wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We have integrated our algorithm with  OpenAI gym-based " Highway-Env " simulator and demonstrate the benefits in terms of improved navigation in different scenarios .
Revised:  We have integrated our algorithm with OpenAI gym-based " Highway-Env " simulator and demonstrate the benefits of our navigation algorithm in terms of reducing collisions by 3.25 - 26.90\% and handling scenarios with 2.5 \times higher traffic density .
Edit intent: meaning-changed
Original: We study the following variant of the classic bin packing problem. The input is {\em   a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Revised: We study the following variant of the classic {\em a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Edit intent: clarity
Original:  The input is {\em   a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Revised:  The input is {\em bin packing a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Edit intent: clarity
Original:  The input is {\em   a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Revised:  The input is {\em problem. Given a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Edit intent: clarity
Original:  The input is {\em   a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Revised:  The input is {\em a set of items of various sizes , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Edit intent: clarity
Original:  The input is {\em   a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Revised:  The input is {\em a set of items I = \{ 1 , %DIFDELCMD < \dots %%% partitioned into groups , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Edit intent: clarity
Original:  The input is {\em   a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Revised:  The input is {\em a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Edit intent: coherence
Original:  The input is {\em   a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Revised:  The input is {\em a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 find a packing of the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Edit intent: clarity
Original:  The input is {\em   a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Revised:  The input is {\em a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of identical (unit-size) bins, such that no two items of the same group are packed in the same bin.
Edit intent: clarity
Original:  The input is {\em   a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are packed in the same bin.
Revised:  The input is {\em a set of items I = \{ 1 , %DIFDELCMD < \dots %%% , N \} with corresponding sizess_1, ...,s_N \in (0 , 1 , partitioned into n groupsG_1, ...,G_n. The goal is to pack the items in a minimum number of unit size bins, such that no two items of the same group are assigned to the same bin.
Edit intent: clarity
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  Our main result is an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em {\em .
Edit intent: clarity
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for the problem, improving upon the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em {\em .
Edit intent: clarity
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em {\em .
Edit intent: clarity
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 %DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em {\em .
Edit intent: clarity
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% 2. \% In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em {\em .
Edit intent: meaning-changed
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em {\em .
Edit intent: fluency
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), the items are packed in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em {\em .
Edit intent: fluency
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em {\em .
Edit intent: fluency
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance . As a key tool, we apply a novel{\em {\em {\em .
Edit intent: meaning-changed
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em Shift \& Swap {\em {\em .
Edit intent: clarity
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em technique which generalizes the classic linear shifting technique to scenarios allowing conflicts between items. The major challenge of packing{\em {\em .
Edit intent: meaning-changed
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em small {\em .
Edit intent: fluency
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em items using only a small number of extra bins is tackled through an intricate combination of enumeration and a greedy-based approach that utilizes the rounded solution of a{\em .
Edit intent: meaning-changed
Original:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em  {\em  {\em  .
Revised:  In this paper, we present an{\em asymptotic polynomial time approximation scheme (APTAS) for group bin packing, thus improving the best known ratio of 2 Adany et al., 2016%DIFDELCMD < ]%%% . In particular, for any instance I and a fixed \varepsilon \eps \in (0,1), our scheme packs the items in at most (1+ \varepsilon \eps )OPT(I) +1 bins, where OPT(I) is the minimum number of bins required for packing the instance {\em {\em {\em linear program .
Edit intent: meaning-changed
Original:  The pipeline trains a deep convolutional neural network that takes as input an image of the scene with object and target. It computes a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion for a manipulator arm to dynamically manipulate the cable to perform a task with varying obstacle and target locations.
Revised:  The framework finds a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion for a manipulator arm to dynamically manipulate the cable to perform a task with varying obstacle and target locations.
Edit intent: clarity
Original:  It computes a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion for a manipulator arm to dynamically manipulate the cable to perform a task with varying obstacle and target locations.
Revised:  It computes a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion that dynamically manipulates the cable to perform a task with varying obstacle and target locations.
Edit intent: clarity
Original:  It computes a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion for a manipulator arm to dynamically manipulate the cable to perform a task with varying obstacle and target locations.
Revised:  It computes a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion for a manipulator arm to dynamically manipulate the cable to perform tasks with varying obstacle and target locations.
Edit intent: fluency
Original:  The trajectory function computes high-speed minimum-jerk arcing motions that are constrained to remain within joint limits and to travel through the 3D apex point by repeatedly solving quadratic programs for shorter time horizons to find the shortest and fastest feasible motion.
Revised:  The trajectory function computes minimum-jerk arcing motions that are constrained to remain within joint limits and to travel through the 3D apex point by repeatedly solving quadratic programs for shorter time horizons to find the shortest and fastest feasible motion.
Edit intent: clarity
Original:  The trajectory function computes high-speed minimum-jerk arcing motions that are constrained to remain within joint limits and to travel through the 3D apex point by repeatedly solving quadratic programs for shorter time horizons to find the shortest and fastest feasible motion.
Revised:  The trajectory function computes high-speed minimum-jerk motions that are constrained to remain within joint limits and to travel through the 3D apex point by repeatedly solving quadratic programs for shorter time horizons to find the shortest and fastest feasible motion.
Edit intent: clarity
Original:  The trajectory function computes high-speed minimum-jerk arcing motions that are constrained to remain within joint limits and to travel through the 3D apex point by repeatedly solving quadratic programs for shorter time horizons to find the shortest and fastest feasible motion.
Revised:  The trajectory function computes high-speed minimum-jerk arcing motions that are constrained to remain within joint limits and to travel through the 3D apex point by repeatedly solving quadratic programs to find the shortest and fastest feasible motion.
Edit intent: clarity
Original:  Results suggest that the robot using the learned apex point can achieve success rates of 81.7\% in vaulting, 65.0\% in knocking, and 60.0\% in weaving, while a baseline with a fixed apex across the three tasks achieves respective success rates of 51.7\%, 36.7\%, and 15.0\%, and a baseline with human-specified  task-specific apex points achieves 66.7\%, 56.7\%, and 15.0\% success rate respectively  . Code, data, and supplementary materials are available at URL //
Revised:  Results suggest that a baseline with a fixed apex across the three tasks achieves respective success rates of 51.7\%, 36.7\%, and 15.0\%, and a baseline with human-specified task-specific apex points achieves 66.7\%, 56.7\%, and 15.0\% success rate respectively . Code, data, and supplementary materials are available at URL //
Edit intent: coherence