wanyu/IteraTeR_v2 huggingface dataset:
Original:  It  affects collaborative filtering algorithms and it has been proven to be a very good baseline in terms of results accuracy.
Revised:  It strongly affects collaborative filtering algorithms and it has been proven to be a very good baseline in terms of results accuracy.
Edit intent: clarity
Original:  It  affects collaborative filtering algorithms and it has been proven to be a very good baseline in terms of results accuracy.
Revised:  It affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy.
Edit intent: clarity
Original:  Even though we miss an actual personalization, global popularity of items in a catalogue can be used effectively to recommend items to users.
Revised:  Even though we miss an actual personalization, global popularity can be effectively used to recommend items to users.
Edit intent: clarity
Original:  Although the proposed approach results quite light in terms of computational effort, its accuracy results highly competitive compared to state of the art model-based collaborative approaches  .
Revised:  Although the proposed approach , compared to state of the art model-based collaborative approaches .
Edit intent: clarity
Original:  Our main result is an algorithm that samples high-weight entries from a low-rank approximation of the input matrix in time independent of m and n, given natural sampling assumptions on that input matrix(k)\log(mn)), only polynomially slower than the quantum algorithm} . As a consequence, we show that Kerenidis and Prakash's quantum machine learning (QML)
Revised:  Our main result is an algorithm that samples high-weight entries from a low-rank approximation of the input matrix in time independent of m and n, given natural sampling assumptions on that input matrix(k)\log(mn)), only polynomially slower than the quantum algorithm} . As a consequence, Kerenidis and Prakash's quantum machine learning (QML)
Edit intent: coherence
Original: We consider a variant of the vehicle routing problem on trees in which the goal is to  route a fleet of vehicles  to serve a set of clients so that the makespan, the longest distance traveled by one of the vehicles , is minimized.
Revised: We consider a variant of the vehicle routing problem on trees in which the goal is to optimally route a fleet of vehicles to serve a set of clients so that the makespan, the longest distance traveled by one of the vehicles , is minimized.
Edit intent: clarity
Original: We consider a variant of the vehicle routing problem on trees in which the goal is to  route a fleet of vehicles  to serve a set of clients so that the makespan, the longest distance traveled by one of the vehicles , is minimized.
Revised: We consider a variant of the vehicle routing problem on trees in which the goal is to route a fleet of vehicles to serve a set of clients , is minimized.
Edit intent: clarity
Original:  This simplification relies on partitioning the tree into clusters such that there exists a near-optimal solution in which every tour that visits a given cluster takes on one of a few forms.
Revised:  This simplification relies on partitioning the tree into clusters such that there exists a near-optimal solution in which every vehicle that visits a given cluster takes on one of a few forms.
Edit intent: clarity
Original:  In particular, there are at most two tours serving clients in any cluster.
Revised:  In particular, only a small number of vehicles serve clients in any cluster.
Edit intent: clarity
Original: Motivation: High-throughput sequencing of large immune repertoires has enabled the de- velopment of methods to predict the probability of generation by V(D)J recombination of T- and B-cell receptors of any specific nucleotide sequence.
Revised: Motivation: High-throughput sequencing of large immune repertoires has enabled the development of methods to predict the probability of generation by V(D)J recombination of T- and B-cell receptors of any specific nucleotide sequence.
Edit intent: fluency
Original: Existing methods that aim to automatically cluster data into physically meaningful subsets typically require assumptions regarding the number, size, or shape of the coherent subgroups.
Revised: The clustering of data into physically meaningful subsets typically require assumptions regarding the number, size, or shape of the coherent subgroups.
Edit intent: clarity
Original: Existing methods that aim to automatically cluster data into physically meaningful subsets typically require assumptions regarding the number, size, or shape of the coherent subgroups.
Revised: Existing methods that aim to automatically cluster data into physically meaningful subsets often requires assumptions regarding the number, size, or shape of the coherent subgroups.
Edit intent: clarity
Original:  We anticipate straightforward translation to many other fields where existing analysis tools , such as k-means and traditional hierarchical clustering, require ad hoc assumptions on the data structure or lack the interpretability of the present method .
