wanyu/IteraTeR_full_sent huggingface dataset:
Original: Financial time series admits inherent uncertainty and randomness that changes over time. To clearly describe volatility uncertainty of the time series, we assume that the volatility of risky assets holds value between the minimum volatility and maximum volatility of the assets. This study establishes autoregressive models to determine the maximum and minimum volatilities, where the ratio of minimum volatility to maximum volatility can measure volatility uncertainty.
Revised: Based on law of large numbers and central limit theorem under nonlinear expectation, we introduce a new method of using G-normal distribution to measure financial risks. Applying max-mean estimators and small windows method, we establish autoregressive models to determine the maximum and minimum volatilities, where the ratio of minimum volatility to maximum volatility can measure volatility uncertainty.
Edit intent: meaning-changed
Original:  This study establishes autoregressive models to determine the maximum and minimum volatilities, where the ratio of minimum volatility to maximum volatility can measure volatility uncertainty. By utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset  .
Revised:  This study establishes autoregressive models to determine the parameters of G-normal distribution, i.e., the return, maximal and minimal volatilities of the time series. Utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset .
Edit intent: meaning-changed
Original:  By utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset  .
Revised:  By utilizing the value at risk (VaR) predictor model under G-normal distribution, we show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset .
Edit intent: meaning-changed
Original:  By utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset  .
Revised:  By utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the G-VaR model gives an excellent performance in predicting the VaR for a benchmark dataset .
Edit intent: clarity
Original:  By utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset  .
Revised:  By utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset comparing to many well-known VaR predictors .
Edit intent: meaning-changed
Original:  In this paper, we propose a data-driven method to learn and interpret flight's 4D approach and landing parameters to facilitate comprehensible and actionable insights of landing dynamicsfor aircrew and air traffic controller (ATCO) in real-time . Specifically, we develop a tunnel Gaussian process (TGP) model to gain an insight into the landing dynamics of aircraft using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight.
Revised:  In this paper, we propose a data-driven method to learn and interpret flight's 4D approach and landing parameters to facilitate comprehensible and actionable insights of flight dynamics . Specifically, we develop a tunnel Gaussian process (TGP) model to gain an insight into the landing dynamics of aircraft using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight.
Edit intent: coherence
Original:  In this paper, we propose a data-driven method to learn and interpret flight's 4D approach and landing parameters to facilitate comprehensible and actionable insights of landing dynamicsfor aircrew and air traffic controller (ATCO) in real-time . Specifically, we develop a tunnel Gaussian process (TGP) model to gain an insight into the landing dynamics of aircraft using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight.
Revised:  In this paper, we propose a data-driven method to learn and interpret flight's 4D approach and landing parameters to facilitate comprehensible and actionable insights of landing dynamicsfor aircrew and air traffic controller (ATCO) in real-time . Specifically, we develop two variants of tunnel Gaussian process (TGP) model to gain an insight into the landing dynamics of aircraft using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight.
Edit intent: meaning-changed
Original:  In this paper, we propose a data-driven method to learn and interpret flight's 4D approach and landing parameters to facilitate comprehensible and actionable insights of landing dynamicsfor aircrew and air traffic controller (ATCO) in real-time . Specifically, we develop a tunnel Gaussian process (TGP) model to gain an insight into the landing dynamics of aircraft using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight.
Revised:  In this paper, we propose a data-driven method to learn and interpret flight's 4D approach and landing parameters to facilitate comprehensible and actionable insights of landing dynamicsfor aircrew and air traffic controller (ATCO) in real-time . Specifically, we develop a tunnel Gaussian process (TGP) models to elucidate aircraft's approach and landing dynamics using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight.
Edit intent: clarity
Original:  We examine TGP qualitatively and quantitatively by synthesizing two complex trajectory datasets  . Empirically, TGP reconstructed the structure of the synthesized trajectories .
Revised:  We examine TGP qualitatively and quantitatively by synthesizing two complex trajectory datasets and compared TGP against existing methods on trajectory learning . Empirically, TGP reconstructed the structure of the synthesized trajectories .
Edit intent: meaning-changed
Original:  In this paper, we present our ongoing effort to build an environment to facilitate the research for models of this capability. In this environment, there are no explicit definitions of tasks or rewards given when accomplishing those tasks. Rather the models experience the experience of the human infants from fetus to 12 months. The agent should learn to speak the first words as a human child does. We expect the environment will contribute to the research for HLAI.
Revised:  In this paper, we present a classification of intelligence according to how individual agents learn and propose a definition and a test for HLAI. The main idea is that language acquisition without explicit rewards can be a sufficient test for HLAI.
Edit intent: coherence
Original:  It employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue.
Revised:  Seq2Seq-DU employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue.
