wanyu/IteraTeR_v2 huggingface dataset:
Original:  This indicator takes into account not only the prestige of the citing scientific publication but also its closeness to the cited journal using the cosine of the angle between the vectors of the two publications' citation profiles.
Revised:  This indicator takes into account not only the prestige of the citing scientific journal but also its closeness to the cited journal using the cosine of the angle between the vectors of the two publications' citation profiles.
Edit intent: fluency
Original:  This indicator takes into account not only the prestige of the citing scientific publication but also its closeness to the cited journal using the cosine of the angle between the vectors of the two publications' citation profiles.
Revised:  This indicator takes into account not only the prestige of the citing scientific publication but also its closeness to the cited journal using the cosine of the angle between the vectors of the two journals' cocitation profiles.
Edit intent: fluency
Original:  To eliminate the size effect, the accumulated prestige is divided by the fraction of the publication 's citable papers , thus eliminating the decreasing tendency of this type of indicator and giving meaning to the scores.
Revised:  To eliminate the size effect, the accumulated prestige is divided by the fraction of the journal 's citable papers , thus eliminating the decreasing tendency of this type of indicator and giving meaning to the scores.
Edit intent: clarity
Original:  To eliminate the size effect, the accumulated prestige is divided by the fraction of the publication 's citable papers , thus eliminating the decreasing tendency of this type of indicator and giving meaning to the scores.
Revised:  To eliminate the size effect, the accumulated prestige is divided by the fraction of the publication 's citable documents , thus eliminating the decreasing tendency of this type of indicator and giving meaning to the scores.
Edit intent: clarity
Original:  In addition, the SJR2 was better distributed than the JIF by Subject Area and almost as well distributed as the SNIP, and better than both at the lower level of Specific Subject Areas.
Revised:  In addition, the SJR2 was better distributed than the JIF by Subject Area and almost as equalized as the SNIP, and better than both at the lower level of Specific Subject Areas.
Edit intent: clarity
Original:   There are even programs for which compact symbolic execution trees are finite even though the classic symbolic execution trees are infinite.
Revised:  There are even programs for which compact symbolic execution trees are finite while classic symbolic execution trees are infinite.
Edit intent: clarity
Original:  As a result, the system does not exhibit an ordered polar state.
Revised:  As a result, the system does not exhibit an ordered state.
Edit intent: clarity
Original:  The isotropic fluid phase separates at a packing fraction well below close packing and exhibits the large number fluctuations and clustering found ubiquitously in active systems.
Revised:  The isotropic fluid phase separates well below close packing and exhibits the large number fluctuations and clustering found ubiquitously in active systems.
Edit intent: clarity
Original:  Our work shows that this behavior is a generic property of systems that are driven out of equilibrium by an input of energy on each particles, as provided for instance by self propulsion.
Revised:  Our work shows that this behavior is a generic property of systems that are driven out of equilibrium locally, as for instance by self propulsion.
Edit intent: clarity
Original:  The presented result is based on a deterministic argument, which differs from other well-known deterministic results for LASSO such as mutual incoherence and restricted isometry property.
Revised:  The proposed result is based on a deterministic argument, which differs from other well-known deterministic results for LASSO such as mutual incoherence and restricted isometry property.
Edit intent: clarity
Original:  The presented result is based on a deterministic argument, which differs from other well-known deterministic results for LASSO such as mutual incoherence and restricted isometry property.
Revised:  The presented result is based on a deterministic argument, which differs from other well-known results based on mutual coherence and restricted isometry property.
Edit intent: clarity
Original: This article focuses on automatically generating polynomial equa- tions that are inductive loop invariants of computer programs.
Revised: This article focuses on automatically generating polynomial equations that are inductive loop invariants of computer programs.
Edit intent: fluency
Original:  The proposed algorithm applies to two models of computation, one deterministic and one non-deterministic .
Revised:  Though the proposed algorithm is not complete, it is efficient and can be applied to a broader range of problems compared to existing methods targeting similar problems .
Edit intent: coherence
Original:  The efficiency of our approach is testified by ex- periments on a large collection of programs.
Revised:  The efficiency of our approach is testified by experiments on a large collection of programs.
Edit intent: fluency
Original:  In this paper we consider the downlink of a wireless network with N Access Points ( %DIFDELCMD < {\sf %%% AP 's) and M clients, where each client is connected to several out-of-band %DIFDELCMD < {\sf %%% AP 's, and requests delay-sensitive traffic (e.g., real-time video).
