wanyu/IteraTeR_full_sent huggingface dataset:
Original: This work presents a novel  approach to simultaneously track a robot with respect to multiple rigid entities, including the environment and additional dynamic objects in a scene . Previous approaches treat dynamic parts of a scene as outliers and are thus limited to  small amount of dynamics , or rely on prior information for all objects in the scene to enable robust camera tracking.
Revised: This work presents a novel approach to simultaneously segment, track and reconstruct the static background and large dynamic rigid objects that can occlude major portions of the camera view . Previous approaches treat dynamic parts of a scene as outliers and are thus limited to small amount of dynamics , or rely on prior information for all objects in the scene to enable robust camera tracking.
Edit intent: meaning-changed
Original: This work presents a novel  approach to simultaneously track a robot with respect to multiple rigid entities, including the environment and additional dynamic objects in a scene . Previous approaches treat dynamic parts of a scene as outliers and are thus limited to  small amount of dynamics , or rely on prior information for all objects in the scene to enable robust camera tracking.
Revised: This work presents a novel approach to simultaneously track a robot with respect to multiple rigid entities, including the environment and additional dynamic objects in a scene . Previous approaches treat dynamic parts of a scene as outliers and are thus limited to a small amount of dynamics , or rely on prior information for all objects in the scene to enable robust camera tracking.
Edit intent: fluency
Original: This work presents a novel  approach to simultaneously track a robot with respect to multiple rigid entities, including the environment and additional dynamic objects in a scene . Previous approaches treat dynamic parts of a scene as outliers and are thus limited to  small amount of dynamics , or rely on prior information for all objects in the scene to enable robust camera tracking.
Revised: This work presents a novel approach to simultaneously track a robot with respect to multiple rigid entities, including the environment and additional dynamic objects in a scene . Previous approaches treat dynamic parts of a scene as outliers and are thus limited to small amount of changes in the scene , or rely on prior information for all objects in the scene to enable robust camera tracking.
Edit intent: clarity
Original:  Here, we propose to formulate localisation and object tracking as the same underlying problem and simultaneously track multiple rigid transformations, thereforeenabling simultaneous localisation and object tracking for mobile manipulators in dynamic scenes .
Revised:  Here, we propose to treat all dynamic parts as one rigid body and simultaneously segment and track both static and dynamic components. We, therefore, enable simultaneous localisation and object tracking for mobile manipulators in dynamic scenes .
Edit intent: meaning-changed
Original:  Here, we propose to formulate localisation and object tracking as the same underlying problem and simultaneously track multiple rigid transformations, thereforeenabling simultaneous localisation and object tracking for mobile manipulators in dynamic scenes .
Revised:  Here, we propose to formulate localisation and object tracking as the same underlying problem and simultaneously track multiple rigid transformations, thereforeenabling simultaneous localisation and reconstruction of both the static background and rigid dynamic components in environments where dynamic objects cause large occlusion .
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Original:   The usability of the soft jig was evaluated from the viewpoint of the versatility and fixing performance for various shapes and postures of parts in assembly operations .
Revised:  We propose a method to configure parts-fixing on the soft jig based on contact relations, reachable directions, and the center of gravity of the parts that are fixed on the jig. The usability of the soft jig was evaluated from the viewpoint of the versatility and fixing performance for various shapes and postures of parts in assembly operations .
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Original:  The interference of deformable parts with other parts can be easily investigated by scaling models . A simulation experiment was conducted using the proposed method, and the results show the possibility of obtaining Pareto-optimal solutions of assembly sequences for a 3D CAD model with 33 parts including a deformable part.
Revised:  The interference of deformable parts with other parts can be easily investigated by scaling parts . A simulation experiment was conducted using the proposed method, and the results show the possibility of obtaining Pareto-optimal solutions of assembly sequences for a 3D CAD model with 33 parts including a deformable part.
Edit intent: clarity
Original:  Furthermore, we discuss the resulted post-learning policies , show that they are almost optimal in the underlying game sense, and provide numerical hints of almost welfare-optimal of the resulted policies .
