wanyu/IteraTeR_v2 huggingface dataset:
Original:  For any desired \varepsilon > 0, we construct a family of binary codes of positive rate which can be efficiently list-decoded from any combination of \gamma fraction of insertions and \delta fraction of deletions as long as \gamma+2\delta\leq 1- \varepsilon .
Revised:  For any desired \varepsilon > 0, we construct a family of binary codes of positive rate which can be efficiently list-decoded from any combination of \gamma fraction of insertions and \delta fraction of deletions as long as \gamma+2\delta\leq 1- \epsilon .
Edit intent: clarity
Original:  2014]. Finally we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with information rate zero ) into an efficiently decodable code family with constant rate.
Revised:  2014]. Finally we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with vanishing information rate ) into an efficiently decodable code family with constant rate.
Edit intent: clarity
Original:  This question has been open even for binary codes, including the restriction to the binary insertion-only setting, where the best known results was that a \gamma\leq 0.707 fraction of insertions is tolerable by some binary code family.
Revised:  This question has been open even for binary codes, including the restriction to the binary insertion-only setting, where the best-known result was that a \gamma\leq 0.707 fraction of insertions is tolerable by some binary code family.
Edit intent: fluency
Original:  2014]. Finally  we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with vanishing information rate) into an efficiently decodable code family with constant rate.
Revised:  2014]. Finally , we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with vanishing information rate) into an efficiently decodable code family with constant rate.
Edit intent: fluency
Original: Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \mu m)) over\mu m cellular structures (\approx O(10 \mu m\mu m )) to the global tissue architecture (\gtrapprox O(1 mm )).
Revised: Histopathologic diagnosis relies on integration of simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \mu m)) over\mu m cellular structures (\approx O(10 \mu m\mu m )) to the global tissue architecture (\gtrapprox O(1 mm )).
Edit intent: clarity
Original: Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \mu m)) over\mu m cellular structures (\approx O(10 \mu m\mu m )) to the global tissue architecture (\gtrapprox O(1 mm )).
Revised: Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \\mu m cellular structures (\approx O(10 \mu m\mu m )) to the global tissue architecture (\gtrapprox O(1 mm )).
Edit intent: fluency
Original: Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \mu m)) over\mu m cellular structures (\approx O(10 \mu m\mu m )) to the global tissue architecture (\gtrapprox O(1 mm )).
Revised: Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \mu m)) over\mu m)) through cellular structures (\approx O(10 \mu m\mu m )) to the global tissue architecture (\gtrapprox O(1 mm )).
Edit intent: fluency
Original: Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \mu m)) over\mu m cellular structures (\approx O(10 \mu m\mu m )) to the global tissue architecture (\gtrapprox O(1 mm )).
Revised: Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \mu m)) over\mu m cellular structures (\approx O(10 \\mu m )) to the global tissue architecture (\gtrapprox O(1 mm )).
Edit intent: fluency
Original: Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \mu m)) over\mu m cellular structures (\approx O(10 \mu m\mu m )) to the global tissue architecture (\gtrapprox O(1 mm )).
Revised: Histopathologic diagnosis is dependent on simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \mu m)) over\mu m cellular structures (\approx O(10 \mu m\mu m )) to the global tissue architecture (\gtrapprox O(1 \ )).
Edit intent: clarity
Original:  Bearing in mind which information is employed by human pathologists , we introduce and examine different strategies for the integration of multiple and widely separate spatial scales into common U-Net-based architectures.
Revised:  To explicitly mimic how human pathologists combine multi-scale information , we introduce and examine different strategies for the integration of multiple and widely separate spatial scales into common U-Net-based architectures.
Edit intent: clarity
Original: Histopathologic diagnosis relies on integration of simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \%DIFDELCMD < \mu m%%%  )) through cellular structures (\approx O(10 \%DIFDELCMD < \mu m%%%  )) to the global tissue architecture (\gtrapprox O(1 \ )).
Revised: Histopathologic diagnosis relies on simultaneous integration of information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \%DIFDELCMD < \mu m%%% )) through cellular structures (\approx O(10 \%DIFDELCMD < \mu m%%% )) to the global tissue architecture (\gtrapprox O(1 \ )).
