wanyu/IteraTeR_v2 huggingface dataset:
Original:  We show that, under realistic relaxed LDP policies , for analytical workloads such as linear counting queries , multi-dimensional range  queries, and quantile queries, we can achieve significant gains in utility.
Revised:  We show that, under such privacy relaxations , for analytical workloads such as linear counting queries , multi-dimensional range queries, and quantile queries, we can achieve significant gains in utility.
Edit intent: clarity
Original:  We show that, under realistic relaxed LDP policies , for analytical workloads such as linear counting queries , multi-dimensional range  queries, and quantile queries, we can achieve significant gains in utility.
Revised:  We show that, under realistic relaxed LDP policies , for analytical workloads such as linear counting , multi-dimensional range queries, and quantile queries, we can achieve significant gains in utility.
Edit intent: fluency
Original:  We show that, under realistic relaxed LDP policies , for analytical workloads such as linear counting queries , multi-dimensional range  queries, and quantile queries, we can achieve significant gains in utility.
Revised:  We show that, under realistic relaxed LDP policies , for analytical workloads such as linear counting queries , multi-dimensional range counting queries, and quantile queries, we can achieve significant gains in utility.
Edit intent: fluency
Original:  We believe that the primitives we design for policy-aware LDP will be useful in developing mechanisms for other non-trivial analytical taskswith lower errors , and encourage the adoption of LDP in practice.
Revised:  We believe that the primitives we design for E-LDP will be useful in developing mechanisms for other non-trivial analytical taskswith lower errors , and encourage the adoption of LDP in practice.
Edit intent: clarity
Original:  We believe that the primitives we design for policy-aware LDP will be useful in developing mechanisms for other non-trivial analytical taskswith lower errors , and encourage the adoption of LDP in practice.
Revised:  We believe that the primitives we design for policy-aware LDP will be useful in developing mechanisms for other analytical tasks , and encourage the adoption of LDP in practice.
Edit intent: clarity
Original:  To conserve their limited flight range, drones can seamlessly hop between and ride on top of public transit vehicles (e.g., buses and trams).
Revised:  To conserve energy, drones hop between public transit vehicles (e.g., buses and trams).
Edit intent: clarity
Original:  We design a novel comprehensive algorithmic framework that strives to minimize the maximum time to complete any delivery.
Revised:  We design a comprehensive algorithmic framework that strives to minimize the maximum time to complete any delivery.
Edit intent: clarity
Original:  First, the upper layer assigns drones to package delivery sequences with a provably near-optimal polynomial-time task allocation algorithm.
Revised:  First, the upper layer assigns drones to package delivery sequences with a near-optimal polynomial-time task allocation algorithm.
Edit intent: clarity
Original:  We present extensive experiments supporting the efficiency of our approach on settings with up to 200 drones, 5000 packages, and large transit networks of up to 8000 stops in San Francisco and the Washington DCarea .
Revised:  Experiments demonstrate the efficiency of our approach on settings with up to 200 drones, 5000 packages, and large transit networks of up to 8000 stops in San Francisco and the Washington DCarea .
Edit intent: clarity
Original:  We present extensive experiments supporting the efficiency of our approach on settings with up to 200 drones, 5000 packages, and large transit networks of up to 8000 stops in San Francisco and the Washington DCarea .
Revised:  We present extensive experiments supporting the efficiency of our approach on settings with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DCarea .
Edit intent: clarity
Original:  We present extensive experiments supporting the efficiency of our approach on settings with up to 200 drones, 5000 packages, and large transit networks of up to 8000 stops in San Francisco and the Washington DCarea .
Revised:  We present extensive experiments supporting the efficiency of our approach on settings with up to 200 drones, 5000 packages, and large transit networks of up to 8000 stops in San Francisco and Washington DC .
