wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Here, we consider only simple weighted base pair counting  to obtain BPPart algorithm for Base-pair Partition function and BPMax for Base-pair Maximization, which use 9 and 2 tables respectively.
Revised:  Here, we consider only simple weighted base pair counting is considered to obtain BPPart algorithm for Base-pair Partition function and BPMax for Base-pair Maximization, which use 9 and 2 tables respectively.
Edit intent: coherence
Original:  Here, we consider only simple weighted base pair counting  to obtain BPPart algorithm for Base-pair Partition function and BPMax for Base-pair Maximization, which use 9 and 2 tables respectively.
Revised:  Here, we consider only simple weighted base pair counting to obtain BPPart for Base-pair Partition function and BPMax for Base-pair Maximization, which use 9 and 2 tables respectively.
Edit intent: clarity
Original:  They are empirically 225 and 1350 fold faster than piRNA. A correlation of 0.855 and 0.836 was achieved between piRNA and BPPart and between piRNA and BPMax, respectively, in 37 degrees on 50, 500 experimentally characterized RRIs. Those correlations increase to 0.920 and 0.904 in -180 degrees.
Revised:  They are empirically 225 and 1350 fold faster than piRNA. A correlation of 0.855 and 0.836 was achieved between piRNA and BPPart and between piRNA and BPMax, respectively, in 37 degrees , and 0.920 and 0.904 in -180 degrees.
Edit intent: coherence
Original:  Model slicing could be viewed as an elastic computation solution without requiring more computation resources, but by slightly sacrificing prediction accuracy. In a nutshell, partially ordered relation is introduced to the basic components of each layer in the model , namely neurons in dense layers and channels in convolutional layers . Specifically, if one component participates in the forward pass , then all of its preceding components are also activated.
Revised:  Model slicing could be viewed as an elastic computation solution without requiring more computational resources. Succinctly, each layer in the model , namely neurons in dense layers and channels in convolutional layers . Specifically, if one component participates in the forward pass , then all of its preceding components are also activated.
Edit intent: coherence
Original:  In a nutshell, partially ordered relation is introduced to the basic components of each layer in the model , namely neurons in dense layers and channels in convolutional layers . Specifically, if one component participates in the forward pass , then all of its preceding components are also activated.
Revised:  In a nutshell, partially ordered relation is introduced to the basic components of each layer in the model is divided into groups of contiguous block of basic components (i.e. neurons in dense layers and channels in convolutional layers . Specifically, if one component participates in the forward pass , then all of its preceding components are also activated.
Edit intent: meaning-changed
Original:  In a nutshell, partially ordered relation is introduced to the basic components of each layer in the model , namely neurons in dense layers and channels in convolutional layers . Specifically, if one component participates in the forward pass , then all of its preceding components are also activated. Dynamically trained under such structural constraint, we can slice a narrower sub-model during inference whose run-time memory and computational operation consumption is roughly quadratic to the width controlled by a single parameter slice rate.
Revised:  In a nutshell, partially ordered relation is introduced to the basic components of each layer in the model , namely neurons in dense layers and channels in convolutional layers ), and then partially ordered relation is introduced to these groups by enforcing that groups participated in each forward pass always starts from the first group to the dynamically-determined rightmost group. Trained by dynamically indexing the rightmost group with a single parameter slice rate, the network is engendered to build up group-wise and residual representation. Then during inference, a sub-model during inference whose run-time memory and computational operation consumption is roughly quadratic to the width controlled by a single parameter slice rate.
Edit intent: meaning-changed
Original:  Dynamically trained under such structural constraint, we can slice a narrower sub-model during inference whose run-time memory and computational operation consumption is roughly quadratic to the width controlled by a single parameter slice rate.
Revised:  Dynamically trained under such structural constraint, we can slice a narrower sub-model with fewer groups can be readily deployed for efficiency whose computation is roughly quadratic to the width controlled by a single parameter slice rate.
Edit intent: meaning-changed
Original:  Succinctly, each layer in the model is divided into groups of  contiguous block of basic components (i.e. neurons in dense layers and channels in convolutional layers), and then partially ordered relation is introduced to these groups by enforcing that groups participated in each forward pass always starts from the first group to the dynamically-determined rightmost group.
Revised:  Succinctly, each layer in the model is divided into groups of a contiguous block of basic components (i.e. neurons in dense layers and channels in convolutional layers), and then partially ordered relation is introduced to these groups by enforcing that groups participated in each forward pass always starts from the first group to the dynamically-determined rightmost group.
Edit intent: fluency
Original: We first pose the Unsupervised Continual Learning (UCL ) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time.
Revised: We first pose the Unsupervised Progressive Learning (UPL ) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time.
Edit intent: meaning-changed
Original: We first pose the Unsupervised Continual Learning (UCL ) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time.
Revised: We first pose the Unsupervised Continual Learning (UCL ) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes increases with time.