Edit intent: fluency
Original: Histopathologic diagnosis relies on integration of simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \%DIFDELCMD < \mu m%%%  )) through cellular structures (\approx O(10 \%DIFDELCMD < \mu m%%%  )) to the global tissue architecture (\gtrapprox O(1 \ )).
Revised: Histopathologic diagnosis relies on integration of simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 %DIFDELCMD < \mu m%%% )) through cellular structures (\approx O(10 \%DIFDELCMD < \mu m%%% )) to the global tissue architecture (\gtrapprox O(1 \ )).
Edit intent: fluency
Original: Histopathologic diagnosis relies on integration of simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \%DIFDELCMD < \mu m%%%  )) through cellular structures (\approx O(10 \%DIFDELCMD < \mu m%%%  )) to the global tissue architecture (\gtrapprox O(1 \ )).
Revised: Histopathologic diagnosis relies on integration of simultaneous information from a broad range of scales, ranging from nuclear aberrations (\approx O(0.1 \%DIFDELCMD < \mu m%%% )) through cellular structures (\approx O(10 %DIFDELCMD < \mu m%%% )) to the global tissue architecture (\gtrapprox O(1 \ )).
Edit intent: fluency
Original:  To explicitly mimic how human pathologists combine multi-scale information, we introduce a family of multi-encoder fully-convolutional neural networks with deep fusion.
Revised:  To explicitly mimic how human pathologists combine multi-scale information, we introduce a family of multi-encoder FCNs with deep fusion.
Edit intent: clarity
Original:  We present a simple block for merging model paths with differing spatial scales in a spatial relationship-preserving fashion, which can readily be included in standard encoder-decoder networks and at various levels .
Revised:  We present a simple block for merging model paths with differing spatial scales in a spatial relationship-preserving fashion, which can readily be included in standard encoder-decoder networks .
Edit intent: coherence
Original:  Additionally, a context classification gate block is suggested as an alternative for the incorporation of solely global context.
Revised:  Additionally, a context classification gate block is proposed as an alternative for the incorporation of solely global context.
Edit intent: clarity
Original:  Additionally, a context classification gate block is suggested as an alternative for the incorporation of solely global context.
Revised:  Additionally, a context classification gate block is suggested as an alternative for the incorporation of global context.
Edit intent: clarity
Original:  The setups benefit from both local as well as global context and particularly from a combination of both.
Revised:  They benefit from local as well as global context and particularly from a combination of both.
Edit intent: clarity
Original:  The setups benefit from both local as well as global context and particularly from a combination of both.
Revised:  The setups benefit from both local as well as global context and particularly a combination of both.
Edit intent: fluency
Original:  If feature maps from different scales are fused, doing so in a spatial relationship preserving manner is found to be beneficial.
Revised:  If feature maps from different scales are fused, doing so in a manner preserving spatial relationships was found to be beneficial.
Edit intent: fluency
Original:  Additional path fusions are shown to be possible at low computational cost which considerably increases the set of possible multi-scale architectures .
Revised:  Additional path fusions were shown to be possible at low computational cost which considerably increases the set of possible multi-scale architectures .
Edit intent: fluency
Original:  The findings demonstrate the potential of the introduced family of human-inspired, end-to-end trainable, multi-scale multi-encoder fully-convolutional neural networks to improve deep histopathologic diagnosis by extensive integration of largely different spatial scales.
Revised:  The findings demonstrate the potential of the presented family of human-inspired, end-to-end trainable, multi-scale multi-encoder fully-convolutional neural networks to improve deep histopathologic diagnosis by extensive integration of largely different spatial scales.
Edit intent: clarity
Original:  The findings demonstrate the potential of the introduced family of human-inspired, end-to-end trainable, multi-scale multi-encoder fully-convolutional neural networks to improve deep histopathologic diagnosis by extensive integration of largely different spatial scales.
Revised:  The findings demonstrate the potential of the introduced family of human-inspired, end-to-end trainable, multi-scale multi-encoder FCNs to improve deep histopathologic diagnosis by extensive integration of largely different spatial scales.
