wanyu/IteraTeR_full_sent huggingface dataset:
Original:  we explain why this is related to Wasserstein distance by establishing a purely spectral bound on the Wasserstein distance on graphs that mirrors R. Peyr\'e 's estimate in the continuous setting.
Revised:  we explain why this is related to Wasserstein distance by establishing a purely spectral bound on the Wasserstein distance on graphs that mirrors R. Peyre 's estimate in the continuous setting.
Edit intent: fluency
Original:  Existing approaches to detect malicious campaigns make strict assumptions about coordinated behaviours, such as malicious accounts perform synchronized actions or share features assumed to be indicative of coordination. Others require part of the malicious accounts in the campaign to be revealed in order to detect the rest.
Revised:  Existing approaches to detect malicious campaigns make strict assumptions about coordinated behaviours, or require part of the malicious accounts in the campaign to be revealed in order to detect the rest.
Edit intent: clarity
Original:  Such assumptions significantly limit the effectiveness of existing approaches. In contrast , we propose AMDN (Attentive Mixture Density Network ) to automatically uncover coordinated group behaviours from account activities and interactions between accounts, based on temporal point processes.
Revised:  To address these drawbacks , we propose AMDN (Attentive Mixture Density Network ) to automatically uncover coordinated group behaviours from account activities and interactions between accounts, based on temporal point processes.
Edit intent: clarity
Original:  In contrast , we propose AMDN (Attentive Mixture Density Network ) to automatically uncover coordinated group behaviours from account activities and interactions between accounts, based on temporal point processes.
Revised:  In contrast , we propose a generative model, AMDN-HAGE (Attentive Mixture Density Network ) to automatically uncover coordinated group behaviours from account activities and interactions between accounts, based on temporal point processes.
Edit intent: meaning-changed
Original:  In contrast , we propose AMDN (Attentive Mixture Density Network ) to automatically uncover coordinated group behaviours from account activities and interactions between accounts, based on temporal point processes. Furthermore, we leverage the learned model to understand and explain the behaviours of coordinated accounts in disinformation campaigns. We find that the average influence between coordinated accountsis the highest, whereas these accounts are not much influenced by regular accounts. We evaluate the effectiveness of the proposed method on Twitter data related to Russian interference in US Elections.
Revised:  In contrast , we propose AMDN (Attentive Mixture Density Network ) to automatically uncover coordinated group behaviours from account activities and hidden group behaviours based on Temporal Point Processes (TPP) and Gaussian Mixture Model (GMM), to capture inherent characteristics of coordination which is, accounts that coordinate must strongly influence each other's activities, and collectively appear anomalous from normal accounts. To address the challenges of optimizing the proposed model, we provide a bilevel optimization algorithm with theoretical guarantee on convergence. We verified the effectiveness of the proposed method on Twitter data related to Russian interference in US Elections.
Edit intent: meaning-changed
Original:  We evaluate the effectiveness of the proposed method on Twitter data related to Russian interference in US Elections.Additionally, we identify disinformation campaigns in COVID-19 data collected from Twitter, and provide the first evidence and analysis of existence of coordinated disinformation campaigns in the ongoing pandemic .
Revised:  We evaluate the effectiveness of the proposed method and training algorithm on real-world social network data collected from Twitter related to coordinated campaigns from Russia's Internet Research Agency targeting the 2016 U.S. Presidential Elections, and to identify coordinated campaigns related to the COVID-19 data collected from Twitter, and provide the first evidence and analysis of existence of coordinated disinformation campaigns in the ongoing pandemic .
Edit intent: meaning-changed
Original: Additionally, we identify disinformation campaigns in COVID-19 data collected from Twitter, and provide the first evidence and analysis of existence of coordinated disinformation campaigns in the ongoing pandemic .
Revised: Additionally, we identify disinformation campaigns in COVID-19 pandemic. Leveraging the learned model, we find that the average influence between coordinated account pairs is the highest.On COVID-19, we found coordinated group spreading anti-vaccination, anti-masks conspiracies that suggest the pandemic is a hoax and political scam .
