wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In particular, we develop an online sketching algorithm that can compress N vectors into a tiny sketch consisting of small arrays of counters whose size scales as O(N ^{b\log^2{N}) } ) in O(N^{b+1} \log^3{N}) time} , where b < 1 depending on the stability of the near-neighbor search.
Revised:  In particular, we develop an online sketching algorithm can compress an N-element dataset to a sketch of size O(N ^{b\log^2{N}) } ) in O(N^{b+1} \log^3{N}) time} , where b < 1 depending on the stability of the near-neighbor search.
Edit intent: clarity
Original:  In particular, we develop an online sketching algorithm that can compress N vectors into a tiny sketch consisting of small arrays of counters whose size scales as O(N ^{b\log^2{N}) } ) in O(N^{b+1} \log^3{N}) time} , where b < 1 depending on the stability of the near-neighbor search.
Revised:  In particular, we develop an online sketching algorithm that can compress N vectors into a tiny sketch consisting of small arrays of counters whose size scales as O(N \log^2{N}) } ) in O(N^{b+1} \log^3{N}) time} , where b < 1 depending on the stability of the near-neighbor search.
Edit intent: clarity
Original:  In particular, we develop an online sketching algorithm that can compress N vectors into a tiny sketch consisting of small arrays of counters whose size scales as O(N ^{b\log^2{N}) } ) in O(N^{b+1} \log^3{N}) time} , where b < 1 depending on the stability of the near-neighbor search.
Revised:  In particular, we develop an online sketching algorithm that can compress N vectors into a tiny sketch consisting of small arrays of counters whose size scales as O(N ^{b\log^2{N}) } ^b \log^3{N) in O(N^{b+1} \log^3{N}) time} , where b < 1 depending on the stability of the near-neighbor search.
Edit intent: meaning-changed
Original:  In particular, we develop an online sketching algorithm that can compress N vectors into a tiny sketch consisting of small arrays of counters whose size scales as O(N ^{b\log^2{N}) } ) in O(N^{b+1} \log^3{N}) time} , where b < 1 depending on the stability of the near-neighbor search.
Revised:  In particular, we develop an online sketching algorithm that can compress N vectors into a tiny sketch consisting of small arrays of counters whose size scales as O(N ^{b\log^2{N}) } ) in O(N^{b+1} \log^3{N}) time} , where b < 1 when the query satisfies a data-dependent near-neighbor search.
Edit intent: clarity
Original:  In particular, we develop an online sketching algorithm that can compress N vectors into a tiny sketch consisting of small arrays of counters whose size scales as O(N ^{b\log^2{N}) } ) in O(N^{b+1} \log^3{N}) time} , where b < 1 depending on the stability of the near-neighbor search. This sketch is sufficient to identify the top-v near-neighbors with high probability. To the best of our knowledge, this is the first near-neighbor search algorithm that breaks the linear memory (O(N)) barrier. We achieve sub-linear memory by combining advances in locality sensitive hashing (LSH) based estimation, especially the recently-published ACE algorithm, with compressed sensing and heavy hitter techniques.
Revised:  In particular, we develop an online sketching algorithm that can compress N vectors into a tiny sketch consisting of small arrays of counters whose size scales as O(N ^{b\log^2{N}) } ) in O(N^{b+1} \log^3{N}) time} , where b < 1 depending on the stability of the near-neighbor stability condition. We achieve data-dependent sublinear space by combining recent advances in locality sensitive hashing (LSH) based estimation, especially the recently-published ACE algorithm, with compressed sensing and heavy hitter techniques.
Edit intent: meaning-changed
Original:  We achieve sub-linear memory by combining advances in locality sensitive hashing (LSH) based estimation, especially the recently-published ACE algorithm, with compressed sensing and heavy hitter techniques. We provide strong theoretical guarantees; in particular, our analysis sheds new light on the memory-accuracy tradeoff in the near-neighbor search setting and the role of sparsity in compressed sensing , which could be of independent interest.
