wanyu/IteraTeR_full_sent huggingface dataset:
Original: We give a novel representation of the Moran Genealogy Process, a continuous-time Markov process on the space of size-n genealogies with the demography of the classical Moran process . We derive the generator and unique stationary distribution of the process and establish its uniform ergodicity. In particular, we show that any initial distribution converges exponentially to the probability measure identical to that of the Kingman coalescent. We go on to show that one-time sampling projects this stationary distribution onto a smaller-size version of itself. Next, we extend the Moran genealogy process to include sampling through time.
Revised: We give a novel representation of the Moran Genealogy Process, a continuous-time Markov process on the space of size-n genealogies with the demography of the classical Moran process , sampled through time. To do so, we begin by defining the Moran Genealogy Process using a novel representation. We then extend this process to include sampling through time.
Edit intent: clarity
Original:  The contexts are mapped to vector states in the two-dimensional quantum Hilbert space describing a dichotomic decision alternative in relation to which the contexts are subjectively recognized. The obtained triad of quantum cognitive representations functions as a minimal carrier of semantic relations between the contexts , which are quantified by phase relations between the corresponding quantum representation states.
Revised:  The contexts are mapped to vector states in the two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a minimal carrier of semantic relations between the contexts , which are quantified by phase relations between the corresponding quantum representation states.
Edit intent: meaning-changed
Original:  The obtained triad of quantum cognitive representations functions as a minimal carrier of semantic relations between the contexts , which are quantified by phase relations between the corresponding quantum representation states. The described quantum model of subjective semantics supports interpretable vector calculus which is geometrically visualized in the Bloch sphere view of quantum cognitive states .
Revised:  The obtained triad of quantum cognitive representations functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory .
Edit intent: meaning-changed
Original:  In this paper, we consider these problems in the streaming model where one string is available via oracle queries and the other string comes as a stream of characters .
Revised:  In this paper, we consider these problems in the asymmetric streaming model introduced by Andoni et al. (FOCS'10) and Saks and Seshadhri (SODA'13). In this model we have random access to one string and streaming access the other string comes as a stream of characters .
Edit intent: meaning-changed
Original:  Our main contribution is a constant factor approximation algorithm in this setting for ED with memory\tilde O(n^{\delta}) for any  \delta > 0. In addition to this, we present an upper bound of \tilde O  (n) on the memory needed to approximate ED or LCS within a factor 1+ o(1) in our setting .
Revised:  Our main contribution is a constant factor approximation algorithm for ED with memory\tilde O(n^{\delta}) for any \delta > 0. In addition to this, we present an upper bound of \tilde O (n) on the memory needed to approximate ED or LCS within a factor 1+ o(1) in our setting .
Edit intent: clarity
Original:  Our main contribution is a constant factor approximation algorithm in this setting for ED with memory\tilde O(n^{\delta}) for any  \delta > 0. In addition to this, we present an upper bound of \tilde O  (n) on the memory needed to approximate ED or LCS within a factor 1+ o(1) in our setting .
Revised:  Our main contribution is a constant factor approximation algorithm in this setting for ED with the memory of\tilde O(n^{\delta}) for any \delta > 0. In addition to this, we present an upper bound of \tilde O (n) on the memory needed to approximate ED or LCS within a factor 1+ o(1) in our setting .
Edit intent: clarity
Original:  Our main contribution is a constant factor approximation algorithm in this setting for ED with memory\tilde O(n^{\delta}) for any  \delta > 0. In addition to this, we present an upper bound of \tilde O  (n) on the memory needed to approximate ED or LCS within a factor 1+ o(1) in our setting .
Revised:  Our main contribution is a constant factor approximation algorithm in this setting for ED with memory\tilde O(n^{\delta}) for any constant \delta > 0. In addition to this, we present an upper bound of \tilde O (n) on the memory needed to approximate ED or LCS within a factor 1+ o(1) in our setting .
Edit intent: clarity
Original:  Our main contribution is a constant factor approximation algorithm in this setting for ED with memory\tilde O(n^{\delta}) for any  \delta > 0. In addition to this, we present an upper bound of \tilde O  (n) on the memory needed to approximate ED or LCS within a factor 1+ o(1) in our setting .
Revised:  Our main contribution is a constant factor approximation algorithm in this setting for ED with memory\tilde O(n^{\delta}) for any \delta > 0. In addition to this, we present an upper bound of \tilde O _\epsilon (n) on the memory needed to approximate ED or LCS within a factor 1+ o(1) in our setting .
