wanyu/IteraTeR_v2 huggingface dataset:
Original:  More so, we demonstrate how, by leveraging the structure of the learned generative process, it can be `inverted' to perform unsupervised instance segmentation.
Revised:  More so, we demonstrate how, by leveraging the structure of the learned generative process, one can `invert' the learned generative model to perform unsupervised instance segmentation.
Edit intent: clarity
Original:  Compared to prior work on purely unsupervised object-centric image generation our results on CLEVR are state of the art .
Revised:  Compared to prior work on purely unsupervised object-centric approaches to image generation .
Edit intent: clarity
Original: The Nonvanishing Problem asks if a coefficient of a polynomial is nonzero.
Revised: The nonvanishing problem asks if a coefficient of a polynomial is nonzero.
Edit intent: fluency
Original:  Thereby, in amenable cases, Nonvanishing is in the complexity class %DIFDELCMD < {\sf %%% NP \cap%DIFDELCMD < {\sf %%% coNP of problems with "good characterizations".
Revised:  Thereby, in amenable cases, nonvanishing is in the complexity class %DIFDELCMD < {\sf %%% NP \cap%DIFDELCMD < {\sf %%% coNP of problems with "good characterizations".
Edit intent: fluency
Original:  Thereby, in amenable cases, Nonvanishing is in the complexity class %DIFDELCMD < {\sf %%% NP \cap%DIFDELCMD < {\sf %%% coNP of problems with "good characterizations".
Revised:  Thereby, in amenable cases, Nonvanishing is in the complexity class %DIFDELCMD < {\sf %%% \cap%DIFDELCMD < {\sf %%% coNP of problems with "good characterizations".
Edit intent: coherence
Original:  Thereby, in amenable cases, Nonvanishing is in the complexity class %DIFDELCMD < {\sf %%% NP \cap%DIFDELCMD < {\sf %%% coNP of problems with "good characterizations".
Revised:  Thereby, in amenable cases, Nonvanishing is in the complexity class %DIFDELCMD < {\sf %%% NP %DIFDELCMD < {\sf %%% coNP of problems with "good characterizations".
Edit intent: fluency
Original:  Thereby, in amenable cases, Nonvanishing is in the complexity class %DIFDELCMD < {\sf %%% NP \cap%DIFDELCMD < {\sf %%% coNP of problems with "good characterizations".
Revised:  Thereby, in amenable cases, Nonvanishing is in the complexity class %DIFDELCMD < {\sf %%% NP \cap%DIFDELCMD < {\sf %%% NP\cap coNP of problems with "good characterizations".
Edit intent: fluency
Original:  This paper focuses on the case of Schubert polynomials.
Revised:  This report discusses the case of Schubert polynomials.
Edit intent: clarity
Original:  We give a tableau criterion for Nonvanishing , from which we deduce the first polynomial time algorithm.
Revised:  We give a tableau criterion for nonvanishing , from which we deduce the first polynomial time algorithm.
Edit intent: fluency
Original:  The formulation is fully formulated in curvilinear coordinates leading to compact and elegant expressions for the kinematics, balance laws and constitutive relations.
Revised:  The formulation is fully expressed in curvilinear coordinates leading to compact and elegant expressions for the kinematics, balance laws and constitutive relations.
Edit intent: clarity
Original: With aggressively shrinking process technologies , physical design faces severe challenges and early detection of failures is mandated.
Revised: With aggressively shrinking process nodes , physical design faces severe challenges and early detection of failures is mandated.
Edit intent: clarity
Original:  Motivated by this philosophy , we propose a novel global routing method using monotone staircase routing regions (channels), defined at the floorplanning stage.
Revised:  Motivated by this , we propose a novel global routing method using monotone staircase routing regions (channels), defined at the floorplanning stage.
Edit intent: clarity
Original:  Motivated by this philosophy , we propose a novel global routing method using monotone staircase routing regions (channels), defined at the floorplanning stage.
Revised:  Motivated by this philosophy , we propose an early global routing framework using pattern routing following the floorplanning stage.
