wanyu/IteraTeR_v2 huggingface dataset:
Original:  We investigate two techniques for generating the template directions.
Revised:  To this end, we investigate two techniques for generating the template directions.
Edit intent: clarity
Original:  The second technique uses Principle Component Analysis (PCA) of sample trajectories for generating templates.
Revised:  The second technique uses Principal Component Analysis (PCA) of sample trajectories for generating templates.
Edit intent: fluency
Original:  We have implemented our approach in a Python based tool called Kaa and improve its performance by two main enhancements.
Revised:  We have implemented our approach in a Python-based tool called Kaa and improve its performance by two main enhancements.
Edit intent: fluency
Original: There exist several ways of integrating unmanned aerial vehicles (UAVs)  into wireless sensor networks (WSNs) for smart city applications . Among the others , a UAV can be employed as a relay in a "store-carry and forward" fashion by uploading data from ground sensors and meters and, then, downloading it to a central unit.
Revised: Unmanned aerial vehicles (UAVs) into wireless sensor networks (WSNs) for smart city applications . Among the others , a UAV can be employed as a relay in a "store-carry and forward" fashion by uploading data from ground sensors and meters and, then, downloading it to a central unit.
Edit intent: clarity
Original: There exist several ways of integrating unmanned aerial vehicles (UAVs)  into wireless sensor networks (WSNs) for smart city applications . Among the others , a UAV can be employed as a relay in a "store-carry and forward" fashion by uploading data from ground sensors and meters and, then, downloading it to a central unit.
Revised: There exist several ways of integrating unmanned aerial vehicles (UAVs) into wireless sensor networks (WSNs) for smart city applications . Among the others , a UAV can be employed as a relay in a "store-carry and forward" fashion by uploading data from ground sensors and metering devices and, then, downloading it to a central unit.
Edit intent: clarity
Original:  As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when  the jammer is mobile, too, e.g., it is mounted on an UAV or inside a terrestrial vehicle.
Revised:  As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when also the jammer is mobile, too, e.g., it is mounted on an UAV or inside a terrestrial vehicle.
Edit intent: clarity
Original:  As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when  the jammer is mobile, too, e.g., it is mounted on an UAV or inside a terrestrial vehicle.
Revised:  As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when the jammer is mobile, e.g., it is mounted on an UAV or inside a terrestrial vehicle.
Edit intent: coherence
Original:  As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when  the jammer is mobile, too, e.g., it is mounted on an UAV or inside a terrestrial vehicle.
Revised:  As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when the jammer is mobile, too, e.g., it is mounted on a UAV or inside a terrestrial vehicle.
Edit intent: fluency
Original:  In this paper, we investigate anti-jamming communications in UAV-aided WSNs operating over doubly-selective channels  .
Revised:  In this paper, we investigate anti-jamming communications for UAV-aided WSNs operating over doubly-selective channels .
Edit intent: style
Original:  In such a scenario, the signals transmitted by the legitimate transmitters (sensors and meters in the uploading phase or the UAV in the downloading phase) and the malicious mobile jammer undergo both time dispersion due to multipath propagation effects and frequency dispersion caused by Doppler shifts .
Revised:  In such a scenario, the signals transmitted by the UAV and the malicious mobile jammer undergo both time dispersion due to multipath propagation effects and frequency dispersion caused by Doppler shifts .
Edit intent: clarity
Original:  In such a scenario, the signals transmitted by the legitimate transmitters (sensors and meters in the uploading phase or the UAV in the downloading phase) and the malicious mobile jammer undergo both time dispersion due to multipath propagation effects and frequency dispersion caused by Doppler shifts .
Revised:  In such a scenario, the signals transmitted by the legitimate transmitters (sensors and meters in the uploading phase or the UAV in the downloading phase) and the malicious mobile jammer undergo both time dispersion due to multipath propagation effects and frequency dispersion caused by their mobility .
Edit intent: clarity
Original:  Such parameters are estimated from data through the use of algorithms exploiting the almost-cyclostationarity properties of the received signal  . Simulation results corroborate the antijamming capabilities of the proposed method, for different mobility scenario of the jammer.
Revised:  Such parameters are estimated from data through the use of algorithms that exploit the almost-cyclostationarity properties of the received signal . Simulation results corroborate the antijamming capabilities of the proposed method, for different mobility scenario of the jammer.
Edit intent: clarity
Original:  Such parameters are estimated from data through the use of algorithms exploiting the almost-cyclostationarity properties of the received signal  . Simulation results corroborate the antijamming capabilities of the proposed method, for different mobility scenario of the jammer.
