wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this work  we develop a data-driven framework that enables editing of these complex camera positioning parameters in a semantic space (e.g. calm, enjoyable, establishing).
Revised:  In this work , we develop a data-driven framework that enables editing of these complex camera positioning parameters in a semantic space (e.g. calm, enjoyable, establishing).
Edit intent: fluency
Original:  Supplementary video : URL
Revised:  Data and video found at : URL
Edit intent: clarity
Original:  All models but one are host specific to either domestic pigs or wild boar , despite the fact that spillover events between the domestic and wild compartments can play an important role in  outbreaks.
Revised:  All models but one are host specific , being developed for either domestic pigs or wild boar , despite the fact that spillover events between the domestic and wild compartments can play an important role in outbreaks.
Edit intent: clarity
Original:  All models but one are host specific to either domestic pigs or wild boar , despite the fact that spillover events between the domestic and wild compartments can play an important role in  outbreaks.
Revised:  All models but one are host specific to either domestic pigs or wild boar despite the fact that spillover events between the domestic and wild compartments can play an important role in outbreaks.
Edit intent: fluency
Original:  All models but one are host specific to either domestic pigs or wild boar , despite the fact that spillover events between the domestic and wild compartments can play an important role in  outbreaks.
Revised:  All models but one are host specific to either domestic pigs or wild boar , despite the fact that spillover events between domestic pigs and wild boar are evidenced to play an important role in outbreaks.
Edit intent: clarity
Original:  The development of models incorporating this transmission is crucial.
Revised:  The development of models incorporating such transmission routes is crucial.
Edit intent: clarity
Original:  Almost all existing SBR studies model user preferences only based on the current session while neglecting the global item-transition information from the other sessions . Specifically, we first propose a basic GNN-based session recommendation model on SBR problem by solely using session-level item-transition information on session graph.
Revised:  Most existing SBR studies model user preferences only based on the current session while neglecting the global item-transition information from the other sessions . Specifically, we first propose a basic GNN-based session recommendation model on SBR problem by solely using session-level item-transition information on session graph.
Edit intent: clarity
Original:  Almost all existing SBR studies model user preferences only based on the current session while neglecting the global item-transition information from the other sessions . Specifically, we first propose a basic GNN-based session recommendation model on SBR problem by solely using session-level item-transition information on session graph.
Revised:  Almost all existing SBR studies model the user preferences based only on the current session while neglecting the global item-transition information from the other sessions . Specifically, we first propose a basic GNN-based session recommendation model on SBR problem by solely using session-level item-transition information on session graph.
Edit intent: fluency
Original:  Almost all existing SBR studies model user preferences only based on the current session while neglecting the global item-transition information from the other sessions . Specifically, we first propose a basic GNN-based session recommendation model on SBR problem by solely using session-level item-transition information on session graph.
Revised:  Almost all existing SBR studies model user preferences only based on the current session while neglecting the item-transition information from the other sessions . Specifically, we first propose a basic GNN-based session recommendation model on SBR problem by solely using session-level item-transition information on session graph.
Edit intent: clarity
Original:  Almost all existing SBR studies model user preferences only based on the current session while neglecting the global item-transition information from the other sessions . Specifically, we first propose a basic GNN-based session recommendation model on SBR problem by solely using session-level item-transition information on session graph.
Revised:  Almost all existing SBR studies model user preferences only based on the current session while neglecting the global item-transition information from the other sessions . Specifically, we first propose a basic GNN-based framework (BGNN), which solely uses session-level item-transition information on session graph.
Edit intent: clarity
Original:  Then, we also propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences of the current session via fully exploiting global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session-level item representation;
Revised:  Then, we also propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session-level item representation;
Edit intent: clarity
Original:  Then, we also propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences of the current session via fully exploiting global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session-level item representation;
Revised:  Then, we also propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences of the current session via fully exploiting global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session level item representation;
Edit intent: fluency
Original:  and ( b ) Constrained-based model SRGI-CM  , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Revised:  and ( b ) Constrained-based Model ( SRGI-CM , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Edit intent: fluency
Original:  and ( b ) Constrained-based model SRGI-CM  , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Revised:  and ( b ) Constrained-based model SRGI-CM ) , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Edit intent: fluency
Original:  and ( b ) Constrained-based model SRGI-CM  , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Revised:  and ( b ) Constrained-based model SRGI-CM , which treats the global-level item-transition information as a constraint to ensure the learned item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Edit intent: fluency
Original:  and ( b ) Constrained-based model SRGI-CM  , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Revised:  and ( b ) Constrained-based model SRGI-CM , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the global item-transition . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Edit intent: clarity
Original:  and ( b ) Constrained-based model SRGI-CM  , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Revised:  and ( b ) Constrained-based model SRGI-CM , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conducted on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Edit intent: fluency
Original:  and ( b ) Constrained-based model SRGI-CM  , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Revised:  and ( b ) Constrained-based model SRGI-CM , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrate that both SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Edit intent: fluency
Original:  and ( b ) Constrained-based model SRGI-CM  , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Revised:  and ( b ) Constrained-based model SRGI-CM , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM and SRGI-CM ) outperform the state-of-the-art methods consistently.