Revised:  We achieve sub-linear memory by combining advances in locality sensitive hashing (LSH) based estimation, especially the recently-published ACE algorithm, with compressed sensing . Our results shed new light on the memory-accuracy tradeoff in the near-neighbor search setting and the role of sparsity in compressed sensing , which could be of independent interest.
Edit intent: coherence
Original:  in particular, our analysis sheds new light on the memory-accuracy tradeoff in the near-neighbor search setting and the role of sparsity in compressed sensing , which could be of independent interest. We rigorously evaluate our framework, which we call RACE (Repeated ACE) data structures on a friend recommendation task on the Google plus graph with more than 100,000 high-dimensional vectors. RACE provides compression that is orders of magnitude better than the random projection based alternative, which is unsurprising given the theoretical advantage. We anticipate that RACE will enable both new theoretical perspectives on near-neighbor search and new methodologies for applications like high-speed data mining, internet-of-things (IoT), and beyond .
Revised:  in particular, our analysis sheds new light on the memory-accuracy tradeoff in the near-neighbor search . The techniques presented reveal a deep connection between the fundamental compressed sensing (or heavy hitters) recovery problem and near-neighbor search and new methodologies for applications like high-speed data mining, internet-of-things (IoT), and beyond .
Edit intent: coherence
Original: Certainty Equivalent is a utility-based measure that performs as a measure in which investors are indifferent between this measure and investment that holds some uncertainty. Therefore, it plays an essential role in utility-based decision making. One of the most extensively used risk measures is the Value at Risk, which is investigated and used by both researchers and practitioners as a powerful tool that measures the risk under some quantile level which allows focusing on the extreme amount of losses.
Revised: We generalize Quasi-Linear Means by restricting to the tail of the risk distribution and show that this can be a useful quantity in risk management since it comprises in its general form the Value at Risk, which is investigated and used by both researchers and practitioners as a powerful tool that measures the risk under some quantile level which allows focusing on the extreme amount of losses.
Edit intent: meaning-changed
Original:  One of the most extensively used risk measures is the Value at Risk, which is investigated and used by both researchers and practitioners as a powerful tool that measures the risk under some quantile level which allows focusing on the extreme amount of losses. In this paper, we propose a natural generalization of the Certainty Equivalent measure that, similar to the Value at Risk measure, is focusing on the tail distribution of the risk, and thus, focusing on extreme financial and insurance risk events .
Revised:  One of the most extensively used risk measures is the Value at Risk, the Tail Value at Risk measure, is focusing on the tail distribution of the risk, and thus, focusing on extreme financial and insurance risk events .
Edit intent: coherence
Original:  In this paper, we propose a natural generalization of the Certainty Equivalent measure that, similar to the Value at Risk measure, is focusing on the tail distribution of the risk, and thus, focusing on extreme financial and insurance risk events .
Revised:  In this paper, we propose a natural generalization of the Certainty Equivalent measure that, similar to the Value at Risk and the Entropic Risk Measure in a unified way .
Edit intent: meaning-changed
Original:  Our Lagrangian framework is also applicable to the Fisher-Rao regularization because, as we show, it is equivalent to our regularized dynamical OMT formulation .
Revised:  While formally equivalent to the Eulerian based Schr\"odinger bridge OMT regularization scheme, the Fisher-Rao approach allows a simple and interpretable methodology for studying the flows of interest in the present work .
Edit intent: meaning-changed
Original:  Applying our Lagrangian analysis to the glymphatic system , we successfully distinguish between flow patterns under two different anesthetics . We should also note that the Fisher-Rao regularization makes direct contact with some of the very nice recent work on entropic regularization for the computation of optimal mass transport as well as the Schr\"odinger bridge theory. The latter provides a stochastic aspect to the approach described in the present work .
Revised:  Applying our Lagrangian analysis to the glymphatic system , we successfully distinguish between flow patterns under two different anesthetics , providing deeper insights into altered states of waste drainage .
Edit intent: clarity
Original:  For ambiguity averse smooth second order and variational preferences, the mechanism reveals inner bounds for the belief interval, which are sharp under additional assumptions  .