Revised:  Such parameters are estimated from data through the use of algorithms exploiting the almost-cyclostationarity properties of the received signal . Simulation results corroborate the anti-jamming capabilities of the proposed method, for different mobility scenario of the jammer.
Edit intent: fluency
Original:  Such parameters are estimated from data through the use of algorithms exploiting the almost-cyclostationarity properties of the received signal  . Simulation results corroborate the antijamming capabilities of the proposed method, for different mobility scenario of the jammer.
Revised:  Such parameters are estimated from data through the use of algorithms exploiting the almost-cyclostationarity properties of the received signal . Simulation results corroborate the antijamming capabilities of the proposed method, for different mobility scenarios of the jammer.
Edit intent: fluency
Original:  The main idea of the framework is to reformulate the underlying problem into another problem with a parameter-independent diffusion coefficient and a parameter-dependent (and solution-dependent) right-hand side, a fixed-point iteration is then employed to compute the solution of the reformulated problem.
Revised:  The main idea of the framework is to reformulate the underlying problem into another problem with parameter-independent diffusion coefficient and a parameter-dependent (and solution-dependent) right-hand side, a fixed-point iteration is then employed to compute the solution of the reformulated problem.
Edit intent: fluency
Original:  High-resolution haptic sensors are still not widely available, and their application is often bound by robustness issues .
Revised:  High-resolution haptic sensors are still not widely available, and their application is often bound by the resolution-robustness dilemma .
Edit intent: clarity
Original:  A route towards high-resolution and robust sensors is to embed a few sensor units (taxels) into a flexible surface material and use signal processing to achieve sensing with super-resolution accuracy.
Revised:  A route towards high-resolution and robust skin embeds a few sensor units (taxels) into a flexible surface material and use signal processing to achieve sensing with super-resolution accuracy.
Edit intent: clarity
Original:  A route towards high-resolution and robust sensors is to embed a few sensor units (taxels) into a flexible surface material and use signal processing to achieve sensing with super-resolution accuracy.
Revised:  A route towards high-resolution and robust sensors is to embed a few sensor units (taxels) into a flexible surface material and uses signal processing to achieve sensing with super-resolution accuracy.
Edit intent: fluency
Original:  This theory is based on sensor isolines and allows us to predict force sensitivity and accuracy in force magnitude and contact position as a spatial quantity.
Revised:  This theory is based on sensor isolines and allows us to predict force sensitivity and accuracy in contact position and force magnitude as a spatial quantity.
Edit intent: fluency
Original:  We evaluate the influence of different factors, such as the elastic properties of the material,  using finite element simulations .
Revised:  We evaluate the influence of different factors, such as elastic properties of the material, using finite element simulations .
Edit intent: fluency
Original:  In particular, deep reinforcement learning (DRL) has recently been employed to train DNNs that act as control policies for various types of real-world systems.
Revised:  In particular, deep reinforcement learning (DRL) has recently been employed to train DNNs that realize control policies for various types of real-world systems.
Edit intent: clarity
Original:  In this work, we present the whiRL 2.0 tool, which implements a new approach for verifying complex properties of interest for such DRL systems.
Revised:  In this work, we present the whiRL 2.0 tool, which implements a new approach for verifying complex properties of interest for DRL systems.
Edit intent: clarity
Original:  We propose techniques for performing k-induction and automated invariant inference on such systems, and use these techniques for proving safety and liveness properties of interest that were previously impossible to verify due to the scalability barriers of prior approaches.
Revised:  We propose techniques for performing k-induction and semi-automated invariant inference on such systems, and use these techniques for proving safety and liveness properties of interest that were previously impossible to verify due to the scalability barriers of prior approaches.
Edit intent: clarity
Original:  We propose techniques for performing k-induction and automated invariant inference on such systems, and use these techniques for proving safety and liveness properties of interest that were previously impossible to verify due to the scalability barriers of prior approaches.
Revised:  We propose techniques for performing k-induction and automated invariant inference on such systems, and leverage these techniques for proving safety and liveness properties of interest that were previously impossible to verify due to the scalability barriers of prior approaches.
Edit intent: clarity
Original:  We propose techniques for performing k-induction and automated invariant inference on such systems, and use these techniques for proving safety and liveness properties of interest that were previously impossible to verify due to the scalability barriers of prior approaches.
Revised:  We propose techniques for performing k-induction and automated invariant inference on such systems, and use these techniques for proving safety and liveness properties that were previously impossible to verify due to the scalability barriers of prior approaches.
Edit intent: clarity
Original: Background  The learning of genotype-phenotype associations and history of human disease by doing detailed and precise analysis of phenotypic abnormalities can be defined as deep phenotyping.