Revised:  Furthermore, we discuss the resulted post-learning policies , show that they are almost optimal in the underlying game sense, i.e., they form a Nash equilibrium. Furthermore, we propose in this work a Q-learning algorithm, requiring predictive observation of two subsequent opponent's actions, yielding an optimal strategy given that the latter applies a stationary strategy, and discuss the existence of the Nash equilibrium in the underlying information asymmetrical game .
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Original: We design a probabilistic algorithm that , given \epsilon>0 and a polynomial system F given by black-box evaluation functions, outputs an approximate zero of F, in the sense of Smale, with probability at least 1-\epsilon.
Revised: This work studies the average complexity of solving structured polynomial systems that are characterized by a low evaluation cost, as opposed to the dense random model previously used. Firstly, we design a continuation algorithm that computes, with high probability, an approximate zero of a polynomial system F given by black-box evaluation functions, outputs an approximate zero of F, in the sense of Smale, with probability at least 1-\epsilon.
Edit intent: meaning-changed
Original: We design a probabilistic algorithm that , given \epsilon>0 and a polynomial system F given by black-box evaluation functions, outputs an approximate zero of F, in the sense of Smale, with probability at least 1-\epsilon.
Revised: We design a probabilistic algorithm that , given \epsilon>0 and a polynomial system given only as black-box evaluation functions, outputs an approximate zero of F, in the sense of Smale, with probability at least 1-\epsilon.
Edit intent: clarity
Original: We design a probabilistic algorithm that , given \epsilon>0 and a polynomial system F given by black-box evaluation functions, outputs an approximate zero of F, in the sense of Smale, with probability at least 1-\epsilon.
Revised: We design a probabilistic algorithm that , given \epsilon>0 and a polynomial system F given by black-box evaluation program. Secondly, we introduce a universal model of random polynomial systems with prescribed evaluation complexity L. Combining both, we show that we can compute an approximate zero of F, in the sense of Smale, with probability at least 1-\epsilon.
Edit intent: meaning-changed
Original: We design a probabilistic algorithm that , given \epsilon>0 and a polynomial system F given by black-box evaluation functions, outputs an approximate zero of F, in the sense of Smale, with probability at least 1-\epsilon. When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs }  (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average.
Revised: We design a probabilistic algorithm that , given \epsilon>0 and a polynomial system F given by black-box evaluation functions, outputs an approximate zero of a random structured polynomial system with n equations of degree at most } (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average.
Edit intent: meaning-changed
Original:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs }  (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average.
Revised:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs \delta} (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average.
Edit intent: meaning-changed
Original:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs }  (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average.
Revised:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs } in n variables with only poly (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average.
Edit intent: meaning-changed
Original:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs }  (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average.
Revised:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs } (n, %DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average.
Edit intent: clarity
Original:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs }  (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average.
Revised:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs } (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% }%DIFDELCMD < \right) %%% operations on average.
Edit intent: meaning-changed
Original:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs }  (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% operations on average. Here n is the number of variables, \delta the maximum degree, L(F) denotes the evaluation cost of F, and \Gamma(F) reflects an aspect of the numerical condition of F. Moreover, we prove that for inputs given by random Gaussian algebraic branching programs of size (n,\delta), the algorithm runs on average in time polynomial in n and \delta.
Revised:  When applying this algorithm to u \cdot F, where u is uniformly random in the product of unitary groups, the algorithm performs } (n, \delta) \cdot L (F) \cdot%DIFDELCMD < \left( %%% \Gamma(F) \log \Gamma(F) + \log \log \epsilon^{-1 }%DIFDELCMD < \right) %%% (n,\delta), the algorithm runs on average in time polynomial in n and \delta.
Edit intent: meaning-changed
Original:  Moreover, we prove that for inputs given by random Gaussian algebraic branching programs of size (n,\delta), the algorithm runs on average in time polynomial in n and \delta. Our result may be interpreted as an affirmative answer to a refined version of  Smale's 17th question, concerned with systems of\emph{structured polynomial equations .
Revised:  Moreover, we prove that for inputs given by random Gaussian algebraic branching programs of size \delta Smale's 17th question, concerned with systems of\emph{structured polynomial equations .
Edit intent: clarity
Original:  Our result may be interpreted as an affirmative answer to a refined version of  Smale's 17th question, concerned with systems of\emph{structured polynomial equations .
