wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Moreover, we discussed the future perspective for RNA-Seq studies on bladder cancer and recommend the application of new technology called single cell sequencing (scRNA-Seq) to further understand bladder cancer . Keywords: Transcriptome profiling, RNA-sequencing, genomics, bioinformatics, bladder cancer
Revised:  Moreover, we discussed the future perspective for RNA-Seq studies on bladder cancer and recommend the application of new technology called single cell sequencing (scRNA-Seq) to further understand the disease . Keywords: Transcriptome profiling, RNA-sequencing, genomics, bioinformatics, bladder cancer
Edit intent: clarity
Original:  Finally, TR or interpolation being a building block of several algorithms, we show how the proposed estimators can be easily adapted to avoid expensive intermediate steps in well-known algorithms such as generalized semi-supervised learning, label propagation, Newton's method and iteratively reweighted least square .
Revised:  TR is a component in many well-known algorithms, and we show how the proposed estimators can be easily adapted to avoid expensive intermediate steps in well-known algorithms such as generalized semi-supervised learning, label propagation, Newton's method and iteratively reweighted least square .
Edit intent: clarity
Original:  Finally, TR or interpolation being a building block of several algorithms, we show how the proposed estimators can be easily adapted to avoid expensive intermediate steps in well-known algorithms such as generalized semi-supervised learning, label propagation, Newton's method and iteratively reweighted least square .
Revised:  Finally, TR or interpolation being a building block of several algorithms, we show how the proposed estimators can be easily adapted to avoid expensive intermediate steps in generalized semi-supervised learning, label propagation, Newton's method and iteratively reweighted least square .
Edit intent: clarity
Original:  Finally, TR or interpolation being a building block of several algorithms, we show how the proposed estimators can be easily adapted to avoid expensive intermediate steps in well-known algorithms such as generalized semi-supervised learning, label propagation, Newton's method and iteratively reweighted least square .
Revised:  Finally, TR or interpolation being a building block of several algorithms, we show how the proposed estimators can be easily adapted to avoid expensive intermediate steps in well-known algorithms such as generalized semi-supervised learning, label propagation, Newton's method and iteratively reweighted least squares .
Edit intent: fluency
Original:  We propose a self-supervised method for incrementally refining visual descriptors to improve performance in the task of object-level visual data association.
Revised: Object-level data association is central to robotic applications such as tracking-by-detection and object-level simultaneous localization and mapping. While current learned visual data association methods outperform hand-crafted algorithms, many rely on large collections of domain-specific training examples that can be difficult to obtain without prior knowledge. Additionally, such methods often remain fixed during inference-time and do not harness observed information to better their performance. We propose a self-supervised method for incrementally refining visual descriptors to improve performance in the task of object-level visual data association.
Edit intent: meaning-changed
Original:  Finally, we demonstrate a MOTA score of 21.25\% on the 2D-MOT-2015 dataset using visual information alone, outperforming methods that incorporate motion information.
Revised:  Finally, we demonstrate that our approach surpasses other visual data-association methods applied to a tracking-by-detection task, and show that it provides better performance-gains when compared to other methods that attempt to adapt to observed information.
Edit intent: clarity
Original:  Utilizing a learning-based mechanism, UniTrigger can generate a fixed phrase that  when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class.
Revised:  Utilizing a learning-based mechanism, UniTrigger generate a fixed phrase that when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class.
Edit intent: fluency
Original:  Utilizing a learning-based mechanism, UniTrigger can generate a fixed phrase that  when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class.
Revised:  Utilizing a learning-based mechanism, UniTrigger can generate a fixed phrase that , when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class.
Edit intent: fluency
Original:  To defend against this new attack method that may cause significant harm,  we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework.
Revised:  To defend against this new attack method that can cause significant harm, we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework.
Edit intent: coherence
Original:  To defend against this new attack method that may cause significant harm,  we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework.
Revised:  To defend against this new attack method that may cause significant harm, in this paper, we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework.
