wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The sorting algorithm returns the given numbers in sorted order  stored within a bit-vector of N bits  whereas our ranking algorithms construct data structures that allows us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism that runs in O(n) time and uses O(n) bits on n-node trees  .
Revised:  The sorting algorithm returns the given numbers in sorted order stored within a bit-vector of N bits whereas our ranking algorithms construct data structures that allow us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism that runs in O(n) time and uses O(n) bits on n-node trees .
Edit intent: fluency
Original:  The sorting algorithm returns the given numbers in sorted order  stored within a bit-vector of N bits  whereas our ranking algorithms construct data structures that allows us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism that runs in O(n) time and uses O(n) bits on n-node trees  .
Revised:  The sorting algorithm returns the given numbers in sorted order stored within a bit-vector of N bits whereas our ranking algorithms construct data structures that allows us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism , which runs in O(n) time and uses O(n) bits on n-node trees .
Edit intent: coherence
Original:  The sorting algorithm returns the given numbers in sorted order  stored within a bit-vector of N bits  whereas our ranking algorithms construct data structures that allows us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism that runs in O(n) time and uses O(n) bits on n-node trees  .
Revised:  The sorting algorithm returns the given numbers in sorted order stored within a bit-vector of N bits whereas our ranking algorithms construct data structures that allows us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism that runs in O(n) time and uses O(n) bits on n-node trees . The previous best linear-time algorithm for tree isomorphism uses \Theta(n \log n) bits .
Edit intent: meaning-changed
Original: Despite significant progress in sequencing technology, there are many cellular enzymatic activities that remain unknown. We develop a new method, referred to as SUNDRY (Similarity-weighting for UNlabeled Data in a Residual HierarchY), for training enzyme-specific predictors that take as input a query substrate moleculeand return whether the enzymewould act on that substrate or not. When addressing this enzyme promiscuity prediction problem , a major challenge is the lack of abundant labeled data , especially the shortage of labeled data for negative cases (enzyme-substrate pairs where the enzyme does not act to transform the substrate to a product molecule).
Revised: As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, as defined via the Enzyme Commission, EC, numbers, are likely to interact with a given query molecule. Our data consists of enzyme-substrate interactions from the BRENDA database. Some interactions are attributed to natural selection and involve the enzyme's natural substrates. The majority of the interactions however involve non-natural substrates, thus reflecting promiscuous enzymatic activities. We frame this enzyme promiscuity prediction problem , a major challenge is the lack of abundant labeled data , especially the shortage of labeled data for negative cases (enzyme-substrate pairs where the enzyme does not act to transform the substrate to a product molecule).
Edit intent: meaning-changed
Original:  When addressing this enzyme promiscuity prediction problem , a major challenge is the lack of abundant labeled data , especially the shortage of labeled data for negative cases (enzyme-substrate pairs where the enzyme does not act to transform the substrate to a product molecule). To overcome this issue, our proposed method can learn to classify a target enzyme by sharing information from related enzymes via known tree hierarchies. Our method can also incorporate three types of data: those molecules known to be catalyzed by an enzyme (positive cases), those with unknown relationships (unlabeled cases), and molecules labeled as inhibitors for the enzyme .
Revised:  When addressing this enzyme promiscuity prediction problem as a multi-label classification task. We maximally utilize inhibitor and unlabelled data to train prediction models that can take advantage of known hierarchical relationships between enzyme classes. We report that a hierarchical multi-label neural network, EPP-HMCNF, is the best model for solving this problem, outperforming k-nearest neighbors similarity-based and other machine learning models.
Edit intent: clarity
Original:  We refer to inhibitors as hard negative cases because they may be difficult to classify well: they bind to the enzyme, like positive cases, but are not transformed by the enzyme. Our method uses confidence scores derived from structural similarity to treat unlabeled examples as weighted negatives. We compare our proposed hierarchy-aware predictor against a baseline that cannot share information across related enzymes. Using data from the BRENDA database, we show that each of our contributions (hierarchical sharing, per-example confidence weighting of unlabeled data based on molecular similarity, and including inhibitors as hard-negative examples) contributes towards a better characterization of enzyme promiscuity.
