wanyu/IteraTeR_v2 huggingface dataset:
Original:  We hope that this type of measure will be widely adopted, in order to help the community to better evaluate the significance of their work, while bringing computational cost -- and in fine carbon emissions -- in the spotlight of AI research.
Revised:  We hope that this type of measure will be widely adopted, in order to help the community to evaluate their work, while bringing computational cost -- and in fine carbon emissions -- in the spotlight of AI research.
Edit intent: clarity
Original:  We hope that this type of measure will be widely adopted, in order to help the community to better evaluate the significance of their work, while bringing computational cost -- and in fine carbon emissions -- in the spotlight of AI research.
Revised:  We hope that this type of measure will be widely adopted, in order to help the community to better evaluate the significance of their work, while putting computational cost -- and in fine carbon emissions -- in the spotlight of AI research.
Edit intent: coherence
Original:  We hope that this type of measure will be widely adopted, in order to help the community to better evaluate the significance of their work, while bringing computational cost -- and in fine carbon emissions -- in the spotlight of AI research.
Revised:  We hope that this type of measure will be widely adopted, in order to help the community to better evaluate the significance of their work, while bringing computational cost -- and in fine carbon emissions -- in the spotlight of deep learning research.
Edit intent: clarity
Original: In this paper, we have proposed an algorithm to combine multiple cheap Inertial Measurement Unit (IMU) sensors to calculate 3D-orientations accurately.
Revised: In this paper, we propose an algorithm to combine multiple cheap Inertial Measurement Unit (IMU) sensors to calculate 3D-orientations accurately.
Edit intent: coherence
Original:  Our algorithm, the %DIFDELCMD < {\em %%% Best Axis Composition (BAC), chooses dynamically the most fitted axes among IMUs to improve the estimation performance.
Revised:  Our algorithm, the %DIFDELCMD < {\em %%% Best Axes Composition (BAC), chooses dynamically the most fitted axes among IMUs to improve the estimation performance.
Edit intent: fluency
Original:  We have compared our approach with a probabilistic Multiple IMU (MIMU) approach, and we have validated our algorithm in our collected dataset.
Revised:  We compare our approach with a probabilistic Multiple IMU (MIMU) approach, and we have validated our algorithm in our collected dataset.
Edit intent: fluency
Original:  We have compared our approach with a probabilistic Multiple IMU (MIMU) approach, and we have validated our algorithm in our collected dataset.
Revised:  We have compared our approach with a probabilistic Multiple IMU (MIMU) approach, and we validate our algorithm in our collected dataset.
Edit intent: fluency
Original:  As a second application of submodular order functions, we show an intriguing algorithmic connection to the maximization of monotone submodular functions in the streaming model.
Revised:  As a second application of submodular order functions, we show an intriguing connection to the maximization of monotone submodular functions in the streaming model.
Edit intent: clarity
Original:  Tested with intrusion alerts collected through Collegiate Penetration Testing Competition, SAGE produces AGsthat reflect the strategies used by  participating teams.
Revised:  Tested with intrusion alerts collected through Collegiate Penetration Testing Competition, SAGE produces AGsthat reflect the strategies used by the participating teams.
Edit intent: fluency
Original:  The resulting AGs are succinct, interpretable, and enable analysts to derive actionable insights , e.g., attackers tend to follow shorter paths after they have discovered a longer one .
Revised:  The AGs are succinct, interpretable, and enable analysts to derive actionable insights , e.g., attackers tend to follow shorter paths after they have discovered a longer one .
Edit intent: clarity
Original:  The resulting AGs are succinct, interpretable, and enable analysts to derive actionable insights , e.g., attackers tend to follow shorter paths after they have discovered a longer one .
Revised:  The resulting AGs are succinct, interpretable, and enable analysts to derive actionable insights , e.g., that attackers will often follow shorter paths after they have discovered a longer one .
Edit intent: clarity
Original:  Therefore, we propose iPOKE - invertible Prediction of Object Kinematics - that, conditioned on an initial frame and a local poke, allows to sample object kinematics and establishes a one-to-one correspondence to the corresponding plausible videos, thereby providing a controlled stochastic video synthesis.
Revised:  Therefore, we propose iPOKE -- invertible Prediction of Object Kinematics - that, conditioned on an initial frame and a local poke, allows to sample object kinematics and establishes a one-to-one correspondence to the corresponding plausible videos, thereby providing a controlled stochastic video synthesis.