Edit intent: fluency
Original:  and ( b ) Constrained-based model SRGI-CM  , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM ) outperform the state-of-the-art methods consistently.
Revised:  and ( b ) Constrained-based model SRGI-CM , which treats the global-level item-transition information as a constraint to ensure the learnt item embeddings are consistent with the graph structure . Extensive experiments conduct on three popular benchmark datasets demonstrates that both ( SRGI-FM ) and ( SRGI-CM outperform the state-of-the-art methods consistently.
Edit intent: fluency
Original: We develop a denotational semantics of  Linear Logic with least and greatest fixed points in a general categorical setting  based on the notion of Seely category and on strong functors acting on them.
Revised: We develop a denotational semantics of Linear Logic with least and greatest fixed points in a general categorical setting is based on the notion of Seely category and on strong functors acting on them.
Edit intent: fluency
Original:  We exhibit a simple instance of this setting in the category of sets and relations, where least and greatest fixed points are interpreted in the same way , and in a category of sets equipped with a notion of totality (non-uniform totality spaces) and relations preserving them, where least and greatest fixed points have distinct interpretations  .
Revised:  We exhibit a simple instance of this setting in the category of sets and relations, least and greatest fixed points are interpreted in the same way , and in a category of sets equipped with a notion of totality (non-uniform totality spaces) and relations preserving them, where least and greatest fixed points have distinct interpretations .
Edit intent: coherence
Original:  We exhibit a simple instance of this setting in the category of sets and relations, where least and greatest fixed points are interpreted in the same way , and in a category of sets equipped with a notion of totality (non-uniform totality spaces) and relations preserving them, where least and greatest fixed points have distinct interpretations  .
Revised:  We exhibit a simple instance of this setting in the category of sets and relations, where least and greatest fixed points are interpreted in the same way , and in a category of sets equipped with a notion of totality (non-uniform totality spaces) and relations preserving them, least and greatest fixed points have distinct interpretations .
Edit intent: coherence
Original: The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz (%DIFDELCMD < \muWave%%% ) networks.
Revised: The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz %DIFDELCMD < \muWave%%% ) networks.
Edit intent: fluency
Original: The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz (%DIFDELCMD < \muWave%%% ) networks.
Revised: The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz (%DIFDELCMD < \muWave%%% networks.
Edit intent: fluency
Original:  We study the Nash Equilibrium as well as the cases where players deviate from it.
Revised:  We study the Nash Equilibrium as well as the case where players deviate from it.
Edit intent: fluency
Original: Randomized iterative methods have attracted much attention in recent years because they can approximately solve large-scale linear systems of equations without accessing the entire coefficient matrix.
Revised: Randomized iterative algorithms have attracted much attention in recent years because they can approximately solve large-scale linear systems of equations without accessing the entire coefficient matrix.
Edit intent: clarity
Original:  In this paper, we propose two novel pseudoinverse-free randomized block iterative methods for solving consistent and inconsistent linear systems.
Revised:  In this paper, we propose two novel pseudoinverse-free randomized block iterative algorithms for solving consistent and inconsistent linear systems.
Edit intent: clarity
Original:  Our methods require two user-defined random matrices: one for row sampling and the other for column sampling.
Revised:  The proposed algorithms require two user-defined random matrices: one for row sampling and the other for column sampling.
Edit intent: clarity
Original:  The well-known doubly stochastic Gauss--Seidel, randomized Kaczmarz, randomized coordinate descent, and randomized extended Kaczmarz methods are special cases of our methods corresponding to specially selected random matrices . Because our methods allow for a much wider selection of these two random matrices, a number of new specific methods can be obtained.
Revised:  We can recover the well-known doubly stochastic Gauss--Seidel, randomized Kaczmarz, randomized coordinate descent, and randomized extended Kaczmarz methods are special cases of our methods corresponding to specially selected random matrices . Because our methods allow for a much wider selection of these two random matrices, a number of new specific methods can be obtained.
Edit intent: coherence
Original:  The well-known doubly stochastic Gauss--Seidel, randomized Kaczmarz, randomized coordinate descent, and randomized extended Kaczmarz methods are special cases of our methods corresponding to specially selected random matrices . Because our methods allow for a much wider selection of these two random matrices, a number of new specific methods can be obtained.
Revised:  The well-known doubly stochastic Gauss--Seidel, randomized Kaczmarz, randomized coordinate descent, and randomized extended Kaczmarz algorithms by choosing appropriate random matrices used in our algorithms . Because our methods allow for a much wider selection of these two random matrices, a number of new specific methods can be obtained.
