wanyu/IteraTeR_full_sent huggingface dataset:
Original: Developed countries are increasingly relying on gas storage to ensure security of supply. In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Revised: In this paper, we define and study the Brownian bridge with random length and pinning point. One of the main results of this work is that, unlike the deterministic case, the bridge fails to be Markovian for pinning points, the law of which is absolutely continuous with respect to the Lebesgue measure. As an application, we propose a new approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Edit intent: meaning-changed
Original:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Revised:  In this article we consider an approach to gas storage valuation . We model the flow of information concerning the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Edit intent: clarity
Original:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Revised:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holders of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Edit intent: fluency
Original:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Revised:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Edit intent: clarity
Original:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Revised:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas , by using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Edit intent: clarity
Original:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Revised:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge starting from zero and conditioned to be equal to a constant x in the time of injection and a constant y in the time of withdrawal.
Edit intent: coherence
Original:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Revised:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x at the time of injection and a constant y in the time of withdrawal.
Edit intent: clarity
Original:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Revised:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection , and to a constant y in the time of withdrawal.
Edit intent: fluency
Original:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y in the time of withdrawal.
Revised:  In this article we consider an approach to gas storage valuation in which the information about the time at which the holder of a gas storage contract should choose to inject or withdraw gas is modelled using a Brownian bridge that starts at zero and is conditioned to equal a constant x in the time of injection and a constant y at the time of withdrawal.
Edit intent: clarity
Original:  This enables  to catch some empirical facts on the behavior of gas storage markets: when the Brownian bridge process is away from the boundaries x and y, the holder of the gas storage contract can be relatively sure that the best decision is to do nothing.
Revised:  This enables us to catch some empirical facts on the behavior of gas storage markets: when the Brownian bridge process is away from the boundaries x and y, the holder of the gas storage contract can be relatively sure that the best decision is to do nothing.
Edit intent: fluency
Original:  This enables  to catch some empirical facts on the behavior of gas storage markets: when the Brownian bridge process is away from the boundaries x and y, the holder of the gas storage contract can be relatively sure that the best decision is to do nothing.
Revised:  This enables to catch some empirical facts on the behavior of gas storage markets: when the Brownian bridge process is away from the boundaries x and y, the holders of the gas storage contract can be relatively sure that the best decision is to do nothing.
Edit intent: fluency
Original:  However, when the bridge information process absorbs y, the holder of the contract should take the decision of withdrawing gas on the other hand, they should take the decision to inject gas when the process absorbs x . In this sense the Brownian bridge information process leaks information concerning the time at which the holder of a storage contract can choose to inject gas, do nothing, or withdraw gas.
Revised:  However, when this process absorbs y, the holder of the contract should take the decision of withdrawing gas on the other hand, they should take the decision to inject gas when the process absorbs x . In this sense the Brownian bridge information process leaks information concerning the time at which the holder of a storage contract can choose to inject gas, do nothing, or withdraw gas.
Edit intent: clarity
Original:  However, when the bridge information process absorbs y, the holder of the contract should take the decision of withdrawing gas on the other hand, they should take the decision to inject gas when the process absorbs x . In this sense the Brownian bridge information process leaks information concerning the time at which the holder of a storage contract can choose to inject gas, do nothing, or withdraw gas.
Revised:  However, when the bridge information process absorbs y, the holders of the contract should take the decision of withdrawing gas on the other hand, they should take the decision to inject gas when the process absorbs x . In this sense the Brownian bridge information process leaks information concerning the time at which the holder of a storage contract can choose to inject gas, do nothing, or withdraw gas.
Edit intent: fluency
Original:  However, when the bridge information process absorbs y, the holder of the contract should take the decision of withdrawing gas on the other hand, they should take the decision to inject gas when the process absorbs x . In this sense the Brownian bridge information process leaks information concerning the time at which the holder of a storage contract can choose to inject gas, do nothing, or withdraw gas.
Revised:  However, when the bridge information process absorbs y, the holder of the contract should take the decision of withdrawing gas , otherwise, when the process absorbs x . In this sense the Brownian bridge information process leaks information concerning the time at which the holder of a storage contract can choose to inject gas, do nothing, or withdraw gas.
Edit intent: coherence
Original:  However, when the bridge information process absorbs y, the holder of the contract should take the decision of withdrawing gas on the other hand, they should take the decision to inject gas when the process absorbs x . In this sense the Brownian bridge information process leaks information concerning the time at which the holder of a storage contract can choose to inject gas, do nothing, or withdraw gas. The issue of storage valuation is not limited to gas markets, storages also plays a significant, balancing role in, for example, oil markets, soft commodity markets and even electricity. The principle of our approach is applicable to those markets as well. In this paper we define and study the Brownian bridge with random length and pinning point. Its main objectives is to see if the properties of Brownian bridges with deterministic length and pinning point remain valid in case its length and pinning point are random. Amongst other we prove that the bridge fails to be Markovian for pinning points having a law, which is absolutely continuous with respect to the Lebesgue measure .
Revised:  However, when the bridge information process absorbs y, the holder of the contract should take the decision of withdrawing gas on the other hand, they should take the decision to inject gas when the process absorbs x , they should take the decision of injecting gas .
Edit intent: meaning-changed
Original:  Here we built a spatially explicit model which predicts the spatial distribution of visits to crop of Apis mellifera by simulating each day the trips of honey bees in the crops , the demographical dynamic of each hive , and their honey production.