Edit intent: meaning-changed
Original:  Through comprehensive experiments across five public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1\% FPR in most cases, while showing a difference of only around 2\% of F1 score on average in predicting for clean inputs .
Revised:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1\% FPR in most cases, while showing a difference of only around 2\% of F1 score on average in predicting for clean inputs .
Edit intent: meaning-changed
Original:  Through comprehensive experiments across five public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1\% FPR in most cases, while showing a difference of only around 2\% of F1 score on average in predicting for clean inputs .
Revised:  Through comprehensive experiments across five public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1\% FPR in most cases, while losing the prediction accuracy (in F1 score on average in predicting for clean inputs .
Edit intent: clarity
Original:  Through comprehensive experiments across five public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1\% FPR in most cases, while showing a difference of only around 2\% of F1 score on average in predicting for clean inputs .
Revised:  Through comprehensive experiments across five public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1\% FPR in most cases, while showing a difference of only around 2\% of F1 ) for clean inputs .
Edit intent: clarity
Original:  Through comprehensive experiments across five public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1\% FPR in most cases, while showing a difference of only around 2\% of F1 score on average in predicting for clean inputs . We also show that DARCY with multiple trapdoors is  robust under different assumptions with respect to attackers' knowledge and skills.
Revised:  Through comprehensive experiments across five public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1\% FPR in most cases, while showing a difference of only around 2\% of F1 score on average in predicting for clean inputs by less than 1\% on average. We finally show that DARCY with multiple trapdoors is robust under different assumptions with respect to attackers' knowledge and skills.
Edit intent: clarity
Original:  Utilizing a learning-based mechanism, UniTrigger generate a fixed phrase that, when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class.
Revised:  Utilizing a learning-based mechanism, UniTrigger generates a fixed phrase that, when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class.
Edit intent: fluency
Original:  To defend against this new attack method that can cause significant harm, in this paper, we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework . DARCY adaptively searches and injects multiple trapdoors into an NN model to "bait and catch" potential attacks.
Revised:  To defend against this attack that can cause significant harm, in this paper, we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework . DARCY adaptively searches and injects multiple trapdoors into an NN model to "bait and catch" potential attacks.
Edit intent: clarity
Original:  To defend against this new attack method that can cause significant harm, in this paper, we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework . DARCY adaptively searches and injects multiple trapdoors into an NN model to "bait and catch" potential attacks.
Revised:  To defend against this new attack method that can cause significant harm, in this paper, we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework against UniTrigger. DARCY greedily searches and injects multiple trapdoors into an NN model to "bait and catch" potential attacks.
Edit intent: meaning-changed
Original:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1 \% FPR in most cases, while losing the prediction accuracy (in F1) for clean inputs by less than 1\% on average.
Revised:  Through comprehensive experiments across four public datasets, we show that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1 \% FPR in most cases, while losing the prediction accuracy (in F1) for clean inputs by less than 1\% on average.
Edit intent: clarity
Original:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1 \% FPR in most cases, while losing the prediction accuracy (in F1) for clean inputs by less than 1\% on average.
Revised:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 2 \% FPR in most cases, while losing the prediction accuracy (in F1) for clean inputs by less than 1\% on average.
Edit intent: meaning-changed
Original:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1 \% FPR in most cases, while losing the prediction accuracy (in F1) for clean inputs by less than 1\% on average.
Revised:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1 \% FPR in most cases, while maintaining the prediction accuracy (in F1) for clean inputs by less than 1\% on average.
Edit intent: clarity
Original:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1 \% FPR in most cases, while losing the prediction accuracy (in F1) for clean inputs by less than 1\% on average.
Revised:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1 \% FPR in most cases, while losing the prediction accuracy (in F1) for clean inputs within a 1\% on average.
Edit intent: clarity
Original:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1 \% FPR in most cases, while losing the prediction accuracy (in F1) for clean inputs by less than 1\% on average. We finally show that DARCY with multiple trapdoors is also robust under different assumptions concerning attackers' knowledge and skills  .