Revised:  For ambiguity averse smooth second order and variational preferences, the mechanism reveals inner bounds for the belief interval, which are sharp under additional assumptions . In an experimental study, participants perceive almost as much ambiguity for natural events (generated by the stock exchange and by a prisoners dilemma game) as for the Ellsberg Urn, indicating that ambiguity may play a role in real-world decision making .
Edit intent: meaning-changed
Original: In the (1+\varepsilon,r) -approximate-near-neighbor problem for curves (ANNC) under some distance measure \delta, the goal is to construct a data structure for a given set C of curves that supports approximate near-neighbor queries: Given a query curve Q, if there exists a curve C\inC such that \delta(Q,C)\le r, then return a curve C'\inC with \delta(Q,C')\le(1+\varepsilon)r.
Revised: In the (1+\varepsilon,r) -approximate near-neighbor problem for curves (ANNC) under some distance measure \delta, the goal is to construct a data structure for a given set C of curves that supports approximate near-neighbor queries: Given a query curve Q, if there exists a curve C\inC such that \delta(Q,C)\le r, then return a curve C'\inC with \delta(Q,C')\le(1+\varepsilon)r.
Edit intent: fluency
Original:  There exists an efficient reduction from the (1+\varepsilon) -approximate-nearest-neighbor problem to ANNC, where in the former problem the answer to a query is a curve C\inC with \delta(Q,C)\le(1+\varepsilon)\cdot\delta(Q,C^*), where C^* is the curve of C closest to Q. Given a set C of n curves, each consisting of m points in d dimensions, we construct a data structure for ANNC that uses n\cdot O(1{\varepsilon})^{md} storage space and has O(md \log(\frac{nm{\varepsilon})
Revised:  There exists an efficient reduction from the (1+\varepsilon) -approximate nearest-neighbor problem to ANNC, where in the former problem the answer to a query is a curve C\inC with \delta(Q,C)\le(1+\varepsilon)\cdot\delta(Q,C^*), where C^* is the curve of C closest to Q. Given a set C of n curves, each consisting of m points in d dimensions, we construct a data structure for ANNC that uses n\cdot O(1{\varepsilon})^{md} storage space and has O(md \log(\frac{nm{\varepsilon})
Edit intent: fluency
Original: approximate-nearest-neighbor problem to ANNC, where in the former problem the answer to a query is a curve C\inC with \delta(Q,C)\le(1+\varepsilon)\cdot\delta(Q,C^*), where C^* is the curve of C closest to Q. Given a set C of n curves, each consisting of m points in d dimensions, we construct a data structure for ANNC that uses n\cdot O(1{\varepsilon})^{md} storage space and has O(md \log(\frac{nm{\varepsilon})
Revised: approximate-nearest-neighbor problem to ANNC, where in the former problem the answer to a query is a curve C\inC with \delta(Q,C)\le(1+\varepsilon)\cdot\delta(Q,C^*), where C^* is the curve of C closest to Q. Given a set C of n curves, each consisting of m points in d dimensions, we construct a data structure for ANNC that uses n\cdot O(1{\varepsilon})^{md} storage space and has O(md {\varepsilon})
Edit intent: meaning-changed
Original:  Our approach consists of a discretization of space based on the input curves, which allows us to prepare a small set of curves that captures all possible queries approximately. Our method is simple and deterministic, yet, somewhat surprisingly, it is more efficient than all previous methods. We also apply our method to a version of approximate range counting for curves and achieve similar bounds.
Revised:  Our method is simple to implement, deterministic, and results in an exponential improvement in both query time and storage space compared with all previous work. Further, we also consider the asymmetric version of ANNC, where the length of the query curves is k \ll m, and obtain essentially the same storage and query bounds as above, except that m is replaced by k. Finally, we apply our method to a version of approximate range counting for curves and achieve similar bounds.