Edit intent: meaning-changed
Original:  This kernel exhibits computational characteristics that are commonly seen in many high-performance computing (HPC) applications, and are expected to be very helpful to a general audience of HPC developers and computational scientists, as they pursue more performance on NVIDIA GPUs.abstract 
Revised:  This kernel exhibits computational characteristics that are commonly seen in many high-performance computing (HPC) applications, and are expected to be very helpful to a general audience of HPC developers and computational scientists, as they pursue more performance on NVIDIA GPUs.abstract \% of the theoretical peak, 6.7 TFLOP/s, at nominal frequency 1312 MHz, and 70\% of the more customized peak based on our 58\% FMA ratio, 5.3 TFLOP/s. An array of techniques used to analyze this OpenACC kernel and optimize its performance are shown, including the use of hierarchical Roofline performance model and the performance tool Nsight Compute. This kernel exhibits computational characteristics that are commonly seen in many high-performance computing (HPC) applications, and are expected to be very helpful to a general audience of HPC developers and computational scientists, as they pursue more performance on NVIDIA GPUs.
Edit intent: meaning-changed
Original: In this paper, we address computation of the degree \rm deg Det A of Dieudonn\'e determinant \rm Det A of \[ A = k=1^m A_k x_k t^{c_k}, \] where A_k are n \times n matrices over a field K, x_k are noncommutative variables, t is a variable commuting  x_k, c_k are integers, and the degree is considered for t.
Revised: In this paper, we address computation of the degree \rm deg Det A of Dieudonn\'e determinant \rm Det A of \[ A = k=1^m A_k x_k t^{c_k}, \] where A_k are n \times n matrices over a field K, x_k are noncommutative variables, t is a variable commuting with x_k, c_k are integers, and the degree is considered for t.
Edit intent: clarity
Original:  We extend this framework by incorporating a cost scaling technique, and show that \rm deg Det A can be computed in time polynomial of n,m,\log_2 C, where C:= \max_k |c_k| , which says that \rm deg Det A is given by linear optimization over an integral polytope with respect to objective vector c = (c_k).
Revised:  We extend this framework by incorporating a cost scaling technique, and show that \rm deg Det A can be computed in time polynomial of n,m,\log_2 C, where C:= \max_k |c_k| . We give a polyhedral interpretation of \mathop{\rm deg Det, which says that \rm deg Det A is given by linear optimization over an integral polytope with respect to objective vector c = (c_k).
Edit intent: meaning-changed
Original: The reader may be familiar with various problems involving prisoners and lockers. A typical set-up is that there are n lockers into which a random permutation of n cards are inserted. Then n prisoners enter the locker room one at a time and are allowed to open half of the lockers in an attempt to find their own card. The team of prisoners wins if every one of them is successful. The surprising result is that there is a strategy which wins with probability about 1-\ln 2. A modified problem in which helpful Alice enters before the prisoners, inspects the whole permutationand swaps two cards improves the winning probability to 1. In our problem, there are n lockers and n cards and a helpful Alice just as before, but there is only one prisoner,Bob.If Bob may only open one locker,their chance of success is less than \frac{2.4{n}, but our main result is that, if Bob can open two lockers, their chance of success is c_n{n} where c_n\rightarrow \infty as n\rightarrow \infty.
Revised: {n}, but our main result is that, if Bob can open two lockers, their chance of success is c_n{n} where c_n\rightarrow \infty as n\rightarrow \infty.
Edit intent: meaning-changed
Original:  For this }  , Alice and Bob have to achieve effective communication within the locker room.
Revised:  For this } We want to efficiently find a specific object in a large unstructured set, which we model by a random n-permutation, and we have to do it by revealing just a single element. Clearly, without any help this task is hopeless and the best one can do is select the element at random, and achieve the success probability \frac{1 , Alice and Bob have to achieve effective communication within the locker room.
Edit intent: meaning-changed
Original:  For this }  , Alice and Bob have to achieve effective communication within the locker room. We show asymptotically matching upper and lower bounds for their optimal probability of success.
Revised:  For this } , even without knowing the object sought? We show that by providing advice of just one integer in \{0,1,...,n-1\}, one can improve the success probability considerably, by a \Theta(logn{loglogn show asymptotically matching upper and lower bounds for their optimal probability of success.
Edit intent: meaning-changed
Original: Our analysis relies on a close relationship of this problem to some intrinsic properties of random permutations related to the rencontres number (which is the number of n-permutations with a given number of fixed points) .
