wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Revised:  Utilizing the \boxplus \backslash \boxminus operations and further defining an oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Edit intent: fluency
Original:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Revised:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the on-manifold system. Such a canonical form enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Edit intent: clarity
Original:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Revised:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold constraints from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Edit intent: clarity
Original:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Revised:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system behaviors in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Edit intent: clarity
Original:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Revised:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic and symbolic Kalman filter framework .
Edit intent: clarity
Original:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework . This proposed Kalman filter framework can be used by only casting the system model into the canonical form without going through the cumbersome hand-derivation of the on-manifold Kalman filter. This is particularly useful when the robotic system is of high dimension.
Revised:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework that are naturally evolving on the manifold.
Edit intent: clarity
Original:  Furthermore, the manifold-embedding Kalman filter is implemented as a toolkit in C++ , with which an user needs only to define the system, and call the respective filter steps (e.g., propagation , update) according to the events (e. g., reception of input, reception of measurement).
Revised:  Furthermore, the on-manifold Kalman filter is implemented as a toolkit in C++ , with which an user needs only to define the system, and call the respective filter steps (e.g., propagation , update) according to the events (e. g., reception of input, reception of measurement).
Edit intent: clarity
Original:  Furthermore, the manifold-embedding Kalman filter is implemented as a toolkit in C++ , with which an user needs only to define the system, and call the respective filter steps (e.g., propagation , update) according to the events (e. g., reception of input, reception of measurement).
Revised:  Furthermore, the manifold-embedding Kalman filter is implemented as a toolkit in C++ packages which enables users to implement an on-manifold Kalman filter just like the normal one in \mathbb{R user needs only to define the system, and call the respective filter steps (e.g., propagation , update) according to the events (e. g., reception of input, reception of measurement).
Edit intent: meaning-changed
Original:  Furthermore, the manifold-embedding Kalman filter is implemented as a toolkit in C++ , with which an user needs only to define the system, and call the respective filter steps (e.g., propagation , update) according to the events (e. g., reception of input, reception of measurement).
Revised:  Furthermore, the manifold-embedding Kalman filter is implemented as a toolkit in C++ , with which an user needs only to provide the system-specific descriptions, and then call the respective filter steps (e.g., propagation , update) according to the events (e. g., reception of input, reception of measurement).
Edit intent: meaning-changed
Original:  Furthermore, the manifold-embedding Kalman filter is implemented as a toolkit in C++ , with which an user needs only to define the system, and call the respective filter steps (e.g., propagation , update) according to the events (e. g., reception of input, reception of measurement).
Revised:  Furthermore, the manifold-embedding Kalman filter is implemented as a toolkit in C++ , with which an user needs only to define the system, and call the respective filter steps (e.g., predict , update) according to the events (e. g., reception of input, reception of measurement).
Edit intent: meaning-changed
Original:  Furthermore, the manifold-embedding Kalman filter is implemented as a toolkit in C++ , with which an user needs only to define the system, and call the respective filter steps (e.g., propagation , update) according to the events (e. g., reception of input, reception of measurement).
Revised:  Furthermore, the manifold-embedding Kalman filter is implemented as a toolkit in C++ , with which an user needs only to define the system, and call the respective filter steps (e.g., propagation , update) without dealing with any of the manifold constraints.
Edit intent: meaning-changed
Original:  The existing implementation supports full iterated Kalman filtering for systems on manifold \mathcal{S = \mathbb{R}^m \times }  SO(3) \times \cdots \times SO(3) \times \mathbb{S^2\times \cdots \times \mathbb{S}^2 or any of its sub-manifolds}  , and is extendable to other types of manifold when necessary.
Revised:  The existing implementation supports full iterated Kalman filtering for systems on = \mathbb{R}^m \times } SO(3) \times \cdots \times SO(3) \times \mathbb{S^2\times \cdots \times \mathbb{S}^2 or any of its sub-manifolds} , and is extendable to other types of manifold when necessary.
Edit intent: meaning-changed
Original:  The existing implementation supports full iterated Kalman filtering for systems on manifold \mathcal{S = \mathbb{R}^m \times }  SO(3) \times \cdots \times SO(3) \times \mathbb{S^2\times \cdots \times \mathbb{S}^2 or any of its sub-manifolds}  , and is extendable to other types of manifold when necessary.
