wanyu/IteraTeR_full_sent huggingface dataset:
Original:   Here we present a systematic method to prove 29 out of the first 100 Centers listed in Clark Kimberling's Encyclopedia are elliptic. We also derive conditions under which loci are algebraic .
Revised:  Here we present two rigorous methods to detect when any given Center produces an elliptic locus: a first one which is a hybrid of numeric and computer algebra techniques (good for fast detection only), and a second one based on the Theory of Resultants, which computes the implicit two-variable polynomial whose zero set contains the locus .
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Original: In this paper we present invariants of the 1d family of 3-periodics (triangular orbits) in an Elliptic Billiard (EB) , previously mentioned [ 14 ], including: (i) explicit Expressions classic invariants: perimeter and Joachimsthal's quantity;
Revised: New invariants in the family of 3-periodics (triangular orbits) in an Elliptic Billiard (EB) , previously mentioned [ 14 ], including: (i) explicit Expressions classic invariants: perimeter and Joachimsthal's quantity;
Edit intent: clarity
Original: In this paper we present invariants of the 1d family of 3-periodics (triangular orbits) in an Elliptic Billiard (EB) , previously mentioned [ 14 ], including: (i) explicit Expressions classic invariants: perimeter and Joachimsthal's quantity;
Revised: In this paper we present invariants of the 1d family of 3-periodics in an Elliptic Billiard (EB) , previously mentioned [ 14 ], including: (i) explicit Expressions classic invariants: perimeter and Joachimsthal's quantity;
Edit intent: meaning-changed
Original: In this paper we present invariants of the 1d family of 3-periodics (triangular orbits) in an Elliptic Billiard (EB) , previously mentioned [ 14 ], including: (i) explicit Expressions classic invariants: perimeter and Joachimsthal's quantity;
Revised: In this paper we present invariants of the 1d family of 3-periodics (triangular orbits) in an Elliptic Billiard (EB) were introduced in [ 14 ], including: (i) explicit Expressions classic invariants: perimeter and Joachimsthal's quantity;
Edit intent: coherence
Original: In this paper we present invariants of the 1d family of 3-periodics (triangular orbits) in an Elliptic Billiard (EB) , previously mentioned [ 14 ], including: (i) explicit Expressions classic invariants: perimeter and Joachimsthal's quantity;
Revised: In this paper we present invariants of the 1d family of 3-periodics (triangular orbits) in an Elliptic Billiard (EB) , previously mentioned [ 20 ], including: (i) explicit Expressions classic invariants: perimeter and Joachimsthal's quantity;
Edit intent: meaning-changed
Original: In this paper we present invariants of the 1d family of 3-periodics (triangular orbits) in an Elliptic Billiard (EB) , previously mentioned [ 14 ], including: (i) explicit Expressions classic invariants: perimeter and Joachimsthal's quantity; (ii) consevation of Inradius-to-Circumradius; (iii) stationarity of the Cosine Circle of the Excentral Triangle and the fact that it is exterior to the EB. Finally we present an entirely new N=3 invariant .
Revised: In this paper we present invariants of the 1d family of 3-periodics (triangular orbits) in an Elliptic Billiard (EB) , previously mentioned [ 14 ], stemming from both experimental and theoretical work. These include relationships between radii, angles and areas of triangular members of the family, as well as a special stationary circle. Here we present the proofs promised there as well as a few new related facts .
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Original:  We apply an Iterative Back Translation (IBT) training scheme on in-domain monolingual data, which repeatedly uses a Transformer-based NMT model to create in-domain pseudo-parallel sentence pairs in one translation direction on the fly and then use them to train the model in the other direction.
Revised:  In this work, we propose a simple but effect approach to the semi-supervised domain adaptation scenario of NMT, where the aim is to improve the performance of a translation model on the target domain consisting of only non-parallel data with the help of supervised source domain data. This approach iteratively trains a Transformer-based NMT model to create in-domain pseudo-parallel sentence pairs in one translation direction on the fly and then use them to train the model in the other direction.