Revised: Our analysis relies on a close relationship of such problems to some intrinsic properties of random permutations related to the rencontres number (which is the number of n-permutations with a given number of fixed points) .
Edit intent: clarity
Original: Our analysis relies on a close relationship of this problem to some intrinsic properties of random permutations related to the rencontres number (which is the number of n-permutations with a given number of fixed points) .
Revised: Our analysis relies on a close relationship of this problem to some intrinsic properties of rendom permutations related to the rencontres number (which is the number of n-permutations with a given number of fixed points) .
Edit intent: meaning-changed
Original:  We also perform an in-depth comparison of the two main language model based WSD strategies, i.e., fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data  .
Revised:  We also perform an in-depth comparison of the two main language model based WSD strategies, i.e., fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data . In fact, a simple feature extraction strategy based on the averaging of contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements beyond this small number of examples .
Edit intent: meaning-changed
Original:  Another problem is to enable the execution of complex trajectories, requiring the spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics require adequate robustness against noise.
Revised:  Another problem is to enable the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics require adequate robustness against noise.
Edit intent: coherence
Original:  Another problem is to enable the execution of complex trajectories, requiring the spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics require adequate robustness against noise.
Revised:  Another problem is to enable the execution of complex trajectories, requiring the spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise.
Edit intent: clarity
Original:  In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network , as the basis for a neuromorphic algorithm for robotic control. For this, we identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm.
Revised:  In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network . We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm.
Edit intent: clarity
Original:  We  show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales.
Revised:  We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales.
Edit intent: meaning-changed
Original:  While being discrete , our embedding supports vector arithmetic operations similar to continuous embeddings by interpreting each word as a set of propositional statements describing a rule. The formulation of our vector arithmetic closely reflects the logical structure originating from the symbolic sequential decision making formalism ( classical/STRIPS planning ).
Revised:  While being discrete , our embedding supports vector arithmetic operations similar to continuous embeddings by interpreting each word as a set of propositional statements describing a transition rule in classical/STRIPS planning ).
Edit intent: clarity
Original:  The formulation of our vector arithmetic closely reflects the logical structure originating from the symbolic sequential decision making formalism ( classical/STRIPS planning ). Contrary to the conventional wisdom that discrete representation cannot perform well due to the lack of ability to capture the uncertainty, our representation is competitive against the continuous representations in several downstream tasks. We demonstrate that our embedding is directly compatible with the symbolic, classical planning solvers by performing a "paraphrasing" task.
Revised:  The formulation of our vector arithmetic closely reflects the logical structure originating from the symbolic sequential decision making formalism ( classical/STRIPS planning formalism. This makes the embedding directly compatible with the symbolic, classical planning solvers by performing a "paraphrasing" task.
Edit intent: coherence
Original:  We demonstrate that our embedding is directly compatible with the symbolic, classical planning solvers by performing a "paraphrasing" task. Due to the discrete/logical decision making in classical algorithms with deterministic (non-probabilistic) completeness, and also because it does not require additional training on the paraphrasing dataset, our system can negatively answer a paraphrasing query (inexistence of solutions), and can answer that only some approximate solutions exist -- A feature that is missing in the recent, huge, purely neural language models such as GPT-3 .
Revised:  We demonstrate that our embedding is directly compatible with the symbolic, classical planning solvers .
Edit intent: coherence
Original:  This framework relies on Bayesian meta-learning for efficient inference of system dynamics with calibrated uncertainty. We leverage the model structure to construct confidence bounds which hold throughout the learning process, and factor this uncertainty into a model-based planning framework. By decomposing the problem of control under uncertainty into discrete exploration and exploitation phases, our framework extends to problems with high initial uncertainty while maintaining probabilistic safety and persistent feasibility guarantees during every phase of operation. We validate our approach on the problem of a nonlinear free flying space robot manipulating a payload in cluttered environments, and show it can safely learn and reach a goal .
Revised:  This framework relies on Bayesian meta-learning with last-layer adaptation: the expressiveness of neural-network features trained offline, paired with efficient last-layer online adaptation, enables the derivation of tight confidence sets which contract around the true dynamics as the model adapts online. We exploit these confidence sets to plan trajectories that guarantee the safety of the system. Our approach handles problems with high dynamics uncertainty where reaching the goal safely is initially infeasible by first exploring to gather data and reduce uncertainty, before autonomously exploiting the acquired information to safely perform the task. Under reasonable assumptions, we prove that our framework provides safety guarantees in the form of a single joint chance constraint. Furthermore, we use this theoretical analysis to motivate regularization of the model to improve performance. We extensively demonstrate our approach in simulation and on hardware .
