wanyu/IteraTeR_full_sent huggingface dataset:
Original:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are , to correctly predict  effect of model size and data size variation during pre-training on transfer.
Revised:  These observations hold across both full and few-shot transfer and indicate that scaling laws pointing to improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are , to correctly predict effect of model size and data size variation during pre-training on transfer.
Edit intent: fluency
Original:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are , to correctly predict  effect of model size and data size variation during pre-training on transfer.
Revised:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should be revised by taking into account the degree of how distinct the source and target data distributions are , to correctly predict effect of model size and data size variation during pre-training on transfer.
Edit intent: meaning-changed
Original:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are , to correctly predict  effect of model size and data size variation during pre-training on transfer.
Revised:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the type and proximity of the source and target data distributions are , to correctly predict effect of model size and data size variation during pre-training on transfer.
Edit intent: meaning-changed
Original:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are , to correctly predict  effect of model size and data size variation during pre-training on transfer.
Revised:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data , to correctly predict effect of model size and data size variation during pre-training on transfer.
Edit intent: clarity
Original:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are , to correctly predict  effect of model size and data size variation during pre-training on transfer.
Revised:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are , to correctly predict the effect of model size and data size variation during pre-training on transfer.
Edit intent: fluency
Original:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are , to correctly predict  effect of model size and data size variation during pre-training on transfer.
Revised:  These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are , to correctly predict effect of model and data scale during pre-training on transfer.
Edit intent: clarity
Original:  (Repository for reproducing the experiments will be made available. )
Revised:  Remarkably, in full shot transfer to a large X-Ray chest imaging target (PadChest), the largest model pre-trained on ImageNet-21k slightly outperforms best models pre-trained on large X-Ray chest imaging data. This indicates possibility to obtain high quality models for domain-specific transfer even without access to large domain-specific data, by pre-training instead on comparably very large, generic source data.
Edit intent: meaning-changed
Original:  Here we present an approach to estimate the carbon storage in trees based on fusing multispectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species , which are crucial attributes in carbon storage quantification.
Revised:  We present an approach to estimate the carbon storage in trees based on fusing multispectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species , which are crucial attributes in carbon storage quantification.
Edit intent: coherence
Original:  Here we present an approach to estimate the carbon storage in trees based on fusing multispectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species , which are crucial attributes in carbon storage quantification.
Revised:  Here we present an approach to estimate the carbon storage in trees based on fusing multi-spectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species , which are crucial attributes in carbon storage quantification.
Edit intent: fluency
Original:  Here we present an approach to estimate the carbon storage in trees based on fusing multispectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species , which are crucial attributes in carbon storage quantification.
Revised:  Here we present an approach to estimate the carbon storage in trees based on fusing multispectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species -- key attributes to carbon storage quantification.
Edit intent: clarity
Original:  We propose to use mBART, a pretrained multilingual sequence-to-sequence model , for the textual-only translations. Further, we bring the visual information to a textual domain by extracting object tags from the image and enhance the input for the multimodal task .
Revised:  We propose to use mBART, a pretrained multilingual sequence-to-sequence model . For multimodal translation, we propose to enhance the textual input by bringing the visual information to a textual domain by extracting object tags from the image and enhance the input for the multimodal task .