Edit intent: clarity
Original:  Experimental results on MCNC/GSRC floorplanning benchmarks show 100\\%DIF < routability while congestion in the routing regions restricted to 100%DIFDELCMD < \\%%% %DIF < . The routed net length for all t-terminal (t\geq 2) nets is %DIF > routing completion, with no over-congestion in the  comparable with the steiner length computed by FLUTE.
Revised:  Experimental results on a set of floorplanning benchmark circuits show 100\\%DIF < routability while congestion in the routing regions restricted to 100%DIFDELCMD < \\%%% %DIF < . The routed net length for all t-terminal (t\geq 2) nets is %DIF > routing completion, with no over-congestion in the comparable with the steiner length computed by FLUTE.
Edit intent: clarity
Original:  Experimental results on MCNC/GSRC floorplanning benchmarks show 100\\%DIF < routability while congestion in the routing regions restricted to 100%DIFDELCMD < \\%%% %DIF < . The routed net length for all t-terminal (t\geq 2) nets is %DIF > routing completion, with no over-congestion in the  comparable with the steiner length computed by FLUTE.
Revised:  Experimental results on MCNC/GSRC floorplanning benchmarks show 100\\%DIF < routability while congestion in the routing regions restricted to %DIFDELCMD < \\%%% %DIF < . The routed net length for all t-terminal (t\geq 2) nets is %DIF > routing completion, with no over-congestion in the comparable with the steiner length computed by FLUTE.
Edit intent: fluency
Original:  Experimental results on MCNC/GSRC floorplanning benchmarks show 100\\%DIF < routability while congestion in the routing regions restricted to 100%DIFDELCMD < \\%%% %DIF < . The routed net length for all t-terminal (t\geq 2) nets is %DIF > routing completion, with no over-congestion in the  comparable with the steiner length computed by FLUTE.
Revised:  Experimental results on MCNC/GSRC floorplanning benchmarks show 100\\%DIF < routability while congestion in the routing regions restricted to 100%DIFDELCMD < \\%%% %DIF < . The routed net length for all t-terminal (t\geq 2) nets is %DIF > routing completion, with no over-congestion in the routing regions reported. The wirelength for the t-terminal (t\geq 2) nets is comparable with the steiner length computed by FLUTE.
Edit intent: clarity
Original:  Experimental results on MCNC/GSRC floorplanning benchmarks show 100\\%DIF < routability while congestion in the routing regions restricted to 100%DIFDELCMD < \\%%% %DIF < . The routed net length for all t-terminal (t\geq 2) nets is %DIF > routing completion, with no over-congestion in the  comparable with the steiner length computed by FLUTE.
Revised:  Experimental results on MCNC/GSRC floorplanning benchmarks show 100\\%DIF < routability while congestion in the routing regions restricted to 100%DIFDELCMD < \\%%% %DIF < . The routed net length for all t-terminal (t\geq 2) nets is %DIF > routing completion, with no over-congestion in the comparable with the Steiner length computed by FLUTE.
Edit intent: fluency
Original: As the No Free Lunch theorem formally states 1%DIFDELCMD < ]%%% , algorithms for detecting communities in networks must make tradeoffs .
Revised:  1%DIFDELCMD < ]%%% , algorithms for detecting communities in networks must make tradeoffs .
Edit intent: coherence
Original: As the No Free Lunch theorem formally states 1%DIFDELCMD < ]%%% , algorithms for detecting communities in networks must make tradeoffs .
Revised: As the No Free Lunch theorem formally states %DIFDELCMD < ]%%% , algorithms for detecting communities in networks must make tradeoffs .
Edit intent: fluency
Original:  On synthetic networks, we show how tuning for node metadata relates to the detectability limit, and on empirical networks, we show how increased tuning for node metadata yields increased mutual information with the metadata at a cost in the traditional map equation.
Revised:  On synthetic networks, we show how tuning for node metadata relates to the detectability limit, and on empirical networks, we show how our algorithm can achieve greater mutual information with the metadata at a cost in the traditional map equation.