Edit intent: meaning-changed
Original:  Our main contribution is a constant factor approximation algorithm in this setting for ED with memory\tilde O(n^{\delta}) for any  \delta > 0. In addition to this, we present an upper bound of \tilde O  (n) on the memory needed to approximate ED or LCS within a factor 1+ o(1) in our setting .
Revised:  Our main contribution is a constant factor approximation algorithm in this setting for ED with memory\tilde O(n^{\delta}) for any \delta > 0. In addition to this, we present an upper bound of \tilde O (n) on the memory needed to approximate ED or LCS within a factor 1+ \epsilon .
Edit intent: meaning-changed
Original:  We use our characterization to study joint belief distributions in which agents are informed regarding the state, and yet receive no information regarding the other's posterior. We study a related class of Bayesian persuasion problems with a single sender and multiple receivers, and explore the extreme points of the set of feasible distributions.
Revised:  We use these characterizations to study information structures with independent posteriors. We also study persuasion problems with a single sender and multiple receivers, and explore the extreme points of the set of feasible distributions.
Edit intent: clarity
Original: Network representation learning has become an active research area in recent years with many new methods showcasing their performance on downstream prediction tasks such as Link Prediction. Despite the efforts of the community to ensure reproducibility of research by providing method implementations, important issues remain. The complexity of the evaluation pipelines and  abundance of design choices have led to difficulties in quantifying the progress in the field and identifying the state-of-the-art .
Revised: Network embedding methods map a network's nodes to vectors in an embedding space, in such a way that these representations are useful for estimating some notion of similarity or proximity between pairs of nodes in the network. The quality of these node representations is then showcased through results of downstream prediction tasks. Commonly used benchmark tasks such as link prediction, however, present complex evaluation pipelines and abundance of design choices have led to difficulties in quantifying the progress in the field and identifying the state-of-the-art .
Edit intent: meaning-changed
Original:  In this work, we analyse 17 network embedding methods on 7 real-world datasets and find , using a consistent evaluation pipeline,  only thin progress over the recent years. Also, many embedding methodsare outperformed by simple heuristics.
Revised:  In this work, we analyse 17 network embedding methods on 7 real-world datasets and find , using a consistent evaluation pipeline, only thin progress has been made over the last years. The newly conducted benchmark that we present here, including 17 embedding methods, also shows that many approaches are outperformed even by simple heuristics.
Edit intent: meaning-changed
Original:  Although the standard OPE and OPL assume the same distribution of covariate between the historical and evaluation data, there often exists a problem of a covariate shift , i.e., the distribution of the covariate of the historical data is different from that of the evaluation data.
Revised:  Although the standard OPE and OPL assume the same distribution of covariate between the historical and evaluation data, a covariate shift often exists , i.e., the distribution of the covariate of the historical data is different from that of the evaluation data.
Edit intent: clarity
Original:  Then, we propose doubly robust and efficient estimators for OPE and OPL under a covariate shift by using an estimator of the density ratio between the distributions of the historical and evaluation data  .
Revised:  Then, we propose doubly robust and efficient estimators for OPE and OPL under a covariate shift by using a nonparametric estimator of the density ratio between the distributions of the historical and evaluation data .
Edit intent: meaning-changed
Original:  Then, we propose doubly robust and efficient estimators for OPE and OPL under a covariate shift by using an estimator of the density ratio between the distributions of the historical and evaluation data  .
Revised:  Then, we propose doubly robust and efficient estimators for OPE and OPL under a covariate shift by using an estimator of the density ratio between the distributions of the historical and evaluation data distributions .
Edit intent: clarity
Original:  In practice  however, some agents may not prescribe to the dominant behavioral model or may act in ways that are arbitrarily inconsistent . Existing algorithms heavily depend on the  model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents.
Revised:  In practice , however, some agents may not prescribe to the dominant behavioral model or may act in ways that are arbitrarily inconsistent . Existing algorithms heavily depend on the model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents.
Edit intent: fluency
Original:  In practice  however, some agents may not prescribe to the dominant behavioral model or may act in ways that are arbitrarily inconsistent . Existing algorithms heavily depend on the  model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents.
Revised:  In practice however, some agents may not prescribe to the dominant behavioral model or may act in ways that are seemingly arbitrarily irrational . Existing algorithms heavily depend on the model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents.
Edit intent: clarity
Original:  In practice  however, some agents may not prescribe to the dominant behavioral model or may act in ways that are arbitrarily inconsistent . Existing algorithms heavily depend on the  model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents.
