wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Online evaluations show that the  system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the  adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Revised:  Online evaluations show that the system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the strategy adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Edit intent: meaning-changed
Original:  Online evaluations show that the  system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the  adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Revised:  Online evaluations show that the system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the adoption rate of advertisers .
Edit intent: coherence
Original:  We introduce new methods for incorporating VIsual LAyout structures (VILA)  , e.g., the grouping of page texts into text lines or text blocks, into language models to further improve performance.
Revised:  We introduce new methods for incorporating VIsual LAyout structures (VILA) structures , e.g., the grouping of page texts into text lines or text blocks, into language models to further improve performance.
Edit intent: meaning-changed
Original:  We show that the I-VILA approach, which simply adds special tokens denoting boundaries between layout structures into model inputs, can lead to +1~4.5 F1 Score improvements in token classificationtasks .
Revised:  We show that the I-VILA approach, which simply adds special tokens denoting the boundaries of layout structures into model inputs, can lead to +1~4.5 F1 Score improvements in token classificationtasks .
Edit intent: clarity
Original:  We show that the I-VILA approach, which simply adds special tokens denoting boundaries between layout structures into model inputs, can lead to +1~4.5 F1 Score improvements in token classificationtasks .
Revised:  We show that the I-VILA approach, which simply adds special tokens denoting boundaries between layout structures into model inputs, can lead to 1.9\% Macro F1 Score improvements in token classificationtasks .
Edit intent: meaning-changed
Original:  We show that the I-VILA approach, which simply adds special tokens denoting boundaries between layout structures into model inputs, can lead to +1~4.5 F1 Score improvements in token classificationtasks .
Revised:  We show that the I-VILA approach, which simply adds special tokens denoting boundaries between layout structures into model inputs, can lead to +1~4.5 F1 improvements for token classification .
Edit intent: clarity
Original:  Moreover, we design a hierarchical model  H-VILA that encodes these layout structures and record a up-to 70\% efficiency boost without hurting prediction accuracy. The experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring VILA awareness and a new dataset covering 19 scientific disciplines with gold annotations . Pre-trained weights, benchmark datasets, and source code will be available at URL
Revised:  Moreover, we design a hierarchical model H-VILA that encodes these layout structures and record a up-to 47\% inference time reduction with less than 1.5\% Macro F1 loss for the text classification models. Experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring VILA awareness and a new dataset covering 19 scientific disciplines with gold annotations . Pre-trained weights, benchmark datasets, and source code will be available at URL
Edit intent: meaning-changed
Original:  The experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring VILA awareness and a new dataset covering 19 scientific disciplines with gold annotations . Pre-trained weights, benchmark datasets, and source code will be available at URL
Revised:  The experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring classification uniformity within visual groups and a new dataset covering 19 scientific disciplines with gold annotations . Pre-trained weights, benchmark datasets, and source code will be available at URL
Edit intent: meaning-changed
Original:  The experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring VILA awareness and a new dataset covering 19 scientific disciplines with gold annotations . Pre-trained weights, benchmark datasets, and source code will be available at URL
Revised:  The experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring VILA awareness and a new dataset of gold annotations covering papers from 19 scientific disciplines with gold annotations . Pre-trained weights, benchmark datasets, and source code will be available at URL
Edit intent: clarity
Original:  The experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring VILA awareness and a new dataset covering 19 scientific disciplines with gold annotations . Pre-trained weights, benchmark datasets, and source code will be available at URL
Revised:  The experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring VILA awareness and a new dataset covering 19 scientific disciplines . Pre-trained weights, benchmark datasets, and source code will be available at URL
Edit intent: clarity
Original: We provide a theory as to why the locus of a triangle center over Poncelet 3-periodics in an ellipse pair is an ellipse or not. For the confocal pair (elliptic billiard), we show that if the  center can be expressed as a fixed affine combination of barycenter, circumcenter, and mittenpunkt (which is stationary over the confocal family) , then its locus will be an ellipse.
