wanyu/IteraTeR_full_sent huggingface dataset:
Original:  A quantized representation of probability in the brain was also posited and supported by experimental data [7].  ][  In this paper, we hypothesize that intertemporal choices may also be quantized.
Revised:  A quantized representation of probability in the brain was also posited and supported by experimental data [7]. ][5 In this paper, we hypothesize that intertemporal choices may also be quantized.
Edit intent: meaning-changed
Original:  A quantized representation of probability in the brain was also posited and supported by experimental data [7].  ][  In this paper, we hypothesize that intertemporal choices may also be quantized.
Revised:  A quantized representation of probability in the brain was also posited and supported by experimental data [7]. ][ . In this paper, we hypothesize that intertemporal choices may also be quantized.
Edit intent: fluency
Original:  In this paper, we propose a novel method for domain adaptation of single-task text classification problems based on a simple but effective idea of diversity-based generalization that does not require unlabeled target data  .
Revised:  In this paper, we propose a novel method for domain adaptation of single-task text classification problems based on a simple but effective idea of diversity-based generalization that does not require unlabeled target data but still matches the state-of-the-art in performance .
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Original:  In this work, we present a system that uses rule based annotators to augment training data for neural models, and for slot filling to increase the diversity of the populated knowledge graph. We also propose a representative set sampling method to use the populated knowledge graph data for downstream applications. We introduce new resources and discuss our results .
Revised:  In this work, we present a system that uses rule based annotators and a graph neural network for missing link prediction, to populate a more complete, fair and diverse knowledge base from the TACRED dataset .
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Original: An emerging area of research is the study of large-scale migration interactions as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration interactions that connect those places.
Revised: An emerging area of research is the study of macroscale migration patterns as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration interactions that connect those places.
Edit intent: clarity
Original: An emerging area of research is the study of large-scale migration interactions as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration interactions that connect those places.
Revised: An emerging area of research is the study of large-scale migration interactions as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration ties that connect those places.
Edit intent: clarity
Original:  We describe properties of global migration networks and outline network diagnostics and methods that are relevant to the study of such networks. We then present key findings and propose areas of future research to overcome current challenges. Research on migration networks is multidisciplinary; it connects migration studies to diverse research areas that include sociology, geography, regional science, network science, applied mathematics, computer science, and other areas. Consequently, a major objective of the present chapter is to highlight and foster interdisciplinary conversations .
Revised:  We then propose ways to leverage network analysis in concert with digital technologies and online geolocated data to examine the structure and dynamics of migration networks. The implementation of such approaches for studying migration networks faces many challenges, including ethical ones, methodological ones, socio-technological ones (e.g., data availability and reuse), and research reproducibility. We detail these challenges, and we then consider possible ways of linking digital geolocated data to administrative and survey data as a way of harnessing new technologies to construct increasingly realistic migration networks (e.g., using multiplex networks). We also briefly discuss new methods (e.g., multilayer network analysis) in network analysis and adjacent fields (e.g., machine learning) that can help advance understanding of macroscale patterns of migration .
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Original:  Functional network models often employ rate units that are continuously coupled by analog signals. Is there a benefit of discrete signaling? By a unified mean-field theory we show that large random networks of rate and binary units have identical second order statistics. Yet their stimulus processing properties are radically different: We discover a chaotic sub-manifold in binary networks that does not exist in rate models. Its dimensionality increases with time after stimulus onset and reaches a fixed point depending on the synaptic coupling strength. Low dimensional stimuli are transiently expanded into higher-dimensional representations within this manifold. High noise resilience persists not only near the edge of chaos, but throughout the chaotic regime. In rate models of spiking activity, the effective spiking noise suppresses chaos, severely impairing classification performance.