Revised:  We anticipate straightforward translation to many other fields where existing analysis tools require ad hoc assumptions on the data structure or lack the interpretability of the present method .
Edit intent: coherence
Original:  To illustrate the effectiveness of the method in the absence of a priori assumptions  we apply it to two exemplary problems in fluid dynamics.
Revised:  To illustrate the effectiveness of the method in the absence of a priori assumptions , we apply it to two exemplary problems in fluid dynamics.
Edit intent: fluency
Original: More than 80\% of today's data is unstructured in nature, and these unstructured datasets evolving over time.
Revised: More than 80\% of today's data is unstructured in nature, and these unstructured datasets evolve over time.
Edit intent: fluency
Original:  A large part of the evolving unstructured data are text documents generated by media outlets, scholarly articles in digital libraries, and social media.
Revised:  A large part of these datasets are text documents generated by media outlets, scholarly articles in digital libraries, and social media.
Edit intent: clarity
Original:  Vector space models have been developed to analyze text documents using data mining and machine learning algorithms.
Revised:  Vector space models were developed to analyze text documents using data mining and machine learning algorithms.
Edit intent: clarity
Original:  Vector space models have been developed to analyze text documents using data mining and machine learning algorithms.
Revised:  Vector space models have been developed to analyze text data using data mining and machine learning algorithms.
Edit intent: clarity
Original:  While ample vector space models exist for text data, the evolution aspect of evolving text corpora is still missing in vector-based representations.
Revised:  While ample vector space models exist for text data, the evolutionary aspect of ever-changing text corpora is still missing in vector-based representations.
Edit intent: clarity
Original:  The advent of word embeddings has given a way to create a contextual vector space, but the embeddings do not consider the temporal aspects of the feature space successfully yet.
Revised:  The advent of word embeddings has enabled us to create a contextual vector space, but the embeddings do not consider the temporal aspects of the feature space successfully yet.
Edit intent: clarity
Original:  The advent of word embeddings has given a way to create a contextual vector space, but the embeddings do not consider the temporal aspects of the feature space successfully yet.
Revised:  The advent of word embeddings has given a way to create a contextual vector space, but the embeddings fail to consider the temporal aspects of the feature space successfully yet.
Edit intent: clarity
Original:  The advent of word embeddings has given a way to create a contextual vector space, but the embeddings do not consider the temporal aspects of the feature space successfully yet.
Revised:  The advent of word embeddings has given a way to create a contextual vector space, but the embeddings do not consider the temporal aspects of the feature space successfully . This paper presents an approach to include temporal aspects in feature spaces.
Edit intent: coherence
Original:  The inclusion of the time aspect in the feature space will provide vectors for every natural language element, such as words or entities, at every timestamp.
Revised:  The inclusion of the time aspect in the feature space provides vectors for every natural language element, such as words or entities, at every timestamp.
Edit intent: clarity
Original:  Such temporal word vectors will provide the ability to track how the meaning of a word changes over time, in terms of the changes in its neighborhood.
Revised:  Such temporal word vectors allow us to track how the meaning of a word changes over time, in terms of the changes in its neighborhood.
Edit intent: clarity
Original:  Such temporal word vectors will provide the ability to track how the meaning of a word changes over time, in terms of the changes in its neighborhood.
Revised:  Such temporal word vectors will provide the ability to track how the meaning of a word changes over time, by studying the changes in its neighborhood.
Edit intent: clarity
Original:  In this paper, we present the potential benefits of a time-reflective vector space model for temporal text data that is able to capture short and long-term changes in the meaning of words.
Revised:  In this paper, we present a time-reflective vector space model for temporal text data that is able to capture short and long-term changes in the meaning of words.
Edit intent: clarity
Original:  We present qualitative and quantitative evaluations using semantic evolution tracking as the target application.
Revised:  We present qualitative and quantitative evaluations using the tracking of semantic evolution as the target application.
Edit intent: clarity
Original:  In fact, researchers have proved that even perfect {\em  scheduling can only achieve R = O(1{\ln N}).