Edit intent: clarity
Original:  Our results show that the framework can compute solutions within a few seconds (up to 2 minutes for the largest settings ) on commodity hardware, and that drones travel up to 450 \\%DIF < of their flight range by using public transit.abstract %DIF > of their flight range with 
Revised:  Our results show that the framework computes solutions within a few seconds (up to 2 minutes for the largest settings ) on commodity hardware, and that drones travel up to 450 \\%DIF < of their flight range by using public transit.abstract %DIF > of their flight range with 
Edit intent: clarity
Original:  Our results show that the framework can compute solutions within a few seconds (up to 2 minutes for the largest settings ) on commodity hardware, and that drones travel up to 450 \\%DIF < of their flight range by using public transit.abstract %DIF > of their flight range with 
Revised:  Our results show that the framework can compute solutions within a few seconds (up to 2 minutes for the largest settings ) on commodity hardware, and that drones travel up to 450 \\%DIF < of their flight range by using public transit.abstract %DIF > of their flight range with public transit.
Edit intent: fluency
Original:  Our results show that the framework computes solutions  within a few seconds (up to 2 minutes at most) on commodity hardware, and that drones travel up to 450 \\%DIF < of their flight range with public transit. %DIF > of their flight range with public transit.
Revised:  Our results show that the framework computes solutions within a few seconds on commodity hardware, and that drones travel up to 450 \\%DIF < of their flight range with public transit. %DIF > of their flight range with public transit.
Edit intent: clarity
Original:  Our results show that the framework computes solutions  within a few seconds (up to 2 minutes at most) on commodity hardware, and that drones travel up to 450 \\%DIF < of their flight range with public transit. %DIF > of their flight range with public transit.
Revised:  Our results show that the framework computes solutions within a few seconds (up to 2 minutes at most) on commodity hardware, and that drones travel up to 450 \\%DIF < of their flight range with %DIF > of their flight range with public transit.
Edit intent: fluency
Original: We consider the problem of controlling a large fleet of drones to deliver packages simultaneously across broad urban areas.
Revised: We consider the problem of routing a large fleet of drones to deliver packages simultaneously across broad urban areas.
Edit intent: clarity
Original:  First, the upper layer assigns drones to package delivery sequences with a near-optimal polynomial-time task allocation algorithm.
Revised:  First, the upper layer assigns drones to package delivery sequences with an approximately optimal polynomial time allocation algorithm.
Edit intent: clarity
Original:  Then, the lower layer executes the allocation by periodically routing the fleet over the transit network while employing efficientbounded-suboptimal multi-agent pathfinding techniques tailored to our setting.
Revised:  Then, the lower layer executes the allocation by periodically routing the fleet over the transit network , using efficient, bounded suboptimal multi-agent pathfinding techniques tailored to our setting.
Edit intent: clarity
Original:  Experiments demonstrate the efficiency of our approach on settings with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and Washington DC.
Revised:  We demonstrate the efficiency of our approach on settings with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and Washington DC.
Edit intent: clarity
Original:  Experiments demonstrate the efficiency of our approach on settings with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and Washington DC.
Revised:  Experiments demonstrate the efficiency of our approach on simulations with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and Washington DC.
Edit intent: clarity
Original:  Experiments demonstrate the efficiency of our approach on settings with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and Washington DC.
Revised:  Experiments demonstrate the efficiency of our approach on settings with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and Washington DC.
Edit intent: fluency
Original:  Our results show that the framework computes solutions typically within a few seconds on commodity hardware , and that drones travel up to 360%DIFDELCMD < \\%%% %DIF < of their flight range with public transit.
Revised:  Our results show that the framework computes solutions typically within a few seconds on commodity hardware %DIFDELCMD < \\%%% %DIF < of their flight range with public transit.
Edit intent: clarity
Original: We consider the problem of routing a large fleet of drones to deliver packages simultaneously across broad urban areas.
Revised: We consider the problem of controlling a large fleet of drones to deliver packages simultaneously across broad urban areas.
Edit intent: clarity
Original:  Besides flying directly, drones can use public transit vehicles (e.g., buses and trams) as temporary modes of transportation to conserve energy;
Revised:  To conserve energy, drones hop between public transit vehicles (e.g., buses and trams) as temporary modes of transportation to conserve energy;
Edit intent: clarity
Original:  We present a comprehensive algorithmic framework that strives to minimize the maximum time to complete any delivery and addresses the multifaceted computational challenges of our problem through a two-layer approach.
