wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show our result by proving  that for every k\geq 1, s\geq 1, t\geq 1, the class of (K_k,K_{1,s}^1,P_t)-free graphs has bounded mim-width and that a corresponding branch decomposition is "quickly computable"  .
Revised:  We show our result by proving boundedness of an underlying width parameter. Namely, we show that for every k\geq 1, s\geq 1, t\geq 1, the class of (K_k,K_{1,s}^1,P_t)-free graphs has bounded mim-width and that a corresponding branch decomposition is "quickly computable" .
Edit intent: meaning-changed
Original:  We show our result by proving  that for every k\geq 1, s\geq 1, t\geq 1, the class of (K_k,K_{1,s}^1,P_t)-free graphs has bounded mim-width and that a corresponding branch decomposition is "quickly computable"  .
Revised:  We show our result by proving that for every k\geq 1, s\geq 1, t\geq 1, the class of (K_k,K_{1,s}^1,P_t)-free graphs has bounded mim-width and that a corresponding branch decomposition is "quickly computable" for these graphs .
Edit intent: clarity
Original:  It is found that traditional factors should almost always be included in the feature matrix and moving average rotations of the data can provide important gains for various forecasting targets  .
Revised:  It is found that traditional factors should almost always be included in the feature matrix and moving average rotations of the data can provide important gains for various forecasting targets . Also, we note that while predicting directly the average growth rate is equivalent to averaging separate horizon forecasts when using OLS-based techniques, the latter can substantially improve on the former when regularization and/or nonparametric nonlinearities are involved .
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Original:  However, existing epidemic models lack the ability to capture this complex decision-making process, which is shaped by an interplay of factors including government-mandated policy interventions, expected socio-economic costs, perceived infection risks and social influences.
Revised:  However, existing models lack the ability to capture this complex decision-making process, which is shaped by an interplay of factors including government-mandated policy interventions, expected socio-economic costs, perceived infection risks and social influences.
Edit intent: clarity
Original:  However, existing epidemic models lack the ability to capture this complex decision-making process, which is shaped by an interplay of factors including government-mandated policy interventions, expected socio-economic costs, perceived infection risks and social influences. Here, we introduce a novel parsimonious  model, grounded in evolutionary game theory, able to capture decision-making dynamics over heterogeneous time scales.
Revised:  However, existing epidemic models lack the ability to capture this complex interdependence over the entire course of the epidemic. We introduce a novel parsimonious model, grounded in evolutionary game theory, able to capture decision-making dynamics over heterogeneous time scales.
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Original:  Here, we introduce a novel parsimonious  model, grounded in evolutionary game theory, able to capture decision-making dynamics over heterogeneous time scales. Using real data, we analyse three case studies in the spreading of gonorrhoea, the 1918--19 Spanish flu and COVID-19. Behavioural factors shaping the course of the epidemic are intelligibly mapped onto a minimal set of model parameters , and their interplay gives rise to characteristic phenomena , such as sustained periodic outbreaks, multiple epidemic waves, or  a successful eradication of the disease.
Revised:  Here, we introduce a novel parsimonious model, grounded in evolutionary game theory, able to capture decision-making and epidemics co-evolve, shaped by an interplay of factors mapped onto a minimal set of model parameters , and their interplay gives rise to characteristic phenomena , such as sustained periodic outbreaks, multiple epidemic waves, or a successful eradication of the disease.
Edit intent: clarity
Original:  Behavioural factors shaping the course of the epidemic are intelligibly mapped onto a minimal set of model parameters , and their interplay gives rise to characteristic phenomena , such as sustained periodic outbreaks, multiple epidemic waves, or  a successful eradication of the disease.
Revised:  Behavioural factors shaping the course of the epidemic are intelligibly mapped onto a minimal set of model parameters ---including government-mandated interventions, socio-economic costs, perceived infection risks and social influences. This interplay gives rise to characteristic phenomena , such as sustained periodic outbreaks, multiple epidemic waves, or a successful eradication of the disease.
Edit intent: meaning-changed
Original:  Behavioural factors shaping the course of the epidemic are intelligibly mapped onto a minimal set of model parameters , and their interplay gives rise to characteristic phenomena , such as sustained periodic outbreaks, multiple epidemic waves, or  a successful eradication of the disease.