Edit intent: meaning-changed
Original:  We apply an Iterative Back Translation (IBT) training scheme on in-domain monolingual data, which repeatedly uses a Transformer-based NMT model to create in-domain pseudo-parallel sentence pairs in one translation direction on the fly and then use them to train the model in the other direction. Evaluated on three domains of German-to-English translation task with no supervised data, this simple technique alone (without any out-of-domain parallel data) can already surpass all previous domain adaptation methods---up to + 9.48 BLEU over the strongest previous method , and up to +27.77 BLEU over the unadapted baseline.
Revised:  We apply an Iterative Back Translation (IBT) training scheme on in-domain monolingual data, which repeatedly uses a Transformer-based NMT model via three training objectives: language modeling, back-translation, and supervised translation. We evaluate this method on two adaptation settings: adaptation between specific domains and adaptation from a general domain to specific domains, and on two language pairs: German to English and Romanian to English. With substantial performance improvement achieved---up to + 9.48 BLEU over the strongest previous method , and up to +27.77 BLEU over the unadapted baseline.
Edit intent: meaning-changed
Original:  Evaluated on three domains of German-to-English translation task with no supervised data, this simple technique alone (without any out-of-domain parallel data) can already surpass all previous domain adaptation methods---up to + 9.48 BLEU over the strongest previous method , and up to +27.77 BLEU over the unadapted baseline. Moreover, given available supervised out-of-domain data on German-to-English and Romanian-to-English language pairs, we can further enhance the performance and obtain up to + 19.31 BLEU improvement over the strongest baseline, and +47.69 BLEU increment against the unadapted model .
Revised:  Evaluated on three domains of German-to-English translation task with no supervised data, this simple technique alone (without any out-of-domain parallel data) can already surpass all previous domain adaptation methods---up to + 19.31 BLEU improvement over the strongest baseline, and +47.69 BLEU increment against the unadapted model .
Edit intent: coherence
Original:  Moreover, given available supervised out-of-domain data on German-to-English and Romanian-to-English language pairs, we can further enhance the performance and obtain up to + 19.31 BLEU improvement over the strongest baseline, and +47.69 BLEU increment against the unadapted model .
Revised:  Moreover, given available supervised out-of-domain data on German-to-English and Romanian-to-English language pairs, we can further enhance the performance and obtain up to + 19.31 BLEU over the strongest baseline, and +47.69 BLEU increment against the unadapted model .
Edit intent: clarity
Original:  Moreover, given available supervised out-of-domain data on German-to-English and Romanian-to-English language pairs, we can further enhance the performance and obtain up to + 19.31 BLEU improvement over the strongest baseline, and +47.69 BLEU increment against the unadapted model .
Revised:  Moreover, given available supervised out-of-domain data on German-to-English and Romanian-to-English language pairs, we can further enhance the performance and obtain up to + 19.31 BLEU improvement over the strongest baseline, and +47.69 BLEU improvement over the unadapted model---we present this method as a simple but tough-to-beat baseline in the field of semi-supervised domain adaptation for NMT .
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Original:  Furthermore, we show that the embeddings capture meaningful "axes" that encompass all disciplines in the knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground a periodical on a given spectrum.
Revised:  Furthermore, we show that the embeddings capture meaningful "axes" that encompass knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground a periodical on a given spectrum.
Edit intent: clarity
Original:  Furthermore, we show that the embeddings capture meaningful "axes" that encompass all disciplines in the knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground a periodical on a given spectrum.
Revised:  Furthermore, we show that the embeddings capture meaningful "axes" that encompass all disciplines in the knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground periodicals on a given spectrum.
Edit intent: fluency
Original:  Furthermore, we show that the embeddings capture meaningful "axes" that encompass all disciplines in the knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground a periodical on a given spectrum. Using this new capacity, we test the hypothesis of the hierarchy of the sciences, showing that, in most disciplines such as Social Sciences and Life Sciences, most widely cited papers tend to appear in "harder" periodicals. Our framework may offer novel quantification methods in science of science, which may in turn facilitate the study of how knowledge is created URLanized.
Revised:  Furthermore, we show that the embeddings capture meaningful "axes" that encompass all disciplines in the knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground a periodical on a given dimension. By offering novel quantification in science of science, which may in turn facilitate the study of how knowledge is created URLanized.