Revised:  We present a comprehensive algorithmic framework that strives to minimize the maximum time to complete any delivery . We address the multifaceted complexity of the problem through a two-layer approach.
Edit intent: clarity
Original:  First, the upper layer assigns drones to package delivery sequences with an approximately optimal polynomial time allocation algorithm.
Revised:  First, the upper layer assigns drones to package delivery sequences with a near-optimal polynomial-time task allocation algorithm.
Edit intent: clarity
Original:  Then, the lower layer executes the allocation by periodically routing the fleet over the transit network , using efficient , bounded suboptimal multi-agent pathfinding techniques tailored to our setting.
Revised:  Then, the lower layer executes the allocation by periodically routing the fleet over the transit network while employing efficient bounded-suboptimal multi-agent pathfinding techniques tailored to our setting.
Edit intent: clarity
Original:  We demonstrate the efficiency of our approach on simulations with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DCMetropolitan Area.
Revised:  Experiments demonstrate the efficiency of our approach on simulations with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DCMetropolitan Area.
Edit intent: clarity
Original:  We demonstrate the efficiency of our approach on simulations with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DCMetropolitan Area.
Revised:  We demonstrate the efficiency of our approach on settings with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DCMetropolitan Area.
Edit intent: clarity
Original:  We demonstrate the efficiency of our approach on simulations with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DCMetropolitan Area.
Revised:  We demonstrate the efficiency of our approach on simulations with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DCMetropolitan Area.
Edit intent: fluency
Original:  Our framework computes solutions for most settings within a few seconds on commodity hardware and enables drones to extend their effective range by a factor of nearly four using transit.
Revised:  Our framework computes solutions typically within a few seconds on commodity hardware and enables drones to extend their effective range by a factor of nearly four using transit.
Edit intent: clarity
Original:  Our framework computes solutions for most settings within a few seconds on commodity hardware and enables drones to extend their effective range by a factor of nearly four using transit.
Revised:  Our framework computes solutions for most settings within a few seconds on commodity hardware 
Edit intent: coherence
Original:  Traditional  Machine Learning (ML) approaches require the data to be centralized in a cloud server  .
Revised:  Traditional Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center .
Edit intent: clarity
Original:  Recently, in light of increasingly stringent data privacy legislation and growing privacy concerns, the concept of Federated Learning (FL) has been introduced.
Revised:  Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced.
Edit intent: fluency
Original:  Finally, we discuss the important challenges , open issues and future research directions in FL .
Revised:  Finally, we discuss the important challenges and future research directions in FL .
Edit intent: clarity
Original:  Finally, we discuss the important challenges , open issues and future research directions in FL .
Revised:  Finally, we discuss the important challenges , open issues and future research directions in FL 
Edit intent: fluency
Original:  We conclude by highlighting the broad range of exciting research opportunities for social neuroscientists who are interested in using network analysis to study social systems.
Revised:  We conclude by highlighting a broad range of exciting research opportunities for social neuroscientists who are interested in using network analysis to study social systems.
Edit intent: fluency
Original: Speech perception is a key to verbal communication.
Revised: Speech perception is key to verbal communication.
Edit intent: fluency
Original:  This study aimed to understand the improvement for vocoded speech intelligibility in cochlear implant (CI) simulation through two potential methods: Speech Enhancement (SE) and Audiovisual Integration.
Revised:  This study aimed to understand the improvement of vocoded speech intelligibility in cochlear implant (CI) simulation through two potential methods: Speech Enhancement (SE) and Audiovisual Integration.
Edit intent: fluency
Original:  Furthermore, the audiovisual integration is reported to supply better speech comprehension compared to audio-only information.
Revised:  Furthermore, audiovisual integration is reported to supply better speech comprehension compared to audio-only information.
Edit intent: fluency
Original:  Experimental results confirmed the effectiveness of the FCN-based  SE and audiovisual integration and positively recommended these two methods becoming a blended feature in a CI processor to increase the speech intelligibility for CI users under noisy conditions.
Revised:  Experimental results confirmed the effectiveness of the FCN-based SE and audiovisual integration and positively recommended these two methods could become a blended feature in a CI processor to increase the speech intelligibility for CI users under noisy conditions.