Revised:  The existing implementation supports full iterated Kalman filtering for systems on manifold \mathcal{S = \mathbb{R}^m \times } any manifold composed of \mathbb{R SO(3) \times \cdots \times SO(3) \times \mathbb{S^2\times \cdots \times \mathbb{S}^2 or any of its sub-manifolds} , and is extendable to other types of manifold when necessary.
Edit intent: meaning-changed
Original:  The existing implementation supports full iterated Kalman filtering for systems on manifold \mathcal{S = \mathbb{R}^m \times }  SO(3) \times \cdots \times SO(3) \times \mathbb{S^2\times \cdots \times \mathbb{S}^2 or any of its sub-manifolds}  , and is extendable to other types of manifold when necessary.
Revised:  The existing implementation supports full iterated Kalman filtering for systems on manifold \mathcal{S = \mathbb{R}^m \times } SO(3) ^2\times \cdots \times \mathbb{S}^2 or any of its sub-manifolds} , and is extendable to other types of manifold when necessary.
Edit intent: meaning-changed
Original:  The existing implementation supports full iterated Kalman filtering for systems on manifold \mathcal{S = \mathbb{R}^m \times }  SO(3) \times \cdots \times SO(3) \times \mathbb{S^2\times \cdots \times \mathbb{S}^2 or any of its sub-manifolds}  , and is extendable to other types of manifold when necessary.
Revised:  The existing implementation supports full iterated Kalman filtering for systems on manifold \mathcal{S = \mathbb{R}^m \times } SO(3) \times \cdots \times SO(3) \times \mathbb{S^2\times \cdots \times \mathbb{S}^2 or any of its sub-manifolds} and \mathbb{S , and is extendable to other types of manifold when necessary.
Edit intent: meaning-changed
Original:  However, a major drawback of using DRL is the Data hungry training regime of DRL that requires millions of trial and error attempts, impractical in real robotic hardware. We propose a multi-subtask reinforcement learning method where complex tasks can be decomposed into low-level subtasks  .
Revised:  However, a major drawback of using DRL is the Data hungry training regime of DRL that requires millions of trial and error attempts, which is impractical when running experiments on robotic systems. To address this issue, we propose a multi-subtask reinforcement learning method where complex tasks can be decomposed into low-level subtasks .
Edit intent: clarity
Original:  The trained subtasks can be choreographed by a high-level synthesizer . As a test bed , we use a pick and place robotic simulator , and transfer the learnt behaviour in a real robotic system.
Revised:  Trained subtasks can then be composed with a high-level synthesizer . As a test bed , we use a pick and place robotic simulator , and transfer the learnt behaviour in a real robotic system.
Edit intent: clarity
Original:  The trained subtasks can be choreographed by a high-level synthesizer . As a test bed , we use a pick and place robotic simulator , and transfer the learnt behaviour in a real robotic system.
Revised:  The trained subtasks can be choreographed by a high-level choreographer . As a test bed , we use a pick and place robotic simulator , and transfer the learnt behaviour in a real robotic system.
Edit intent: clarity
Original:  The trained subtasks can be choreographed by a high-level synthesizer . As a test bed , we use a pick and place robotic simulator , and transfer the learnt behaviour in a real robotic system.
Revised:  The trained subtasks can be choreographed by a high-level synthesizer . As a testbed , we use a pick and place robotic simulator , and transfer the learnt behaviour in a real robotic system.
Edit intent: fluency
Original:  The trained subtasks can be choreographed by a high-level synthesizer . As a test bed , we use a pick and place robotic simulator , and transfer the learnt behaviour in a real robotic system. We show that our method outperforms imitation learning based method and reaches  high success rate compared to an end-to-end learning approach.
Revised:  The trained subtasks can be choreographed by a high-level synthesizer . As a test bed , we use a pick and place robotic simulator to demonstrate our methodology, and show that our method outperforms imitation learning based method and reaches high success rate compared to an end-to-end learning approach.