Edit intent: clarity
Original:  To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM) , which loosely models the function of a cortical column in the mammalian brain.
Revised:  To solve the UPL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM) , which loosely models the function of a cortical column in the mammalian brain.
Edit intent: clarity
Original:  To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM) , which loosely models the function of a cortical column in the mammalian brain.
Revised:  To solve the UCL problem, we propose an architecture that involves an online clustering module, called Self-Taught Associative Memory (STAM) , which loosely models the function of a cortical column in the mammalian brain.
Edit intent: meaning-changed
Original:  To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM) , which loosely models the function of a cortical column in the mammalian brain. Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns , URLetting outliers, and top-down predictions.
Revised:  To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM) . Layered hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns , URLetting outliers, and top-down predictions.
Edit intent: clarity
Original:  Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns , URLetting outliers, and top-down predictions. We illustrate the operation of STAMs in the context of learning handwritten digits in a continual manner with only 3-12 labeled examplesper class. STAMs suggest a promising direction to solve the UCL problemwithout catastrophic URLetting .
Revised:  Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns , URLetting outliers, and storing only prototypical representations rather than specific examples. The goal of this paper is to introduce the UPL problem, describe the STAM architecture, and evaluate the latter in the UPL context .
Edit intent: clarity
Original:  To solve the UPL problem , we propose an architecture that involves an online clustering module , called Self-Taught Associative Memory (STAM). Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples.
Revised:  To solve the UPL problem we propose an architecture that involves an online clustering module , called Self-Taught Associative Memory (STAM). Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples.
Edit intent: fluency
Original:  To solve the UPL problem , we propose an architecture that involves an online clustering module , called Self-Taught Associative Memory (STAM). Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples.
Revised:  To solve the UPL problem , we propose an architecture that involves a module called Self-Taught Associative Memory (STAM). Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples.
Edit intent: clarity
Original:  The goal of this paper is to introduce the UPL problem, describe the STAM architecture , and evaluate the latter in the UPL context.
Revised:  We evaluate STAM representations using clustering and classification tasks, relying on limited labeled data for the latter. Even though there are no prior approaches that are directly applicable to the UPL problem, describe the STAM architecture , and evaluate the latter in the UPL context.
Edit intent: meaning-changed
Original: We first pose the Unsupervised Progressive Learning (UPL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes increases with time .
Revised: We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary stream of unlabeled data in which the number of object classes increases with time .
Edit intent: clarity
Original: We first pose the Unsupervised Progressive Learning (UPL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes increases with time .
Revised: We first pose the Unsupervised Progressive Learning (UPL) problem: learning salient representations from a non-stationary and unlabeled data stream, and identifies a growing number of features that persist over time even though the data is not stored or replayed .
Edit intent: meaning-changed
Original:  To solve the UPL problem we propose an architecture that involves a module called Self-Taught Associative Memory (STAM)  . Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples.
Revised:  To solve the UPL problem we propose the Self-Taught Associative Memory (STAM) . Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples.
Edit intent: clarity
Original:  To solve the UPL problem we propose an architecture that involves a module called Self-Taught Associative Memory (STAM)  . Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples.
Revised:  To solve the UPL problem we propose an architecture that involves a module called Self-Taught Associative Memory (STAM) architecture . Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples.
Edit intent: meaning-changed
Original:  To solve the UPL problem we propose an architecture that involves a module called Self-Taught Associative Memory (STAM)  . Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples.
Revised:  To solve the UPL problem we propose an architecture that involves a module called Self-Taught Associative Memory (STAM) . Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical features rather than specific examples.
Edit intent: clarity
Original:  Even though there are no prior approaches that are directly applicable to the UPL problem, we compare the STAM architecture to a couple of unsupervised and self-supervised deep learning approaches adapted in the UPL context.
Revised:  Even though there are no prior approaches that are directly applicable to the UPL problem, we evaluate the STAM architecture to a couple of unsupervised and self-supervised deep learning approaches adapted in the UPL context.
Edit intent: clarity
Original:  Even though there are no prior approaches that are directly applicable to the UPL problem, we compare the STAM architecture to a couple of unsupervised and self-supervised deep learning approaches adapted in the UPL context.
Revised:  Even though there are no prior approaches that are directly applicable to the UPL problem, we compare the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context.
Edit intent: clarity
Original: We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, and identifies a growing number of features that persist over time even though the data is not stored or replayed.
Revised: We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed.
Edit intent: coherence
Original:  We evaluate STAM representations using classification and clustering tasks. Even though there are no prior approaches that are directly applicable to the UPLproblem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context .
Revised:  We evaluate STAM representations using clustering and classification tasks. While there are no prior approaches that are directly applicable to the UPLproblem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context .
Edit intent: coherence
Original:  Even though there are no prior approaches that are directly applicable to the UPLproblem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context .
Revised:  Even though there are no existing learning scenarios that are directly applicable to the UPLproblem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context .