Revised:  Our result may be interpreted as an affirmative answer to a refined version of ) L operations with high probability. This exceeds the expectations implicit in Smale's 17th question, concerned with systems of\emph{structured polynomial equations .
Edit intent: meaning-changed
Original: We investigate the general problem of conditioned dialogue , in which a condition label is used as input to designate the type of the target response such as a persona. A major challenge for conditioned dialogue generation is the lack of substantial dialogue data labeled with conditions. Thus, we propose to complement the labeled dialogue data with labeled non-dialogue text data , and fine-tune BERT based on them.
Revised: Conditioned dialogue generation suffers from the scarcity of labeled responses. In this work, we exploit labeled non-dialogue text data , and fine-tune BERT based on them.
Edit intent: clarity
Original:  Thus, we propose to complement the labeled dialogue data with labeled non-dialogue text data , and fine-tune BERT based on them. Our fine-tuning approach utilizes BERT for both encoder and decoder via different input representations and self-attention masks in order to distinguish the source and target side. On the target (generation ) side, we use a new attention routing mechanism to choose between generating a generic word or condition-related word at each position. Our model is instantiated to persona- and topic-related dialogue .
Revised:  Thus, we propose to complement the labeled dialogue data with labeled non-dialogue text data related to the condition, which are much easier to collect. We propose a multi-task learning approach to leverage both labeled dialogue and text data. The 3 tasks jointly optimize the same pre-trained Transformer -- conditioned dialogue generation task on the labeled dialogue data, conditioned language encoding task and conditioned language generation task on the labeled text data .
Edit intent: meaning-changed
Original:  In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for zero-shot entity linking by 2.5 points of accuracy, and our joint inference model further improves entity linking by 1.8 points.
Revised:  In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for entity linking by 2.5 points of accuracy, and our joint inference model further improves entity linking by 1.8 points.
Edit intent: clarity
Original:  In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for zero-shot entity linking by 2.5 points of accuracy, and our joint inference model further improves entity linking by 1.8 points.
Revised:  In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for zero-shot entity linking by 3.0 points of accuracy, and our joint inference model further improves entity linking by 1.8 points.
Edit intent: meaning-changed
Original:  In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for zero-shot entity linking by 2.5 points of accuracy, and our joint inference model further improves entity linking by 1.8 points.
Revised:  In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for zero-shot entity linking by 2.5 points of accuracy, and our clustering-based inference model further improves entity linking by 1.8 points.
Edit intent: meaning-changed
Original:  In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for zero-shot entity linking by 2.5 points of accuracy, and our joint inference model further improves entity linking by 1.8 points.
Revised:  In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for zero-shot entity linking by 2.5 points of accuracy, and our joint inference model further improves entity linking by 2.3 points.
Edit intent: meaning-changed
Original:  This work presents the first demonstration of magnetic microscale rocker robot (microrocker bot) capable of bidirectional movement on flat as well as biological surfaces, when actuated by a single compact electromagnet .
Revised:  This work presents a novel control mechanism and contact design that enables bidirectional steering via biasing the neutral position of the microrobot. Equipped with rockers to contact the substrate, the microrobot, hence microrocker bot, is capable of well-controlled forward and backward movement on flat as well as biological surfaces, when actuated by a single compact electromagnet .
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Original:  The 100um by 113um by 36um robot was 3D printed via two-photon lithography and subsequently coated with a nickel (Ni) thin film. When actuated by an externally applied magnetic sawtooth field, the robot demonstrated stick-slip motion enabled by its rockers.
Revised:  The 100um by 113um by 36um robot was 3D printed via two-photon lithography and subsequently deposited with nickel thin films. Under a relatively small static magnetic field, the microrocker bot tilts either forward or backward to align the thin film magnetization direction with the magnetic field lines. When combined with an oscillating magnetic field, the robot demonstrated stick-slip motion enabled by its rockers.
Edit intent: meaning-changed
Original:  When actuated by an externally applied magnetic sawtooth field, the robot demonstrated stick-slip motion enabled by its rockers. The controllable bidirectional motion is enabled by adjusting the DC offset of the waveform, which tilts the robot and biases it towards either forward or backward motion .