Edit intent: fluency
Original:  Therefore, we propose iPOKE - invertible Prediction of Object Kinematics - that, conditioned on an initial frame and a local poke, allows to sample object kinematics and establishes a one-to-one correspondence to the corresponding plausible videos, thereby providing a controlled stochastic video synthesis.
Revised:  Therefore, we propose iPOKE - invertible Prediction of Object Kinematics -- that, conditioned on an initial frame and a local poke, allows to sample object kinematics and establishes a one-to-one correspondence to the corresponding plausible videos, thereby providing a controlled stochastic video synthesis.
Edit intent: fluency
Original:  To data , various DL approaches have been proposed to solve the ME issues and improve MER performance.
Revised:  To date , various DL approaches have been proposed to solve the ME issues and improve MER performance.
Edit intent: clarity
Original:  The current approaches operate by intelligently balancing computational effort between searching the graph and evaluating edges.
Revised:  The existing algorithms operate by intelligently balancing computational effort between searching the graph and evaluating edges.
Edit intent: clarity
Original:  However these algorithms are designed to run as a single process and do not leverage the multi-threading capability of modern processors.
Revised:  However , they are designed to run as a single process and do not leverage the multi-threading capability of modern processors.
Edit intent: clarity
Original:  However these algorithms are designed to run as a single process and do not leverage the multi-threading capability of modern processors.
Revised:  However these algorithms are designed to run as a single process and do not leverage the multithreading capability of modern processors.
Edit intent: fluency
Original:  In this work  we propose a massively parallelized, bounded suboptimal, lazy search algorithm (MPLP) that harnesses modern multi-core processors.
Revised:  In this work , we propose a massively parallelized, bounded suboptimal, lazy search algorithm (MPLP) that harnesses modern multi-core processors.
Edit intent: fluency
Original: A graph neural network (GNN) based access point (AP) selection algorithm for cell-free massive multiple-input multiple-output (MIMO)  is proposed.
Revised: A graph neural network (GNN) based access point (AP) selection algorithm for cell-free massive multiple-input multiple-output (MIMO) systems is proposed.
Edit intent: clarity
Original:  Two graphs, a homogeneous graph which includes only AP nodes representing the structure of the APs in the network, and a heterogeneous graph which includes both the AP nodes and user equipment (UE) nodes are constructed to represent a cell-free massive MIMO network.
Revised:  Two graphs, a homogeneous graph which includes only AP nodes representing the structure of the APs in the network, and a heterogeneous graph which includes both AP nodes and user equipment (UE) nodes are constructed to represent a cell-free massive MIMO network.
Edit intent: fluency
Original:  Numerical results show that compared to  proximity-based AP selection algorithms, the proposed GNN based algorithm predicts more potential links with a limited number of reference signal receive power (RSRP) measurements.
Revised:  The numerical results show that compared to proximity-based AP selection algorithms, the proposed GNN based algorithm predicts more potential links with a limited number of reference signal receive power (RSRP) measurements.
Edit intent: coherence
Original:  Numerical results show that compared to  proximity-based AP selection algorithms, the proposed GNN based algorithm predicts more potential links with a limited number of reference signal receive power (RSRP) measurements.
Revised:  Numerical results show that compared to the proximity-based AP selection algorithms, the proposed GNN based algorithm predicts more potential links with a limited number of reference signal receive power (RSRP) measurements.
Edit intent: fluency
Original:  Unlike the other AP selection algorithms in the literature , the proposed algorithm does not assume the knowledge of RSRP measurements of every AP-UE combination for optimal AP selection .
Revised:  Unlike the other AP selection algorithms , the proposed algorithm does not assume the knowledge of RSRP measurements of every AP-UE combination for optimal AP selection .
Edit intent: clarity
Original:  Unlike the other AP selection algorithms in the literature , the proposed algorithm does not assume the knowledge of RSRP measurements of every AP-UE combination for optimal AP selection .
Revised:  Unlike the other AP selection algorithms in the literature , the proposed algorithm does not require measured and sorted signal strengths of all the neighbouring APs .
Edit intent: clarity
Original:  However, these rhythms are not fully exploited in the design of active implantable medical devices.
Edit intent: coherence
Original:  This integrated algorithm allows for stimulation to be adjusted to the ultradian, circadian , and infradian patterns observed in patients through slowly-varying temporal adjustments of stimulation and algorithm sub-components, while also enabling adaption of stimulation based on immediate physiological needs such as a breakthrough seizure or change of posture.