Revised:  In fact, researchers have proved that even {\em scheduling can only achieve R = O(1{\ln N}).
Edit intent: clarity
Original:  Here N is the number of nodes in the network, while R is the {\em medium utilization rate .
Revised:  Here N is the number of nodes in the network, and R is the {\em medium utilization rate .
Edit intent: coherence
Original:  In this work, our vision is to achieve R = \Theta(1), and avoid all the complexities in scheduling.
Revised:  In this work, our vision is to achieve R = \Theta(1), while avoiding all the complexities in scheduling.
Edit intent: coherence
Original:  To this end, this paper proposes {\em cross-sender bit-mixing coding (or BMCin short{\em  ), which does not rely on scheduling.
Revised:  To this end, this paper proposes {\em cross-sender bit-mixing coding ({\em ), which does not rely on scheduling.
Edit intent: clarity
Original: In this paper we develop an efficient procedure for computing a (scaled) Hadamard product forcommutative polynomials.
Revised: commutative polynomials.
Edit intent: clarity
Original: In this paper we develop an efficient procedure for computing a (scaled) Hadamard product forcommutative polynomials.
Revised: In this paper we develop an efficient procedure for computing a (scaled) Hadamard product for polynomials.
Edit intent: clarity
Original:  Our main algorithmic results include the following: 1) Given an arithmetic circuit C of poly (n) size computing a polynomial f\inF[X] and a parameter k0pt . k-MLC: Compute the sum of the coefficients of all degree-k multilinear monomials in the polynomial f. k-MMD: Test if there is a nonzero degree-k multilinear monomial in the polynomial f.
Revised:  Our main algorithmic results include the following: 1) Given an arithmetic circuit C of poly (n) size given as input that computes a polynomial f\inF[X] and a parameter k0pt . k-MLC: Compute the sum of the coefficients of all degree-k multilinear monomials in the polynomial f. k-MMD: Test if there is a nonzero degree-k multilinear monomial in the polynomial f.
Edit intent: clarity
Original:  for some constant c to compute the sum of the coefficients of multilinear monomials of degree k in f, that answers an open question mentioned by Koutis and Williams in~\mbox{ KW160pt . 2)
Revised:  (where c is a constant), answering an open question mentioned by Koutis and Williams in~\mbox{ KW160pt . 2)
Edit intent: clarity
Original:  for some constant c to compute the sum of the coefficients of multilinear monomials of degree k in f, that answers an open question mentioned by Koutis and Williams in~\mbox{ KW160pt . 2)
Revised:  for some constant c to compute the sum of the coefficients of multilinear monomials of degree k in f, that answers an open question of Koutis and Williams in~\mbox{ KW160pt . 2)
Edit intent: clarity
Original:  for some constant c to compute the sum of the coefficients of multilinear monomials of degree k in f, that answers an open question mentioned by Koutis and Williams in~\mbox{ KW160pt . 2)
Revised:  for some constant c to compute the sum of the coefficients of multilinear monomials of degree k in f, that answers an open question mentioned by Koutis and Williams 0pt . 2)
Edit intent: clarity
Original:  Given an arithmetic circuit C of size s computing a polynomial f\inF[X] (where F could be any field where the field arithmetic is efficient), and a parameter k, } }\hspace{0pt} . As corollaries, we show O^*(}\binom{n}{\downarrow k/2} we give a randomized algorithm of run time 4.32^k\cdot \text{poly (n, s) to check if f contains a multilinear monomial of degree kor not .
Revised:  Given an arithmetic circuit C of size s computing a polynomial f\inF[X] (where F could be any field where the field arithmetic is efficient), and a parameter k, } }\hspace{0pt} . As corollaries, we show O^*(}\binom{n}{\downarrow k/2} we give a randomized algorithm of run time 4.32^k\cdot poly (n, s) to check if f contains a multilinear monomial of degree kor not .
Edit intent: coherence
Original:  Our algorithm uses \text{poly (n,k ,s ) space.
Revised:  Our algorithm uses only poly (n,k ,s ) space.
Edit intent: clarity
Original:  Our algorithm uses \text{poly (n,k ,s ) space.
