wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our method distinguishes between links that are common to all networks, links that are specific to only one of the compared networks, and links that are different in that their sign changes between networks. Importantly, we developed a statistical framework to normalize between these different categories of common or changed networklinks. Our approach also allows us to categorize nodes as dominated by common, specific, or differentiated links. We demonstrate the usage of our new method by presenting the analysis of a dataset consisting of different types of cancers. We revealed common , specific, and differentiated links between cancer types and show that each category of links is enriched for genes with distinct Gene Ontology groups. This suggests that our method can detect functional differences. CoDiNA has been implemented in an R package that is available from CRAN .
Revised:  Our method distinguishes between links that are common , specific or different to the networks. Applying CoDiNA to a neurogenesis study identified genes for neuron differentiation. Experimentally overexpressing one candidate resulted in significant disturbance in the underlying neurogenesis' gene regulatory network. We compared data from adults and children with active tuberculosis to test for signatures of HIV. We also identified common and distinct network features for particular cancer types with CoDiNA. These studies show that CoDiNA successfully detects genes associated with the diseases .
Edit intent: meaning-changed
Original:  We investigate the relationship of this result with the stocks' capitalization and we show that a linear dependence from the logarithm of the capitalization  does not explain the observed stylized fact  .
Revised:  We investigate the relationship of this result with the stocks' capitalization and on the kurtosis of stocks' log-returns in order to search for possible confounding effects. We show that a linear dependence from the logarithm of the capitalization does not explain the observed stylized fact .
Edit intent: meaning-changed
Original:  We investigate the relationship of this result with the stocks' capitalization and we show that a linear dependence from the logarithm of the capitalization  does not explain the observed stylized fact  .
Revised:  We investigate the relationship of this result with the stocks' capitalization and we show that a linear dependence from the logarithm of the capitalization and the logarithm of kurtosis does not explain the observed stylized fact .
Edit intent: meaning-changed
Original:  We investigate the relationship of this result with the stocks' capitalization and we show that a linear dependence from the logarithm of the capitalization  does not explain the observed stylized fact  .
Revised:  We investigate the relationship of this result with the stocks' capitalization and we show that a linear dependence from the logarithm of the capitalization does not explain the observed stylized fact , which we interpret as being originated from a deeper relationship .
Edit intent: meaning-changed
Original:  The model is then presented under the physical measure, and its state-space representation is derived, in order to estimate the model's parameters with the Kalman filter for time series of corn, cotton, soybean, sugar and wheat futures from 2007 to 2017.
Revised:  The model is then presented under the physical measure, and its state-space representation is derived, in order to estimate the parameters with the Kalman filter for time series of corn, cotton, soybean, sugar and wheat futures from 2007 to 2017.
Edit intent: clarity
Original:  We confirm the importance of correctly modelling the Samuelson effect in order to account for futures with different maturities. We also see that the seasonal model significantly outperforms the nested non-seasonal model in all five markets, and  show which seasonality patterns are particularly well-suited for each market .
Revised:  The seasonal model significantly outperforms the nested non-seasonal model in all five markets, and show which seasonality patterns are particularly well-suited for each market .
Edit intent: coherence
Original:  We also see that the seasonal model significantly outperforms the nested non-seasonal model in all five markets, and  show which seasonality patterns are particularly well-suited for each market .
Revised:  We also see that the seasonal model significantly outperforms the nested non-seasonal model in all five markets, and show which seasonality patterns are particularly well-suited in each case. We also confirm the importance of correctly modelling the Samuelson effect in order to account for futures with different maturities. Our results are clearly confirmed in a robustness check carried out with an alternative dataset of constant maturity futures for the same agricultural markets .
Edit intent: meaning-changed
Original:  Specifically we show a polynomial time construction for a coreset of size O( \sqrt{d\log (1/\epsilon) / \epsilon ), and we show a near-matching lower bound of size \Omega(  d/ \epsilon).
Revised:  Specifically we show a polynomial time construction for a coreset of size O( \sqrt{d / \epsilon ), and we show a near-matching lower bound of size \Omega( d/ \epsilon).
