over permutation modules that could have other applications.
Revised:  A novel feature of our algorithm is that we construct the quantum Fourier transform over permutation modules that, which could have other applications.
Edit intent: fluency
Original: The Min-Hashing approach to sketching has become an important tool in data analysis, search , and classification.
Revised: The Min-Hashing approach to sketching has become an important tool in data analysis, information retrial , and classification.
Edit intent: clarity
Original: We present a neurosymbolic approach to the lifelong learning of algorithmic tasks that mix perception and procedural reasoning.
Revised: We present a neurosymbolic framework for the lifelong learning of algorithmic tasks that mix perception and procedural reasoning.
Edit intent: clarity
Original:  Reusing highlevel concepts across domains and learning complex procedures are two key challenges in lifelong learning.
Revised:  Reusing high-level concepts across domains and learning complex procedures are two key challenges in lifelong learning.
Edit intent: fluency
Original:  Reusing highlevel concepts across domains and learning complex procedures are two key challenges in lifelong learning.
Revised:  Reusing highlevel concepts across domains and learning complex procedures are key challenges in lifelong learning.
Edit intent: clarity
Original:  Concretely, our approach, called HOUDINI, represents neural networks as strongly typed, end-to-end differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions.
Revised:  Our framework, called HOUDINI, represents neural networks as strongly typed, end-to-end differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions.
Edit intent: clarity
Original:  Concretely, our approach, called HOUDINI, represents neural networks as strongly typed, end-to-end differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions.
Revised:  Concretely, our approach, called HOUDINI, represents neural networks as strongly typed, differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions.
Edit intent: clarity
Original:  Our learning algorithm consists of: (1) a  program synthesizer that performs a type-directed search over programs in this language , and decides on the library functions that should be reused and the architectures that should be used to combine them  ;
Revised:  Our learning algorithm consists of: (1) a program synthesizer that performs a type-directed search over programs in this language , and decides on the library functions to reuse, and the architectures that should be used to combine them ;
Edit intent: fluency
Original:  Our learning algorithm consists of: (1) a  program synthesizer that performs a type-directed search over programs in this language , and decides on the library functions that should be reused and the architectures that should be used to combine them  ;
Revised:  Our learning algorithm consists of: (1) a program synthesizer that performs a type-directed search over programs in this language , and decides on the library functions that should be reused and the architectures to combine them ;
Edit intent: clarity
Original:  and (2) a neural module that trains synthesized programs using stochastic gradient descent.
Revised:  and (2) a neural module that trains these programs using stochastic gradient descent.
Edit intent: clarity
Original:  Our experiments show that our type-directed search technique is able to significantly prune the search space of programs, and that the overall approach transfers high-level concepts more effectively than monolithic neural networks as well as traditional transfer learning  .
Revised:  Our experiments show that HOUDINI transfers high-level concepts more effectively than monolithic neural networks as well as traditional transfer learning .
Edit intent: clarity
Original:  Our experiments show that our type-directed search technique is able to significantly prune the search space of programs, and that the overall approach transfers high-level concepts more effectively than monolithic neural networks as well as traditional transfer learning  .
Revised:  Our experiments show that our type-directed search technique is able to significantly prune the search space of programs, and that the overall approach transfers high-level concepts more effectively than traditional transfer learning .
Edit intent: clarity
Original:  Typically, this goal is approached by minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect .
Revised:  Typically, this goal is approached by minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise incidentally .
Edit intent: clarity
Original:  Typically, this goal is approached by minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise incidentally .
Revised:  Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise incidentally .
Edit intent: clarity
Original:  Typically, this goal is approached by minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise incidentally .
Revised:  Typically, this goal is approached by minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect .
Edit intent: clarity
Original:  In this work, we propose instead to directly target a later desired task by meta-learning an unsupervised learning rule , which leads to representations useful for that task.
Revised:  In this work, we propose instead to directly target later desired tasks by meta-learning an unsupervised learning rule , which leads to representations useful for that task.
Edit intent: fluency
Original:  In this work, we propose instead to directly target a later desired task by meta-learning an unsupervised learning rule , which leads to representations useful for that task.
Revised:  In this work, we propose instead to directly target a later desired task by meta-learning an unsupervised learning rule which leads to representations useful for that task.
Edit intent: fluency
Original:  Here, our desired task (meta-objective) is the performance of the representation on semi-supervised classification  , and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations that perform well under this meta-objective .
Revised:  Here, our desired task (meta-objective) is the performance of the representation on semi-supervised classification performance , and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations that perform well under this meta-objective .
Edit intent: clarity
Original:  Here, our desired task (meta-objective) is the performance of the representation on semi-supervised classification  , and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations that perform well under this meta-objective .
Revised:  Here, our desired task (meta-objective) is the performance of the representation on semi-supervised classification , and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task .
Edit intent: clarity
Original:  Additionally, we constrain our unsupervised update rule to a be a biologically-motivated, neuron-local function, which enables it to generalize to novel neural network architectures  .