Revised:  Even if the timings of the individual events are strongly chaotic (microscopic chaos), the rate of events might still be non-chaotic or at the edge of what is known as rate chaos. Such edge-of-chaos dynamics are beneficial to the computational power of neuronal networks. We analyze both types of chaotic dynamics in densely connected networks of asynchronous binary neurons, by developing and applying a model-independent field theory for neuronal networks. We find a strongly size-dependent transition to microscopic chaos. We then expose the conceptual difficulty at the heart of the effective spiking noise suppresses chaos, severely impairing classification performance.
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Original:  In rate models of spiking activity, the effective spiking noise suppresses chaos, severely impairing classification performance. Chaotic rate networks without effective spiking noise also show the transient performance boost. The transitions to chaos in the two models do not coincide and have qualitatively different causes. Our theory mechanistically explains these observations. These findings have several implications. 1) Discrete state networksreach optimal performance with weaker synapses; implying lower energetic costs for synaptic transmission. 2) The classification mechanism is robust to noise, compatible with fluctuations in biophysical systems . 3) Optimal performance is reached after only a single activation per participating neuron ;
Revised:  In rate models of spiking activity, the definition of rate chaos, identify two reasonable definitions, and show that for neither of them the binary network dynamics crosses a transition to rate chaos . 3) Optimal performance is reached after only a single activation per participating neuron ;
Edit intent: coherence
Original:  2) The classification mechanism is robust to noise, compatible with fluctuations in biophysical systems . 3) Optimal performance is reached after only a single activation per participating neuron ;
Revised:  2) The classification mechanism is robust to noise, compatible with fluctuations in biophysical systems . The analysis of diverging trajectories in chaotic networks also allows us to study classification of linearly non-separable classes of stimuli in a reservoir computing approach. We show that microscopic chaos rapidly expands the dimensionality of the representation while, crucially, the number of dimensions corrupted by noise lags behind. This translates to a transient peak in the networks' classification performance even deeply in the chaotic regime, challenging the view that computational performance is always optimal near the edge of chaos. This is a general effect in high dimensional chaotic systems, and not specific to binary networks: We also demonstrate it in a continuous 'rate' network, a spiking LIF network, and an LSTM network. For binary and LIF networks, classification performance peaks rapidly within one activation per participating neuron ;
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Original:  2) The classification mechanism is robust to noise, compatible with fluctuations in biophysical systems . 3) Optimal performance is reached after only a single activation per participating neuron ; demonstrating event-based computation with short latencies.
Revised:  2) The classification mechanism is robust to noise, compatible with fluctuations in biophysical systems . 3) Optimal performance is reached after only a single activation per participating neuron , demonstrating fast event-based computation with short latencies.
Edit intent: clarity
Original:  demonstrating event-based computation with short latencies. 4) The chaotic sub-manifold predicts a transient increase of variability after stimulus onset. Our results thus provide a hitherto unknown link between recurrent and chaotic dynamics of functional networks, neuronal variability, and dimensionality of neuronal responses .
Revised:  demonstrating event-based computation that may be exploited by biological neural systems, for which we propose testable predictions .
Edit intent: clarity
Original: Cortical neurons communicate with spikes, which are discrete events in time . Even if the timings of the individual events are strongly chaotic (microscopic chaos), the rate of events might still be non-chaotic or at the edge of what is known as rate chaos. Such edge-of-chaos dynamics are beneficial to the computational power of neuronal networks. We analyze both types of chaotic dynamics in densely connected networks of asynchronous binary neurons, by developing and applying a model-independent field theory for neuronal networks . We find a strongly size-dependent transition to microscopic chaos. We then expose the conceptual difficulty at the heart of the definition of rate chaos, identify two reasonable definitions, and show that for neither of them the binary network dynamics crosses a transition to rate chaos. The analysis of diverging trajectories in chaotic networks also allows us to study classification of linearly non-separable classes of stimuli in a reservoir computing approach. We show that microscopic chaos rapidly expands the dimensionality of the representation while, crucially, the number of dimensions corrupted by noise lags behind. This translates to a transient peak in the networks' classification performance even deeply in the chaotic regime, challenging the view that computational performance is always optimal near the edge of chaos. This is a general effect in high dimensional chaotic systems, and not specific to binary networks : We also demonstrate it in a continuous 'rate' network, a spiking LIF network, and an LSTM network.