Revised: We provide a theory as to why the locus of a triangle center 's locus over a Poncelet 3-periodic family. We show that if the center can be expressed as a fixed affine combination of barycenter, circumcenter, and mittenpunkt (which is stationary over the confocal family) , then its locus will be an ellipse.
Edit intent: coherence
Original:  For the confocal pair (elliptic billiard), we show that if the  center can be expressed as a fixed affine combination of barycenter, circumcenter, and mittenpunkt (which is stationary over the confocal family) , then its locus will be an ellipse.
Revised:  For the confocal pair (elliptic billiard), we show that if the triangle center can be expressed as a fixed affine combination of barycenter, circumcenter, and mittenpunkt (which is stationary over the confocal family) , then its locus will be an ellipse.
Edit intent: meaning-changed
Original:  For the confocal pair (elliptic billiard), we show that if the  center can be expressed as a fixed affine combination of barycenter, circumcenter, and mittenpunkt (which is stationary over the confocal family) , then its locus will be an ellipse.
Revised:  For the confocal pair (elliptic billiard), we show that if the center can be expressed as a fixed affine combination of barycenter, circumcenter, and a third, stationary point over some family , then its locus will be an ellipse.
Edit intent: meaning-changed
Original:  Finally we write out expressions for the convex quartic locus of the incenter for a generic Poncelet family, conjecturing it can only be an ellipse if the pair is confocal  .
Revised:  Finally , we derive a (long) expression for the loci of the incenter for a generic Poncelet family, conjecturing it can only be an ellipse if the pair is confocal .
Edit intent: clarity
Original:  Finally we write out expressions for the convex quartic locus of the incenter for a generic Poncelet family, conjecturing it can only be an ellipse if the pair is confocal  .
Revised:  Finally we write out expressions for the convex quartic locus of the incenter and excenters over a generic Poncelet family, conjecturing it can only be an ellipse if the pair is confocal .
Edit intent: meaning-changed
Original:  Finally we write out expressions for the convex quartic locus of the incenter for a generic Poncelet family, conjecturing it can only be an ellipse if the pair is confocal  .
Revised:  Finally we write out expressions for the convex quartic locus of the incenter for a generic Poncelet 3-periodic family, showing they are roots of a quartic. We conjecture (i) those loci are convex, and (ii) that they can only be an ellipse if the pair is confocal .
Edit intent: meaning-changed
Original:  Finally we write out expressions for the convex quartic locus of the incenter for a generic Poncelet family, conjecturing it can only be an ellipse if the pair is confocal  .
Revised:  Finally we write out expressions for the convex quartic locus of the incenter for a generic Poncelet family, conjecturing it can only be ellipses if the pair is confocal .
Edit intent: fluency
Original:  Finally we write out expressions for the convex quartic locus of the incenter for a generic Poncelet family, conjecturing it can only be an ellipse if the pair is confocal  .
Revised:  Finally we write out expressions for the convex quartic locus of the incenter for a generic Poncelet family, conjecturing it can only be an ellipse if the pair is confocal , i.e., within a 1d subspace of the 5d space of ellipse pairs which admit 3-periodics .
Edit intent: meaning-changed
Original: Quantum Approximate Optimization Algorithm (QAOA) has been studied widely in the literature, primarily for finding an approximate value of the maximum cut size of a graph. QAOA is composed of a problem hamiltonian and a mixer hamiltonian which are applied alternately for p \geq 1 layers. The circuit for this algorithm requires 2m CNOT gatesin each layer, where m is the number of edges in the graph. CNOT gate is one of the primary sources of error in modern quantum computers.
Revised: Quantum Approximate Optimization Algorithm (QAOA) is studied primarily to find approximate solutions to combinatorial optimization problems. For a graph with n vertices and m edges, a depth p QAOA for the Max-cut problem requires 2\cdot m \cdot p CNOT gates. CNOT is one of the primary sources of error in modern quantum computers.