Revised:  Through comprehensive experiments across four public datasets, we demonstrate that DARCY detects UniTrigger's adversarial attacks with up to 99\% TPR and less than 1 \% FPR in most cases, while losing the prediction accuracy (in F1) for clean inputs by less than 1\% margin. We also demonstrate that DARCY with multiple trapdoors is also robust under different assumptions concerning attackers' knowledge and skills .
Edit intent: clarity
Original: When should restrictive measures be taken to contain the COVID-19 pandemic explosion? An important role for interpreting the pandemic data in a fully rational manner, and thus proactively supporting the political decision makers, is played by signal processing tools. In this paper, we exploit quickest-detection theory applied to pandemic time-series to predict the explosion of COVID-19 infection on a large scale.
Revised: This paper develops an easily-implementable version of Page's CUSUM quickest-detection theory applied to pandemic time-series to predict the explosion of COVID-19 infection on a large scale.
Edit intent: meaning-changed
Original:  In this paper, we exploit quickest-detection theory applied to pandemic time-series to predict the explosion of COVID-19 infection on a large scale. The focus of the work is to develop a version of the celebrated Page's CUSUM test, specifically tailored to pandemic data. Its application to the publicly-available sequence of new positive individuals per day from different countries is briefly illustrated .
Revised:  In this paper, we exploit quickest-detection test, designed to work in certain composite hypothesis scenarios with time-varying data statistics. The decision statistic can be cast in a recursive form and is particularly suited for on-line analysis. By back-testing our approach on publicly-available sequence of new positive individuals per day from different countries is briefly illustrated .
Edit intent: meaning-changed
Original:  As a case study, we compare these two types of models for COVID-19 forecasting and notice that physics-based  models significantly outperform deep learning models.
Revised:  We first formalize the learning of physics-based models as AutoODE, which leverages automatic differentiation to estimate the model parameters. Through a benchmark study on COVID-19 forecasting and notice that physics-based models significantly outperform deep learning models.
Edit intent: meaning-changed
Original:  As a case study, we compare these two types of models for COVID-19 forecasting and notice that physics-based  models significantly outperform deep learning models. We present a hybrid approach, AutoODE-COVID, which combines a novel compartmental model with automatic differentiation.
Revised:  As a case study, we compare these two types of models for COVID-19 forecasting and notice that physics-based models significantly outperform deep learning .
Edit intent: coherence
Original: We present a Symmetry-based abstraction refinement algorithm SymAR that is directed towards safety verification of large-scale scenarios with complex dynamical systems. The abstraction maps modes with symmetric dynamics to a single abstract mode and refinements recursively split the modes when safety checks fail. We show how symmetry abstractionscan be applied effectively to closed-loop control systems, including non-symmetric deep neural network (DNN) controllers. For such controllers, we transform their inputs and outputs to enforce symmetry and make the closed loop system amenable for abstraction. We implemented SymAR in Python and used it to verify paths with 100s of segments in 2D and 3D scenarios followed by a six dimensional DNN-controlled quadrotor, and also a ground vehicle.
Revised: We presentSceneChecker, a tool for verifying scenarios involving vehicles executing complex plans in large cluttered workspaces. SceneChecker converts the scenario verification problem to a standard hybrid system verification problem, and solves it effectively by exploiting structural properties in the plan and the vehicle dynamics. SceneChecker uses symmetry abstractions, a novel refinement algorithm, and also a ground vehicle.
Edit intent: meaning-changed
Original:  We implemented SymAR in Python and used it to verify paths with 100s of segments in 2D and 3D scenarios followed by a six dimensional DNN-controlled quadrotor, and also a ground vehicle. Our experiments show significant savings, up to 10x in some cases, in verification time over existing methods .