Revised: Cortical neurons communicate with spikes, which are discrete events in time and value. Functional network models often employ rate units that are continuously coupled by analog signals. Is there a qualitative difference implied by these two forms of signaling? We develop a unified mean-field theory for large random networks to show that first- and second-order statistics in rate and binary networks are in fact identical if rate neurons receive the right amount of noise. Their response to presented stimuli, however, can be radically different. We quantify these differences by studying how nearby state trajectories evolve over time, asking to what extent the dynamics is chaotic. Chaos in the two models is found to be qualitatively different. In binary networks we find a network-size-dependent transition to chaos and a chaotic submanifold whose dimensionality expands stereotypically with time, while rate networks with matched statistics are nonchaotic. Dimensionality expansion in chaotic binary networks : We also demonstrate it in a continuous 'rate' network, a spiking LIF network, and an LSTM network.
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Original:  This is a general effect in high dimensional chaotic systems, and not specific to binary networks : We also demonstrate it in a continuous 'rate' network, a spiking LIF network, and an LSTM network. For binary and LIF networks , classification performance peaks rapidly within one activation per participating neuron, demonstrating fast event-based computation that may be exploited by biological neural systems, for which we propose testable predictions .
Revised:  This is a general effect in high dimensional chaotic systems, and not specific to binary networks aids classification in reservoir computing and optimal performance is reached within about a single activation per neuron; a fast mechanism for computation that we demonstrate also in spiking networks. A generalization of this mechanism extends to rate networks in their respective chaotic regimes .
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Original: Modern neural network training relies heavily on data augmentation for improved generalization. After the initial success of label-preserving augmentations, there has been a recent surge of interest in label-perturbing approaches, which combine features and labels across training samples to smooth the learned decision surface .
Revised: The moments (a.k.a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time. However, in the field of image generation, the moments play a much more central role. Studies have shown that the moments extracted from instance normalization and positional normalization can roughly capture style and shape information of an image. Instead of being discarded, these moments are instrumental to the generation process .
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Original:  In this paper , we propose a new augmentation method that leverages the first and second moments extracted and re-injected by feature normalization. We replace the moments of the learned features of one training image by those of another, and also interpolate the target labels  .
Revised:  In this paper , we propose a new augmentation method that encourages the model to utilize the moment information also for recognition models. Specifically, we replace the moments of the learned features of one training image by those of another, and also interpolate the target labels .
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Original:  We also show that this LP has an \Omega(\log^* m) integrality gap  even for the problem of selecting intervals on a line . Moreover, we show logarithmic gaps for problems without geometric structure, showing that some structure is needed to get good results using these techniques .
Revised:  We also show that this LP has an \Omega(\log^* m) integrality gap even for the problem of selecting intervals on a line ; here \log^* m is the iterated logarithm function .
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Original: We consider the minimum set cover problem in a dynamic setting. Here, we are given a universe of elements \mathcal{U and a collection of sets S \subseteq 2^{U} as input, where each set s \in S has a cost 1/C < c_s < 1 for some parameter C > 1. The input keeps changing via a sequence of updates.
Revised: We consider the minimum set cover problem and a collection of sets S \subseteq 2^{U} as input, where each set s \in S has a cost 1/C < c_s < 1 for some parameter C > 1. The input keeps changing via a sequence of updates.
Edit intent: clarity
Original:  Let ndenote the }  number of sets in \mathcal{S number of elements in the universe and the maximum frequency of an element across all the updates.
Revised:  Let ndenote the } , a challenge is to minimize the update time while guaranteeing close to the optimal \min(O(\log n), f) approximation factor. (Throughout, m, n, f, and C are parameters denoting the maximum number of sets in \mathcal{S number of elements in the universe and the maximum frequency of an element across all the updates.