Revised:  Our algorithm uses \text{poly (n,k ) space.
Edit intent: clarity
Original:  3) If the given circuit C is a depth-three homogeneous circuit computing f \in \mathbb{Q}[X] of degree k, we give a }\emph{deterministic parameterized algorithm of run time 4^k \cdot \text{poly(n,s) to detect degree kmultilinear terms, and an algorithm of run time 2^k \cdot \text{poly}(n,s) to compute the sum of their coefficients in f.
Revised:  3) If the given circuit C is a depth-three homogeneous circuit computing f \in \mathbb{Q}[X] of degree k, we give a }\emph{ parameterized algorithm of run time 4^k \cdot \text{poly(n,s) to detect degree kmultilinear terms, and an algorithm of run time 2^k \cdot \text{poly}(n,s) to compute the sum of their coefficients in f.
Edit intent: clarity
Original:  3) If the given circuit C is a depth-three homogeneous circuit computing f \in \mathbb{Q}[X] of degree k, we give a }\emph{deterministic parameterized algorithm of run time 4^k \cdot \text{poly(n,s) to detect degree kmultilinear terms, and an algorithm of run time 2^k \cdot \text{poly}(n,s) to compute the sum of their coefficients in f.
Revised:  3) If the given circuit C is a depth-three homogeneous circuit computing f \in \mathbb{Q}[X] of degree k, we give a }\emph{deterministic (n,s) to detect degree kmultilinear terms, and an algorithm of run time 2^k \cdot \text{poly}(n,s) to compute the sum of their coefficients in f.
Edit intent: clarity
Original: The backpropagation of error algorithm (BP) is often said to be impossible to implement in a real brain.
Revised: The backpropagation of error algorithm (BP) is impossible to implement in a real brain.
Edit intent: clarity
Original:  The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might implement or approximate BP.
Revised:  The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP.
Edit intent: clarity
Original:  Here we present the first results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance.
Revised:  Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance.
Edit intent: clarity
Original:  Our approach, called GrangerBusca, is validated on nine datasets from the Snap repository .
Revised:  Our approach, called GrangerBusca, is validated on nine datasets .
Edit intent: clarity
Original:  This is an advance in relation to most prior efforts which focus mostly on subsets of the Memetracker dataset . Regarding accuracy, GrangerBusca is three times more accurate (in Precision@10) than the state of the art for the commonly explored subsets of the Memetrackerdata .
Revised:  This is an advance in relation to most prior efforts which focus mostly on subsets of the Memetracker data . Regarding accuracy, GrangerBusca is three times more accurate (in Precision@10) than the state of the art for the commonly explored subsets of the Memetrackerdata .
Edit intent: fluency
Original:  This is an advance in relation to most prior efforts which focus mostly on subsets of the Memetracker dataset . Regarding accuracy, GrangerBusca is three times more accurate (in Precision@10) than the state of the art for the commonly explored subsets of the Memetrackerdata .
Revised:  This is an advance in relation to most prior efforts which focus mostly on subsets of the Memetracker dataset . Regarding accuracy, GrangerBusca is three times more accurate (in Precision@10) than the state of the art for the commonly explored subsets Memetracker .
Edit intent: fluency
Original:  Here, we propose a new approach  based on convex duality instead of martingale measures duality: our prices will be expressed using Fenchel conjugate and bi-conjugate.
Revised:  We propose a new approach based on convex duality instead of martingale measures duality: our prices will be expressed using Fenchel conjugate and bi-conjugate.
Edit intent: coherence
Original:  Here, we propose a new approach  based on convex duality instead of martingale measures duality: our prices will be expressed using Fenchel conjugate and bi-conjugate.
Revised:  Here, we propose a new approach based on convex duality instead of martingale measures duality: Our prices will be expressed using Fenchel conjugate and bi-conjugate.
Edit intent: fluency
Original:  This naturally leads to a weak condition of (NA ) called Absence of Immediate Profit (AIP).
Revised:  This naturally leads to a weak condition of NA called Absence of Immediate Profit (AIP).
Edit intent: fluency
Original:  It asserts that the price of the zero claim should be zero or equivalently that the super-hedging cost of some call option should be non-negative.
