wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Dimensionality reduction revealed that the LM-embeddings from unlabelled data (only protein sequences) captured important biophysical properties of the protein alphabet, namely the amino acids, and their well orchestrated interplay in governing the shapeof proteins.
Revised:  Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties of the protein alphabet, namely the amino acids, and their well orchestrated interplay in governing the shapeof proteins.
Edit intent: fluency
Original:  Dimensionality reduction revealed that the LM-embeddings from unlabelled data (only protein sequences) captured important biophysical properties of the protein alphabet, namely the amino acids, and their well orchestrated interplay in governing the shapeof proteins. In the analogy of NLP, this implied having learned some of the grammar of the language of life realized in protein sequences. 
Revised:  Dimensionality reduction revealed that the LM-embeddings from unlabelled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. 
Edit intent: clarity
Original:  In the analogy of NLP, this implied having learned some of the grammar of the language of life realized in protein sequences. 
Revised:  In the analogy of NLP, this implied having learned some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. The official GitHub repository: URL
Edit intent: meaning-changed
Original:  The obtained results endorse an implementation of recurrent circuits similar to the primate ventral stream in artificial vision models as a way to achieve adequate object generalization abilitiesacross unexperienced manipulations .
Revised:  The obtained results show that humans contrast strongly with artificial feedforward architectures when it comes to visual core object recognition of manipulated images. In general, these findings are in line with a growing body of literature, that hints towards recurrence as a crucial factor for adequate generalization abilities .
Edit intent: clarity
Original: The remarkable empirical finding that extensive fine-scale diversity is stable on long time scales, despite selective pressures, has spurred the study of several models, which can explain species formationand stability. However, the focus has been more on the possible mechanisms which sustain coexistence, rather than on their universality and robustness. We thus consider a model that includes generic forms of many-body non-linear interactions, which are non-local and depend on a set of parameters. Despite the complicated dynamics, we are able to aptly identify a region in the parameter space where the dynamics manifests universal and robust features which extend to the weakly non-linear regime.
Revised: The remarkable empirical finding that extensive fine-scale diversity is stable on long time scales, despite selective pressures, has spurred the study of several models, which can explain pattern formation. The modulations of patterns, occurring on long spatial and temporal scales, can not be captured by linear approximation analysis. Here, we show that, starting from a general model with long range couplings displaying patterns, the spatio-temporal evolution of large scale modulations at the onset of instability is ruled by the well-known Ginzburg-Landau equation, independently of the weakly non-linear regime.
Edit intent: meaning-changed
Original:  Despite the complicated dynamics, we are able to aptly identify a region in the parameter space where the dynamics manifests universal and robust features which extend to the weakly non-linear regime. Large scale variations of patterns are well captured by a Ginzburg-Landau equation , whose form does not depend on the details of the underlying models.
Revised:  Despite the complicated dynamics, we are able to aptly identify a region in the parameter space where the dynamics manifests universal and robust features which extend to the details of the dynamics. Hence, we demonstrate the validity of such equation in the description of the behavior of a wide class of systems. We introduce a novel mathematical framework that is also able to retrieve the analytical expressions of the coefficients appearing in the Ginzburg-Landau equation , whose form does not depend on the details of the underlying models.
Edit intent: meaning-changed
Original:  Large scale variations of patterns are well captured by a Ginzburg-Landau equation , whose form does not depend on the details of the underlying models. The mathematical framework we have introduced has much larger applicability than the one considered here, possibly including pattern formation in models with very different physical features .
Revised:  Large scale variations of patterns are well captured by a Ginzburg-Landau equation as functions of the model parameters. Such framework can include higher order nonlocal interactions and has much larger applicability than the one considered here, possibly including pattern formation in models with very different physical features .
Edit intent: meaning-changed
Original:  The mathematical framework we have introduced has much larger applicability than the one considered here, possibly including pattern formation in models with very different physical features . The analytical predictions yield a good agreement when compared to the numerical integration of a competitive Lotka-Volterra equation with non-local interactions .
Revised:  The mathematical framework we have introduced has much larger applicability than the one considered here, possibly including pattern formation in models with very different physical features .
Edit intent: coherence
Original:  In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines in the field of oncology. The corpus is one of the largest corpora of German medical text to date.
Revised:  In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest corpora of German medical text to date.
Edit intent: clarity
Original:  In this paper, we demonstrate that a neural decoder trained on neural activity signals of one subject can be used torobustly decode the motor intentions of a different subject with high reliability.
Revised:  In this paper, we demonstrate that a neural decoder trained on neural activity signals of one subject can be used torobustly consider the problem of cross-subject decoding, where neural activity data collected from the prefrontal cortex of a given subject (destination) is used to decode motor intentions from the neural activity of a different subject with high reliability.