Revised:  In practice however, some agents may not prescribe to the dominant behavioral model or may act in ways that are arbitrarily inconsistent . Existing algorithms heavily depend on the behavioral model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents.
Edit intent: fluency
Original:  How do we design learning algorithms that are robust to the presence of arbitrarily irrational agents? We address this question for a number of canonical game-theoretic applications by designing a robust algorithm for the fundamental problem of multidimensional binary search . The performance of our algorithm degrades gracefully with the number of corrupted rounds, which correspond to irrational agents and need not be known in advance. As binary search is the key primitive in algorithms for contextual pricing, Stackelberg Security Games, and other game-theoretic applications, we immediately obtain robust algorithms for these settings .
Revised:  We initiate the study of contextual search when some of the agents can behave in ways inconsistent with the underlying behavioral model. In particular, we provide two algorithms, one built on robustifying multidimensional binary search methods and one on translating the setting to a proxy setting appropriate for gradient descent .
Edit intent: clarity
Original:  When multiple hierarchical clusterings of the data are possible, it is useful to represent uncertainty in the clustering through various probabilistic quantities  . Existing approaches represent uncertainty for a range of models;
Revised:  When multiple hierarchical clusterings of the data are possible, it is useful to represent uncertainty in the clustering through various probabilistic quantities , such as the distribution over tree structures and the marginal probabilities of subtrees . Existing approaches represent uncertainty for a range of models;
Edit intent: meaning-changed
Original:  Rather than scaling with the number of hierarchical clusterings of n elements (\omega(n n!/ 2^{n-1 , our approach runs in time and space proportional to the significantly smaller powerset of n . Despite still being large, these algorithms enable exact inference in small-data applications and are also interesting from a theoretical perspective.
Revised:  Rather than scaling with the number of hierarchical clusterings of N elements ((2N-3)!!) , our approach runs in time and space proportional to the significantly smaller powerset of n . Despite still being large, these algorithms enable exact inference in small-data applications and are also interesting from a theoretical perspective.
Edit intent: meaning-changed
Original:  Rather than scaling with the number of hierarchical clusterings of n elements (\omega(n n!/ 2^{n-1 , our approach runs in time and space proportional to the significantly smaller powerset of n . Despite still being large, these algorithms enable exact inference in small-data applications and are also interesting from a theoretical perspective.
Revised:  Rather than scaling with the number of hierarchical clusterings of n elements (\omega(n n!/ 2^{n-1 , our approach runs in time and space proportional to the significantly smaller powerset of N . Despite still being large, these algorithms enable exact inference in small-data applications and are also interesting from a theoretical perspective.
Edit intent: coherence
Original:  Rather than scaling with the number of hierarchical clusterings of n elements (\omega(n n!/ 2^{n-1 , our approach runs in time and space proportional to the significantly smaller powerset of n . Despite still being large, these algorithms enable exact inference in small-data applications and are also interesting from a theoretical perspective.
Revised:  Rather than scaling with the number of hierarchical clusterings of n elements (\omega(n n!/ 2^{n-1 , our approach runs in time and space proportional to the significantly smaller powerset of n . Despite still being large, there are many important applications at the practically-computable range of N < 20.
Edit intent: meaning-changed
Original:  however, they only provideapproximate inference. This paper presents dynamic-programming algorithms and proofs for exact inference in hierarchical clustering at small but useful scales.
Revised:  however, they only provideapproximate Typically approximate algorithms are used for inference due to the combinatorial number of possible hierarchical clusterings. In contrast to existing methods, we present novel dynamic-programming algorithms and proofs for exact inference in hierarchical clustering at small but useful scales.
Edit intent: meaning-changed
Original:  This paper presents dynamic-programming algorithms and proofs for exact inference in hierarchical clustering at small but useful scales. We are able to compute the partition function, MAP hierarchical clustering , and marginal probabilities of sub-hierarchies and clusters.
Revised:  This paper presents dynamic-programming algorithms and proofs for exact inference in hierarchical clustering based on a novel trellis data structure, and we prove that we can exactly compute the partition function, MAP hierarchical clustering , and marginal probabilities of sub-hierarchies and clusters.
Edit intent: meaning-changed
Original:  Our method supports a wide range of hierarchical clustering models and only requires a cluster compatibility function. Rather than scaling with the number of hierarchical clusterings of N elements (( 2N-3)!! ), our approach runs in time and space proportional to the significantly smaller powerset of N. Despite still being large, there are many important applications at the practically-computable range of N < 20.