Edit intent: clarity
Original:  We propose several characterizations of the (AIP ) condition and also study the relation with (NA) and a stronger notion of (AIP) linked to the no-free lunch condition.
Revised:  We propose several characterizations of AIP and study the relation with (NA) and a stronger notion of (AIP) linked to the no-free lunch condition.
Edit intent: coherence
Original:  At the heart of our approach lies a distributed algorithm called Random-Push: this algorithm approximates a natural notion of Most Recently Used (MRU) tree (essentially an approximate working set), by first performing move-to-front, but then pushing less recently accessed items down the tree using a random walk  .
Revised:  At the heart of our approach lies a distributed algorithm called Random-Push: this algorithm approximates a natural notion of Most Recently Used (MRU) tree (essentially an approximate working set), by first performing move-to-front, but then pushing less recently used communication partners down the tree using a random walk .
Edit intent: clarity
Original:  At the heart of our approach lies a distributed algorithm called Random-Push: this algorithm approximates a natural notion of Most Recently Used (MRU) tree (essentially an approximate working set), by first performing move-to-front, but then pushing less recently accessed items down the tree using a random walk  .
Revised:  At the heart of our approach lies a distributed algorithm called Random-Push: this algorithm approximates a natural notion of Most Recently Used (MRU) tree (essentially an approximate working set), by first performing move-to-front, but then pushing less recently accessed items down the tree , along a random walk .
Edit intent: clarity
Original:  In order to investigate the influence of the network topology on collective response, we consider an archetypal model of distributed decision-making---the leader-follower linear consensus---and study the collective capacity of the system to follow a dynamic driving signal (the `leader' ) for a range of topologies and system sizes.
Revised:  In order to investigate the influence of the network topology on collective response, we consider an archetypal model of distributed decision-making---the leader-follower linear consensus---and study the collective capacity of the system to follow a dynamic driving signal (the "leader" ) for a range of topologies and system sizes.
Edit intent: fluency
Original: The stable throughput region of the two-user interference channel is investigated here .
Revised: We consider the two-user interference channel is investigated here .
Edit intent: clarity
Original:  First, the stability region  for the general case is characterized.
Revised:  First, the stability region is characterized for the general case is characterized.
Edit intent: fluency
Original:  Second, we study the cases where the receivers treat interference as noise or perform successive interference cancellation.
Revised:  Second, we explore two different interference harnessing strategies at the receiver: treating interference as noise or perform successive interference cancellation.
Edit intent: clarity
Original:  Second, we study the cases where the receivers treat interference as noise or perform successive interference cancellation.
Revised:  Second, we study the cases where the receivers treat interference as noise and successive interference cancellation.
Edit intent: clarity
Original:  Furthermore, we provide conditions for the convexity of the stability region and for which a certain interference management strategy leads to broader stability region  .
Revised:  Furthermore, we provide conditions for the convexity of the stability region and for which a certain receiver strategy leads to broader stability region .
Edit intent: clarity
Original:  By dense we mean that |E(G)|\geq\alpha|V(G)|^2 for some fixed \alpha>0, and a maximal \varepsilon-near perfect matching is a maximal matching which covers at least (1-\varepsilon)|V(G)| vertices.
Revised:  By dense we mean that |E(G)|\geq\alpha|V(G)|^2 for some fixed \alpha>0, and a maximal \varepsilon-near perfect matching is a maximal matching which covers at least (1-\varepsilon)|V(G)| vertices.
Edit intent: fluency
Original:  The upper bound of such graphs is always 1/2(n\log n).
Revised:  An upper bound for such graphs is always 1/2(n\log n).
Edit intent: fluency
Original:  The upper bound of such graphs is always 1/2(n\log n).
Revised:  The upper bound of such graphs is 1/2(n\log n).
Edit intent: clarity
Original:  Our algorithm is based on analysis of counting the number of maximal near perfect matchings of generalized quasirandom graphs.
Revised:  Our algorithm is based on analyzing the number of maximal near perfect matchings of generalized quasirandom graphs.
Edit intent: clarity