Edit intent: meaning-changed
Original:  Notably, we find that (1) newer DRAM technologies such as DDR4 and HMC often do not outperform older technologies such as DDR3, due to higher access latencies and, in the case of HMC, poor exploitation of locality;
Revised:  Notably, we find that (1) newer DRAM types such as DDR4 and HMC often do not outperform older technologies such as DDR3, due to higher access latencies and, in the case of HMC, poor exploitation of locality;
Edit intent: clarity
Original:  Notably, we find that (1) newer DRAM technologies such as DDR4 and HMC often do not outperform older technologies such as DDR3, due to higher access latencies and, in the case of HMC, poor exploitation of locality;
Revised:  Notably, we find that (1) newer DRAM technologies such as DDR4 and HMC often do not outperform older types such as DDR3, due to higher access latencies and, in the case of HMC, poor exploitation of locality;
Edit intent: clarity
Original:  (2) there is no single memory type that can cater to all of the SoC accelerators (e.g., GDDR5 significantly outperforms other memories for multimedia  , while HMC significantly outperforms other memories for networking );
Revised:  (2) there is no single DRAM type that can cater to all of the SoC accelerators (e.g., GDDR5 significantly outperforms other memories for multimedia , while HMC significantly outperforms other memories for networking );
Edit intent: clarity
Original:  (2) there is no single memory type that can cater to all of the SoC accelerators (e.g., GDDR5 significantly outperforms other memories for multimedia  , while HMC significantly outperforms other memories for networking );
Revised:  (2) there is no single memory type that can cater to all components of a heterogeneous system (e.g., GDDR5 significantly outperforms other memories for multimedia , while HMC significantly outperforms other memories for networking );
Edit intent: meaning-changed
Original:  (2) there is no single memory type that can cater to all of the SoC accelerators (e.g., GDDR5 significantly outperforms other memories for multimedia  , while HMC significantly outperforms other memories for networking );
Revised:  (2) there is no single memory type that can cater to all of the SoC accelerators (e.g., GDDR5 significantly outperforms other memories for multimedia acceleration , while HMC significantly outperforms other memories for networking );
Edit intent: meaning-changed
Original:  (2) there is no single memory type that can cater to all of the SoC accelerators (e.g., GDDR5 significantly outperforms other memories for multimedia  , while HMC significantly outperforms other memories for networking );
Revised:  (2) there is no single memory type that can cater to all of the SoC accelerators (e.g., GDDR5 significantly outperforms other memories for multimedia , while HMC significantly outperforms other memories for network acceleration );
Edit intent: clarity
Original: Econometric analyses in the happiness literature typically use subjective well-being (SWB) data to compare the mean of observed or latent happiness across samples. Recent critiques show that comparing the mean of ordinal data is only valid under strong assumptions that are usually rejected by SWB data. This leads to an open question whether much of the empirical studies in the economics of happiness literature have been futile. In order to salvage some of the prior results and avoid future issues, we suggest regression analysis of SWB (and other ordinal data) should focus on the median rather than the mean.
Revised: The mean rank of happiness outcomes between groups has often been estimated using ordered probit and logit models. However, it has recently been highlighted that such ranking is not identified in most applications. Can we then learn anything from a mean rank between groups that is reported by standard statistical softwares such as STATA? We argue it can instead be interpreted as the median rank, which is identified even when the mean rank is not. We thus suggest focusing on ranking happiness outcomes (and other ordinal data) should focus on the median rather than the mean.
Edit intent: meaning-changed
Original:  Median comparisons using parametric models such as the ordered probit and logit can be readily carried out using familiar statistical softwares like STATA. We also show a previously assumed impractical task of estimating a semiparametric median ordered-response model is also possible by using a novel constrained mixed integer optimization technique.
Revised:  The median ranking can also be performed semiparametrically and we provide a new constrained mixed integer optimization technique.
Edit intent: coherence
Original:  We also show a previously assumed impractical task of estimating a semiparametric median ordered-response model is also possible by using a novel constrained mixed integer optimization technique. We use GSS data to show the famous Easterlin Paradox from the happiness literature holds for the US independent of any parametric assumption.
Revised:  We also show a previously assumed impractical task of estimating a semiparametric median ordered-response model is also possible by using a novel constrained mixed integer optimization procedure for implementation. To illustrate, we use General Social Survey data to show the famous Easterlin Paradox from the happiness literature holds for the US independent of any parametric assumption.