Revised:  Behavioural factors shaping the course of the epidemic are intelligibly mapped onto a minimal set of model parameters , and their interplay gives rise to a range of characteristic phenomena that can be captured within this general framework , such as sustained periodic outbreaks, multiple epidemic waves, or a successful eradication of the disease.
Edit intent: meaning-changed
Original:  Behavioural factors shaping the course of the epidemic are intelligibly mapped onto a minimal set of model parameters , and their interplay gives rise to characteristic phenomena , such as sustained periodic outbreaks, multiple epidemic waves, or  a successful eradication of the disease.
Revised:  Behavioural factors shaping the course of the epidemic are intelligibly mapped onto a minimal set of model parameters , and their interplay gives rise to characteristic phenomena , such as sustained periodic outbreaks, multiple epidemic waves, or prompt behavioural response ensuring a successful eradication of the disease.
Edit intent: clarity
Original:  Our modelenables a direct assessment of the epidemiological and socio-economic impact of different policy interventions implemented to combat epidemic outbreaks. Besides the common-sense finding that stringent non-pharmaceutical interventions are essential to taming the initial phases of the outbreak, the duration of such interventions and the way they are phased out are key for an eradication in the medium-to-long term. Surprisingly, our findings reveal that social influence is a double-edged sword in the control of epidemics, helping strengthen collective adoption of self-protective behaviours during the early stages of the epidemic, but then accelerating their rejection upon lifting of non-pharmaceutical containment interventions .
Revised:  The model's potentialities are demonstrated by three case studies based on real-world gonorrhoea, 1918--19 Spanish flu and COVID-19 .
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Original:  Likewise, individual decision-making is highly influenced by the overwhelming pressure of epidemic spreading. However , existing models lack the ability to capture this complex interdependence over the entire course of the epidemic.
Revised:  Likewise, individual decision-making and epidemics on a network. Although such a co-evolution is deeply intertwined in the real-world , existing models lack the ability to capture this complex interdependence over the entire course of the epidemic.
Edit intent: meaning-changed
Original:  However , existing models lack the ability to capture this complex interdependence over the entire course of the epidemic. We introduce a novel parsimonious network model , grounded in evolutionary game theory, in which decision-making and epidemics co-evolve, shaped by an interplay of factors mapped onto a minimal set of model parameters ---including government-mandated interventions, socio-economic costs, perceived infection risks and social influences. This interplay gives rise to a range of characteristic phenomenathat can be captured within this general framework, such as sustained periodic outbreaks, multiple epidemic waves, or prompt behavioural response ensuring a successful eradication of the disease.
Revised:  However , existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our model offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks, multiple epidemic waves, or prompt behavioural response ensuring a successful eradication of the disease.
Edit intent: clarity
Original:  This interplay gives rise to a range of characteristic phenomenathat can be captured within this general framework, such as sustained periodic outbreaks, multiple epidemic waves, or prompt behavioural response ensuring a successful eradication of the disease. The model's potentialities are demonstrated by three case studiesbased on real-world gonorrhoea, 1918--19 Spanish flu and COVID-19 .
Revised:  This interplay gives rise to a range of characteristic phenomenathat can be captured within this general framework, such as sustained periodic outbreaks, as illustrated through two real-world case studies .
Edit intent: clarity
Original: We introduce a parsimonious game-theoretic behavioral--epidemic model, in which an interplay of realistic factors shapes the co-evolution of individual decision-making and epidemics on a network.
Revised: The spreading dynamics of an epidemic and the collective behavioral pattern of the population over which it spreads are deeply intertwined and the latter can critically shape the outcome of the former. Motivated by this, we design a parsimonious game-theoretic behavioral--epidemic model, in which an interplay of realistic factors shapes the co-evolution of individual decision-making and epidemics on a network.
Edit intent: meaning-changed
Original:  Our model offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks , as illustrated through two real-world case studies .
Revised:  Our model offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks . The framework also allows to assess the effectiveness of different policy interventions on ensuring a collective response that successfully eradicates the outbreak. Two case studies, inspired by real-world diseases, are presented to illustrate the potentialities of the proposed model .
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Original: Motivation: Recognizing named entities (NER) and their associated attributes like negation are core tasks in natural language processing.However, manually labeling data for entity tasks is time consuming and expensive, creating barriers to using machine learning in new medical applications.