Edit intent: clarity
Original:  Under reasonable assumptions, we prove that our framework provides safety guarantees in the form of a single joint chance constraint. Furthermore, we use this theoretical analysis to motivate regularization of the model to improve performance .
Revised:  Under reasonable assumptions, we prove that our framework has high-probability guarantees of satisfying all constraints at all times jointly. This analysis also motivates two regularizers of last-layer meta-learners that improve online adaptation capabilities as well as performance by reducing the size of the confidence sets .
Edit intent: meaning-changed
Original:  In addition to learning approaches, we also compare to inverse-kinematics (IK) acting on planned footsteps , and show that our fully-learned framework outperforms IK in adverse settings on both A1 and Daisy simulations.
Revised:  In addition to learning approaches, we also compare to inverse-kinematics (IK) acting on desired robot motion , and show that our fully-learned framework outperforms IK in adverse settings on both A1 and Daisy simulations.
Edit intent: clarity
Original:  On hardware, we show the Daisy hexapod achieving multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples, reinforcing the robustness and sample-efficiency of our approach.
Revised:  On hardware, we show the Daisy hexapod achieve multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples, reinforcing the robustness and sample-efficiency of our approach.
Edit intent: fluency
Original:  On hardware, we show the Daisy hexapod achieving multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples, reinforcing the robustness and sample-efficiency of our approach.
Revised:  On hardware, we show the Daisy hexapod achieving multiple locomotion tasks, in an unstructured outdoor setting, with only 2000 hardware samples, reinforcing the robustness and sample-efficiency of our approach.
Edit intent: clarity
Original:  The results show that AMBERT outperforms the existing best performing models in almost all cases, particularly the improvements are significant for Chinese  .
Revised:  The results show that AMBERT outperforms the existing best performing models in almost all cases, particularly the improvements are significant for Chinese . We also develop a method to improve the efficiency of AMBERT in inference, which still performs better than BERT with the same computational cost as BERT .
Edit intent: meaning-changed
Original:  Extending these arguments, we introduce a new class of control architectures we call generalised BTs or k-BTs and show how they can extend the applicability of BTs to some of the aforementioned challenging BT use-cases while preserving the BT principles .
Revised:  Extending these arguments, we introduce a new class of control architectures we call generalised BTs or k-BTs and show how they can extend the applicability of BTs to some of the aforementioned challenging BT use cases while preserving the BT principles .
Edit intent: fluency
Original:  Extending these arguments, we introduce a new class of control architectures we call generalised BTs or k-BTs and show how they can extend the applicability of BTs to some of the aforementioned challenging BT use-cases while preserving the BT principles . We compare BTs to a number of other control architectures within this framework, and show which forms of decision-making can and cannot be equivalently represented by BTs. This allows us to construct a hierarchy of architectures and to show how BTs fit into such a hierarchy .
Revised:  Extending these arguments, we introduce a new class of control architectures we call generalised BTs or k-BTs and show how they can extend the applicability of BTs to some of the aforementioned challenging BT use-cases while preserving the BT principles .
Edit intent: coherence
Original: We prove new optimality results for adaptive mesh refinement algorithms for non-symmetric, indefinite, and time-dependent problems by proposing a generalization of quasi-orthogonality which follows directly from the uniform inf-sup stability of the underlying problem.
Revised: We prove new optimality results for adaptive mesh refinement algorithms for non-symmetric, and time-dependent problems by proposing a generalization of quasi-orthogonality which follows directly from the uniform inf-sup stability of the underlying problem.
Edit intent: clarity
Original: We prove new optimality results for adaptive mesh refinement algorithms for non-symmetric, indefinite, and time-dependent problems by proposing a generalization of quasi-orthogonality which follows directly from the uniform inf-sup stability of the underlying problem.
Revised: We prove new optimality results for adaptive mesh refinement algorithms for non-symmetric, indefinite, and time-dependent problems by proposing a generalization of quasi-orthogonality which follows directly from the coercivity of the underlying problem.
Edit intent: clarity