Edit intent: coherence
Original: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f  (x) = g  (\langle x, \theta\rangle) for convex g  : \mathbb R \to \mathbb R and  \theta \in \mathbb R^d  . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log(  diam  \mathcal K))  where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Revised: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f _t (x) = g (\langle x, \theta\rangle) for convex g : \mathbb R \to \mathbb R and \theta \in \mathbb R^d . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log( diam \mathcal K)) where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Edit intent: fluency
Original: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f  (x) = g  (\langle x, \theta\rangle) for convex g  : \mathbb R \to \mathbb R and  \theta \in \mathbb R^d  . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log(  diam  \mathcal K))  where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Revised: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f (x) = g _t (\langle x, \theta\rangle) for convex g : \mathbb R \to \mathbb R and \theta \in \mathbb R^d . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log( diam \mathcal K)) where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Edit intent: clarity
Original: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f  (x) = g  (\langle x, \theta\rangle) for convex g  : \mathbb R \to \mathbb R and  \theta \in \mathbb R^d  . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log(  diam  \mathcal K))  where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Revised: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f (x) = g (\langle x, \theta\rangle) for convex g _t : \mathbb R \to \mathbb R and \theta \in \mathbb R^d . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log( diam \mathcal K)) where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Edit intent: clarity
Original: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f  (x) = g  (\langle x, \theta\rangle) for convex g  : \mathbb R \to \mathbb R and  \theta \in \mathbb R^d  . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log(  diam  \mathcal K))  where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Revised: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f (x) = g (\langle x, \theta\rangle) for convex g : \mathbb R \to \mathbb R and unknown \theta \in \mathbb R^d . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log( diam \mathcal K)) where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Edit intent: clarity
Original: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f  (x) = g  (\langle x, \theta\rangle) for convex g  : \mathbb R \to \mathbb R and  \theta \in \mathbb R^d  . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log(  diam  \mathcal K))  where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Revised: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f (x) = g (\langle x, \theta\rangle) for convex g : \mathbb R \to \mathbb R and \theta \in \mathbb R^d that is homogeneous over time . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log( diam \mathcal K)) where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Edit intent: meaning-changed
Original: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f  (x) = g  (\langle x, \theta\rangle) for convex g  : \mathbb R \to \mathbb R and  \theta \in \mathbb R^d  . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log(  diam  \mathcal K))  where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Revised: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f (x) = g (\langle x, \theta\rangle) for convex g : \mathbb R \to \mathbb R and \theta \in \mathbb R^d . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log( n diam \mathcal K)) where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Edit intent: fluency
Original: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f  (x) = g  (\langle x, \theta\rangle) for convex g  : \mathbb R \to \mathbb R and  \theta \in \mathbb R^d  . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log(  diam  \mathcal K))  where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Revised: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f (x) = g (\langle x, \theta\rangle) for convex g : \mathbb R \to \mathbb R and \theta \in \mathbb R^d . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log( diam ( \mathcal K)) where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Edit intent: fluency
Original: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f  (x) = g  (\langle x, \theta\rangle) for convex g  : \mathbb R \to \mathbb R and  \theta \in \mathbb R^d  . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log(  diam  \mathcal K))  where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Revised: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f (x) = g (\langle x, \theta\rangle) for convex g : \mathbb R \to \mathbb R and \theta \in \mathbb R^d . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log( diam \mathcal K)) ) where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Edit intent: fluency
Original: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f  (x) = g  (\langle x, \theta\rangle) for convex g  : \mathbb R \to \mathbb R and  \theta \in \mathbb R^d  . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log(  diam  \mathcal K))  where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set . Hence, this class of functions is at most logarithmically harder than the linear case .
Revised: We analyse adversarial bandit convex optimisation with an adversary that is restricted to playing functions of the form f (x) = g (\langle x, \theta\rangle) for convex g : \mathbb R \to \mathbb R and \theta \in \mathbb R^d . We provide a short information-theoretic proof that the minimax regret is at most O(d n \log( diam \mathcal K)) where n is the number of interactions, d the dimension and diam(\mathcal K) is the diameter of the constraint set .
Edit intent: meaning-changed
Original:  In both kinds of market environments, the players have  individual risk tolerance coefficients . In the incomplete market setting, these risk tolerances are constants (CARA utilities) , while in the complete market, they are assumed to be wealth-independent random variables depending, among others, on upcoming market information .
Revised:  In both kinds of market environments, the players have individual risk tolerance coefficients (CARA utilities) , while in the complete market, they are assumed to be wealth-independent random variables depending, among others, on upcoming market information .
Edit intent: clarity
Original:  In the incomplete market setting, these risk tolerances are constants (CARA utilities) , while in the complete market, they are assumed to be wealth-independent random variables depending, among others, on upcoming market information .
Revised:  In the incomplete market setting, these risk tolerances are constants (CARA utilities) . We also consider the general case of random individual risk tolerances and analyze the related games in a complete market setting. This randomness makes the problem substantially more complex as it leads to (N or a continuum of) auxiliary ''individual'' It\^{o .
Edit intent: meaning-changed
Original:  His algorithm achieves an approximation ratio of 10 and it is based on a dynamic program that intuitively recursively partitions the input plane into special polygons called corner-clipped rectangles (CCRs) . In this paper, we present a 4-approximation algorithm for MISR which is based on a similar recursive partitioning scheme.
Revised:  His algorithm achieves an approximation ratio of 10 and it is based on a dynamic program that intuitively recursively partitions the input plane into special polygons called corner-clipped rectangles , without intersecting certain special horizontal line segments called fences . In this paper, we present a 4-approximation algorithm for MISR which is based on a similar recursive partitioning scheme.