Edit intent: clarity
Original:  We present mBART -- a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages ;
Revised:  We present mBART -- a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages ;
Edit intent: clarity
Original:  We present mBART -- a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages ; previous MT pre-training has focused only on the encoder, decoder, or reconstructing parts of the text.
Revised:  We present mBART -- a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages , while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text.
Edit intent: coherence
Original:  Here, to address this situation and examine the performance of the HMM, we compare it with the Gaussian mixture model (GMM), which is a statistically simpler model than the HMM with no assumption of Markovianity .
Revised:  Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is a statistically simpler model than the HMM with no assumption of Markovianity .
Edit intent: clarity
Original:  Here, to address this situation and examine the performance of the HMM, we compare it with the Gaussian mixture model (GMM), which is a statistically simpler model than the HMM with no assumption of Markovianity . We do so by applying both models to synthetic and empirical resting-state functional magnetic resonance imaging (fMRI) data.
Revised:  Here, to address this situation and examine the performance of the HMM, we compare it with the Gaussian mixture model (GMM), which is a statistically simpler model than the HMM with no assumption of Markovianity , by applying both models to synthetic and empirical resting-state functional magnetic resonance imaging (fMRI) data.
Edit intent: coherence
Original:  We find that the GMM allows us to interpret the sequence of the estimated hidden states as a time series obeying some patterns . We show that GMMs are often better than HMMs in terms of the accuracy and consistency of estimating the time course of the hidden state.
Revised:  We find that the GMM allows us to interpret the sequence of the estimated hidden states as a time series obeying some patterns and is often better than HMMs in terms of the accuracy and consistency of estimating the time course of the hidden state.
Edit intent: coherence
Original:  In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods ( 27 studies), gait-based methods ( 23 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods ( 33 studies).
Revised:  In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods ( 28 studies), gait-based methods ( 23 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods ( 33 studies).
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Original:  In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods ( 27 studies), gait-based methods ( 23 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods ( 33 studies).
Revised:  In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods ( 27 studies), gait-based methods ( 19 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods ( 33 studies).
Edit intent: meaning-changed
Original:  In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods ( 27 studies), gait-based methods ( 23 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods ( 33 studies).
Revised:  In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods ( 27 studies), gait-based methods ( 23 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods ( 34 studies).
Edit intent: clarity
Original:  Since, goal-oriented dialogs naturally exhibit a hierarchical structure over utterances and related annotations, deep generative data augmentation for the task requires the generative model to be aware of the hierarchical nature .
Revised:  Due to the inherent hierarchical structure of goal-oriented dialogs naturally exhibit a hierarchical structure over utterances and related annotations, deep generative data augmentation for the task requires the generative model to be aware of the hierarchical nature .
Edit intent: clarity
Original:  We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling  complete aspects of goal-oriented dialogs, including linguistic features and underlying structured annotations, namely dialog acts and goals.
Revised:  We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling the complete aspects of goal-oriented dialogs, including linguistic features and underlying structured annotations, namely dialog acts and goals.
Edit intent: fluency
Original:  We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling  complete aspects of goal-oriented dialogs, including linguistic features and underlying structured annotations, namely dialog acts and goals.
Revised:  We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling complete aspects of goal-oriented dialogs, including linguistic features and underlying structured annotations, namely speaker information, dialog acts, and goals.
Edit intent: clarity
Original:  We also propose two training policies to mitigate issues that arise with training VAE-based models. Experiments show that our hierarchical model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialog state trackers, ultimately improving the dialog state tracking performances on various dialog domains.
Revised:  We also propose two training policies to mitigate issues that arise from training complex variational models, we propose appropriate training strategies. Experiments on various dialog datasets show that our hierarchical model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialog state trackers, ultimately improving the dialog state tracking performances on various dialog domains.
Edit intent: clarity
Original:  Experiments show that our hierarchical model is able to generate realistic and novel samples that improve the robustness of state-of-the-art dialog state trackers, ultimately improving the dialog state tracking performances on various dialog domains. Surprisingly, the ability to jointly generate dialog features enables our model to outperform previous state-of-the-arts in related subtasks, such as language generation and user simulation  .
