wanyu/IteraTeR_full_sent huggingface dataset:
Original: A question has been whirling in the musical fraternity for generations. Which comes first? Melody or lyrics? The answer to which is still unknown. It is well established that the lyrics play a very important role in portraying the emotional content of a particular recitation or song. The audience intuitively listens to the lyrics of the song or recitation and tends to get an idea of the mood of the song or recitation. But what would happen if the lyric part is separated from the melody and conveyed to the audience as a separate entity altogether. Would the emotional content of the recitation remain the same or the meaning would change altogether? A recitation is a way of combining the words together so that they have a sense of rhythm and thus an emotional content is imbibed within.
Revised: A recitation is a way of combining the words together so that they have a sense of rhythm and thus an emotional content is imbibed within.
Edit intent: coherence
Original:  The recitations as well as the speech were analyzed with the help of a latest non linear technique called Detrended Fluctuation Analysis  that gives a scaling exponent  , which is essentially the measure of long range correlations present in the signal.
Revised:  The recitations as well as the speech were analyzed with the help of a latest non linear technique called Detrended Fluctuation Analysis (DFA) that gives a scaling exponent , which is essentially the measure of long range correlations present in the signal.
Edit intent: meaning-changed
Original:  The recitations as well as the speech were analyzed with the help of a latest non linear technique called Detrended Fluctuation Analysis  that gives a scaling exponent  , which is essentially the measure of long range correlations present in the signal.
Revised:  The recitations as well as the speech were analyzed with the help of a latest non linear technique called Detrended Fluctuation Analysis that gives a scaling exponent \alpha , which is essentially the measure of long range correlations present in the signal.
Edit intent: meaning-changed
Original:  Our analysis shows that the scaling exponent for all parts of recitation were much higher in general as compared to their counterparts in speech  .
Revised:  Our analysis shows that the scaling exponent for all parts of recitation were much higher in general as compared to their counterparts in speech . We have also established a critical value from our analysis, above which a mere speech may become a recitation. The case may be similar to the conventional phase transition, wherein the measurement of external condition at which the transformation occurs (generally temperature) is called phase transition. Further, we have also categorized the 5 recitations on the basis of their emotional content with the help of the same DFA technique. Analysis with a greater variety of recitations is being carried out to yield more interesting results .
Edit intent: meaning-changed
Original:  The odds ratio in the sample is our main estimand of interest and we articulate its relationship with causal parameters under various scenarios. It turns out that the odds ratio is generally a sharp upper bound for counterfactual relative risk under some monotonicity assumptions, without resorting to strong ignorability, nor to the rare-disease assumption.
Revised:  The odds ratio is shown to be a sharp upper bound for counterfactual relative risk under some monotonicity assumptions, without resorting to strong ignorability, nor to the rare-disease assumption.
Edit intent: clarity
Original:  It turns out that the odds ratio is generally a sharp upper bound for counterfactual relative risk under some monotonicity assumptions, without resorting to strong ignorability, nor to the rare-disease assumption.
Revised:  It turns out that the odds ratio is generally a sharp upper bound on causal relative risk under some monotonicity assumptions, without resorting to strong ignorability, nor to the rare-disease assumption.
Edit intent: meaning-changed
Original:  It turns out that the odds ratio is generally a sharp upper bound for counterfactual relative risk under some monotonicity assumptions, without resorting to strong ignorability, nor to the rare-disease assumption.
Revised:  It turns out that the odds ratio is generally a sharp upper bound for counterfactual relative risk under the monotone treatment response and monotone treatment selection assumptions, without resorting to strong ignorability, nor to the rare-disease assumption.
Edit intent: meaning-changed
Original:  Further , we propose semparametrically efficient , easy-to-implement, machine-learning-friendly estimators of the aggregated (log) odds ratio by exploiting an explicit form of the efficient influence function.
Revised:  Sharp bounds on causal attributable risk are also obtained under the same assumptions. Paying special attention to the (conditional) odds ratio , we propose semparametrically efficient , easy-to-implement, machine-learning-friendly estimators of the aggregated (log) odds ratio by exploiting an explicit form of the efficient influence function.