Revised: Background : The learning of genotype-phenotype associations and history of human disease by doing detailed and precise analysis of phenotypic abnormalities can be defined as deep phenotyping.
Edit intent: fluency
Original:  Methods  In this study, we developed a framework to predict links between human phenotype ontology (HPO) and genes.
Revised:  Methods : In this study, we developed a framework to predict links between human phenotype ontology (HPO) and genes.
Edit intent: fluency
Original: A common approach to solve a combinatorial optimization problem is to first solve a continous relaxation and then round the  fractional solution.
Revised: A common approach to tackle a combinatorial optimization problem is to first solve a continous relaxation and then round the fractional solution.
Edit intent: fluency
Original: A common approach to solve a combinatorial optimization problem is to first solve a continous relaxation and then round the  fractional solution.
Revised: A common approach to solve a combinatorial optimization problem is to first solve a continuous relaxation and then round the fractional solution.
Edit intent: fluency
Original: A common approach to solve a combinatorial optimization problem is to first solve a continous relaxation and then round the  fractional solution.
Revised: A common approach to solve a combinatorial optimization problem is to first solve a continous relaxation and then round the obtained fractional solution.
Edit intent: clarity
Original:  For the latter, the framework of contention resolution schemes (or CR schemes)  introduced by Chekuri, Vondrak, and Zenklusen, has become a general and successful tool.
Revised:  For the latter, the framework of contention resolution schemes (or CR schemes) , introduced by Chekuri, Vondrak, and Zenklusen, has become a general and successful tool.
Edit intent: fluency
Original:  For the latter, the framework of contention resolution schemes (or CR schemes)  introduced by Chekuri, Vondrak, and Zenklusen, has become a general and successful tool.
Revised:  For the latter, the framework of contention resolution schemes (or CR schemes) introduced by Chekuri, Vondrak, and Zenklusen, is a general and successful tool.
Edit intent: clarity
Original:  In this work, we provide a simple and explicit monotone CR scheme with a balancedness factor of 1 - e\left({n}}\right) ^{ -k } k^k/k! for uniform matroids of rank k (which matches the balancedness of 1-1/e for k=1)\left({n}}\right) , and show that this is optimal.
Revised:  In this work, we provide a simple and explicit monotone CR scheme with a balancedness of 1 - e\left({n}}\right) ^{ -k } k^k/k! for uniform matroids of rank k (which matches the balancedness of 1-1/e for k=1)\left({n}}\right) , and show that this is optimal.
Edit intent: clarity
Original:  In this work, we provide a simple and explicit monotone CR scheme with a balancedness factor of 1 - e\left({n}}\right) ^{ -k } k^k/k! for uniform matroids of rank k (which matches the balancedness of 1-1/e for k=1)\left({n}}\right) , and show that this is optimal.
Revised:  In this work, we provide a simple and explicit monotone CR scheme with a balancedness factor of 1 - \:\left({n}}\right) ^{ -k } k^k/k! for uniform matroids of rank k (which matches the balancedness of 1-1/e for k=1)\left({n}}\right) , and show that this is optimal.
Edit intent: clarity
Original:  In this work, we provide a simple and explicit monotone CR scheme with a balancedness factor of 1 - e\left({n}}\right) ^{ -k } k^k/k! for uniform matroids of rank k (which matches the balancedness of 1-1/e for k=1)\left({n}}\right) , and show that this is optimal.
Revised:  In this work, we provide a simple and explicit monotone CR scheme with a balancedness factor of 1 - e\left({n}}\right) ^{ -k } k^k/k! for uniform matroids of rank k (which matches the balancedness of 1-1/e for k=1)\left({n}}\right)^k , and show that this is optimal.
Edit intent: fluency
Original:  While this bound can be obtained by combining previously known results, these require defining an exponential-sized linear program and using random sampling and the ellipsoid algorithm.
Revised:  While this bound can be obtained by combining previously known results, these require defining an exponential-sized linear program , as well as using random sampling and the ellipsoid algorithm.
Edit intent: coherence
Original: Path planning in uncertain environments is a key enabler of true vehicle autonomy.
Revised: Path planning in an uncertain environment is a key enabler of true vehicle autonomy.
Edit intent: fluency
Original:  An important capability of such planning is the prediction of vehicle dispersion covariances about candidate paths .
Revised:  An important capability of such planning is the prediction of vehicle dispersion covariances about a candidate path .
Edit intent: fluency
Original:  This work develops a new closed-loop linear covariance (CL-LinCov) framework applicable to  wide range of autonomous system architectures.
Revised:  This work develops a new closed-loop linear covariance (CL-LinCov) framework applicable to a wide range of autonomous system architectures.