Revised:  Experiments show that our model improves the downstream dialog trackers' robustness via generative data augmentation. We also discover additional benefits of our unified approach to modeling goal-oriented dialogs: dialog response generation and user simulation .
Edit intent: clarity
Original:  Surprisingly, the ability to jointly generate dialog features enables our model to outperform previous state-of-the-arts in related subtasks, such as language generation and user simulation  .
Revised:  Surprisingly, the ability to jointly generate dialog features enables our model to outperform previous state-of-the-arts in related subtasks, such as language generation and user simulation , where our model outperforms previous strong baselines .
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Original:  a policy gradient reinforcement learning technique uses that reward to fine-tune the language model weights. Using this fine-tuning technique , with 24,000 sentences from a science fiction plot summary dataset, halves the percentage of generated text containing non-normative behavior from 35.1\%to 15.7\% .
Revised:  a policy gradient reinforcement learning technique and a normative text classifier to produce reward and punishment values. We evaluate our technique on five data sets using automated and human participant experiments. The normative text classifier is 81-90\% accurate when compared to gold-standard human judgments of normative and non-normative generated text. Our normative fine-tuning technique is able to reduce non-normative behavior from 35.1\%to 15.7\% .
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Original:  Using this fine-tuning technique , with 24,000 sentences from a science fiction plot summary dataset, halves the percentage of generated text containing non-normative behavior from 35.1\%to 15.7\% .
Revised:  Using this fine-tuning technique , with 24,000 sentences from a science fiction plot summary dataset, halves the percentage of generated text containing non-normative text by 27-61\%, depending on the data set .
Edit intent: meaning-changed
Original:  Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeats Monte Carlo (MC) simulations over and over , specifically tens of thousands of times or more per potential seed set, to reliably estimate the influence .
Revised:  Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeat Monte Carlo (MC) simulations over and over , specifically tens of thousands of times or more per potential seed set, to reliably estimate the influence .
Edit intent: fluency
Original:  Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeats Monte Carlo (MC) simulations over and over , specifically tens of thousands of times or more per potential seed set, to reliably estimate the influence .
Revised:  Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeats Monte Carlo (MC) simulations over and over to reliably estimate the influence .
Edit intent: clarity
Original:  Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeats Monte Carlo (MC) simulations over and over , specifically tens of thousands of times or more per potential seed set, to reliably estimate the influence .
Revised:  Since it is an NP-hard problem, many approximate/heuristic methods have been developed, and a number of them repeats Monte Carlo (MC) simulations over and over , specifically tens of thousands of times or more per potential seed set, to reliably estimate the influence (i.e., the number of infected nodes) of a seed set.
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Original:  In our experiments, MONSTOR achieves near-perfect accuracy on unseen real social networkswith little sacrifice of accuracy in IM use cases.
Revised:  In our experiments, MONSTOR provided highly accurate estimates, achieving 0.998 or higher Pearson and Spearman correlation coefficients in unseen real-world social networks. Moreover, IM algorithms equipped with MONSTOR are more accurate than state-of-the-art competitors in 67\% of IM use cases.
Edit intent: meaning-changed
Original:  Policy makers need to be aware of a subtle, but decisive difference between the actual and the commonly implied meanings of SCC that substantially limits its applicability as compared to the current practice .
Revised:  Policy makers need to be aware of a subtle, but decisive difference between the actual and the commonly implied meanings of SCC that substantially limits its applicability in practice. The presented results call for a critical revision of the SCC concept and the SCC calculation methods in integrated assessment models .
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Original: We introduce a new multimodal retrieval task - TV show Retrieval (TVR), in which a short video moment has to be localized from a large video (with subtitle) corpus, given a natural language query. Different from previous moment retrieval tasks dealing with videos only, TVR requires the system to understand both the video and the associated subtitle text , making it a more realistictask.
Revised: We introduce a new multimodal retrieval task - TV show Retrieval (TVR), a new multimodal retrieval dataset. TVR requires systems to understand both the video and the associated subtitle text , making it a more realistictask.
Edit intent: clarity