Edit intent: clarity
Original:  Experimental results confirmed the effectiveness of the FCN-based  SE and audiovisual integration and positively recommended these two methods becoming a blended feature in a CI processor to increase the speech intelligibility for CI users under noisy conditions.
Revised:  Experimental results confirmed the effectiveness of the FCN-based SE and audiovisual integration and positively recommended these two methods becoming a blended feature in a CI processor to improve the speech intelligibility for CI users under noisy conditions.
Edit intent: clarity
Original:  Experimental results on two public datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.
Revised:  Experimental results on two datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.
Edit intent: clarity
Original:  Under logarithmic source conditions (which have a natural interpretation in term of Sobolev spaces in the aforementioned context), concepts of qualifications as well as order optimal rates of convergence are presented.
Revised:  Under logarithmic source conditions (which have a natural interpretation in terms of Sobolev spaces in the aforementioned context), concepts of qualifications as well as order optimal rates of convergence are presented.
Edit intent: fluency
Original:  Finally, numerical experiments on three test problems strongly attest the superiority of the new method compared to the well known Tikhonov method in instances of exponentially ill-posed problems.
Revised:  Finally, numerical experiments on three test problems attest the better performance of the new method compared to the well known Tikhonov method in instances of exponentially ill-posed problems.
Edit intent: clarity
Original: Exploiting multi-antenna technologies for robust beamsteering to overcome the effects of blockage and beam misalignment is the key to providing seamless multi-Gbps connectivity in 5G-and-beyond mm-wave  networks.
Revised: Exploiting multi-antenna technologies for robust beamsteering to overcome the effects of blockage and beam misalignment is the key to providing seamless multi-Gbps connectivity in millimeter-wave ( mm-wave networks.
Edit intent: clarity
Original: Exploiting multi-antenna technologies for robust beamsteering to overcome the effects of blockage and beam misalignment is the key to providing seamless multi-Gbps connectivity in 5G-and-beyond mm-wave  networks.
Revised: Exploiting multi-antenna technologies for robust beamsteering to overcome the effects of blockage and beam misalignment is the key to providing seamless multi-Gbps connectivity in 5G-and-beyond mm-wave ) networks.
Edit intent: fluency
Original:  We systematically collect fine-grained 3D angle-of-arrival  and angle-of-departure  data, totaling over 50,000 received signal strength measurements.
Revised:  We systematically collect fine-grained 3D angle-of-arrival (AoA) and angle-of-departure data, totaling over 50,000 received signal strength measurements.
Edit intent: clarity
Original:  We systematically collect fine-grained 3D angle-of-arrival  and angle-of-departure  data, totaling over 50,000 received signal strength measurements.
Revised:  We systematically collect fine-grained 3D angle-of-arrival and angle-of-departure (AoD) data, totaling over 50,000 received signal strength measurements.
Edit intent: clarity
Original:  Our results show a limited number of 2-4 link opportunities per receiver location, indicating that the mm-wave multipath richness in a European town is surprisingly similar to that of dense urban metropolises.
Revised:  Our results show a limited number of 2--4 link opportunities per receiver location, indicating that the mm-wave multipath richness in a European town is surprisingly similar to that of dense urban metropolises.
Edit intent: fluency
Original:  By contrast, the loss for the horn antenna is monotonically increasing with the misalignment.
Revised:  By contrast, the loss for horn antennas is monotonically increasing with the misalignment.
Edit intent: fluency
Original: We strengthen ] }h_{f\left(x\right)}d\mu\geq C\cdotvar}\left(\right)\left(\left({\sumInf_{i}^{2}\left(f\right)}}\right)\right), where h_{f}}\left(\right) }\left(\right)  several classical inequalities concerning the influences of a Boolean function, showing that near-maximizers must have large vertex boundaries.
Revised: We strengthen ] }h_{f\left(x\right)}d\mu\geq C\cdotvar}\left(f\right)\left(\left({\sumInf_{i}^{2}\left(f\right)}}\right)\right), where h_{f}}\left(\right) }\left(\right) several classical inequalities concerning the influences of a Boolean function, showing that near-maximizers must have large vertex boundaries.