Edit intent: clarity
Original: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following  problem: Given parameters d\in \mathbb{N {\bf  , \alpha>\beta\geq 0 and unit vectors x,y\in \mathbb{R {\bf  ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Revised: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following inner product estimation problem: Given parameters d\in \mathbb{N {\bf , \alpha>\beta\geq 0 and unit vectors x,y\in \mathbb{R {\bf ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Edit intent: clarity
Original: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following  problem: Given parameters d\in \mathbb{N {\bf  , \alpha>\beta\geq 0 and unit vectors x,y\in \mathbb{R {\bf  ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Revised: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following problem: Given parameters d\in {\bf , \alpha>\beta\geq 0 and unit vectors x,y\in \mathbb{R {\bf ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Edit intent: clarity
Original: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following  problem: Given parameters d\in \mathbb{N {\bf  , \alpha>\beta\geq 0 and unit vectors x,y\in \mathbb{R {\bf  ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Revised: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following problem: Given parameters d\in \mathbb{N {\bf N , \alpha>\beta\geq 0 and unit vectors x,y\in \mathbb{R {\bf ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Edit intent: fluency
Original: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following  problem: Given parameters d\in \mathbb{N {\bf  , \alpha>\beta\geq 0 and unit vectors x,y\in \mathbb{R {\bf  ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Revised: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following problem: Given parameters d\in \mathbb{N {\bf , \alpha>\beta\geq 0 and unit vectors x,y\in {\bf ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Edit intent: clarity
Original: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following  problem: Given parameters d\in \mathbb{N {\bf  , \alpha>\beta\geq 0 and unit vectors x,y\in \mathbb{R {\bf  ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Revised: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following problem: Given parameters d\in \mathbb{N {\bf , \alpha>\beta\geq 0 and unit vectors x,y\in \mathbb{R {\bf R ^{d} consider the task of distinguishing between the cases \langle x, y\rangle\leq\beta and \langle x, y\rangle\geq \alpha where \langle x, y\rangle = i=1^d x_i y_i is the inner product of vectors x and y.
Edit intent: fluency
Original:  The goal is to distinguish these cases with information on each vector encoded independently in a bit string of the shortest length possible.
Revised:  The goal is to distinguish these cases based on information on each vector encoded independently in a bit string of the shortest length possible.
Edit intent: clarity
Original:  The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative `` opinion" to all agents about whether the traded asset price will go up or down.
Revised:  The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative " opinion" to all agents about whether the traded asset price will go up or down.
Edit intent: fluency
Original: The ability of accurately ranking candidate architectures is the key to the performance of neural architecture search~(NAS).
Revised: The ability to rank candidate architectures is the key to the performance of neural architecture search~(NAS).
Edit intent: clarity
Original:  One-shot NAS is proposed to cut the expense but shows inferior performance against conventional NAS and is not adequately stable.
Revised:  One-shot NAS is proposed to reduce the expense but shows inferior performance against conventional NAS and is not adequately stable.
Edit intent: fluency
Original:  We  find that the ranking correlation between architectures under one-shot training and the ones under stand-alone  training is poor, which misleads the algorithm to discover better architectures.
Revised:  We investigate into this and find that the ranking correlation between architectures under one-shot training and the ones under stand-alone training is poor, which misleads the algorithm to discover better architectures.
Edit intent: coherence
Original:  (2) Encourage more updates for large and complex architectures than small and simple architectures to balance them by sampling architectures in proportion to their model sizes.
Revised:  (2) Encourage more updates for large architectures than small and simple architectures to balance them by sampling architectures in proportion to their model sizes.
Edit intent: clarity
Original:  (2) Encourage more updates for large and complex architectures than small and simple architectures to balance them by sampling architectures in proportion to their model sizes.
Revised:  (2) Encourage more updates for large and complex architectures than small architectures by sampling architectures in proportion to their model sizes.
Edit intent: clarity
Original:  (3) Make the one-shot training of the supernet independent at each iteration.
Edit intent: coherence
Original:  It leads to a more stable search that all the top architectures perform well enough compared to baseline methods .