Revised: In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and expensive, creating barriers to using machine learning in new medical applications.
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Original: However, manually labeling data for entity tasks is time consuming and expensive, creating barriers to using machine learning in new medical applications. Weakly supervised learning, which automatically builds imperfect training sets from low cost, less accurate labeling rules, offers a potential solution. Medical ontologies are compelling sources for generating labels, however combining multiple ontologies without ground truth data creates challenges due to label noise introduced by conflicting entity definitions. Key questions remain on the extent to which weakly supervised entity classification can be automated using ontologies, or how much additional task-specific rule engineering is required for state-of-the-art performance. Also unclear is how pre-trained language models , such as BioBERT, improve the ability to generalize from imperfectly labeled data. Results: We present Trove, a framework for weakly supervised entity classification using medical ontologies .
Revised: However, manually labeling data for entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies .
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Original:  Results: We present Trove, a framework for weakly supervised entity classification using medical ontologies . We report state-of-the-art, weakly supervised performance on two NER benchmark datasets and establish new baselines for two entity classification tasks in clinical text. We perform within an average of 3.5 F1 points (4.2\%) of NER classifiers trained with hand-labeled data.
Revised:  Results: We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled data.
Edit intent: clarity
Original:  We perform within an average of 3.5 F1 points (4.2\%) of NER classifiers trained with hand-labeled data. Automatically learning label source accuracies to correct for label noise provided an average improvement of 3.9 F1 points. BioBERT provided an average improvement of 0.9 F1 points. We measure the impact of combining large numbers of ontologies and present a case study on rapidly building classifiers for COVID-19 clinical tasks.
Revised:  We perform within an average of 3.5 F1 points (4.2\%) of NER classifiers trained with hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 clinical tasks.
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Original:  We measure the impact of combining large numbers of ontologies and present a case study on rapidly building classifiers for COVID-19 clinical tasks. Our framework demonstrates how a wide range of medical entity classifiers can be quickly constructed using weak supervision and without requiring manually-labeled training data .
Revised:  We measure the impact of combining large numbers of ontologies and present a case study on rapidly building classifiers for COVID-19 presenting symptoms and risk factors .
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Original:  We also introduce two new complexes which we call the Alpha clique and Minibox complexes . We provide results on topological properties of these, as well as computational experiments which show that these can often be used to reduce the number of high-dimensional simplices included in Cech filtrations  and so speed up the computation of persistent homology .
Revised:  We also introduce two new complexes which we call the Alpha clique and Minibox complexes , and prove their equivalence with \v{C which show that these can often be used to reduce the number of high-dimensional simplices included in Cech filtrations and so speed up the computation of persistent homology .
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Original:  We provide results on topological properties of these, as well as computational experiments which show that these can often be used to reduce the number of high-dimensional simplices included in Cech filtrations  and so speed up the computation of persistent homology .
Revised:  We provide results on topological properties of these, as well as computational experiments which show that Minibox filtrations can often be used to reduce the number of high-dimensional simplices included in Cech filtrations and so speed up the computation of persistent homology .
Edit intent: meaning-changed
Original:  We provide results on topological properties of these, as well as computational experiments which show that these can often be used to reduce the number of high-dimensional simplices included in Cech filtrations  and so speed up the computation of persistent homology .
Revised:  We provide results on topological properties of these, as well as computational experiments which show that these can often be used to reduce the number of simplices included in Cech filtrations and so speed up the computation of persistent homology .
Edit intent: clarity
Original:  We provide results on topological properties of these, as well as computational experiments which show that these can often be used to reduce the number of high-dimensional simplices included in Cech filtrations  and so speed up the computation of persistent homology .
Revised:  We provide results on topological properties of these, as well as computational experiments which show that these can often be used to reduce the number of high-dimensional simplices included in Cech filtrations , and so speed up the computation of persistent homology .
Edit intent: fluency
Original:  We provide results on topological properties of these, as well as computational experiments which show that these can often be used to reduce the number of high-dimensional simplices included in Cech filtrations  and so speed up the computation of persistent homology .
Revised:  We provide results on topological properties of these, as well as computational experiments which show that these can often be used to reduce the number of high-dimensional simplices included in Cech filtrations and so speed up persistent homology computations .