Edit intent: meaning-changed
Original: Whole-brain computational modeling based on structural connectivity measures has shown great promise in successfully simulating fMRI BOLD signals that includes temporal co-activation patterns that are highly similar to empirical functional connectivity patterns during resting state.
Revised: Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals that includes temporal co-activation patterns that are highly similar to empirical functional connectivity patterns during resting state.
Edit intent: clarity
Original: Whole-brain computational modeling based on structural connectivity measures has shown great promise in successfully simulating fMRI BOLD signals that includes temporal co-activation patterns that are highly similar to empirical functional connectivity patterns during resting state.
Revised: Whole-brain computational modeling based on structural connectivity measures has shown great promise in successfully simulating fMRI BOLD signals with temporal co-activation patterns that are highly similar to empirical functional connectivity patterns during resting state.
Edit intent: clarity
Original:  From the results , it follows that although the feature-based methods perform better than CNN in terms of accuracy and are computationally cheaper, the performance of CNNis already very good and may be probably improved after carrying out an expanded search for the best performing CNN model on an HPC platform .
Revised:  From the results it follows that although the feature-based methods perform better than CNN in terms of accuracy and are computationally cheaper, the performance of CNNis already very good and may be probably improved after carrying out an expanded search for the best performing CNN model on an HPC platform .
Edit intent: fluency
Original:  From the results , it follows that although the feature-based methods perform better than CNN in terms of accuracy and are computationally cheaper, the performance of CNNis already very good and may be probably improved after carrying out an expanded search for the best performing CNN model on an HPC platform .
Revised:  From the results , it follows that CNN is usually slightly better than the feature-based methods perform better than CNN in terms of accuracy and are computationally cheaper, the performance of CNNis already very good and may be probably improved after carrying out an expanded search for the best performing CNN model on an HPC platform .
Edit intent: meaning-changed
Original:  From the results , it follows that although the feature-based methods perform better than CNN in terms of accuracy and are computationally cheaper, the performance of CNNis already very good and may be probably improved after carrying out an expanded search for the best performing CNN model on an HPC platform .
Revised:  From the results , it follows that although the feature-based methods , but at the costs of much longer processing times. Moreover, there are still some borderline cases, in which the classical methods perform better than CNN .
Edit intent: meaning-changed
Original:  The k-center problem has recently been shown to be NP-hard, even in the case where k=1, i.e. the minimum enclosing ball under the Fr\'echet distance . We extend these results by showing that also the k-median problem is NP-hard for k=1.
Revised:  The k-center problem has recently been shown to be NP-hard, even in the case where k=1, i.e. the minimum enclosing ball under the Fr\'echet distance , we show that also the k-median problem is NP-hard for k=1.
Edit intent: clarity
Original:  Our result generalizes an earlier result by Bulteau et al. from 2018 for a variant of DTW that uses squared distances . Moreover, closing some gaps in the literature, we show positive results for a variant where the center curve may have complexity at most \ell under the discrete Fr\'echet distance.
Revised:  This yields an independent proof of an earlier result by Bulteau et al. from 2018 for a variant of DTW that uses squared distances . Moreover, closing some gaps in the literature, we show positive results for a variant where the center curve may have complexity at most \ell under the discrete Fr\'echet distance.
Edit intent: meaning-changed
Original:  Our result generalizes an earlier result by Bulteau et al. from 2018 for a variant of DTW that uses squared distances . Moreover, closing some gaps in the literature, we show positive results for a variant where the center curve may have complexity at most \ell under the discrete Fr\'echet distance.
Revised:  Our result generalizes an earlier result by Bulteau et al. from 2018 for a variant of DTW that uses squared distances , where the new proof is both simpler and more general. On the positive side, we give approximation algorithms for problem variants where the center curve may have complexity at most \ell under the discrete Fr\'echet distance.
Edit intent: meaning-changed
Original: Here we summarize abundant excuses that support the eye detection can be as a noninvasive approach to early diagnose some brain disorders, including Alzheimer disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ)  and major depressive disorder (MDD).
Revised: The present review presents multiple techniques in which ocular assessments may serve as a noninvasive approach to early diagnose some brain disorders, including Alzheimer disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ) and major depressive disorder (MDD).
Edit intent: clarity