Edit intent: meaning-changed
Original:  Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2  , WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.
Revised:  Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2 , MultiWOZ2.1 , WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.
Edit intent: meaning-changed
Original: We propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments . We show how robotic tasks can be formulated as a free-energy minimization problem , bringing the neuroscientific theory of active inference on a mobile manipulator .
Revised: We propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments , showing how robotic tasks can be formulated as a free-energy minimization problem , bringing the neuroscientific theory of active inference on a mobile manipulator .
Edit intent: fluency
Original:  We show how robotic tasks can be formulated as a free-energy minimization problem , bringing the neuroscientific theory of active inference on a mobile manipulator .
Revised:  We show how robotic tasks can be formulated as a free-energy minimization problem . The proposed approach allows to handle partially observable initial states and improves the robustness of classical BTs against unexpected contingencies while at the same time reducing the number of nodes in a tree .
Edit intent: meaning-changed
Original:  In this framework , the general nominal behavior is specified offline through BTs . A new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as in classical BTs.
Revised:  In this work , the general nominal behavior is specified offline through BTs . A new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as in classical BTs.
Edit intent: clarity
Original:  In this framework , the general nominal behavior is specified offline through BTs . A new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as in classical BTs.
Revised:  In this framework , the general nominal behavior is specified offline through BTs , where a new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as in classical BTs.
Edit intent: coherence
Original:  In this framework , the general nominal behavior is specified offline through BTs . A new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as in classical BTs.
Revised:  In this framework , the general nominal behavior is specified offline through BTs . A new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as in classical BTs.
Edit intent: fluency
Original:  In this framework , the general nominal behavior is specified offline through BTs . A new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as in classical BTs.
Revised:  In this framework , the general nominal behavior is specified offline through BTs . A new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as typically done in BTs.
Edit intent: clarity
Original:  We thus suggest that role recommendation may benefit both the users and developers, i.e., once we are able to make successful role recommendation for those in need, it may dramatically contribute to the productivity of developers and the enthusiasm of users, thus further boosting OSS projects' development.
Revised:  We thus suggest that partner recommendation across different roles may benefit both the users and developers, i.e., once we are able to make successful role recommendation for those in need, it may dramatically contribute to the productivity of developers and the enthusiasm of users, thus further boosting OSS projects' development.
Edit intent: meaning-changed
Original:  We thus suggest that role recommendation may benefit both the users and developers, i.e., once we are able to make successful role recommendation for those in need, it may dramatically contribute to the productivity of developers and the enthusiasm of users, thus further boosting OSS projects' development.
Revised:  We thus suggest that role recommendation may benefit both the users and developers, i.e., once we are able to make successful recommendation for those in need, it may dramatically contribute to the productivity of developers and the enthusiasm of users, thus further boosting OSS projects' development.
Edit intent: fluency
Original:  Our main result is a simple, linear programming based randomized algorithm that, whenever the instance is feasible, leverages assumptions typically met in practice to return an assignment whose: i) expected cost is at most that of an optimal solution, and ii) expected fraction of unassigned requests is at most 1/e.
Revised:  Our main result is an LP-based randomized rounding algorithm that, whenever the instance is feasible, leverages assumptions typically met in practice to return an assignment whose: i) expected cost is at most that of an optimal solution, and ii) expected fraction of unassigned requests is at most 1/e.
Edit intent: meaning-changed
Original:  Our main result is a simple, linear programming based randomized algorithm that, whenever the instance is feasible, leverages assumptions typically met in practice to return an assignment whose: i) expected cost is at most that of an optimal solution, and ii) expected fraction of unassigned requests is at most 1/e.
Revised:  Our main result is a simple, linear programming based randomized algorithm that, whenever the instance is feasible, leverages mild assumptions to return an assignment whose: i) expected cost is at most that of an optimal solution, and ii) expected fraction of unassigned requests is at most 1/e.
Edit intent: clarity
Original:  We can relax the feasibility requirement by including a penalty term for unassigned requests, in which case our performance guarantee is with respect to a modified objective function. Our techniques generalize to a class of set-partitioning problems .
Revised:  We can relax the feasibility requirement by considering the penalty version of the problem, in which a penalty is paid for each unassigned request. We find that, whenever a request is repeatedly unassigned after a number of rounds, with high probability it is so in accordance with the sequence of LP solutions and not because of a rounding error. We additionally introduce a deterministic rounding heuristic inspired by our randomized technique. Our computational experiments show that our rounding algorithms achieve a performance similar to that of the ILP at a reduced computation time, far improving on our theoretical guarantee. The reason for this is that, although the assignment problem is hard in theory, the natural LP relaxation tends to be very tight in practice. We conclude with observations and open questions related to this phenomenon .