Edit intent: fluency
Original: We strengthen ] }h_{f\left(x\right)}d\mu\geq C\cdotvar}\left(\right)\left(\left({\sumInf_{i}^{2}\left(f\right)}}\right)\right), where h_{f}}\left(\right) }\left(\right)  several classical inequalities concerning the influences of a Boolean function, showing that near-maximizers must have large vertex boundaries.
Revised: We strengthen ] }h_{f\left(x\right)}d\mu\geq C\cdotvar}\left(\right)\left(\left({\sumInf_{i}^{2}\left(f\right)}}\right)\right), where h_{f}}\left(x\right) }\left(\right) several classical inequalities concerning the influences of a Boolean function, showing that near-maximizers must have large vertex boundaries.
Edit intent: fluency
Original: We strengthen ] }h_{f\left(x\right)}d\mu\geq C\cdotvar}\left(\right)\left(\left({\sumInf_{i}^{2}\left(f\right)}}\right)\right), where h_{f}}\left(\right) }\left(\right)  several classical inequalities concerning the influences of a Boolean function, showing that near-maximizers must have large vertex boundaries.
Revised: We strengthen ] }h_{f\left(x\right)}d\mu\geq C\cdotvar}\left(\right)\left(\left({\sumInf_{i}^{2}\left(f\right)}}\right)\right), where h_{f}}\left(\right) }\left(f\right) several classical inequalities concerning the influences of a Boolean function, showing that near-maximizers must have large vertex boundaries.
Edit intent: fluency
Original:  An inequality due to Talagrand states that for a Boolean function f, var\left(f\right)\leq Ci=1^{n}\mathrm{Inf_{i}\left(f\right)}{1+\log\left(1/\mathrm{Inf}_{i}\left(f\right)\right)} , where \mathrm{Inf_{i}}%DIFDELCMD < \left(%%% f%DIFDELCMD < \right) %%% denotes the influence of the i-th coordinate .
Revised:  An inequality due to Talagrand states that for a Boolean function f, var\left(f\right)\leq Ci=1^{n}\mathrm{Inf_{i}\left(f\right)}{1+\log\left(1/\mathrm{Inf}_{i}\left(f\right)\right)} _{i}}%DIFDELCMD < \left(%%% f%DIFDELCMD < \right) %%% denotes the influence of the i-th coordinate .
Edit intent: clarity
Original:  An inequality due to Talagrand states that for a Boolean function f, var\left(f\right)\leq Ci=1^{n}\mathrm{Inf_{i}\left(f\right)}{1+\log\left(1/\mathrm{Inf}_{i}\left(f\right)\right)} , where \mathrm{Inf_{i}}%DIFDELCMD < \left(%%% f%DIFDELCMD < \right) %%% denotes the influence of the i-th coordinate .
Revised:  An inequality due to Talagrand states that for a Boolean function f, var\left(f\right)\leq Ci=1^{n}\mathrm{Inf_{i}\left(f\right)}{1+\log\left(1/\mathrm{Inf}_{i}\left(f\right)\right)} , where \mathrm{Inf_{i}}%DIFDELCMD < \left(%%% %DIFDELCMD < \right) %%% denotes the influence of the i-th coordinate .
Edit intent: fluency
Original:  An inequality due to Talagrand states that for a Boolean function f, var\left(f\right)\leq Ci=1^{n}\mathrm{Inf_{i}\left(f\right)}{1+\log\left(1/\mathrm{Inf}_{i}\left(f\right)\right)} , where \mathrm{Inf_{i}}%DIFDELCMD < \left(%%% f%DIFDELCMD < \right) %%% denotes the influence of the i-th coordinate .
Revised:  An inequality due to Talagrand states that for a Boolean function f, var\left(f\right)\leq Ci=1^{n}\mathrm{Inf_{i}\left(f\right)}{1+\log\left(1/\mathrm{Inf}_{i}\left(f\right)\right)} , where \mathrm{Inf_{i}}%DIFDELCMD < \left(%%% f%DIFDELCMD < \right) %%% .
Edit intent: clarity