Edit intent: clarity
Original:  Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for an evolutionary algorithm if that facilitates the inclusion of SND.
Revised:  Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for a GP system if that facilitates the inclusion of SND.
Edit intent: clarity
Original: Despite recent developments, deploying deep neural networks on resource constrained general purpose hardware remains a significant challenge.
Revised: The task of accelerating large neural networks on resource constrained general purpose hardware remains a significant challenge.
Edit intent: clarity
Original: Despite recent developments, deploying deep neural networks on resource constrained general purpose hardware remains a significant challenge.
Revised: Despite recent developments, deploying deep neural networks on general purpose hardware remains a significant challenge.
Edit intent: clarity
Original:  Despite this , a theoretical and computational approach to achieve the understanding of such mechanism is still lacking.
Revised:  Despite this , a theoretical and computational approach demonstrating the underlying mechanisms is still lacking.
Edit intent: clarity
Original:  In this paper we show the effect of sleep-like activity on a simplified thalamo-cortical model which is trained to encode and retrieve images of handwritten digits.
Revised:  In this paper we show the effect of sleep-like activity on a simplified thalamo-cortical model which is trained to encode , retrieve and classify images of handwritten digits.
Edit intent: clarity
Original:  Such effect might be beneficial to the network to obtain better performances in retrieval and classification tasks and to create hierarchies of categories in integrated representations.
Revised:  Such effect might be beneficial to the network to obtain better performances in retrieval and classification tasks and create hierarchies of categories in integrated representations.
Edit intent: fluency
Original:  If spike-timing-dependent-plasticity (STDP) is active during slow oscillations, a differential homeostatic process is observed .
Revised:  During slow oscillations, spike-timing-dependent-plasticity (STDP) is active during slow oscillations, a differential homeostatic process is observed .
Edit intent: fluency
Original:  If spike-timing-dependent-plasticity (STDP) is active during slow oscillations, a differential homeostatic process is observed .
Revised:  If spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process is observed .
Edit intent: clarity
Original:  If spike-timing-dependent-plasticity (STDP) is active during slow oscillations, a differential homeostatic process is observed .
Revised:  If spike-timing-dependent-plasticity (STDP) is active during slow oscillations, a differential homeostatic process .
Edit intent: clarity
Original:  Such effects favour higher performance in retrieval and classification tasks and create hierarchies of categories in integrated representations.
Revised:  Such effects favour higher performances in retrieval and classification tasks and create hierarchies of categories in integrated representations.
Edit intent: clarity
Original:  Also, such mechanism hints at possible applications to artificial learning systems.
Revised:  Such mechanism hints at possible applications to artificial learning systems.
Edit intent: coherence
Original:  Despite the importance of the phenomenon, a theoretical and computational approach demonstrating the underlying mechanisms is still lacking.
Revised:  Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking.
Edit intent: clarity
Original:  This is reflected in a hierarchical organization of post-sleep internal representations .
Revised:  This hierarchical organization of post-sleep internal representations .
Edit intent: clarity
Original:  The model leverages on the interaction between of top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations  .
Revised:  The model leverages on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations .
Edit intent: clarity
Original:  In this paper we present and analyze a variant of Work Stealing that \ws avoids most synchronization overheads by keeping processors' deques entirely private by default, and only exposing work when requested by thieves.
Revised:  In this paper we present and analyze a variant of \ws avoids most synchronization overheads by keeping processors' deques entirely private by default, and only exposing work when requested by thieves.
Edit intent: clarity
Original:  Our analysis shows that the expected execution time is O\left(T_{1}{P} + T_{\infty}\right), and the expected synchronization overheads incurred during the execution are at most O\left(\left(C_{ Compare-And-Swap } + C_{ Memory Fence }\right)PT_{\infty}\right), where C_{ Compare-And-Swap } and C_{ Memory Fence } respectively denote the maximum cost of executing a Compare-And-Swap instruction and a Memory Fence instruction .