Revised:  Additionally, we constrain our unsupervised update rule to a be a biologically-motivated, neuron-local function, which enables it to generalize to different neural network architectures .
Edit intent: clarity
Original:  Using stochastic dynamics techniques, we characterize the value function as well as the indifference price in terms of the solution to a quadratic-exponential backward stochastic differential equation .
Revised:  Using stochastic dynamics techniques, we characterize the value function as well as the indifference price in terms of the solution to a quadratic-exponential backward stochastic differential equations .
Edit intent: fluency
Original:  We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lowest layers and reward-modulated STDP (R-STDP) for the highest ones.
Revised:  We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lower layers and reward-modulated STDP (R-STDP) for the highest ones.
Edit intent: fluency
Original:  We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lowest layers and reward-modulated STDP (R-STDP) for the highest ones.
Revised:  We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lowest layers and reward-modulated STDP (R-STDP) for the higher ones.
Edit intent: fluency
Original:  This approach led to an accuracy of 97.2 \% on MNIST, without \\%DIF > on MNIST, without requiring an external classifier.
Revised:  This approach led to an accuracy of 97.2 \\%DIF > on MNIST, without requiring an external classifier.
Edit intent: coherence
Original:  An understanding of  response of such interdependent networks and the process of post-disaster recovery is the cornerstone of an efficient emergency management plan.
Revised:  An understanding of the response of such interdependent networks and the process of post-disaster recovery is the cornerstone of an efficient emergency management plan.
Edit intent: fluency
Original:  In this study, the interconnectedness among different critical facilities, such as electrical power networks, water networks, highway bridges, and food retailers  is modeled.
Revised:  In this study, the interconnectedness among different critical facilities, such as electrical power networks, water networks, highway bridges, and food retailers , is modeled.
Edit intent: fluency
Original:  Second, we show that when the process stops, for all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, every particle is contained in a large "monochromatic region" of size exponential in N, almost surely.
Revised:  Second, we show that when the process stops, \ for \ all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, every particle is contained in a large "monochromatic region" of size exponential in N, almost surely.
Edit intent: fluency
Original:  Second, we show that when the process stops, for all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, every particle is contained in a large "monochromatic region" of size exponential in N, almost surely.
Revised:  Second, we show that when the process stops, for all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, any particle is contained in a large "monochromatic region" of size exponential in N, almost surely.
Edit intent: clarity
Original:  When particles are placed on the infinite lattice Z^2 rather than on a flat torus, for the values of \tau mentioned above, sufficiently large N, and after a sufficiently long evolution time, every particle is contained in a large monochromatic region of size exponential in N, almost surely.
Revised:  When particles are placed on the infinite lattice Z^2 rather than on a flat torus, for the values of \tau mentioned above, sufficiently large N, and after a sufficiently long evolution time, any particle is contained in a large monochromatic region of size exponential in N, almost surely.
Edit intent: clarity
Original: We consider a long-range interacting particle system in which binary particles  are located at the integer points of a flat torus ./hZ)^2.
Revised: We consider a long-range interacting particle system in which binary particles are located at the nodes of a flat torus ./hZ)^2.
Edit intent: clarity
Original:  } Based on the interactions with other particles in its "neighborhood" and on the value of a common intolerance threshold \tau, every particle decides whether to change its state after an independent and exponentially distributed waiting time .
Revised:  } Based on the states of the neighboring particles and on the value of a common intolerance threshold \tau, every particle decides whether to change its state after an independent and exponentially distributed waiting time .
Edit intent: clarity
Original:  We first prove a shape theorem for the spread of the "affected" nodes during the process dynamics . Second, we show that when the process stops , \ for \ all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, any particle is contained in a large "monochromatic region " of size exponential in N , almost surely.
Revised:  We first prove a shape theorem for the spreading of the "affected" nodes during the process dynamics . Second, we show that when the process stops , \ for \ all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, any particle is contained in a large "monochromatic region " of size exponential in N , almost surely.
Edit intent: fluency
Original:  We first prove a shape theorem for the spread of the "affected" nodes during the process dynamics . Second, we show that when the process stops , \ for \ all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, any particle is contained in a large "monochromatic region " of size exponential in N , almost surely.
Revised:  We first prove a shape theorem for the spread of the "affected" nodes during the process dynamics . Second, we show that when the process stops , \ for \ all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, any particle is contained in a large "monochromatic ball " of size exponential in N , almost surely.
Edit intent: clarity
Original:  We first prove a shape theorem for the spread of the "affected" nodes during the process dynamics . Second, we show that when the process stops , \ for \ all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, any particle is contained in a large "monochromatic region " of size exponential in N , almost surely.
Revised:  We first prove a shape theorem for the spread of the "affected" nodes during the process dynamics . Second, we show that when the process stops , \ for \ all {\tau \in (\tau^*,1-\tau^*) \setminus \{1/2\}} where {\tau^* \approx 0.488}, and when the size of the neighborhood of interaction N is sufficiently large, any particle is contained in a large "monochromatic region " of cardinality exponential in N , almost surely.
Edit intent: clarity