Revised:  It leads to a more stable search .
Edit intent: clarity
Original:  The final discovered architecture shows significant improvements against baselines with a test error rate of 2.60 \% on CIFAR-10 and top-1 accuracy of 74.4\% on ImageNet \\%DIF > on CIFAR-10 and top-1 accuracy of 74.4\% on ImageNet under the mobile setting.
Revised:  The final discovered architecture shows significant improvements against baselines with a test error rate of 2.60 \\%DIF > on CIFAR-10 and top-1 accuracy of 74.4\% on ImageNet under the mobile setting.
Edit intent: fluency
Original:  Code and model checkpoints are publicly availableat URL
Revised:  Code and model checkpoints will be publicly available. The code is available at github.com/renqianluo/NAO_pytorch.
Edit intent: clarity
Original:  With m the number of transitions and n the number of states, the classic O( mn) algorithm has recently been replaced by an O(m log n)\lvert }\rvert  algorithm, which is unfortunately rather complex.
Revised:  With m the number of transitions and n the number of states, the classic O( m n) algorithm was recently replaced by an O(m log n)\lvert }\rvert algorithm, which is unfortunately rather complex.
Edit intent: fluency
Original:  With m the number of transitions and n the number of states, the classic O( mn) algorithm has recently been replaced by an O(m log n)\lvert }\rvert  algorithm, which is unfortunately rather complex.
Revised:  With m the number of transitions and n the number of states, the classic O( mn) algorithm has recently been replaced by an O(m (\log\lvert }\rvert algorithm, which is unfortunately rather complex.
Edit intent: fluency
Original:  This paper combines the ideas from Groote et al. with the ideas from Valmari.
Revised:  This paper combines its ideas with the ideas from Valmari.
Edit intent: clarity
Original: Active inference is a first (Bayesian) principles account of how autonomous agents might operate in dynamic, non-stationary environments.
Revised: Active inference is a first principle account of how autonomous agents might operate in dynamic, non-stationary environments.
Edit intent: clarity
Original: Active inference is a first (Bayesian) principles account of how autonomous agents might operate in dynamic, non-stationary environments.
Revised: Active inference is a first (Bayesian) principles account of how autonomous agents operate in dynamic, non-stationary environments.
Edit intent: clarity
Original:  In contrast to analogous frameworks - by operating in a pure belief-based setting (free energy functional of beliefs about states) - active inference agents can carry out epistemic exploration and naturally account for uncertainty about their environment  .
Revised:  In contrast to analogous frameworks - by operating in a pure belief-based setting , active inference agents can carry out epistemic exploration and naturally account for uncertainty about their environment .
Edit intent: clarity
Original:  We conclude by noting that this formalism can be applied in other engineering applications ;
Revised:  We conclude by noting that this formalism can be applied to more complex settings ;
Edit intent: clarity
Original:  e.g., robotic arm movement, playing Atari games, etc., if appropriate underlying probability distributions (i.e. generative model) can be formulated  .
Revised:  e.g., robotic arm movement, Atari games, etc., if appropriate underlying probability distributions (i.e. generative model) can be formulated .
Edit intent: clarity
Original:  e.g., robotic arm movement, playing Atari games, etc., if appropriate underlying probability distributions (i.e. generative model) can be formulated  .
Revised:  e.g., robotic arm movement, playing Atari games, etc., if appropriate generative models can be formulated .
Edit intent: clarity
Original:  In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in reinforcement learning ;
Revised:  In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in RL ;
Edit intent: clarity
Original:  2) to our knowledge, the first explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline.
Revised:  2) an explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline.
Edit intent: clarity
Original:  2) to our knowledge, the first explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline.
Revised:  2) to our knowledge, the first explicit discrete-state comparison between active inference and RL on an OpenAI gym baseline.
Edit intent: clarity
Original:  We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of reinforcement learning .
Revised:  We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of RL .
Edit intent: clarity
Original:  We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration - and account for uncertainty about their environment - in a Bayes optimal fashion.
Revised:  We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration , and account for uncertainty about their environment - in a Bayes optimal fashion.
Edit intent: fluency