Edit intent: clarity
Original:  Even though there are no prior approaches that are directly applicable to the UPLproblem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context .
Revised:  Even though there are no prior approaches that are directly comparable to UPL, we compare the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context .
Edit intent: clarity
Original:  Even though there are no prior approaches that are directly applicable to the UPLproblem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context .
Revised:  Even though there are no prior approaches that are directly applicable to the UPLproblem, we evaluate the STAM architecture with two recent continual learning models, Memory Aware Synapses (MAS) and Gradient Episodic Memories (GEM), after adapting them in the UPL context .
Edit intent: meaning-changed
Original:  Even though there are no prior approaches that are directly applicable to the UPLproblem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context .
Revised:  Even though there are no prior approaches that are directly applicable to the UPLproblem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL setting .
Edit intent: clarity
Original:  However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires in fact kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood in this case .
Revised:  However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood in this case .
Edit intent: coherence
Original:  However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires in fact kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood in this case .
Revised:  However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires in fact kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood .
Edit intent: coherence
Original:  Only noisy limit cycle attractors can be identified directly from ecological count data (as in inverse modelling), although even in this case, adding kill rate data - including in small amounts - can make the estimates much more precise.
Revised:  Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data - including in small amounts - can make the estimates much more precise.
Edit intent: clarity
Original:  Our framework highlights how to combine data types in dynamical models for multiple species , and may be extended to other biotic interactions for which data on both interaction rates and population counts are available .
Revised:  Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species , and may be extended to other biotic interactions for which data on both interaction rates and population counts are available .
Edit intent: meaning-changed
Original: If K is a simplicial k-complex , the standard van Kampen obstructions tells you whether K can be embedded into \mathbb R^{2k or not (provided k\neq 2).
Revised: Let K be a simplicial k-complex , the standard van Kampen obstructions tells you whether K can be embedded into \mathbb R^{2k or not (provided k\neq 2).
Edit intent: clarity
Original:  We describe how the obstruction changes if we replace \mathbb R^{2k} by a closed PL 2k-manifold satisfying a certain technical condition : We require }  that every map f\colon |K| \to M is homotopic to a map f'\colon |K| \to M such that f'(|K^{(k-1)}|) fits into some 2k-ball in M, where K^{(k-1)} stands for the (k-1)-skeleton of K.
Revised:  We describe how the obstruction changes if we replace \mathbb R^{2k} by a closed PL 2k-manifold satisfying a certain technical condition : We require } and M be a closed PL 2k-manifold. Our aim is to describe an obstruction for embeddability of K into M via the intersection form on M. For description of the obstruction, we need a technical condition that every map f\colon |K| \to M is homotopic to a map f'\colon |K| \to M such that f'(|K^{(k-1)}|) fits into some 2k-ball in M, where K^{(k-1)} stands for the (k-1)-skeleton of K.
Edit intent: meaning-changed
Original:  If M = S^{2k} (or \mathbb R ^{2k}) then the intersection form on M vanishes and our obstruction coincides with the standard van Kampen obstruction.
Revised:  If M = S^{2k} (or \mathbb{R ^{2k}) then the intersection form on M vanishes and our obstruction coincides with the standard van Kampen obstruction.
Edit intent: fluency
Original:  Finally, the \mathbb Z _2-reduction of the obstruction shows how to obtain a non-trivial upper bound for the K\"{u}hnel problem: determine the smallest n so that \Delta_n^{(k)} does not embed into M.
Revised:  Finally, the \mathbb{Z _2-reduction of the obstruction shows how to obtain a non-trivial upper bound for the K\"{u}hnel problem: determine the smallest n so that \Delta_n^{(k)} does not embed into M.
Edit intent: fluency
Original:  Also, the \mathbb Z _2-reduction is computable and, if M is (k-1)-connected, it determines whether there is a map f\colon |K| \to M which has an even number of crossings of f(\sigma) and f(\tau) for each pair (\sigma, \tau) of disjoint k-simplices of K.
Revised:  Also, the \mathbb{Z _2-reduction is computable and, if M is (k-1)-connected, it determines whether there is a map f\colon |K| \to M which has an even number of crossings of f(\sigma) and f(\tau) for each pair (\sigma, \tau) of disjoint k-simplices of K.
Edit intent: coherence
Original:  In this work, we propose an intelligent agent which supervises motion planning of a filming drone  based on aesthetical values of video shots using deep reinforcement learning. Unlike the current state-of-the-art approaches which mostly require explicit guidance by a human expert, our drone learns how to make favorable shot type selections by experience.
Revised:  In this work, we propose an intelligent agent which supervises motion planning of a filming drone based on aesthetical values of video shots . Unlike most of the current state-of-the-art approaches which mostly require explicit guidance by a human expert, our drone learns how to make favorable shot type selections by experience.
Edit intent: coherence