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Original:  In this paper, we demonstrate that a neural decoder trained on neural activity signals of one subject can be used torobustly decode the motor intentions of a different subject with high reliability. This is achieved in spite of the non-stationary nature of neural activity signals and the subject-specific variations of the recording conditions .
Revised:  In this paper, we demonstrate that a neural decoder trained on neural activity signals of one subject can be used torobustly decode the motor intentions of a different subject (source). Approach. We cast the problem of neural activity signals and the subject-specific variations of the recording conditions .
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Original:  Our proposed algorithm for cross-subject mapping of neural activity is based on deep conditional generative models. We verify the results on an experimental data set in which two macaque monkeys perform memory-guided visual saccades to one of eight target locations.
Revised:  Our proposed algorithm uses deep conditional variational autoencoder to infer the representation of the neural activity of the source subject into an adequate feature space of the destination subject where neural decoding takes place. Results. We verify our approach on an experimental data set in which two macaque monkeys perform memory-guided visual saccades to one of eight target locations.
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Original:  The framework can lead to the effect that discount-factors for very large liquidity requirements or projects are an increasing function of time  .
Revised:  The non-linear discounting presented here has several effects, which are relevant in various applications: * If we consider the question of default-free valuation, i.e., factoring in the cost of default protection, the framework can will lead to over-proportional higher values (or cost) for large projects (or damages). The framework can lead to the effect that discount-factors for very large liquidity requirements or projects are an increasing function of time .
Edit intent: meaning-changed
Original:  The framework can lead to the effect that discount-factors for very large liquidity requirements or projects are an increasing function of time  .
Revised:  The framework can lead to the effect that discount-factors for very large liquidity requirements or projects are an increasing function of time . It may even lead to discount factors larger than one. This may have relevance in the assessment of event like climate change. * For the valuation of defaultable products, e.g., like a defaultable swap, the framework leads to the generation of a continuum of (defaultable) par rate curves (interest rate curve) and the valuation of a payer and a receiver swap differs by more than just a sign .
Edit intent: meaning-changed
Original:  We present an alternate statistical method for estimating an n-dimensional integral that is suited even for non-convex domains and scales as O(n) in the samples required, given any distribution of extents of the arbitrary domain and the allowed tolerance in relative error.
Revised:  We analyze a less known alternate method used for estimating n-dimensional integral that is suited even for non-convex domains and scales as O(n) in the samples required, given any distribution of extents of the arbitrary domain and the allowed tolerance in relative error.
Edit intent: clarity
Original:  We present an alternate statistical method for estimating an n-dimensional integral that is suited even for non-convex domains and scales as O(n) in the samples required, given any distribution of extents of the arbitrary domain and the allowed tolerance in relative error. This results due to the possible decomposition of an arbitrary n-volume into an integral of statistically weighted volumes of n-spheres  .
Revised:  We present an alternate statistical method for estimating an n-dimensional volumes, that is agnostic to the convexity and roughness of the body. It results due to the possible decomposition of an arbitrary n-volume into an integral of statistically weighted volumes of n-spheres .
Edit intent: clarity
Original:  This results due to the possible decomposition of an arbitrary n-volume into an integral of statistically weighted volumes of n-spheres  .
Revised:  This results due to the possible decomposition of an arbitrary n-volume into an integral of statistically weighted volumes of n-spheres . We establish its dimensional scaling, and extend it for evaluation of arbitrary integrals over non-convex domains. Our results also show that this method is significantly more efficient than the MCMC approach even when restricted to convex domains, for n \sim < 100. An importance sampling may extend this advantage to larger dimensions .
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Original:  Furthermore, we also highlight the future opportunities in training contextualized code representations on larger code corpus as a solution to low-resource settings and adapting the pretrained code representations to other downstream code-to-text generation tasks.
Revised:  Furthermore, we also highlight the future opportunities in training contextualized code representations on larger code corpus as a solution to low-resource tasks and adapting the pretrained code representations to other downstream code-to-text generation tasks.
Edit intent: clarity
Original:  Furthermore, we also highlight the future opportunities in training contextualized code representations on larger code corpus as a solution to low-resource settings and adapting the pretrained code representations to other downstream code-to-text generation tasks.
Revised:  Furthermore, we also highlight the future opportunities in training contextualized code representations on larger code corpus as a solution to low-resource settings and adapting the contextualized code representation framework to other code-to-text generation tasks.
Edit intent: clarity
Original:  As strong concerns and emotions are expressed in the tweets, we analyzed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes associated with each tweet, including 1)
Revised:  As strong concerns and emotions are expressed in the tweets, we analysed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes associated with each tweet, including 1)
Edit intent: fluency
Original:  To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss possible applications in communication, psychology, public health, economics and epidemiology  .