Revised:  This integrated algorithm allows for stimulation to be adjusted to the ultradian, circadian and infradian patterns observed in patients through slowly-varying temporal adjustments of stimulation and algorithm sub-components, while also enabling adaption of stimulation based on immediate physiological needs such as a breakthrough seizure or change of posture.
Edit intent: fluency
Original:  This algorithmic approach is tested on a canine with severe drug-resistant idiopathic generalized epilepsy exhibiting a characteristic diurnal pattern correlated with sleep-wake cycles.
Revised:  This approach is tested on a canine with severe drug-resistant idiopathic generalized epilepsy exhibiting a characteristic diurnal pattern correlated with sleep-wake cycles.
Edit intent: clarity
Original:  This algorithmic approach is tested on a canine with severe drug-resistant idiopathic generalized epilepsy exhibiting a characteristic diurnal pattern correlated with sleep-wake cycles.
Revised:  This algorithmic approach is tested on a canine with severe drug-resistant idiopathic generalized epilepsy exhibiting a diurnal pattern correlated with sleep-wake cycles.
Edit intent: clarity
Original:  Using combinations of time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters , and fast-adaptive modes based on activity, the canine has experienced no further SE events post-implant at the time of writing ( 7 months), and no significant clusters are observed any longer .
Revised:  Using time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters , and fast-adaptive modes based on activity, the canine has experienced no further SE events post-implant at the time of writing ( 7 months), and no significant clusters are observed any longer .
Edit intent: clarity
Original:  Using combinations of time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters , and fast-adaptive modes based on activity, the canine has experienced no further SE events post-implant at the time of writing ( 7 months), and no significant clusters are observed any longer .
Revised:  Using combinations of time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters as well as fast-adaptive modes based on activity, the canine has experienced no further SE events post-implant at the time of writing ( 7 months), and no significant clusters are observed any longer .
Edit intent: clarity
Original:  Using combinations of time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters , and fast-adaptive modes based on activity, the canine has experienced no further SE events post-implant at the time of writing ( 7 months), and no significant clusters are observed any longer .
Revised:  Using combinations of time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters , and fast-adaptive modes based on activity, the canine experienced no further SE events post-implant at the time of writing ( 7 months), and no significant clusters are observed any longer .
Edit intent: clarity
Original:  Using combinations of time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters , and fast-adaptive modes based on activity, the canine has experienced no further SE events post-implant at the time of writing ( 7 months), and no significant clusters are observed any longer .
Revised:  Using combinations of time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters , and fast-adaptive modes based on activity, the canine has experienced no further SE events post-implant as of the time of writing ( 7 months), and no significant clusters are observed any longer .
Edit intent: clarity
Original:  The use of digitally-enabled chronotherapy as a feedforward signal to augment adaptive neurostimulators could prove a useful algorithmic method where sensitivity to temporal patterns are characteristics of the disease state  .
Revised:  The use of chronotherapy as a feedforward signal to augment adaptive neurostimulators could prove a useful algorithmic method where sensitivity to temporal patterns are characteristics of the disease state .
Edit intent: clarity
Original:  The use of digitally-enabled chronotherapy as a feedforward signal to augment adaptive neurostimulators could prove a useful algorithmic method where sensitivity to temporal patterns are characteristics of the disease state  .
Revised:  The use of digitally-enabled chronotherapy as a feedforward signal to augment adaptive neurostimulators could prove a useful method in conditions where sensitivity to temporal patterns are characteristics of the disease state .
Edit intent: clarity
Original:  The idea behind Group Sampling is that it can gather a group of samples from the same class in the same mini-batch , such that the model is trained upon group normalized samples while alleviating the effect of a single sample.
Revised:  The idea behind Group Sampling is that it can gather a group of samples from the same class into a mini-batch , such that the model is trained upon group normalized samples while alleviating the effect of a single sample.
Edit intent: clarity
Original:  The idea behind Group Sampling is that it can gather a group of samples from the same class in the same mini-batch , such that the model is trained upon group normalized samples while alleviating the effect of a single sample.
Revised:  The idea behind Group Sampling is that it can gather a group of samples from the same class in the same mini-batch . The model is thereby trained using normalized group samples, which helps to alleviate the effects associated with a single sample.
Edit intent: clarity
Original:  Group sampling updates the pipeline of pseudo label generation by guaranteeing the samples to be better divided into the correct classes.