Edit intent: fluency
Original:  Extensions to current CL-LinCov frameworks are made to accommodate 1) the cascaded architecture typical of autonomous vehicles and 2) the dual-use of continuous sensor information for both navigation and control.
Revised:  Important features of the developed framework include the ( 1) the cascaded architecture typical of autonomous vehicles and 2) the dual-use of continuous sensor information for both navigation and control.
Edit intent: clarity
Original:  Extensions to current CL-LinCov frameworks are made to accommodate 1) the cascaded architecture typical of autonomous vehicles and 2) the dual-use of continuous sensor information for both navigation and control.
Revised:  Extensions to current CL-LinCov frameworks are made to accommodate 1) separation of high-level guidance from low-level control, ( 2) the dual-use of continuous sensor information for both navigation and control.
Edit intent: clarity
Original:  The utility of the CL-LinCov information is illustrated by its application to path planning in an uncertain obstacle field via a modified version of the rapidly exploring random tree algorithm.
Revised:  The utility of the CL-LinCov information is illustrated by its application to path planning in an uncertain obstacle field via a modified version of the Rapidly Exploring Random Tree algorithm.
Edit intent: fluency
Original:  We also show that the minimal amount of memory required to solve games using a given Muller condition is exactly the size of a minimal Rabin automaton recognising this condition , if we only allow chromatic memories.
Revised:  We also show that the minimal amount of chromatic memory required in games using a given Muller condition is exactly the size of a minimal Rabin automaton recognising this condition , if we only allow chromatic memories.
Edit intent: clarity
Original:  We also provide a counter-example to a conjecture by Kopczy\'nski : we require more memory if we only consider chromatic memory structures .
Revised:  We also provide a counter-example to a conjecture by Kopczy\'nski .
Edit intent: coherence
Original: It is known that deep neural networks, trained for the classification of a non-sensitive target attribute, can reveal  sensitive attributes of their input data;
Revised: It is known that deep neural networks, trained for the classification of a non-sensitive target attribute, can reveal some sensitive attributes of their input data;
Edit intent: clarity
Original:  We , taking a step forward , show that deep classifiers can be trained to secretly encode a sensitive attribute of users' input data , at inference time, into the classifier's outputs for the target attribute .
Revised:  We take a step forward , show that deep classifiers can be trained to secretly encode a sensitive attribute of users' input data , at inference time, into the classifier's outputs for the target attribute .
Edit intent: fluency
Original:  We , taking a step forward , show that deep classifiers can be trained to secretly encode a sensitive attribute of users' input data , at inference time, into the classifier's outputs for the target attribute .
Revised:  We , taking a step forward and show that deep classifiers can be trained to secretly encode a sensitive attribute of users' input data , at inference time, into the classifier's outputs for the target attribute .
Edit intent: coherence
Original:  We , taking a step forward , show that deep classifiers can be trained to secretly encode a sensitive attribute of users' input data , at inference time, into the classifier's outputs for the target attribute .
Revised:  We , taking a step forward , show that deep classifiers can be trained to secretly encode a sensitive attribute of users' input data into the classifier's outputs for the target attribute .
Edit intent: clarity
Original:  An attack that works even if users have a  white-box view of the classifier, and can keep all internal representations hidden except for the classifier's estimation of the target attribute.
Revised:  An attack that works even if users have a white-box view of the classifier, and can keep all internal representations hidden except for the classifier's outputs for the target attribute.
Edit intent: clarity
Original:  We introduce an information-theoretical formulation of such adversaries and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation: deep models that can be accurate in predicting the target attribute, but also can utilize their outputs to secretly encode a sensitive attribute.
Revised:  We introduce an information-theoretical formulation of such attacks and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation: deep models that can be accurate in predicting the target attribute, but also can utilize their outputs to secretly encode a sensitive attribute.
Edit intent: clarity
Original:  We introduce an information-theoretical formulation of such adversaries and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation: deep models that can be accurate in predicting the target attribute, but also can utilize their outputs to secretly encode a sensitive attribute.
Revised:  We introduce an information-theoretical formulation of such adversaries and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation: classifiers that can be accurate in predicting the target attribute, but also can utilize their outputs to secretly encode a sensitive attribute.
Edit intent: clarity
Original:  We introduce an information-theoretical formulation of such adversaries and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation: deep models that can be accurate in predicting the target attribute, but also can utilize their outputs to secretly encode a sensitive attribute.
Revised:  We introduce an information-theoretical formulation of such adversaries and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation: deep models that can be accurate in predicting their target attribute, but also can utilize their outputs to secretly encode a sensitive attribute.
Edit intent: fluency