Edit intent: meaning-changed
Original:  Here, we construct a likelihood function based on the statistics of the position increments. The corresponding maximum likelihoodestimator for the global diffusion coefficient of multiple trajectories can be efficiently evaluated in \mathcal{O extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate by using the expectation-maximization algorithm.
Revised:  Here, we revisit the problem of extracting diffusion coefficients from single-particle tracking experiments subject to static and dynamic noise using the principle of maximum likelihood. Taking advantage of an efficient real-space formulation, we extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate by using the expectation-maximization algorithm.
Edit intent: clarity
Original:  The corresponding maximum likelihoodestimator for the global diffusion coefficient of multiple trajectories can be efficiently evaluated in \mathcal{O extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate by using the expectation-maximization algorithm.
Revised:  The corresponding maximum likelihoodestimator for the global diffusion coefficient of multiple trajectories can be efficiently evaluated in \mathcal{O extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate with the help of the expectation-maximization algorithm.
Edit intent: clarity
Original: An agent privately observes a technological breakthrough that expands utility possibilities, and must be incentivised to disclose it. The principal controls the agent's utility over time.
Revised: We identify a new and pervasive dynamic agency problem: that of incentivising the prompt disclosure of productive information. To study it, we introduce a model in which a technological breakthrough occurs at an uncertain time and is privately observed by an agent, and a principal must incentivise disclosure via her control of the agent's utility over time.
Edit intent: meaning-changed
Original:  The principal controls the agent's utility over time. Optimal mechanisms keep the agent only just willing to disclose promptly. In an important case, a deadline mechanism is optimal : absent disclosure, the agent enjoys an efficient utility before a deadline, and an inefficiently low utility afterwards. In general, optimal mechanisms feature a (possibly gradual) transition from the former to the latter. Even if monetary transfers are permitted, they may not be used .
Revised:  The principal controls the agent's utility . We uncover a striking deadline structure of optimal mechanisms: they have a simple deadline form in an important special case, and a graduated deadline structure in general .
Edit intent: coherence
Original:  To study it, we introduce a  model in which a technological breakthrough occurs at an uncertain time and is privately observed by an agent, and a principal must incentivise disclosure via her control of the agent's utility.
Revised:  To study it, we introduce a general model in which a technological breakthrough occurs at an uncertain time and is privately observed by an agent, and a principal must incentivise disclosure via her control of the agent's utility.
Edit intent: meaning-changed
Original:  To study the cascades  molecular events underlying the poor prognosis that are due to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed to address these limitations .
Revised:  To study the cascades of molecular events underlying the poor prognosis that are due to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed to address these limitations .
Edit intent: fluency
Original:  To study the cascades  molecular events underlying the poor prognosis that are due to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed to address these limitations .
Revised:  To study the cascades molecular events underlying the poor prognosis that may lead to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed to address these limitations .
Edit intent: clarity
Original:  To study the cascades  molecular events underlying the poor prognosis that are due to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed to address these limitations .
Revised:  To study the cascades molecular events underlying the poor prognosis that are due to limited treatment options for advanced disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed to address these limitations .
Edit intent: fluency
Original:  To study the cascades  molecular events underlying the poor prognosis that are due to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed to address these limitations .
Revised:  To study the cascades molecular events underlying the poor prognosis that are due to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using modern high-throughput sequencing platforms has been employed to address these limitations .
Edit intent: fluency
Original:  To study the cascades  molecular events underlying the poor prognosis that are due to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed to address these limitations .
Revised:  To study the cascades molecular events underlying the poor prognosis that are due to limited treatment options for advance disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using the modern high-throughput sequencing platforms has been employed .
Edit intent: coherence
Original:  Here we review the principles of RNA-Seq technology and summarize the recent studies on human bladder cancer that employed this technique to unravel the pathogenesis of the disease, identify biomarkers, discover pathways and classify the disease state.
Revised:  Here we review the principles of RNA-Seq technology and summarize recent studies on human bladder cancer that employed this technique to unravel the pathogenesis of the disease, identify biomarkers, discover pathways and classify the disease state.
Edit intent: fluency
Original:  Moreover, we discussed the future perspective for RNA-Seq studies on bladder cancer and recommend the application of new technology called single cell sequencing (scRNA-Seq) to further understand bladder cancer . Keywords: Transcriptome profiling, RNA-sequencing, genomics, bioinformatics, bladder cancer
Revised:  Moreover, we discussed the future perspectives for RNA-Seq studies on bladder cancer and recommend the application of new technology called single cell sequencing (scRNA-Seq) to further understand bladder cancer . Keywords: Transcriptome profiling, RNA-sequencing, genomics, bioinformatics, bladder cancer
Edit intent: fluency