Revised:  We show that inhibitor information during training consistently improves predictive power, particularly for EPP-HMCNF. We also show that all promiscuity prediction models perform worse under a realistic data split when compared to a random data split, and when evaluating performance on non-natural substrates compared to natural substrates. We provide Python code for EPP-HMCNF and other models in a repository termed EPP (Enzyme Promiscuity Prediction) at URL
Edit intent: meaning-changed
Original:  The controller is unique in the sense that (1) all data are encoded and processed by a spiking neural network (SNN), and (2) by mimicking the associated brain areas' connectivity , the SNN required no training to operate.
Revised:  The controller is unique in the sense that (1) all data are encoded and processed by a spiking neural network (SNN), and (2) by mimicking the associated brain areas' topology , the SNN required no training to operate.
Edit intent: clarity
Original:  The controller is unique in the sense that (1) all data are encoded and processed by a spiking neural network (SNN), and (2) by mimicking the associated brain areas' connectivity , the SNN required no training to operate.
Revised:  The controller is unique in the sense that (1) all data are encoded and processed by a spiking neural network (SNN), and (2) by mimicking the associated brain areas' connectivity , the SNN is biologically interpretable and requires no training to operate.
Edit intent: meaning-changed
Original:  Here, we report the tracking performance of the robotic head and show that the robotic eye kinematics are similar to those reported in human eye studies  .
Revised:  Here, we report the tracking performance of the robotic head and show that the robotic eye kinematics are similar to those reported in human eye studies and show that a biologically-constrained learning, although not required for the SNN's function, can be used to further refine its performance .
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Original:  The proposed Rumor Spread Power Measurement Model (RSPMM) computes SPR by utilizing a textual-based approach  which entails contextual features to compute the spread power of the rumors in two categories: False Rumor (FR) and True Rumor (TR).
Revised:  The proposed Rumor Spread Power Measurement Model (RSPMM) computes SPR by utilizing a textual-based approach , which entails contextual features to compute the spread power of the rumors in two categories: False Rumor (FR) and True Rumor (TR).
Edit intent: fluency
Original:  Herein, given a sequence of graphs over the same vertex set but changing edge sets, the task is to find short s-t paths in each graph ("snapshot") such that in the resulting path sequence the consecutive s-t paths are "similar".
Revised:  Herein, given a sequence of graphs over the same vertex set but changing edge sets, the task is to find short s-t paths in each graph ("snapshot") such that in the found path sequence the consecutive s-t paths are "similar".
Edit intent: clarity
Original:  Among other results,  we prove parameterized hardness (W[1]-hardness) regarding the size of the path sequence (solution size) for both variants, vertex- and edge-similarity.
Revised:  Among other results, for both variants, vertex- and edge-similarity, we prove parameterized hardness (W[1]-hardness) regarding the size of the path sequence (solution size) for both variants, vertex- and edge-similarity.
Edit intent: fluency
Original:  As one of the main results , we prove that dissimilarity allows for fixed-parameter tractability for the parameter solution size, thereby contrasting our W[1]-hardness proof of the corresponding similarity case  .
Revised:  As one of the main technical results (employing so-called representative sets known from non-temporal settings) , we prove that dissimilarity allows for fixed-parameter tractability for the parameter solution size, thereby contrasting our W[1]-hardness proof of the corresponding similarity case .
Edit intent: meaning-changed
Original:  As one of the main results , we prove that dissimilarity allows for fixed-parameter tractability for the parameter solution size, thereby contrasting our W[1]-hardness proof of the corresponding similarity case  .
Revised:  As one of the main results , we prove that dissimilarity allows for fixed-parameter tractability for the parameter solution size, thereby contrasting our W[1]-hardness proof of the corresponding similarity case . We also provide partially positive results concerning efficient and effective data reduction (kernelization) .
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Original:  A context-free language L is letter-monotonic when L \subseteq a_1^*a_2^* \cdots a_\ell^* for some finite \ell > 0 where each letter is distinct.
Revised:  A context-free language \mathcal{L \subseteq a_1^*a_2^* \cdots a_\ell^* for some finite \ell > 0 where each letter is distinct.
Edit intent: meaning-changed
Original:  The decidability of this problem is in marked contrast to the situation for the (strict) emptiness problem for PFA which is undecidable under the even more severe restrictions of PFA with polynomial ambiguity, commutative matrices and input over a letter-monotonic language as well as  the injectivity problem which is undecidable for PFA over letter-monotonic languages.