Edit intent: clarity
Original:  Furthermore, we investigate the trained subtasks to demonstrate a adaptive behaviour by fine-tuning a subset of subtasks on a different task. Our approach deviates from the end-to-end learning strategy and provide an initial direction towards learning modular task representations that can generate robust behaviours .
Revised:  Moreover, we transfer the learned behavior in a different robotic environment that allows us to exploit sim-to-real transfer and demonstrate the trajectories in a real robotic system. Our training regime is carried out using a central processing unit (CPU)-based system, which demonstrates the data-efficient properties of our approach .
Edit intent: meaning-changed
Original:  In this paper, we describe an image retrieval setup  where the user simultaneously describes an image using both spoken natural language (the "what" ) and mouse traces over an empty canvas (the "where" ) to express the characteristics of the desired target image.
Revised:  In this paper, we propose an image retrieval setup where the user simultaneously describes an image using both spoken natural language (the "what" ) and mouse traces over an empty canvas (the "where" ) to express the characteristics of the desired target image.
Edit intent: clarity
Original:  In this paper, we describe an image retrieval setup  where the user simultaneously describes an image using both spoken natural language (the "what" ) and mouse traces over an empty canvas (the "where" ) to express the characteristics of the desired target image.
Revised:  In this paper, we describe an image retrieval setup with a new form of multimodal queries, where the user simultaneously describes an image using both spoken natural language (the "what" ) and mouse traces over an empty canvas (the "where" ) to express the characteristics of the desired target image.
Edit intent: meaning-changed
Original:  In this paper, we describe an image retrieval setup  where the user simultaneously describes an image using both spoken natural language (the "what" ) and mouse traces over an empty canvas (the "where" ) to express the characteristics of the desired target image.
Revised:  In this paper, we describe an image retrieval setup where the user simultaneously uses both spoken natural language (the "what" ) and mouse traces over an empty canvas (the "where" ) to express the characteristics of the desired target image.
Edit intent: clarity
Original:  In this paper, we describe an image retrieval setup  where the user simultaneously describes an image using both spoken natural language (the "what" ) and mouse traces over an empty canvas (the "where" ) to express the characteristics of the desired target image.
Revised:  In this paper, we describe an image retrieval setup where the user simultaneously describes an image using both spoken natural language (the what ) and mouse traces over an empty canvas (the "where" ) to express the characteristics of the desired target image.
Edit intent: fluency
Original:  In this paper, we describe an image retrieval setup  where the user simultaneously describes an image using both spoken natural language (the "what" ) and mouse traces over an empty canvas (the "where" ) to express the characteristics of the desired target image.
Revised:  In this paper, we describe an image retrieval setup where the user simultaneously describes an image using both spoken natural language (the "what" ) and mouse traces over an empty canvas (the where ) to express the characteristics of the desired target image.
Edit intent: fluency
Original: The ongoing COVID-19 vaccination campaign has so far targeted less than 3 \% of the world population, and  even in countries where the campaign has started  many citizens will not receive their doses for many months.
Revised: The ongoing COVID-19 vaccination campaign has so far targeted less than 10 \% of the world population, and even in countries where the campaign has started many citizens will not receive their doses for many months.
Edit intent: meaning-changed
Original: The ongoing COVID-19 vaccination campaign has so far targeted less than 3 \% of the world population, and  even in countries where the campaign has started  many citizens will not receive their doses for many months.
Revised: The ongoing COVID-19 vaccination campaign has so far targeted less than 3 \% of the world population, and , even in countries where the campaign has started many citizens will not receive their doses for many months.
Edit intent: fluency
Original: The ongoing COVID-19 vaccination campaign has so far targeted less than 3 \% of the world population, and  even in countries where the campaign has started  many citizens will not receive their doses for many months.
Revised: The ongoing COVID-19 vaccination campaign has so far targeted less than 3 \% of the world population, and even in countries where the campaign has started , many citizens will not receive their doses for many months.
Edit intent: fluency
Original:  The number of currently active listings is 11, including one listing selling the Pfizer/BioNTech vaccine, one listing the AstraZeneca/Oxford  vaccine, and 9 listings selling vaccines of unspecified type .