Revised:  When actuated by an externally applied magnetic sawtooth field, the robot demonstrated stick-slip motion in the predisposed direction, dictated by the neutral position tilt .
Edit intent: clarity
Original:  The microrocker bots are further equipped with sharp  tips that can get engagedvia application of DC-only or low frequency magnetic fields. This novel control method offers an attractive solution to replace the multiple bulky coils traditionally used for magnetic actuation and control, as well as allows for a more flexible and simple approach towards microrobotics motion control.
Revised:  The microrocker bots are further equipped with sharp tips that can be selectively engaged.
Edit intent: clarity
Original:  When the frequency and offset of the  sawtooth waveform are optimized, the robot travels up to 87ums (0.87 body length per second) forward and backward with minor deviance from linear trajectories.
Revised:  When the frequency and offset of the actuation sawtooth waveform are optimized, the robot travels up to 87ums (0.87 body length per second) forward and backward with minor deviance from linear trajectories.
Edit intent: meaning-changed
Original:  When the frequency and offset of the  sawtooth waveform are optimized, the robot travels up to 87ums (0.87 body length per second) forward and backward with minor deviance from linear trajectories.
Revised:  When the frequency and offset of the sawtooth waveform are optimized, the robot travels up to 100um/s (1 body length per second) forward and backward with minor deviance from linear trajectories.
Edit intent: meaning-changed
Original:  When the frequency and offset of the  sawtooth waveform are optimized, the robot travels up to 87ums (0.87 body length per second) forward and backward with minor deviance from linear trajectories.
Revised:  When the frequency and offset of the sawtooth waveform are optimized, the robot travels up to 87ums (0.87 body length per second) forward and backward showing very linear trajectories.
Edit intent: meaning-changed
Original:  Finally, to prove the robot's capabilities in direct contact with biological environments , we demonstrate the microbot 's ability to traverse forward and backward on the surface of a Dracaena Fragrans (corn plant), as well as upend on its mechanical tip.
Revised:  Finally, to prove the functionality of the microrocker bots in direct contact with biological environments , we demonstrate the microbot 's ability to traverse forward and backward on the surface of a Dracaena Fragrans (corn plant), as well as upend on its mechanical tip.
Edit intent: clarity
Original:  Finally, to prove the robot's capabilities in direct contact with biological environments , we demonstrate the microbot 's ability to traverse forward and backward on the surface of a Dracaena Fragrans (corn plant), as well as upend on its mechanical tip.
Revised:  Finally, to prove the robot's capabilities in direct contact with biological surfaces , we demonstrate the microbot 's ability to traverse forward and backward on the surface of a Dracaena Fragrans (corn plant), as well as upend on its mechanical tip.
Edit intent: clarity
Original:  Finally, to prove the robot's capabilities in direct contact with biological environments , we demonstrate the microbot 's ability to traverse forward and backward on the surface of a Dracaena Fragrans (corn plant), as well as upend on its mechanical tip.
Revised:  Finally, to prove the robot's capabilities in direct contact with biological environments , we demonstrate the robot 's ability to traverse forward and backward on the surface of a Dracaena Fragrans (corn plant), as well as upend on its mechanical tip.
Edit intent: clarity
Original:  Finally, to prove the robot's capabilities in direct contact with biological environments , we demonstrate the microbot 's ability to traverse forward and backward on the surface of a Dracaena Fragrans (corn plant), as well as upend on its mechanical tip.
Revised:  Finally, to prove the robot's capabilities in direct contact with biological environments , we demonstrate the microbot 's ability to traverse forward and backward on the surface of a Dracaena Fragrans leaf, and upend/engage on its mechanical tip.
Edit intent: meaning-changed
Original:  For many tasks such as repeated infrastructure inspection, item delivery or inventory transport, a route repeating capability rather than full navigation stack can be sufficient and offers potential practical advantages  . Previous teach and repeat research has achieved high performance in difficult conditions generally by using sophisticated, often expensive sensors, and has often had high computational requirements.
Revised:  For many tasks such as repeated infrastructure inspection, item delivery or inventory transport, a route repeating capability can be sufficient and offers potential practical advantages . Previous teach and repeat research has achieved high performance in difficult conditions generally by using sophisticated, often expensive sensors, and has often had high computational requirements.
Edit intent: clarity