Edit intent: clarity
Original:  In this paper, we propose two techniques for reducing the number of CNOT gates in the circuit which are independent of the hardware architecture. For a graph with n vertices, we first propose a technique based on edge coloring that can reduce upto \lfloor n{2} \rfloor CNOT gates in the circuit .
Revised:  In this paper, we propose two techniques for reducing the number of CNOT gates in the circuit . First, we present a method based on Edge Coloring of the input graph that minimizes the the number of cycles (termed as depth of the circuit), and reduces upto \lfloor n{2} \rfloor CNOT gates in the circuit .
Edit intent: meaning-changed
Original:  We analytically derive the criteria for which the reduction in the number of CNOT gates due to the DFS based technique can provide lower error probability even with some increased depth, and show that all graphs conform to this criteria, making this technique universal.
Revised:  We analytically derive the condition for which the reduction in the number of CNOT gates due to the DFS based technique can provide lower error probability even with some increased depth, and show that all graphs conform to this criteria, making this technique universal.
Edit intent: clarity
Original:  We analytically derive the criteria for which the reduction in the number of CNOT gates due to the DFS based technique can provide lower error probability even with some increased depth, and show that all graphs conform to this criteria, making this technique universal.
Revised:  We analytically derive the criteria for which the reduction in CNOT gates overshadows this increase in depth, and show that all graphs conform to this criteria, making this technique universal.
Edit intent: clarity
Original:  We analytically derive the criteria for which the reduction in the number of CNOT gates due to the DFS based technique can provide lower error probability even with some increased depth, and show that all graphs conform to this criteria, making this technique universal. We further show that this proposed optimization holds even in the post transpilation stage of the circuit , which is actually executed in the IBM Quantum hardware .
Revised:  We analytically derive the criteria for which the reduction in the number of CNOT gates due to the DFS based technique can provide lower error probability even with some increased depth, and the error probability of the circuit , which is actually executed in the IBM Quantum hardware .
Edit intent: coherence
Original:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{  and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Revised:  We simulate these two methods for graphs of various sparsity with the ibmq_manhattan noise model \textit{ and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Edit intent: clarity
Original:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{  and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Revised:  We simulate these two techniques for graphs of various sparsity with the \textit{ and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Edit intent: clarity
Original:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{  and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Revised:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{ibmq\_manhattan and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Edit intent: meaning-changed
Original:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{  and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Revised:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{ noise model, and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Edit intent: meaning-changed
Original:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{  and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Revised:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{ and show that the DFS based method outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Edit intent: clarity
Original:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{  and show that the DFS based technique outperforms the edge coloring based technique , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Revised:  We simulate these two techniques for graphs of various sparsity with the ibmq_manhattan noise model \textit{ and show that the DFS based technique outperforms the edge coloring based method , which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
Edit intent: fluency
Original:  It greatly simplifies the pipeline of TAD and runs much faster than previous detectors . Our method achieves state-of-the-art performance on HACS Segments and THUMOS14 and competitive performance on  ActivityNet-1.3.
Revised:  It greatly simplifies the pipeline of TAD and runs much faster than previous detectors , while preserving remarkable detection performance. TadTR achieves state-of-the-art performance on HACS Segments and THUMOS14 and competitive performance on ActivityNet-1.3.
Edit intent: meaning-changed
Original:  Our method achieves state-of-the-art performance on HACS Segments and THUMOS14 and competitive performance on  ActivityNet-1.3.
Revised:  Our method achieves state-of-the-art performance on HACS Segments (+3.35\% average mAP). As a single-network detector, TadTR runs 10\times faster than its comparable competitor. It outperforms existing single-network detectors by a large margin on THUMOS14 and competitive performance on ActivityNet-1.3.