Edit intent: meaning-changed
Original:  Further , we propose semparametrically efficient , easy-to-implement, machine-learning-friendly estimators of the aggregated (log) odds ratio by exploiting an explicit form of the efficient influence function. Using our new estimators , we develop methods for causal inference and illustrate the usefulness of our methods by a real-data example .
Revised:  Further , we propose semparametrically efficient , easy-to-implement, machine-learning-friendly estimators of the aggregated (log) odds ratio . Further , we develop methods for causal inference and illustrate the usefulness of our methods by a real-data example .
Edit intent: coherence
Original:  Using our new estimators , we develop methods for causal inference and illustrate the usefulness of our methods by a real-data example .
Revised:  Using our new estimators , we develop easy-to-implement causal inference procedures for relative and attributable risk. Finally, we showcase our methodology by applying it to two unique datasets in the literature. We find that attending private school may have little effect on entering a very selective university in Pakistan and that dropping out of school could substantially increase relative and attributable risk of joining a criminal gang in Brazil .
Edit intent: meaning-changed
Original:  The second algorithm maintains an O(k)-approximate cut sparsifier of size \tilde  (n) in \tilde  (n^{1/k}) amortized update time, for any k\ge1, which is polylog(n) time when k=\log(n).
Revised:  The second algorithm maintains an O(k)-approximate cut sparsifier of size \tilde O (n) in \tilde (n^{1/k}) amortized update time, for any k\ge1, which is polylog(n) time when k=\log(n).
Edit intent: meaning-changed
Original:  The second algorithm maintains an O(k)-approximate cut sparsifier of size \tilde  (n) in \tilde  (n^{1/k}) amortized update time, for any k\ge1, which is polylog(n) time when k=\log(n).
Revised:  The second algorithm maintains an O(k)-approximate cut sparsifier of size \tilde (n) in \tilde O (n^{1/k}) amortized update time, for any k\ge1, which is polylog(n) time when k=\log(n).
Edit intent: meaning-changed
Original:  The algorithm is based on the Newton-Dinkelbach method for fractional combinatorial optimization . We extend and strengthen previous work of Madani (2002) that showed a weakly polynomial bound for a variant of the Newton-Dinkelbach method for solving deterministic Markov decision processes (DMDPs) , a special class of 2VPI linear programs .
Revised:  The algorithm is based on the Newton-Dinkelbach method , a special class of 2VPI linear programs .
Edit intent: coherence
Original:  We extend and strengthen previous work of Madani (2002) that showed a weakly polynomial bound for a variant of the Newton-Dinkelbach method for solving deterministic Markov decision processes (DMDPs) , a special class of 2VPI linear programs .
Revised:  We extend and strengthen previous work of Madani (2002) that showed a weakly polynomial bound for a variant of the Newton-Dinkelbach method for solving deterministic Markov decision processes (DMDPs) , a well-known technique for solving fractional and parametric optimization problems. This acceleration halves the Bregman divergence between the current iterate and the optimal solution within every two iterations. Using the Bregman divergence as a potential in conjunction with combinatorial arguments, we obtain strongly polynomial algorithms in three applications domains: (i) For linear fractional combinatorial optimization, we show a convergence bound of O(m \log m) iterations; the previous best bound was O(m^2 \log m) by Wang et al. (2006). (ii) We obtain a strongly polynomial label-correcting algorithm for solving linear feasibility systems with two variables per inequality (2VPI) .
Edit intent: meaning-changed
Original:  Every iteration takes O(  m + n \log n) time for DMDPs, and O(mn) time for general 2VPI systems. The key technical idea is a new analysis of the Newton-Dinkelbach method exploiting gauge symmetries of the algorithm.
Revised:  Every iteration takes O( m + n \log n) time for the special case of deterministic Markov Decision Processes (DMDPs). This extends and strengthens a previous result by Madani (2002) that showed a weakly polynomial bound for a variant of the Newton-Dinkelbach method exploiting gauge symmetries of the algorithm.
Edit intent: meaning-changed
Original:  The key technical idea is a new analysis of the Newton-Dinkelbach method exploiting gauge symmetries of the algorithm. This also leads to an acceleration of the Newton-Dinkelbach method for general fractional combinatorial optimization problems. For the special case of linear fractional combinatorial optimization, our method converges in O(m\log m) iterations, improving upon the previous best bound of O(m^2\log m) by Wang et al. ( 2006 ).