Revised:  Here we present a spatially explicit model which predicts the spatial distribution of visits to crop of Apis mellifera by simulating each day the trips of honey bees in the crops , the demographical dynamic of each hive , and their honey production.
Edit intent: clarity
Original:  Here we built a spatially explicit model which predicts the spatial distribution of visits to crop of Apis mellifera by simulating each day the trips of honey bees in the crops , the demographical dynamic of each hive , and their honey production.
Revised:  Here we built a spatially explicit model that predicts the spatial distribution of visits to crop of Apis mellifera by simulating each day the trips of honey bees in the crops , the demographical dynamic of each hive , and their honey production.
Edit intent: fluency
Original:  Here we built a spatially explicit model which predicts the spatial distribution of visits to crop of Apis mellifera by simulating each day the trips of honey bees in the crops , the demographical dynamic of each hive , and their honey production.
Revised:  Here we built a spatially explicit model which predicts the spatial distribution of visits by Apis mellifera to crops, by simulating daily trips of honey bees in the crops , the demographical dynamic of each hive , and their honey production.
Edit intent: clarity
Original:  Here we built a spatially explicit model which predicts the spatial distribution of visits to crop of Apis mellifera by simulating each day the trips of honey bees in the crops , the demographical dynamic of each hive , and their honey production.
Revised:  Here we built a spatially explicit model which predicts the spatial distribution of visits to crop of Apis mellifera by simulating each day the trips of honey bees , the demographical dynamic of each hive , and their honey production.
Edit intent: clarity
Original:  Here we built a spatially explicit model which predicts the spatial distribution of visits to crop of Apis mellifera by simulating each day the trips of honey bees in the crops , the demographical dynamic of each hive , and their honey production.
Revised:  Here we built a spatially explicit model which predicts the spatial distribution of visits to crop of Apis mellifera by simulating each day the trips of honey bees in the crops , the demographical dynamic of each hive and their honey production.
Edit intent: fluency
Original:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites.
Revised:  This model goes beyond existing approaches by including 1) a flower resource affected by the feedback interaction between nectar extraction, pollination, blossoming and repeated visits, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites.
Edit intent: meaning-changed
Original:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites.
Revised:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinators dynamic that allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites.
Edit intent: clarity
Original:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites.
Revised:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinators have only partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites.
Edit intent: fluency
Original:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites.
Revised:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on their surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites.
Edit intent: fluency
Original:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites.
Revised:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the specific and systematic foraging behavior and strategies of Apis mellifera regarding its election of foraging sites.
Edit intent: clarity
Original:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera regarding its election of foraging sites. With this simple but quite realistic model we show the importance of keeping a minimal fraction of natural habitat in an agricultural landscape.
Revised:  This model goes beyond existing approaches by including 1) a flower resource affected by the nectar and pollen extractionby the pollinators, 2) a pollinator growth dynamic which allows competition through short term resource depletion, 3) a probabilistic approach of the foraging behavior, modeling the fact that the pollinator has only a partial knowledge of the resource on its surroundings, and 4) the unique foraging habits of Apis mellifera at the moment of choosing foraging sites, as opposed to those adopted by solitary and wild pollinators. With a balance between simplicity and realism we show the importance of keeping a minimal fraction of natural habitat in an agricultural landscape.
Edit intent: meaning-changed
Original:  We also evaluate the effects of the landscape structure on the pollination, and demonstrate that it exists an optimal size of natural habitat patches which maximize the pollination service for a fixed fraction of natural habitat . Finally, we reveal the effect of the distinct ways of mixing culture on the number of visits to the crop .
Revised:  We also evaluate the effects of the landscape structure on the pollination, and demonstrate that it exists an optimal size of natural habitat patches which maximize the pollination service for a fixed fraction of natural habitat .
Edit intent: coherence
Original:  On this basis, our region proposal module propagates the partial annotations to the whole image and then proposes effective bounding boxes for the bounding box regression and binary mask generation modules.
Revised:  On this basis, our region proposal module propagates the partial annotations to the whole image and then proposes effective bounding boxes for the bounding box-regression and binary mask-generation modules. Our method effectively learns from unlabeled regions thereby improving detection performance. We test our method with various nuclear images.
Edit intent: meaning-changed
Original:  When trained with only 1/4 of the nuclei annotated, the baseline pipeline gives frequent false negatives, while our approach retains  detection accuracy comparable to that of training with the fully annotated data.
Revised:  When trained with only 1/4 of the nuclei annotated, the baseline pipeline gives frequent false negatives, while our approach retains a detection accuracy comparable to that of training with the fully annotated data.
Edit intent: fluency
Original:  When trained with only 1/4 of the nuclei annotated, the baseline pipeline gives frequent false negatives, while our approach retains  detection accuracy comparable to that of training with the fully annotated data.
Revised:  When trained with only 1/4 of the nuclei annotated, the baseline pipeline gives frequent false negatives, while our approach retains detection accuracy comparable to that from training with fully annotated data.
Edit intent: fluency
Original: Market dynamic is studied by quantifying the dependence of the entropy S(\tau,n) of the clusters formed by the  series of the prices p_t and its moving average p_{t,n} on temporal horizon M.
Revised: Market dynamic is quantified in terms of the entropy S(\tau,n) of the clusters formed by the series of the prices p_t and its moving average p_{t,n} on temporal horizon M.
Edit intent: clarity