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Original:  STOC `17%DIFDELCMD < ] %%% designed a deterministic \Theta(\log m )-approximation algorithm for this problem that has an update time of O(f \log m) .
Revised:  %DIFDELCMD < ] %%% designed a deterministic \Theta(\log m )-approximation algorithm for this problem that has an update time of O(f \log m) .
Edit intent: coherence
Original:  STOC `17%DIFDELCMD < ] %%% designed a deterministic \Theta(\log m )-approximation algorithm for this problem that has an update time of O(f \log m) .
Revised:  STOC `17%DIFDELCMD < ] %%% , frequency, and the cost range.) In the high-frequency range, when f=\Omega(\log n), this was achieved by a deterministic O(\log n )-approximation algorithm for this problem that has an update time of O(f \log m) .
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Original:  STOC `17%DIFDELCMD < ] %%% designed a deterministic \Theta(\log m )-approximation algorithm for this problem that has an update time of O(f \log m) .
Revised:  STOC `17%DIFDELCMD < ] %%% designed a deterministic \Theta(\log m )-approximation algorithm with O(f \log m) .
Edit intent: clarity
Original:  STOC `17%DIFDELCMD < ] %%% designed a deterministic \Theta(\log m )-approximation algorithm for this problem that has an update time of O(f \log m) . On the other hand ] , the line of work by Bhattacharya et al.
Revised:  STOC `17%DIFDELCMD < ] %%% designed a deterministic \Theta(\log m )-approximation algorithm for this problem that has an update time of O(f \log n) amortized update time ] , the line of work by Bhattacharya et al.
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Original:  [ FOCS ` 19] very recently led to a deterministic (1+\epsilon)f-approximation algorithm with O(f \log ( Cm )/\epsilon^2)  update time.
Revised:  [ ICALP'15, IPCO'17, FOCS' 19] very recently led to a deterministic (1+\epsilon)f-approximation algorithm with O(f \log ( Cm )/\epsilon^2) update time.
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Original:  [ FOCS ` 19] very recently led to a deterministic (1+\epsilon)f-approximation algorithm with O(f \log ( Cm )/\epsilon^2)  update time.
Revised:  [ FOCS ` 19] led to a deterministic (1+\epsilon)f-approximation algorithm with O(f \log ( Cm )/\epsilon^2) update time.
Edit intent: clarity
Original:  [ FOCS ` 19] very recently led to a deterministic (1+\epsilon)f-approximation algorithm with O(f \log ( Cm )/\epsilon^2)  update time.
Revised:  [ FOCS ` 19] very recently led to a deterministic (1+\epsilon)f-approximation algorithm with O(f \log ( Cn )/\epsilon^2) update time.
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Original:  [ FOCS ` 19] very recently led to a deterministic (1+\epsilon)f-approximation algorithm with O(f \log ( Cm )/\epsilon^2)  update time.
Revised:  [ FOCS ` 19] very recently led to a deterministic (1+\epsilon)f-approximation algorithm with O(f \log ( Cm )/\epsilon^2) amortized update time.
Edit intent: meaning-changed
Original:  In this paper , we obtain the first dynamic algorithm for this problem with near-optimal approximation ratio whose update timeis independent of m, n(\epsilon)) worst-case update time for unweighted dynamic vertex cover (i.e., when f=2 and C=1) by Bhattacharya et al.
Revised:  In this paper we improve the latter update time and provide the first bounds that subsume (and sometimes improve) the state-of-the-art dynamic vertex cover algorithms. We obtain: 1. (1+\epsilon)f-approximation ratio in O(f\log^2 (Cn)/\epsilon^3) worst-case update time: No non-trivial worst-case update time was previously known for dynamic set cover. Our bound subsumes and improves by a logarithmic factor the O(\log^3 n/\text{poly(\epsilon)) worst-case update time for unweighted dynamic vertex cover (i.e., when f=2 and C=1) by Bhattacharya et al.