Edit intent: meaning-changed
Original:  His algorithm achieves an approximation ratio of 10 and it is based on a dynamic program that intuitively recursively partitions the input plane into special polygons called corner-clipped rectangles (CCRs) . In this paper, we present a 4-approximation algorithm for MISR which is based on a similar recursive partitioning scheme.
Revised:  His algorithm achieves an approximation ratio of 10 and it is based on a dynamic program that intuitively recursively partitions the input plane into special polygons called corner-clipped rectangles (CCRs) . In this paper, we present a 3-approximation algorithm for MISR which is based on a similar recursive partitioning scheme.
Edit intent: meaning-changed
Original:  However , we use a  more general class of polygons -- polygons that are horizontally or vertically convex -- which allows us to provide an arguably simpler analysis and already improve the approximation ratio .
Revised:  However , we use a partition into a more general class of polygons -- polygons that are horizontally or vertically convex -- which allows us to provide an arguably simpler analysis and already improve the approximation ratio .
Edit intent: clarity
Original:  However , we use a  more general class of polygons -- polygons that are horizontally or vertically convex -- which allows us to provide an arguably simpler analysis and already improve the approximation ratio .
Revised:  However , we use a more general class of polygons -- polygons that are horizontally or vertically convex -- which allows us to provide an arguably simpler analysis and at the same time already improves the approximation ratio .
Edit intent: coherence
Original:  However , we use a  more general class of polygons -- polygons that are horizontally or vertically convex -- which allows us to provide an arguably simpler analysis and already improve the approximation ratio . Using a new fractional charging argument and fork-fences to guide the partitions , we improve the approximation ratio even more to 4.
Revised:  However , we use a more general class of polygons -- polygons that are horizontally or vertically convex -- which allows us to provide an arguably simpler analysis and already improve the approximation ratio to 6. Then, using a more elaborate charging scheme and a recursive partitioning into general axis-parallel polygons with constant complexity , we improve the approximation ratio even more to 4.
Edit intent: meaning-changed
Original:  Using a new fractional charging argument and fork-fences to guide the partitions , we improve the approximation ratio even more to 4. We hope that our ideas will lead to further progress towards a PTAS for MISR.
Revised:  Using a new fractional charging argument and fork-fences to guide the partitions , we improve our approximation ratio to 3. In particular, our partitioning uses more general fences that can be sequences of up to O(1) line segments each. This and our other new ideas may be useful for future work towards a PTAS for MISR.
Edit intent: meaning-changed
Original:  Specifically, our goal is to design a fairness utility score for each feature which quantifies how this feature influences accurate as well as nondiscriminatory decisions. We first propose information theoretic measures for the impact of different subsets of features on the accuracy and discrimination of the model.
Revised:  In particular, we first propose information theoretic measures for the impact of different subsets of features on the accuracy and discrimination of the model.
Edit intent: coherence
Original:  We first propose information theoretic measures for the impact of different subsets of features on the accuracy and discrimination of the model. Subsequently, we deduce the marginal impact of each feature using Shapley value function  .
Revised:  We first propose information theoretic measures for the impact of different subsets of features on the accuracy and discrimination of the decision outcomes. We then deduce the marginal impact of each feature using Shapley value function .
Edit intent: clarity
Original:  Subsequently, we deduce the marginal impact of each feature using Shapley value function  .
Revised:  Subsequently, we deduce the marginal impact of each feature using Shapley value function ; a solution concept in cooperative game theory used to estimate marginal contributions of players in a coalitional game. Finally, we design a fairness utility score for each feature (for feature selection) which quantifies how this feature influences accurate as well as nondiscriminatory decisions .
Edit intent: meaning-changed
Original: Information extraction (IE) from visually-rich documents (VRDs) has achieved SOTA performance recently thanks to the adaptation of Transformer-based language models, which demonstrates great potential of pre-training methods.
Revised: Information extraction (IE) from visually-rich documents (VRDs) has achieved SOTA performance recently thanks to the adaptation of Transformer-based language models, which shows the great potential of pre-training methods.
Edit intent: fluency
Original:  In this paper, we present a new approach to improve the capability of language model pre-training on VRDs. Firstly, we introduce a new  IE model that is query-based and employs the span extraction formulation instead of the commonly used sequence labelling approach.
Revised:  In this paper, we present a new approach to improve the capability of language model pre-training on VRDs. Firstly, we introduce a new query-based IE model that is query-based and employs the span extraction formulation instead of the commonly used sequence labelling approach.
Edit intent: coherence