Edit intent: meaning-changed
Original:  Specifically we show a polynomial time construction for a coreset of size O( \sqrt{d\log (1/\epsilon) / \epsilon ), and we show a near-matching lower bound of size \Omega(  d/ \epsilon).
Revised:  Specifically we show a polynomial time construction for a coreset of size O( \sqrt{d\log (1/\epsilon) / \varepsilon\cdot \sqrt{\log 1/\varepsilon ), and we show a near-matching lower bound of size \Omega( d/ \epsilon).
Edit intent: meaning-changed
Original:  Specifically we show a polynomial time construction for a coreset of size O( \sqrt{d\log (1/\epsilon) / \epsilon ), and we show a near-matching lower bound of size \Omega(  d/ \epsilon).
Revised:  Specifically we show a polynomial time construction for a coreset of size O( \sqrt{d\log (1/\epsilon) / \epsilon ), and we show a near-matching lower bound of size \Omega( \min\{ d/ \epsilon).
Edit intent: meaning-changed
Original:  Specifically we show a polynomial time construction for a coreset of size O( \sqrt{d\log (1/\epsilon) / \epsilon ), and we show a near-matching lower bound of size \Omega(  d/ \epsilon).
Revised:  Specifically we show a polynomial time construction for a coreset of size O( \sqrt{d\log (1/\epsilon) / \epsilon ), and we show a near-matching lower bound of size \Omega( d/ \varepsilon, 1/\varepsilon^2\}). When d\geq 1/\varepsilon^2, it is known that the size of coreset can be O(1/\varepsilon^2).
Edit intent: meaning-changed
Original:  The upper bound is a polynomial in 1/ \epsilon improvement when d \in [3,1/ \epsilon^2) (for all kernels except the Gaussian kernel which had a previous upper bound of O((1/\epsilon) \log^d (1/\epsilon)) ) and the lower bound is the first known lower bound to depend on d for this problem.
Revised:  The upper bound is a polynomial-in-( 1/ \epsilon improvement when d \in [3,1/ \epsilon^2) (for all kernels except the Gaussian kernel which had a previous upper bound of O((1/\epsilon) \log^d (1/\epsilon)) ) and the lower bound is the first known lower bound to depend on d for this problem.
Edit intent: clarity
Original:  The upper bound is a polynomial in 1/ \epsilon improvement when d \in [3,1/ \epsilon^2) (for all kernels except the Gaussian kernel which had a previous upper bound of O((1/\epsilon) \log^d (1/\epsilon)) ) and the lower bound is the first known lower bound to depend on d for this problem.
Revised:  The upper bound is a polynomial in 1/ \varepsilon) improvement when d \in [3,1/ \epsilon^2) (for all kernels except the Gaussian kernel which had a previous upper bound of O((1/\epsilon) \log^d (1/\epsilon)) ) and the lower bound is the first known lower bound to depend on d for this problem.
Edit intent: meaning-changed
Original:  The upper bound is a polynomial in 1/ \epsilon improvement when d \in [3,1/ \epsilon^2) (for all kernels except the Gaussian kernel which had a previous upper bound of O((1/\epsilon) \log^d (1/\epsilon)) ) and the lower bound is the first known lower bound to depend on d for this problem.
Revised:  The upper bound is a polynomial in 1/ \epsilon improvement when d \in [3,1/ \varepsilon^2 ) and the lower bound is the first known lower bound to depend on d for this problem.
Edit intent: meaning-changed
Original:  This paper formulates a detection problem for distinguishing sustained signals from transient ones.
Revised:  It has been shown that the coherent type-1 feedforward loop with an AND logic at the output (or C1-FFL for short) can be used to discriminate a persistent input signal from a transient one. This has been done by modelling the C1-FFL, and then use the model to show that persistent and transient input signals give, respectively, a non-zero and zero output. Instead of assuming the structure of C1-FFL, this paper shows that it is possible to deduce the C1-FFL model from the requirement of discriminating a persistent signal. We do this by first formulating a statistical detection problem of distinguishing persistent signals from transient ones.