Revised:  Our algorithms scale in time and space proportional to the powerset of N elements which is super-exponentially more efficient than explicitly considering each of the ( 2N-3)!! ), our approach runs in time and space proportional to the significantly smaller powerset of N. Despite still being large, there are many important applications at the practically-computable range of N < 20.
Edit intent: meaning-changed
Original:  Rather than scaling with the number of hierarchical clusterings of N elements (( 2N-3)!! ), our approach runs in time and space proportional to the significantly smaller powerset of N. Despite still being large, there are many important applications at the practically-computable range of N < 20. We demonstrate the advantages of exact inference on synthetic data of interest to Dasgupta's cost as well as on two real world applications, in particle physics at the Large Hadron Collider at CERN and in cancer genomics  .
Revised:  Rather than scaling with the number of hierarchical clusterings of N elements (( 2N-3)!! possible hierarchies. Also, for larger datasets where our exact algorithms become infeasible, we introduce an approximate algorithm based on a sparse trellis that compares well to other benchmarks. Exact methods are relevant to data analyses in particle physics at the Large Hadron Collider at CERN and in cancer genomics .
Edit intent: clarity
Original:  We demonstrate the advantages of exact inference on synthetic data of interest to Dasgupta's cost as well as on two real world applications, in particle physics at the Large Hadron Collider at CERN and in cancer genomics  .
Revised:  We demonstrate the advantages of exact inference on synthetic data of interest to Dasgupta's cost as well as on two real world applications, in particle physics and for finding correlations among gene expression in cancer genomics .
Edit intent: meaning-changed
Original:  We demonstrate the advantages of exact inference on synthetic data of interest to Dasgupta's cost as well as on two real world applications, in particle physics at the Large Hadron Collider at CERN and in cancer genomics  .
Revised:  We demonstrate the advantages of exact inference on synthetic data of interest to Dasgupta's cost as well as on two real world applications, in particle physics at the Large Hadron Collider at CERN and in cancer genomics , and we give examples in both areas, where our algorithms outperform greedy and beam search baselines. In addition, we consider Dasgupta's cost with synthetic data .
Edit intent: meaning-changed
Original:  We introduce MYND: A smartphone application for unsupervised evaluation of BCI control strategies with consumer-grade hardware . Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires.
Revised:  We introduce MYND: A framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies with consumer-grade hardware . Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires.
Edit intent: clarity
Original:  We introduce MYND: A smartphone application for unsupervised evaluation of BCI control strategies with consumer-grade hardware . Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires.
Revised:  We introduce MYND: A smartphone application for unsupervised evaluation of BCI control strategies . Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires.
Edit intent: clarity
Original:  As a use case, we evaluate the BCI control strategies  "Positive memories" and "Music imagery"  in a realistic scenario by combining MYND with a four-channel electroencephalogram (EEG).
Revised:  As a use case, we evaluate two BCI control strategies "Positive memories" and "Music imagery" in a realistic scenario by combining MYND with a four-channel electroencephalogram (EEG).
Edit intent: clarity
Original:  As a use case, we evaluate the BCI control strategies  "Positive memories" and "Music imagery"  in a realistic scenario by combining MYND with a four-channel electroencephalogram (EEG).
Revised:  As a use case, we evaluate the BCI control strategies ( "Positive memories" and "Music imagery" in a realistic scenario by combining MYND with a four-channel electroencephalogram (EEG).
Edit intent: fluency
Original:  As a use case, we evaluate the BCI control strategies  "Positive memories" and "Music imagery"  in a realistic scenario by combining MYND with a four-channel electroencephalogram (EEG).
Revised:  As a use case, we evaluate the BCI control strategies "Positive memories" and "Music imagery" ) in a realistic scenario by combining MYND with a four-channel electroencephalogram (EEG).
Edit intent: fluency
Original:  We introduce a novel framework to model  (non-)Markovian susceptible-infected-recovered (SIR) stochastic processes that are evolving either in continuous or discrete time on networks.
Revised:  We introduce a novel framework to model continuous, discrete, and hybrid forms of (non-)Markovian susceptible-infected-recovered (SIR) stochastic processes that are evolving either in continuous or discrete time on networks.
Edit intent: meaning-changed
Original:  We apply our simulation framework to study hybrid SIR processes that  describe infections as discrete-time Markovian and recovery events as continuous-time non-Markovian processes, which mimic the distribution of cell-cycle times .
Revised:  The hybrid SIR processes that describe infections as discrete-time Markovian and recovery events as continuous-time non-Markovian processes, which mimic the distribution of cell-cycle times .
Edit intent: clarity