Revised:  The key technical idea is a new analysis of the Newton-Dinkelbach method for solving DMDPs. (iii) We give a simplified variant of the parametric submodular function minimization result by Goemans et al. ( 2006 ).
Edit intent: meaning-changed
Original:  For the special case of linear fractional combinatorial optimization, our method converges in O(m\log m) iterations, improving upon the previous best bound of O(m^2\log m) by Wang et al. ( 2006 ).
Revised:  For the special case of linear fractional combinatorial optimization, our method converges in O(m\log m) iterations, improving upon the previous best bound of O(m^2\log m) by Wang et al. ( 2017 ).
Edit intent: meaning-changed
Original: We model the spread of an epidemic through physically proximate human contacts using an Erdos-Renyi random graph representing a city, and simulate outcomes for two kinds of agents , poor and non-poor.
Revised: We create a network model to study the spread of an epidemic through physically proximate human contacts using an Erdos-Renyi random graph representing a city, and simulate outcomes for two kinds of agents , poor and non-poor.
Edit intent: meaning-changed
Original:  Larger populations, higher fractions of poor, and greater intensity of interventions are found to progressively worsen outcomes for the poor , vis-a-vis the non-poor. Increasing population leads to higher infection rates among the poor, supporting concerns that households in large high-density settlements like slums amongst the most at-risk population groups during an outbreak. Related to this, increasing population of the urban poor is found to worsen infection outcomes of the poor, and given the expansion of developing cities into large metropolises, this is an issue of significant concern for public health and economic wellbeing. The poor are also found disproportionately affected by increasing length and intensity of social distancing measures, pointing to the immediate need for countries to have clear guidelines on how social distancing can be practiced in high density slum settlements, given local contexts. Sustainable solutions for the long-term will require concerted investment in environmental and physical infrastructure to improve living conditions .
Revised:  Larger populations, higher fractions of poor, and greater intensity of interventions are found to progressively worsen outcomes for the poor , vis-a-vis the developing world .
Edit intent: coherence
Original:  Finally, addressing these iniquitous outcomes for the poor creates better outcomes for the whole population, including the non-poor.
Revised:  Addressing these challenges requires a deeper, more rigorous understanding of the relationships between structural poverty and epidemy, as well as effective utilization of extant community level infrastructure for primary care in developing cities. Finally, improving iniquitous outcomes for the poor creates better outcomes for the whole population, including the non-poor.
Edit intent: meaning-changed
Original:  We show that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings , and argue that it is also well suited to parallel computation for coalescent models.
Revised:  We show that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings algorithms, and that it is also well suited to parallel computation for coalescent models.
Edit intent: clarity
Original:  We show that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings , and argue that it is also well suited to parallel computation for coalescent models.
Revised:  We show that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings , and argue that it is well suited to parallel computation for coalescent models.
Edit intent: clarity
Original: We construct  zig-zag processes targeting posterior distributions arising in genetics from the Kingman coalescent and two popular models of mutation.
Revised: We construct zig-zag process targeting a posterior distribution defined on a hybrid state space consisting of both discrete and continuous variables. The construction does not require any assumptions on the structure among discrete variables. We demonstrate our method on two examples in genetics based on the Kingman coalescent and two popular models of mutation.
Edit intent: meaning-changed
Original: We construct  zig-zag processes targeting posterior distributions arising in genetics from the Kingman coalescent and two popular models of mutation. We show that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings algorithms, and that it is well suited to parallel computation for coalescent models .
Revised: We construct zig-zag processes targeting posterior distributions arising in genetics from the Kingman coalescent , showing that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings algorithms, and that it is well suited to parallel computation for coalescent models .
Edit intent: clarity
Original:  We show that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings algorithms, and that it is well suited to parallel computation for coalescent models .
Revised:  We show that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis--Hastings algorithms, and that it is well suited to parallel computation for coalescent models .
Edit intent: fluency
Original:  We show that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings algorithms, and that it is well suited to parallel computation for coalescent models .