Edit intent: meaning-changed
Original:  We show that, if the logarithm of this likelihood ratio is positive (i.e. when the signal is likely to be sustained), then this log-likelihood ratio can be approximately computed by a coherent feedforward loop .
Revised:  We show that, if this log-likelihood ratio is positive (i.e. when the signal is likely to be sustained), then this log-likelihood ratio can be approximately computed by a coherent feedforward loop .
Edit intent: clarity
Original:  We show that, if the logarithm of this likelihood ratio is positive (i.e. when the signal is likely to be sustained), then this log-likelihood ratio can be approximately computed by a coherent feedforward loop .
Revised:  We show that, if the logarithm of this likelihood ratio is positive , which happens when the signal is likely to be sustained), then this log-likelihood ratio can be approximately computed by a coherent feedforward loop .
Edit intent: clarity
Original:  We show that, if the logarithm of this likelihood ratio is positive (i.e. when the signal is likely to be sustained), then this log-likelihood ratio can be approximately computed by a coherent feedforward loop .
Revised:  We show that, if the logarithm of this likelihood ratio is positive (i.e. when the signal is likely to be persistent, then it can be approximately computed by a coherent feedforward loop .
Edit intent: clarity
Original:  We show that, if the logarithm of this likelihood ratio is positive (i.e. when the signal is likely to be sustained), then this log-likelihood ratio can be approximately computed by a coherent feedforward loop .
Revised:  We show that, if the logarithm of this likelihood ratio is positive (i.e. when the signal is likely to be sustained), then this log-likelihood ratio can be approximately computed by a C1-FFL .
Edit intent: meaning-changed
Original:  We then show that the indicative match price is strongly mean-reverting because the imbalance is , which we link to strategic behavior. Finally, we investigate how the final auction price reacts to order placement, especially conditional on imbalance improving or worsening events and find a large difference between the opening and closing auctions, emphasizing the role of liquidity and simultaneous trading in the pre-open or open-market order book .
Revised:  We then show that the indicative match price is strongly mean-reverting because the imbalance is ; the final auction price reacts to order placement, especially conditional on imbalance improving or worsening events and find a large difference between the opening and closing auctions, emphasizing the role of liquidity and simultaneous trading in the pre-open or open-market order book .
Edit intent: coherence
Original:  Finally, we investigate how the final auction price reacts to order placement, especially conditional on imbalance improving or worsening events and find a large difference between the opening and closing auctions, emphasizing the role of liquidity and simultaneous trading in the pre-open or open-market order book .
Revised:  Finally, we investigate how the final auction price reacts to a single auction order placement or cancellation in markedly different ways in the opening and closing auctions when computed conditionally on imbalance improving or worsening events and find a large difference between the opening and closing auctions, emphasizing the role of liquidity and simultaneous trading in the pre-open or open-market order book .
Edit intent: meaning-changed
Original:  Finally, we investigate how the final auction price reacts to order placement, especially conditional on imbalance improving or worsening events and find a large difference between the opening and closing auctions, emphasizing the role of liquidity and simultaneous trading in the pre-open or open-market order book .
Revised:  Finally, we investigate how the final auction price reacts to order placement, especially conditional on imbalance improving or worsening events ; the indicative price reverts towards the mid price of the regular limit order book but is not especially bound to the spread .
Edit intent: meaning-changed
Original: How prevalent is preference misrepresentation in matching procedures that incentivize truth-telling, and who pursues it? To address these questions, we administered an online experiment to 1,714 medical students immediately after their participation in the medical residency match .
Revised: How prevalent is preference misrepresentation in matching procedures that incentivize truthful preference reporting is considered one of the major successes of market design research. In this study, we test the degree to which these procedures succeed in eliminating preference misrepresentation. We administered an online experiment to 1,714 medical students immediately after their participation in the medical residency match .
Edit intent: meaning-changed
Original:  When placed in an analogous  matching task, we find that 23\% of match participants act on an incorrect belief that the algorithm can be gamed. Better students are less likely to make these mistakes, due to both higher cognitive ability and a reduced propensity to game when in a strategically advantageous position. We explore additional determinants of gaming attempts, including overconfidence, expectations, advice, and trust.