Revised:  The decidability of this problem is in marked contrast to the situation for the (strict) emptiness problem for PFA which is undecidable under the even more severe restrictions of PFA with polynomial ambiguity, commutative matrices and input over a letter-bounded language as well as the injectivity problem which is undecidable for PFA over letter-monotonic languages.
Edit intent: fluency
Original:  The decidability of this problem is in marked contrast to the situation for the (strict) emptiness problem for PFA which is undecidable under the even more severe restrictions of PFA with polynomial ambiguity, commutative matrices and input over a letter-monotonic language as well as  the injectivity problem which is undecidable for PFA over letter-monotonic languages.
Revised:  The decidability of this problem is in marked contrast to the situation for the (strict) emptiness problem for PFA which is undecidable under the even more severe restrictions of PFA with polynomial ambiguity, commutative matrices and input over a letter-monotonic language as well as to the injectivity problem which is undecidable for PFA over letter-monotonic languages.
Edit intent: fluency
Original:  The decidability of this problem is in marked contrast to the situation for the (strict) emptiness problem for PFA which is undecidable under the even more severe restrictions of PFA with polynomial ambiguity, commutative matrices and input over a letter-monotonic language as well as  the injectivity problem which is undecidable for PFA over letter-monotonic languages.
Revised:  The decidability of this problem is in marked contrast to the situation for the (strict) emptiness problem for PFA which is undecidable under the even more severe restrictions of PFA with polynomial ambiguity, commutative matrices and input over a letter-monotonic language as well as the injectivity problem which is undecidable for PFA over letter-bounded languages.
Edit intent: fluency
Original:  We provide a constructive nondeterministic algorithm to solve the cutpoint isolation problem, even for exponentially ambiguousPFA, and we also show that the problem is at least NP-hard  .
Revised:  We provide a constructive nondeterministic algorithm to solve the cutpoint isolation problem, which holds even when the PFA is exponentially ambiguous. We also show that the problem is at least NP-hard .
Edit intent: coherence
Original:  We provide a constructive nondeterministic algorithm to solve the cutpoint isolation problem, even for exponentially ambiguousPFA, and we also show that the problem is at least NP-hard  .
Revised:  We provide a constructive nondeterministic algorithm to solve the cutpoint isolation problem, even for exponentially ambiguousPFA, and we also show that the problem is at least NP-hard and use our decision procedure to solve several related problems .
Edit intent: meaning-changed
Original:  For example , when the p-value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes.
Revised:  For example when the p-value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes.
Edit intent: fluency
Original:  Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations - measured as the "cliff effect" : the sudden drop in confidence around p-value 0.05 - compared with classic CI visualization and textual representation of the CI with p-values.
Revised:  Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations - measured as the `cliff effect' : the sudden drop in confidence around p-value 0.05 - compared with classic CI visualization and textual representation of the CI with p-values.
Edit intent: fluency
Original: We propose an innovative machine learning paradigm enabling precision medicine for prioritizing cognitive assessments according to their relevance to Alzheimer's disease at the individual patient level. The paradigm tailors the cognitive biomarker discovery and cognitive assessment selection process to the brain morphometric characteristics of each individual patient. We implement this paradigm using a newly developed learning-to-rank method PLTR .
Revised: We propose an innovative machine learning paradigm enabling precision medicine for prioritizing cognitive assessments according to their relevance to Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, determining a proper set of cognitive tests is a widely studied topic. Most existing studies aim to create selection guidelines for a targeted population but not for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method: We adapt a newly developed learning-to-rank method PLTR .
Edit intent: coherence
Original:  We implement this paradigm using a newly developed learning-to-rank method PLTR .
Revised:  We implement this paradigm using a newly developed learning-to-rank method PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments on top of the prioritization list. We also extend PLTR to better separate the most effective cognitive assessments and the less effective ones. Results:
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Original:  Our empirical study on the ADNI data yields promising results to identify and prioritize individual-specific cognitive biomarkers as well as cognitive assessment tasks based on the individual's structural MRI data.
Revised:  Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers as well as cognitive assessment tasks based on the individual's structural MRI data.
Edit intent: meaning-changed
Original:  Our empirical study on the ADNI data yields promising results to identify and prioritize individual-specific cognitive biomarkers as well as cognitive assessment tasks based on the individual's structural MRI data. The resulting top ranked cognitive biomarkers and assessment tasks have the potential to aid personalized diagnosis and disease subtyping  .