Revised:  The number of currently active listings is 34, including eight listings offering the Pfizer/BioNTech vaccine, one listing the AstraZeneca/Oxford vaccine, and 9 listings selling vaccines of unspecified type .
Edit intent: meaning-changed
Original:  The number of currently active listings is 11, including one listing selling the Pfizer/BioNTech vaccine, one listing the AstraZeneca/Oxford  vaccine, and 9 listings selling vaccines of unspecified type .
Revised:  The number of currently active listings is 11, including one listing selling the Pfizer/BioNTech vaccine, six the Moderna, two the AstraZeneca/Oxford vaccine, and 9 listings selling vaccines of unspecified type .
Edit intent: meaning-changed
Original:  The number of currently active listings is 11, including one listing selling the Pfizer/BioNTech vaccine, one listing the AstraZeneca/Oxford  vaccine, and 9 listings selling vaccines of unspecified type .
Revised:  The number of currently active listings is 11, including one listing selling the Pfizer/BioNTech vaccine, one listing the AstraZeneca/Oxford , two the Sputinik V vaccine, and 9 listings selling vaccines of unspecified type .
Edit intent: meaning-changed
Original:  The number of currently active listings is 11, including one listing selling the Pfizer/BioNTech vaccine, one listing the AstraZeneca/Oxford  vaccine, and 9 listings selling vaccines of unspecified type .
Revised:  The number of currently active listings is 11, including one listing selling the Pfizer/BioNTech vaccine, one listing the AstraZeneca/Oxford vaccine, and nine offering fabricated proofs of vaccination .
Edit intent: meaning-changed
Original:  Our motivation is the match between residents and hospitals where, due to the COVID-19 pandemic, interviews for the 2020-21 season of the NRMP match have switched to a virtual format.
Revised:  Our motivation is the match between residents and hospitals where, due to the COVID-19 pandemic, interviews for the 2020-21 season of the National Residency Matching Program were switched to a virtual format.
Edit intent: clarity
Original:  We show that if doctors are willing to accept more interviews  but the hospitals do not increase the number of interviews they offer, no doctor will be better off and potentially many doctors will be harmed.
Revised:  We show that , starting from a situation where the final matching is stable, if doctors can accept more interviews but the hospitals do not increase the number of interviews they offer, no doctor will be better off and potentially many doctors will be harmed.
Edit intent: meaning-changed
Original:  While several methods were designed to partially mitigate this issue, the underlying causes are poorly understood. In this work we investigate the above phenomenon and show that tightness  may not be the determining factor for reduced certified robustness . Concretely, we identify two key features of relaxations that impact training dynamics: continuity and sensitivity.
Revised:  The poor understanding of this paradox has forced recent state-of-the-art certified defenses to focus on designing various heuristics in order to mitigate its effects. In contrast, in this paper we study the underlying causes and show that tightness may not be the determining factor for reduced certified robustness . Concretely, we identify two key features of relaxations that impact training dynamics: continuity and sensitivity.
Edit intent: clarity
Original:  In this work we investigate the above phenomenon and show that tightness  may not be the determining factor for reduced certified robustness . Concretely, we identify two key features of relaxations that impact training dynamics: continuity and sensitivity.
Revised:  In this work we investigate the above phenomenon and show that tightness alone may not be the determining factor for reduced certified robustness . Concretely, we identify two key features of relaxations that impact training dynamics: continuity and sensitivity.
Edit intent: clarity
Original:  In this work we investigate the above phenomenon and show that tightness  may not be the determining factor for reduced certified robustness . Concretely, we identify two key features of relaxations that impact training dynamics: continuity and sensitivity.
Revised:  In this work we investigate the above phenomenon and show that tightness may not be the determining factor . Concretely, we identify two key features of relaxations that impact training dynamics: continuity and sensitivity.
Edit intent: clarity
Original:  In this work we investigate the above phenomenon and show that tightness  may not be the determining factor for reduced certified robustness . Concretely, we identify two key features of relaxations that impact training dynamics: continuity and sensitivity.
Revised:  In this work we investigate the above phenomenon and show that tightness may not be the determining factor for reduced certified robustness . Concretely, we identify two key properties of relaxations that impact training dynamics: continuity and sensitivity.
Edit intent: clarity