Revised:  Our analysis shows that the expected execution time is O\left(T_{1}{P} + T_{\infty}\right), and the expected synchronization overheads incurred during the execution are at most O\left(\left(C_{ Compare-And-Swap } + C_{ MFence }\right)PT_{\infty}\right), where C_{ Compare-And-Swap } and C_{ Memory Fence } respectively denote the maximum cost of executing a Compare-And-Swap instruction and a Memory Fence instruction .
Edit intent: fluency
Original:  Our analysis shows that the expected execution time is O\left(T_{1}{P} + T_{\infty}\right), and the expected synchronization overheads incurred during the execution are at most O\left(\left(C_{ Compare-And-Swap } + C_{ Memory Fence }\right)PT_{\infty}\right), where C_{ Compare-And-Swap } and C_{ Memory Fence } respectively denote the maximum cost of executing a Compare-And-Swap instruction and a Memory Fence instruction .
Revised:  Our analysis shows that the expected execution time is O\left(T_{1}{P} + T_{\infty}\right), and the expected synchronization overheads incurred during the execution are at most O\left(\left(C_{ Compare-And-Swap } + C_{ Memory Fence }\right)PT_{\infty}\right), where C_{ CAS } and C_{ Memory Fence } respectively denote the maximum cost of executing a Compare-And-Swap instruction and a Memory Fence instruction .
Edit intent: clarity
Original:  Our analysis shows that the expected execution time is O\left(T_{1}{P} + T_{\infty}\right), and the expected synchronization overheads incurred during the execution are at most O\left(\left(C_{ Compare-And-Swap } + C_{ Memory Fence }\right)PT_{\infty}\right), where C_{ Compare-And-Swap } and C_{ Memory Fence } respectively denote the maximum cost of executing a Compare-And-Swap instruction and a Memory Fence instruction .
Revised:  Our analysis shows that the expected execution time is O\left(T_{1}{P} + T_{\infty}\right), and the expected synchronization overheads incurred during the execution are at most O\left(\left(C_{ Compare-And-Swap } + C_{ Memory Fence }\right)PT_{\infty}\right), where C_{ Compare-And-Swap } and C_{ MFence } respectively denote the maximum cost of executing a Compare-And-Swap instruction and a Memory Fence instruction .
Edit intent: fluency
Original:  Considering the interpretation of deep learning models has raised extended attention these years , it will be beneficial if we can learn an interpretable structure from deep learning models.
Revised:  Considering the interpretation of deep learning models has raised extended attention these years . It will be beneficial if we can learn an interpretable structure from deep learning models.
Edit intent: fluency
Original:  In this paper, we focus on Recurrent Neural Networks  (RNNs)  whose inner mechanism is still not clearly understood.
Revised:  In this paper, we focus on Recurrent Neural Networks ~ (RNNs) whose inner mechanism is still not clearly understood.
Edit intent: fluency
Original:  We find that Finite State Automaton  (FSA) that processes sequential data has more interpretable inner mechanism  and can be learned from RNNs as the interpretable structure.
Revised:  We find that Finite State Automaton ~ (FSA) that processes sequential data has more interpretable inner mechanism and can be learned from RNNs as the interpretable structure.
Edit intent: fluency
Original:  From the FSA 's point of view, we then analyze how the performance of RNNs are affected by the number of gates , as well as the semantic meaning behind the transition of numerical hidden states.
Revised:  From the FSA 's point of view, we then analyze how the number of gates , as well as the semantic meaning behind the transition of numerical hidden states.
Edit intent: clarity
Original:  These failure modes are more complex, more problematic, and less well understood in the multi-agent setting, at least partially because they are not yet observed in practice .
Revised:  The equivalent failure modes for multi-agent systems are more complex, more problematic, and less well understood in the multi-agent setting, at least partially because they are not yet observed in practice .
Edit intent: clarity
Original:  These failure modes are more complex, more problematic, and less well understood in the multi-agent setting, at least partially because they are not yet observed in practice .
Revised:  These failure modes are more complex, more problematic, and less well understood .