Revised:  We implemented SymAR in Python and used it to verify paths with 100s of segments in 2D and 3D scenarios followed by a six dimensional DNN-controlled quadrotor, and importantly, is built to boost the performance of any existing reachability analysis tool as a plug-in subroutine. We evaluated SceneChecker on several scenarios involving ground and aerial vehicles with nonlinear dynamics and neural network controllers, employing different kinds of symmetries, using different reachability subroutines, and following plans with hundreds of way-points in complex workspaces. Compared to two leading tools, DryVR and Flow*, SceneChecker shows 20x speedup in verification time over existing methods .
Edit intent: meaning-changed
Original:  More precisely, we present a randomized algorithm that performs each operation in time \tilde O(n^{ 4 / 5 }) and after each update, reports the answer to the LIS problem correctly with high probability.
Revised:  More precisely, we present a randomized algorithm that performs each operation in time \tilde O(n^{ 2 / 5 }) and after each update, reports the answer to the LIS problem correctly with high probability.
Edit intent: meaning-changed
Original:  More precisely, we present a randomized algorithm that performs each operation in time \tilde O(n^{ 4 / 5 }) and after each update, reports the answer to the LIS problem correctly with high probability.
Revised:  More precisely, we present a randomized algorithm that performs each operation in time \tilde O(n^{ 4 / 3 }) and after each update, reports the answer to the LIS problem correctly with high probability.
Edit intent: meaning-changed
Original:  In this paper, we present HALO 1.0, an open-ended extensible multi-agent software framework , that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles and a novel compute-centric message passing interface (C^2MPI) specification for enabling the portable and performance-optimized execution of hardware-agnostic application  codes across heterogeneous accelerator resources.
Revised:  In this paper, we present HALO 1.0, an open-ended extensible multi-agent software framework that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles and a novel compute-centric message passing interface (C^2MPI) specification for enabling the portable and performance-optimized execution of hardware-agnostic application codes across heterogeneous accelerator resources.
Edit intent: fluency
Original:  In this paper, we present HALO 1.0, an open-ended extensible multi-agent software framework , that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles and a novel compute-centric message passing interface (C^2MPI) specification for enabling the portable and performance-optimized execution of hardware-agnostic application  codes across heterogeneous accelerator resources.
Revised:  In this paper, we present HALO 1.0, an open-ended extensible multi-agent software framework , that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles and a novel compute-centric message passing interface (C^2MPI) specification for enabling the portable and performance-optimized execution of hardware-agnostic application host codes across heterogeneous accelerator resources.
Edit intent: meaning-changed
Original:  Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows the same hardware-agnostic application codes of the HPC kernels, without any change, to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score  .
Revised:  Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for the host program to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score .
Edit intent: clarity
Original:  Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows the same hardware-agnostic application codes of the HPC kernels, without any change, to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score  .
Revised:  Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows the same hardware-agnostic application codes of the HPC kernels, without any change, to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score . of the documentation of their work .
Edit intent: meaning-changed
Original:  In this way, we fuse the structural features and visual features in the consistent bird-eye view frame, yielding a semantic feature representationwith sensible geometry , namely CORAL.  Comparisons on the Oxford RobotCar show that CORAL has superior performance against other state-of-the-art methods.
Revised:  In this way, we fuse the structural features and visual features in the consistent bird-eye view frame, yielding a semantic representation , namely CORAL. Comparisons on the Oxford RobotCar show that CORAL has superior performance against other state-of-the-art methods.
Edit intent: clarity
Original:  In this way, we fuse the structural features and visual features in the consistent bird-eye view frame, yielding a semantic feature representationwith sensible geometry , namely CORAL.  Comparisons on the Oxford RobotCar show that CORAL has superior performance against other state-of-the-art methods.
Revised:  In this way, we fuse the structural features and visual features in the consistent bird-eye view frame, yielding a semantic feature representationwith sensible geometry , namely CORAL. And the whole network is called CORAL-VLAD. Comparisons on the Oxford RobotCar show that CORAL has superior performance against other state-of-the-art methods.
Edit intent: meaning-changed