Revised:  We show that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings algorithms, and that it is well suited to parallel computation .
Edit intent: clarity
Original: In the last decade, Human Activity Recognition (HAR) has become a very important research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives.
Revised: In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives.
Edit intent: clarity
Original:  HAR is one of the most promising assistive technology tools to support elderly's daily life . However, this class of algorithms requires large amounts of data. Furthermore, not all the HAR application fields generate a significant amount of data and not all of them provide the computational power that DL models in HAR require .
Revised:  HAR is one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities .
Edit intent: clarity
Original:  The time at which the  number of infected individuals  starts decreasing cannot be reliably calculated before it is actually attained . A standard SIR model with confinement shows that infection spread is inhibited only above a threshold . Confinement induces a slow-down in the expansion phase that does not guarantee an eventual control of the epidemic.
Revised:  The time at which the number of infected individuals starts decreasing cannot be reliably calculated before it is actually attained ; neither can the end of the epidemic after the turning point. An SIR model with confinement shows that infection spread is inhibited only above a threshold . Confinement induces a slow-down in the expansion phase that does not guarantee an eventual control of the epidemic.
Edit intent: meaning-changed
Original:  A standard SIR model with confinement shows that infection spread is inhibited only above a threshold . Confinement induces a slow-down in the expansion phase that does not guarantee an eventual control of the epidemic.
Revised:  A standard SIR model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold . Confinement induces a slow-down in the expansion phase that does not guarantee an eventual control of the epidemic.
Edit intent: meaning-changed
Original:  A Bayesian fit to the on-going COVID-19 pandemic in Spain shows that we can infer neither its peaking time nor whether there is a peak at all. The dispersion of possible trajectories grows extremely fast , yielding a short horizon for reliable prediction.
Revised:  A Bayesian fit to the on-going COVID-19 pandemic in Spain shows that a slow-down in the number of newly infected individuals during the expansion phase allows to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast , yielding a short horizon for reliable prediction.
Edit intent: meaning-changed
Original:  The dispersion of possible trajectories grows extremely fast , yielding a short horizon for reliable prediction. As unpredictability is intrinsic, not due to incomplete or wrong data, our study advocates for a scenario of probabilistic forecasting .
Revised:  The dispersion of possible trajectories grows extremely fast . The impossibility to predict in the mid-term is not due to incomplete or wrong data, our study advocates for a scenario of probabilistic forecasting .
Edit intent: clarity
Original:  As unpredictability is intrinsic, not due to incomplete or wrong data, our study advocates for a scenario of probabilistic forecasting .
Revised:  As unpredictability is intrinsic, not due to wrong or incomplete data, since it persists in error-free, synthetically produced data sets, and does not necessarily improve by using larger data sets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective or phenomenological models, and supports that only probabilities of different outcomes can be confidently given .
Edit intent: meaning-changed
Original: Epidemics, such as the present Covid-19 pandemic, usually spread at a rapid rate. Standard models, e.g., the SIR model, have stressed on the importance of finding the susceptible cases to flatten the growth rate of the spread of infection as early as possible. In the present scientific world, location traces in the form of GPS coordinates are logged by mobile device manufacturing and their operating systems developing companies, such as Apple, Samsung, Google etc. However, due to the sensitive nature of this data, it is usually not shared with URLanisations, mainly to protect individual privacy. However, in disaster situations, such as epidemics, data in the form of location traces of a community of people can potentially be helpful to proactively locate susceptible people from the community and enforce quarantine on them as early as possible.
Revised: Epidemics, such as the present Covid-19 pandemic, it is essential to trace, as early as possible, the susceptible people who might have been infected by the disease due to their close proximity with people who were tested positive for the virus. This early contact tracing is likely to limit the rate of spread of the infection within a locality. In this paper, we investigate how effectively and efficiently can such a list of susceptible people be found given a list of infected persons and their locations. To address this problem from an information retrieval (search) perspective, we represent the location of each person at each time instant as a point in a vector space. By using the locations of the form of location traces of a community of people can potentially be helpful to proactively locate susceptible people from the community and enforce quarantine on them as early as possible.
Edit intent: meaning-changed