Edit intent: clarity
Original:  Finally we run computational experiments on random points, which show that Minibox filtrations can often be used to reduce the number of simplices included in \v{C speed up persistent homology computations  .
Revised:  Finally we run computational experiments on random points, which show that Minibox filtrations can often be used to reduce the number of simplices included in \v{C speed up persistent homology computations in homological degrees zero and one by reducing the number of simplices in the filtration .
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Original: We employ the mobility data released by Google and Apple to investigate the effects of social distancing on the spreading dynamics of COVID-19 in Europe. We identifyand quantify different degrees of social distancing and characterise their imprint on the first wave of the pandemic.
Revised: We employ the mobility data released by Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the pandemic.
Edit intent: clarity
Original:  We identifyand quantify different degrees of social distancing and characterise their imprint on the first wave of the pandemic. The analysis allows us to classify countries according to their level of mobility. Furthermore we identify a negative change in the infection rate occurring two to five weeks after the onset of mobility reduction for the European countries studied here .
Revised:  We identifyand quantify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Interestingly we observe a general decrease in the infection rate occurring two to five weeks after the onset of mobility reduction for the European countries studied here .
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Original: We show that the transformer attention mechanism is the update rule of a modern Hopfield network with continuous states . This new Hopfield network can store exponentially (with the dimension  ) many patterns, converges with one update, and has exponentially small retrieval errors.
Revised: We show that the transformer attention mechanism is the update rule of a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension ) many patterns, converges with one update, and has exponentially small retrieval errors.
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Original:  The number of stored patterns is traded off against convergence speed and retrieval error. The new Hopfield network has three types of energy minima (fixed points of the update): (1) global fixed point averaging over all patterns, (2) metastable states averaging over a subset of patterns, and (3) fixed points which store a single pattern.
Revised:  It has three types of energy minima (fixed points of the update): (1) global fixed point averaging over all patterns, (2) metastable states averaging over a subset of patterns, and (3) fixed points which store a single pattern.
Edit intent: clarity
Original:  Transformer and BERT modelsoperate in their first layers preferably in the global averaging regime, while they operate in higher layers in metastable states.
Revised:  The new update rule is equivalent to the attention mechanism used in transformers. This equivalence enables a characterization of the heads of transformer models. These heads perform in the first layers preferably in the global averaging regime, while they operate in higher layers in metastable states.
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Original:  The gradient in transformers is maximal for metastable states, is uniformly distributed for global averaging, and vanishes for a fixed point near a stored pattern. Using the Hopfield network interpretation, we analyzed learning of transformer and BERT models. Learning starts with attention heads that average and then most of them switch to metastable states. However, the majority of heads in the first layers still averages and can be replaced by averaging, e.g. our proposed Gaussian weighting. In contrast, heads in the last layers steadily learn and seem to use metastable states to collect information created in lower layers. These heads seem to be a promising target for improving transformers. Neural networks with Hopfield networks outperform other methods on immune repertoire classification , where the Hopfield net stores several hundreds of thousands of patterns.
Revised:  The new modern Hopfield network can be integrated into deep learning architectures as layers to allow the storage of and access to raw input data, intermediate results, or learned prototypes. These Hopfield layers enable new ways of deep learning, beyond fully-connected, convolutional, or recurrent networks, and provide pooling, memory, association, and attention mechanisms. We demonstrate the broad applicability of the Hopfield layers across various domains. Hopfield layers improved state-of-the-art on three out of four considered multiple instance learning problems as well as on immune repertoire classification , where the Hopfield net stores several hundreds of thousands of patterns.
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Original:  Neural networks with Hopfield networks outperform other methods on immune repertoire classification , where the Hopfield net stores several hundreds of thousands of patterns. We provide a new PyTorch layer called "Hopfield", which allows to equip deep learning architectures with modern Hopfield networks as a new powerful concept comprising pooling, memory, and attention. GitHub : URL
Revised:  Neural networks with Hopfield networks outperform other methods on immune repertoire classification , where the Hopfield net stores several hundreds of thousands of instances. On the UCI benchmark collections of small classification tasks, where deep learning methods typically struggle, Hopfield layers yielded a new state-of-the-art when compared to different machine learning methods. Finally, Hopfield layers achieved state-of-the-art on two drug design datasets. The implementation is available at : URL
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