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Original:  }[ . Specifically, our algorithm is deterministic and it achieves an approximation ratio of (1+\epsilon) f and an update time of O\left((f^2/\epsilon^3)+(f/\epsilon^2) \log C\right) .
Revised:  }[SODA'17 . Specifically, our algorithm is deterministic and it achieves an approximation ratio of (1+\epsilon) f and an update time of O\left((f^2/\epsilon^3)+(f/\epsilon^2) \log C\right) .
Edit intent: meaning-changed
Original:  }[ . Specifically, our algorithm is deterministic and it achieves an approximation ratio of (1+\epsilon) f and an update time of O\left((f^2/\epsilon^3)+(f/\epsilon^2) \log C\right) .
Revised:  }[ . 2. (1+\epsilon) f and an update time of O\left((f^2/\epsilon^3)+(f/\epsilon^2) \log C\right) .
Edit intent: coherence
Original:  }[ . Specifically, our algorithm is deterministic and it achieves an approximation ratio of (1+\epsilon) f and an update time of O\left((f^2/\epsilon^3)+(f/\epsilon^2) \log C\right) .
Revised:  }[ . Specifically, our algorithm is deterministic and it achieves an approximation ratio of (1+\epsilon) f-approximation ratio in O\left((f^2/\epsilon^3)+(f/\epsilon^2) \log C\right) .
Edit intent: meaning-changed
Original:  }[ . Specifically, our algorithm is deterministic and it achieves an approximation ratio of (1+\epsilon) f and an update time of O\left((f^2/\epsilon^3)+(f/\epsilon^2) \log C\right) . Our algorithm is based on the dynamic primal-dual framework, and it carefully combines the ideas from the dynamic vertex cover algorithm of Bhattacharya and Kulkarni [SODA ` 19] and the dynamic set cover algorithm of Bhattacharya et al.
Revised:  }[ . Specifically, our algorithm is deterministic and it achieves an approximation ratio of (1+\epsilon) f and an update time of O\left((f^2/\epsilon^3)+(f/\epsilon^2) \log C\right) amortized update time: This result improves the previous O(f \log (Cn)/\epsilon^2) update time bound for most values of f in the low-frequency range, i.e. whenever f=o(\log n). It is the first that is independent of m and n. It subsumes the constant amortized update time of Bhattacharya and Kulkarni [SODA ` 19] and the dynamic set cover algorithm of Bhattacharya et al.
Edit intent: meaning-changed
Original:  Our algorithm is based on the dynamic primal-dual framework, and it carefully combines the ideas from the dynamic vertex cover algorithm of Bhattacharya and Kulkarni [SODA ` 19] and the dynamic set cover algorithm of Bhattacharya et al.
Revised:  Our algorithm is based on the dynamic primal-dual framework, and it carefully combines the ideas from the dynamic vertex cover algorithm of Bhattacharya and Kulkarni [SODA ' 19] and the dynamic set cover algorithm of Bhattacharya et al.
Edit intent: fluency
Original:  Our algorithm is based on the dynamic primal-dual framework, and it carefully combines the ideas from the dynamic vertex cover algorithm of Bhattacharya and Kulkarni [SODA ` 19] and the dynamic set cover algorithm of Bhattacharya et al.
Revised:  Our algorithm is based on the dynamic primal-dual framework, and it carefully combines the ideas from the dynamic vertex cover algorithm of Bhattacharya and Kulkarni [SODA ` 19] 
Edit intent: clarity
Original: We give a novel representation of the Moran Genealogy Process, a continuous-time Markov process on the space of size-n genealogies with the demography of the classical Moran process .
Revised: We define the Sampled Moran Genealogy Process, a continuous-time Markov process on the space of size-n genealogies with the demography of the classical Moran process .
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