Revised:  To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss possible usage in communication, psychology, public health, economics and epidemiology .
Edit intent: clarity
Original:  To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss possible applications in communication, psychology, public health, economics and epidemiology  .
Revised:  To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss possible applications in communication, psychology, public health, economics and epidemiology research .
Edit intent: clarity
Original: In the online non-metric variant of the facility location problem, there is a given graph consisting of  set F of facilities (each with a certain opening cost),  set C of potential clients, and weighted connections between them.
Revised: In the online non-metric variant of the facility location problem, there is a given graph consisting of a set F of facilities (each with a certain opening cost), set C of potential clients, and weighted connections between them.
Edit intent: fluency
Original: In the online non-metric variant of the facility location problem, there is a given graph consisting of  set F of facilities (each with a certain opening cost),  set C of potential clients, and weighted connections between them.
Revised: In the online non-metric variant of the facility location problem, there is a given graph consisting of set F of facilities (each with a certain opening cost), a set C of potential clients, and weighted connections between them.
Edit intent: fluency
Original:  We give the first online, polynomial-time deterministic algorithm for this problem, with  competitive ratio of O(\log |F| \cdot (\log |C| + \log \log |F|)).
Revised:  We give an online, polynomial-time deterministic algorithm for this problem, with competitive ratio of O(\log |F| \cdot (\log |C| + \log \log |F|)).
Edit intent: clarity
Original:  We give the first online, polynomial-time deterministic algorithm for this problem, with  competitive ratio of O(\log |F| \cdot (\log |C| + \log \log |F|)).
Revised:  We give the first online, polynomial-time deterministic algorithm for this problem, with a competitive ratio of O(\log |F| \cdot (\log |C| + \log \log |F|)).
Edit intent: fluency
Original:  Previously, the only known solution for this problem with a sub-linear competitive ratio was randomized Alon et al. , TALG 2006].
Revised:  Our algorithm improves over the O((\log |C| + \log |F|) \cdot (\log |C| + \log \log |F|))-competitive construction that first reduces the facility location instance to a set cover one and then later solves such instance using the deterministic algorithm by Alon et al. , TALG 2006].
Edit intent: meaning-changed
Original:  Our approach is based on solving a different fractional relaxation than that of Alon et al. , where we combine dual fitting and multiplicative weight updates approaches .
Revised:  This is an asymptotic improvement in a typical scenario where |F| \ll |C|. We achieve this by a more direct approach: we design an algorithm for a fractional relaxation of the non-metric facility location problem with clustered facilities. To handle the constraints of such non-covering LP, we combine the dual fitting and multiplicative weight updates approaches .
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Original:  In this paper, inspired by the well-known sparse signal recovery algorithm called orthogonal matching pursuit (OMP), we introduce a deterministic, greedy edge selection algorithm called universal greedy algorithm (UGA) for graph sparsification.
Revised:  In this paper, inspired by the well-known sparse signal recovery algorithm called orthogonal matching pursuit (OMP), we introduce a deterministic, greedy edge selection algorithm called universal greedy approach (UGA) for graph sparsification.
Edit intent: clarity
Original:  The UGA algorithm^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time  with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Revised:  For a general spectral sparsification problem, e.g., positive subset selection problem from a set of m vectors from \mathbb{R^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Edit intent: meaning-changed
Original:  The UGA algorithm^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time  with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Revised:  The UGA algorithm^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil\frac{n{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Edit intent: meaning-changed
Original:  The UGA algorithm^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time  with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Revised:  The UGA algorithm^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil, where \beta is the ratio between the smallest length and largest length of the vectors. The convergence of the nonnegative UGA algorithm will be established. For the graph sparsification problem, another UGA algorithm will be proposed which can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Edit intent: meaning-changed
Original:  The UGA algorithm^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time  with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Revised:  The UGA algorithm^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a {(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Edit intent: meaning-changed
Original:  The UGA algorithm^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} {1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time  with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Revised:  The UGA algorithm^n, we propose a nonnegative UGA algorithm which needs O(mn^2+ n^3/\epsilon^2) time to find a 1+\epsilon/\beta{1-\epsilon/\beta}-spectral sparsifier with positive coefficients with sparsity }\le\lceil{\epsilon^2}}\rceil can output a \frac{(1+\epsilon)^2{(1-\epsilon)^2}} \frac{1+O(\epsilon){1-O(\epsilon)}} -spectral sparsifier with \lceiln{\epsilon^2}\rceil edges in O(m+n^2/\epsilon^2) time with m edges and n vertices for a general random graph satisfying a mild sufficient condition .
Edit intent: coherence