Edit intent: clarity
Original:  This paper explains why this is the case, then lays out some of the classes of such failure, such as accidental steering, coordination failures, adversarial misalignment, input spoofing or filtering, and goal co-option or direct hacking.
Revised:  This paper explains why this is the case, then lays out some of the classes of such failure, such as accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking.
Edit intent: fluency
Original: Overoptimization failures in machine learning and AI can involve specification gaming, reward hacking, fragility to distributional shifts, and Goodhart's or Campbell's law.
Revised: Overoptimization failures in machine learning and artificial intelligence systems can involve specification gaming, reward hacking, fragility to distributional shifts, and Goodhart's or Campbell's law.
Edit intent: clarity
Original:  These failure modes are an important challenge in building safe AI systems, but multi-agent systems have additional related failure modes .
Revised:  These failure modes are an important challenge in building safe AI systems, and multi-agent systems have additional related failure modes .
Edit intent: coherence
Original:  This paper explains why this is the case, then lays out some of the classes of such failure , such as accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking  .
Revised:  This paper explains why these failure modes are in some sense fundamental. Following this , such as accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking .
Edit intent: clarity
Original: Overoptimization failures in machine learning and artificial intelligence systems can involve specification gaming, reward hacking, fragility to distributional shifts, and Goodhart's or Campbell's law.
Revised: An important challenge for safety in machine learning and artificial intelligence systems can involve specification gaming, reward hacking, fragility to distributional shifts, and Goodhart's or Campbell's law.
Edit intent: clarity
Original:  These failure modes are an important challenge in building safe AI systems, and multi-agent systems have additional failure modes that are closely related.
Revised:  This paper presents additional failure modes for interactions within multi-agent systems have additional failure modes that are closely related.
Edit intent: clarity
Original:  These failure modes are an important challenge in building safe AI systems, and multi-agent systems have additional failure modes that are closely related.
Revised:  These failure modes are an important challenge in building safe AI systems, and multi-agent systems that are closely related.
Edit intent: clarity
Original:  These failure modes for multi-agent systems are more complex, more problematic, and less well understood than the single-agent case .
Revised:  These multi-agent failure modes are more complex, more problematic, and less well understood than the single-agent case .
Edit intent: clarity
Original:  After motivating the discussion with examples from poker-playing AI , the paper explains why these failure modes are in some sense fundamental .
Revised:  After motivating the discussion with examples from poker-playing AI , the paper explains why these failure modes are in some senses unavoidable .
Edit intent: clarity
Original:  Following this, the paper categorizes failure modes, provides definitions, and cites examples for each of  : accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking.
Revised:  Following this, the paper categorizes failure modes, provides definitions, and cites examples for each of the modes : accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking.
Edit intent: fluency
Original:  The paper then discusses ongoing and potential work on mitigation of these failure modes, and what to expect when these failures continue to proliferate .
Revised:  The paper then discusses ongoing and potential work on mitigation of these failure modes, and identifies work which may be useful for the mitigation of these failure modes .
Edit intent: clarity
Original: In this paper we study the single-leg revenue management problem, with no information given about the demand trajectory over time.
Revised: In this paper we study the single-item revenue management problem, with no information given about the demand trajectory over time.
Edit intent: clarity
Original:  A key technical ingredient in our paper is a new `valuation tracking' subroutine, which tracks the possible values for the optimum, and follows the most  inventory-conservative  control which maintains the desired competitive ratio  .
Revised:  A key technical ingredient in our policy is a new `valuation tracking' subroutine, which tracks the possible values for the optimum, and follows the most inventory-conservative control which maintains the desired competitive ratio .
Edit intent: clarity
Original:  A key technical ingredient in our paper is a new `valuation tracking' subroutine, which tracks the possible values for the optimum, and follows the most  inventory-conservative  control which maintains the desired competitive ratio  .
Revised:  A key technical ingredient in our paper is a new "valuation tracking" subroutine, which tracks the possible values for the optimum, and follows the most inventory-conservative control which maintains the desired competitive ratio .
Edit intent: fluency