Edit intent: clarity
Original:  The well-known doubly stochastic Gauss--Seidel, randomized Kaczmarz, randomized coordinate descent, and randomized extended Kaczmarz methods are special cases of our methods corresponding to specially selected random matrices . Because our methods allow for a much wider selection of these two random matrices, a number of new specific methods can be obtained.
Revised:  The well-known doubly stochastic Gauss--Seidel, randomized Kaczmarz, randomized coordinate descent, and randomized extended Kaczmarz methods are special cases of our methods corresponding to specially selected random matrices . Because our algorithms allow for a much wider selection of these two random matrices, a number of new specific methods can be obtained.
Edit intent: clarity
Original:  The well-known doubly stochastic Gauss--Seidel, randomized Kaczmarz, randomized coordinate descent, and randomized extended Kaczmarz methods are special cases of our methods corresponding to specially selected random matrices . Because our methods allow for a much wider selection of these two random matrices, a number of new specific methods can be obtained.
Revised:  The well-known doubly stochastic Gauss--Seidel, randomized Kaczmarz, randomized coordinate descent, and randomized extended Kaczmarz methods are special cases of our methods corresponding to specially selected random matrices . Because our methods allow for a much wider selection of these two random matrices, a number of new specific algorithms can be obtained.
Edit intent: clarity
Original:  We prove the linear convergence ( in the mean square sense ) of our methods . Numerical experiments for linear systems with synthetic and real-world coefficient matrices demonstrate the efficiency of some special cases of our methods.
Revised:  We prove the linear convergence in the mean square sense ) of our methods . Numerical experiments for linear systems with synthetic and real-world coefficient matrices demonstrate the efficiency of some special cases of our methods.
Edit intent: fluency
Original:  We prove the linear convergence ( in the mean square sense ) of our methods . Numerical experiments for linear systems with synthetic and real-world coefficient matrices demonstrate the efficiency of some special cases of our methods.
Revised:  We prove the linear convergence ( in the mean square sense of our algorithms . Numerical experiments for linear systems with synthetic and real-world coefficient matrices demonstrate the efficiency of some special cases of our methods.
Edit intent: clarity
Original: Backdoor attacks , which are a kind of emergent training-time threat to deep neural networks ( DNNS ).
Revised: Backdoor attacks are a kind of emergent training-time threat to deep neural networks ( DNNS ).
Edit intent: coherence
Original: Backdoor attacks , which are a kind of emergent training-time threat to deep neural networks ( DNNS ).
Revised: Backdoor attacks , which are a kind of emergent training-time threat to deep neural networks ( DNNs ).
Edit intent: fluency
Original:  They can manipulate the output of DNNs and posses high insidiousness.
Revised:  They can manipulate the output of DNNs and possess high insidiousness.
Edit intent: fluency
Original:  Nevertheless, the studies on defending textual backdoor defense are little conducted .
Revised:  Nevertheless, there are few studies on defending textual backdoor defense are little conducted .
Edit intent: clarity
Original:  Nevertheless, the studies on defending textual backdoor defense are little conducted .
Revised:  Nevertheless, the studies on defending against textual backdoor attacks .
Edit intent: clarity
Original:  In this paper, we propose a simple and effective textual backdoor defense named ONION, which is based on outlier word detection and might be the first method that can handle all the  attack situations.
Revised:  In this paper, we propose a simple and effective textual backdoor defense named ONION, which is based on outlier word detection and , to the best of our knowledge, is the first method that can handle all the attack situations.
Edit intent: clarity
Original:  However, existing methods are restricted to learning efficient interpolations of static scenes that encode all scene objects into a single neural network, lacking the ability to represent dynamic scenes and decompositions into individual scene objects.
Revised:  However, existing methods are restricted to learning efficient representations of static scenes that encode all scene objects into a single neural network, lacking the ability to represent dynamic scenes and decompositions into individual scene objects.
Edit intent: clarity
Original:  However, existing methods are restricted to learning efficient interpolations of static scenes that encode all scene objects into a single neural network, lacking the ability to represent dynamic scenes and decompositions into individual scene objects.
Revised:  However, existing methods are restricted to learning efficient interpolations of static scenes that encode all scene objects into a single neural network, and lack the ability to represent dynamic scenes and decompositions into individual scene objects.
Edit intent: coherence
Original:  We assess the proposed method on synthetic and real automotive data, validating that our approach learns dynamic scenes - only by observing a video of this scene - and allows for rendering novel photo-realistic views of novel scene compositions with unseen sets of objects at unseen poses.
Revised:  We assess the proposed method on synthetic and real automotive data, validating that our approach learns dynamic scenes -- only by observing a video of this scene - and allows for rendering novel photo-realistic views of novel scene compositions with unseen sets of objects at unseen poses.
Edit intent: fluency