Revised:  When placed in an analogous matching task, we find that 23\% of participants misrepresent their preferences. We explore the factors that predict preference misrepresentation, including cognitive ability, strategic positioning, overconfidence, expectations, advice, and trust.
Edit intent: clarity
Original: We discuss a statistical model of evolution of wealth distribution , growing inequality and economic stratification in macro-economic scale. Evolution is driven by multiplicative stochastic fluctuations governed by the law of proportionate growth as well as by direct interactions between individuals and interactions between the private and public sectors.
Revised: We discuss a statistical model of evolution of wealth distribution , growing inequality and economic stratification in macro-economic system. The evolution is driven by multiplicative stochastic fluctuations governed by the law of proportionate growth as well as by direct interactions between individuals and interactions between the private and public sectors.
Edit intent: clarity
Original:  Evolution is driven by multiplicative stochastic fluctuations governed by the law of proportionate growth as well as by direct interactions between individuals and interactions between the private and public sectors. We concentrate on the effect of accumulated advantage and study the role of taxation and redistribution in the process of reducing wealth inequality.
Revised:  Evolution is driven by multiplicative stochastic fluctuations governed by the law of proportionate growth as well as by direct interactions between individuals and interactions between agents. We are mainly interested in interactions increasing wealth inequality that is in a local implementation of the accumulated advantage principle. Such interactions destabilise the system. They are confronted in the model with a global regulatory mechanism which reduces wealth inequality.
Edit intent: meaning-changed
Original:  We show in particular that capital tax may significantly reduce wealth inequality and lead to a stabilisation of the system. The model predicts a decay of the population into economic classes of quantised wealth levels. The effect of economic stratification manifests in a characteristic multimodal form of the histogram of wealth distribution.
Revised:  There are different scenarios emerging as a net effect of these two competing mechanisms. When the effect of the global regulation (economic interventionism) is too weak the system is unstable and it never reaches equilibrium. When the effect is sufficiently strong the system evolves towards a limiting stationary distribution with a Pareto tail. In between there is a critical phase. In this phase the system may evolve towards a steady state with a multimodal wealth distribution. The corresponding cumulative density function has a characteristic stairway pattern which reflects the effect of economic stratification manifests in a characteristic multimodal form of the histogram of wealth distribution.
Edit intent: meaning-changed
Original:  The effect of economic stratification manifests in a characteristic multimodal form of the histogram of wealth distribution. We observe such a multimodal pattern in empirical data on wealth distribution for  urban areas of China.
Revised:  The effect of economic stratification . The stairs represent wealth levels of economic classes separated by wealth gaps. As we show, the pattern is typical for macro-economic systems with a limited economic freedom. One can find such a multimodal pattern in empirical data on wealth distribution for urban areas of China.
Edit intent: meaning-changed
Original:  The networks eventually break altogether when rote memorization becomes impossible such as when the intra-class variability exceeds their capacity. We further show that another type of feedforward network, called a relational network (RN), which was shown to successfully solve seemingly difficult visual question answering (VQA) problems on the CLEVR datasets, suffers similar limitations.
Revised:  The networks eventually break altogether when rote memorization becomes impossible such as when the intra-class variability exceeds network capacity.
Edit intent: coherence
Original:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed  .
Revised:  In particular, we use a variant of low-rank factorisation, which considers interval uncertainty sets around "known entries", on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed .
Edit intent: meaning-changed
Original:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed  .
Revised:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed .
Edit intent: clarity
Original:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed  .
Revised:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model , R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed .
Edit intent: coherence
Original:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed  .
Revised:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of incoming data to the low-rank "normal" subspace and update the subspace to keep it consistent with the seasonal changes present . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed .
Edit intent: meaning-changed
Original:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed  .
Revised:  In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present an algorithm with a one-sided error bounded by a function of the number of coordinates employed .
Edit intent: clarity