Revised:  Group sampling updates the pipeline of pseudo label generation by guaranteeing that samples are more efficiently divided into the correct classes.
Edit intent: clarity
Original:  Group Sampling regularizes classifier training and representation learning , leading to the statistical stability of feature representation in a progressive fashion.
Revised:  Group sampling regulates the representation learning process, which enhances statistical stability for feature representation in a progressive fashion.
Edit intent: clarity
Original:  Qualitative and quantitative experiments on Market-1501, DukeMTMC-reID, and MSMT17 show that Grouping Sampling improves the state-of-the-arts by up to 2.2 \%~6.1\%.
Revised:  Qualitative and quantitative experiments on Market-1501, DukeMTMC-reID, and MSMT17 show that group sampling improves upon state-of-the-art methods by between 3.3 \%~6.1\%.
Edit intent: clarity
Original:  Code is available at URL
Revised:  Code has been available at URL
Edit intent: clarity
Original:  We develop ageneral framework that allows us to associate methods based on optimization formulations with maximum likelihood problems on random graphs .
Revised:  We establish and exploit connections between node reordering via (a) minimizing an objective function and (b) maximizing the likelihood of a random graph model .
Edit intent: clarity
Original:  In this review, we first introduce several classical paradigms ~( such as graph convolutional neural networks, graph attention networks, graph auto-encoder, graph adversarial methods, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks ) of graph neural networks comprehensively.
Revised:  In this review, we first introduce several classical paradigms , such as graph convolutional neural networks, graph attention networks, graph auto-encoder, graph adversarial methods, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks ) of graph neural networks comprehensively.
Edit intent: fluency
Original:  In this review, we first introduce several classical paradigms ~( such as graph convolutional neural networks, graph attention networks, graph auto-encoder, graph adversarial methods, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks ) of graph neural networks comprehensively.
Revised:  In this review, we first introduce several classical paradigms ~( such as graph convolutional neural networks, graph attention networks, graph auto-encoder, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks ) of graph neural networks comprehensively.
Edit intent: clarity
Original:  In this review, we first introduce several classical paradigms ~( such as graph convolutional neural networks, graph attention networks, graph auto-encoder, graph adversarial methods, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks ) of graph neural networks comprehensively.
Revised:  In this review, we first introduce several classical paradigms ~( such as graph convolutional neural networks, graph attention networks, graph auto-encoder, graph adversarial methods, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks , of graph neural networks comprehensively.
Edit intent: fluency
Original: With the rapid enhancement of computer computing power, deep learning methods , e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance.
Revised: In recent years, with the rapid enhancement of computer computing power, deep learning methods , e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance.
Edit intent: clarity
Original: With the rapid enhancement of computer computing power, deep learning methods , e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance.
Revised: With the rapid enhancement of computing power, deep learning methods , e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance.
Edit intent: fluency
Original: With the rapid enhancement of computer computing power, deep learning methods , e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance.
Revised: With the rapid enhancement of computer computing power, deep learning methods have been widely applied in wireless network widely and achieved impressive performance.
Edit intent: clarity
Original: With the rapid enhancement of computer computing power, deep learning methods , e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance.
Revised: With the rapid enhancement of computer computing power, deep learning methods , e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless networks and achieved impressive performance.
Edit intent: clarity
Original:  In recent years, in order to mine the topology information of graph-structured data in wireless network as well as contextual information, graph neural networks  have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems .
Revised:  To effectively exploit the information of graph-structured data in wireless network as well as contextual information, graph neural networks have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems .
Edit intent: clarity
Original:  In recent years, in order to mine the topology information of graph-structured data in wireless network as well as contextual information, graph neural networks  have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems .
Revised:  In recent years, in order to mine the topology information of graph-structured data as well as contextual information, graph neural networks have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems .
Edit intent: coherence
Original:  In recent years, in order to mine the topology information of graph-structured data in wireless network as well as contextual information, graph neural networks  have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems .
Revised:  In recent years, in order to mine the topology information of graph-structured data in wireless network as well as contextual information, graph neural networks (GNNs) have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems .
Edit intent: clarity
Original:  In recent years, in order to mine the topology information of graph-structured data in wireless network as well as contextual information, graph neural networks  have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems .
Revised:  In recent years, in order to mine the topology information of graph-structured data in wireless network as well as contextual information, graph neural networks have been introduced to address a series of wireless network problems .
Edit intent: clarity