Revised:  Our empirical study on the ADNI data yields promising results to identify and prioritize individual-specific cognitive biomarkers . We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1\% and 19.7\%, respectively, on prioritizing cognitive features. Conclusions: The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The top ranked cognitive biomarkers and assessment tasks have the potential to aid personalized diagnosis and disease subtyping .
Edit intent: meaning-changed
Original:  Given a large number of cognitive assessment tools and time-limited office visits, determining a proper set of cognitive tests is a widely studied topic. Most existing studies aim to create selection guidelines for a targeted population but not for each individual subject.
Revised:  Given a large number of cognitive assessment tools and time-limited office visits, determining a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population but not for each individual subject.
Edit intent: clarity
Original:  In this paper, we propose an instance-based method to find similar questions of a new question , in the sense of their relations, to predict its mentioned relation . The motivation roots in the fact that a relation can be expressed with different forms of question and these forms mostly share similar terms or concepts.
Revised:  In this paper, we propose an instance-based method to find similar questions of a new question which share the same relation, and their corresponding relation is selected as our prediction. The idea of our model roots in the fact that a relation can be expressed with different forms of question and these forms mostly share similar terms or concepts.
Edit intent: clarity
Original:  We obtain a 4.31 point F1-score improvement and a 1.09 point mAP score improvement over current state-of-the-art models on the ClaimBuster Dataset and CLEF2019 Dataset, respectively.
Revised:  We obtain a 4.70 point F1-score improvement and a 1.09 point mAP score improvement over current state-of-the-art models on the ClaimBuster Dataset and CLEF2019 Dataset, respectively.
Edit intent: meaning-changed
Original:  We obtain a 4.31 point F1-score improvement and a 1.09 point mAP score improvement over current state-of-the-art models on the ClaimBuster Dataset and CLEF2019 Dataset, respectively.
Revised:  We obtain a 4.31 point F1-score improvement over current state-of-the-art models on the ClaimBuster Dataset and CLEF2019 Dataset, respectively.
Edit intent: meaning-changed
Original:  Motivated by the study of the mixing set problem and a recent result in the field of real-time systems we investigate the case of harmonic divisors, i.e. a_{i+1}/a_i is an integer for all i<n  .
Revised:  By investigating the case of harmonic divisors, i.e. a_{i+1}/a_i is an integer for all i<n .
Edit intent: coherence
Original:  Motivated by the study of the mixing set problem and a recent result in the field of real-time systems we investigate the case of harmonic divisors, i.e. a_{i+1}/a_i is an integer for all i<n  .
Revised:  Motivated by the study of the mixing set problem and a recent result in the field of real-time systems we investigate the case of harmonic divisors, i.e. a_{i+1}/a_i is an integer for all i<n , which was heavily studied for the mixing set problem as well, we also answer a recent algorithmic question from the field of real-time systems .
Edit intent: meaning-changed
Original:  We present an algorithm to decide the feasibility of an instance in time O(n^2) and we show that  even the smallest feasible solution can be computed in strongly polynomial time O(n^3).
Revised:  We present an algorithm to decide the feasibility of an instance in time O(n^2) and we show that if it exists even the smallest feasible solution can be computed in strongly polynomial time O(n^3).
Edit intent: meaning-changed
Original: The efficiency of a spatial DNN accelerator depends heavily on the compiler 's ability to generate optimized mappings for a given DNN's operators (layers) on to the accelerator's compute and memory resources.
Revised: The efficiency of a spatial DNN accelerator depends heavily on the compiler and its cost model ability to generate optimized mappings for a given DNN's operators (layers) on to the accelerator's compute and memory resources.
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Original:   Searching for the optimal mapping is challenging because of a massive space of possible data-layouts and loop transformations for the DNN layers.
Revised:  But, existing cost models lack a formal boundary over the operators for precise and tractable analysis, which poses adaptability challenges for new DNN operators. To address this challenge, we leverage the recently introduced Maestro Data-Centric (MDC) notation. We develop a formal understanding of DNN operators whose mappings can be described in the MDC notation, because any mapping adhering to the notation is always analyzable by the MDC's cost model. Furthermore, we introduce a transformation for translating mappings into the MDC notation for exploring the mapping space. Searching for the optimal mapping is challenging because of a massive space of possible data-layouts and loop transformations for the DNN layers.
Edit intent: meaning-changed