Edit intent: meaning-changed
Original:  Our method achieves state-of-the-art performance on HACS Segments and THUMOS14 and competitive performance on  ActivityNet-1.3.
Revised:  Our method achieves state-of-the-art performance on HACS Segments and THUMOS14 (+5.0\% average mAP) and ActivityNet (+7.53\% average mAP). When combined with other detectors, it reports 54.1\% mAP at IoU=0.5 on ActivityNet-1.3.
Edit intent: meaning-changed
Original:  Our method achieves state-of-the-art performance on HACS Segments and THUMOS14 and competitive performance on  ActivityNet-1.3.
Revised:  Our method achieves state-of-the-art performance on HACS Segments and THUMOS14 and competitive performance on THUMOS14, and 34.55\% average mAP on ActivityNet-1.3.
Edit intent: meaning-changed
Original:  We crop the transparent images into 8 * 8 and 224 * 224 pixels patches in the same proportion , and then divide the two different pixels patches into foreground and background according to groundtruch .
Revised:  We crop the T-EM images into 8 * 8 and 224 * 224 pixels patches in the same proportion , and then divide the two different pixels patches into foreground and background according to groundtruch .
Edit intent: meaning-changed
Original:  We crop the transparent images into 8 * 8 and 224 * 224 pixels patches in the same proportion , and then divide the two different pixels patches into foreground and background according to groundtruch .
Revised:  We crop the transparent images into 8 * 8 and 224 * 224 pixel patches in the same proportion , and then divide the two different pixels patches into foreground and background according to groundtruch .
Edit intent: fluency
Original:  We crop the transparent images into 8 * 8 and 224 * 224 pixels patches in the same proportion , and then divide the two different pixels patches into foreground and background according to groundtruch .
Revised:  We crop the transparent images into 8 * 8 and 224 * 224 pixels patches in the same proportion and then divide the two different pixels patches into foreground and background according to groundtruch .
Edit intent: fluency
Original:  We crop the transparent images into 8 * 8 and 224 * 224 pixels patches in the same proportion , and then divide the two different pixels patches into foreground and background according to groundtruch .
Revised:  We crop the transparent images into 8 * 8 and 224 * 224 pixels patches in the same proportion , and then divide the two different pixel patches into foreground and background according to groundtruch .
Edit intent: fluency
Original:  We crop the transparent images into 8 * 8 and 224 * 224 pixels patches in the same proportion , and then divide the two different pixels patches into foreground and background according to groundtruch .
Revised:  We crop the transparent images into 8 * 8 and 224 * 224 pixels patches in the same proportion , and then divide the two different pixels patches into foreground and background according to ground truth .
Edit intent: fluency
Original:  We conclude that ViT performs the worst in classifying 8 * 8 pixels patches, but it outperforms most convolutional neural networks in classifying 224 * 224.
Revised:  We conclude that ViT performs the worst in classifying 8 * 8 pixels patches, but it outperforms most convolutional neural networks in classifying 224 * 224 pixel patches.
Edit intent: fluency
Original:  Without additional pre-training, CBNetV2 can be integrated into mainstream detectors, including one-stage and two-stage detectors , as well as anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO.
Revised:  Without additional pre-training, CBNetV2 can be adapt to various backbones, including manual-based and NAS-based , as well as anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO.
Edit intent: meaning-changed
Original:  Without additional pre-training, CBNetV2 can be integrated into mainstream detectors, including one-stage and two-stage detectors , as well as anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO. Also, experiments provide strong evidence showing that composite backbones are more efficient  and resource-friendly than pre-trained wider and deeper networks  ,
Revised:  Without additional pre-training, CBNetV2 can be integrated into mainstream detectors, including one-stage and two-stage detectors , as well as CNN-based and Transformer-based ones. Experiments provide strong evidence showing that composite backbones are more efficient and resource-friendly than pre-trained wider and deeper networks ,
Edit intent: meaning-changed