Revised:  In this paper we consider the downlink of a wireless network with N Access Points ( %DIFDELCMD < {\sf %%% AP 's) and M clients, where each client is connected to several out-of-band %DIFDELCMD < {\sf %%% AP 's, and requests delay-sensitive traffic (e.g., real-time video).
Edit intent: fluency
Original:  Solving this problem is challenging since even the number of different ways of assigning packets to the %DIFDELCMD < {\sf %%% AP 's is N^M.
Revised:  Solving this problem is challenging since even the number of different ways of assigning packets to the %DIFDELCMD < {\sf %%% AP 's is N^M.
Edit intent: fluency
Original:  We show that the additive gap between the capacity of the relaxed problem, denoted by C_{ \text{det }, and C_{ \text{{\sf T } is bounded by 2 \sqrt{N(C_{\text{det , which is asymptotically negligible compared to C_{ \text{det }, when the network is operating at high-throughput regime.
Revised:  We show that the additive gap between the capacity of the relaxed problem, denoted by C_{ det }, and C_{ \text{{\sf T } is bounded by 2 \sqrt{N(C_{\text{det , which is asymptotically negligible compared to C_{ \text{det }, when the network is operating at high-throughput regime.
Edit intent: fluency
Original:  We show that the additive gap between the capacity of the relaxed problem, denoted by C_{ \text{det }, and C_{ \text{{\sf T } is bounded by 2 \sqrt{N(C_{\text{det , which is asymptotically negligible compared to C_{ \text{det }, when the network is operating at high-throughput regime.
Revised:  We show that the additive gap between the capacity of the relaxed problem, denoted by C_{ \text{det }, and C_{ \text{{\sf T } is bounded by 2 \sqrt{N(C_{det , which is asymptotically negligible compared to C_{ \text{det }, when the network is operating at high-throughput regime.
Edit intent: fluency
Original:  We show that the additive gap between the capacity of the relaxed problem, denoted by C_{ \text{det }, and C_{ \text{{\sf T } is bounded by 2 \sqrt{N(C_{\text{det , which is asymptotically negligible compared to C_{ \text{det }, when the network is operating at high-throughput regime.
Revised:  We show that the additive gap between the capacity of the relaxed problem, denoted by C_{ \text{det }, and C_{ \text{{\sf T } is bounded by 2 \sqrt{N(C_{\text{det , which is asymptotically negligible compared to C_{ det }, when the network is operating at high-throughput regime.
Edit intent: clarity
Original:  In addition, our numerical results show that the actual gap between C_{ \text{{\sf T}^{\text{3}}}  } and C_{ \text{det } is in most cases much less than the worst-case gap proven analytically.
Revised:  In addition, our numerical results show that the actual gap between C_{ \text{{\sf T}^{\text{3}}} } and C_{ det } is in most cases much less than the worst-case gap proven analytically.
Edit intent: fluency
Original:  We also extend the analytical results to the case of time-varying channel states and weighted total timely throughput .
Revised:  We extend the analytical results to the case of time-varying channel states and weighted total timely throughput .
Edit intent: clarity
Original: Many populations in nature are fragmented: they consist of local populations occupying separate habitat patches.
Revised: Many populations in nature are fragmented: they consist of local populations occupying separate patches.
Edit intent: clarity
Original:  In the critical case we modify the Aldous--Popovic model by assuming a proper prior for the time of origin.
Revised:  In the critical case we modify the Aldous-Popovic model by assuming a proper prior for the time of origin.
Edit intent: fluency
Original: We present algorithms to compute the Smith normal form of matrices over two families of local rings.
Revised: We present algorithms to compute the Smith Normal Form of matrices over two families of local rings.
Edit intent: fluency
Original:  The algorithms use the black box model which is suitable for sparse and structured matrices.
Revised:  The algorithms use the black-box model which is suitable for sparse and structured matrices.
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: clarity
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F %DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F \in\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: clarity
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( %DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black-box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency
Original:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F z%DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe  is assumed greater than den \Pden .
Revised:  For an n by n \nxn matrix A over the ring F z%DIFDELCMD < ]%%% /(f^e) \Fzfe , where f^e is a power of an irreducible polynomial f in F z\Fz of degree d, our algorithm requires O( \mu%DIFDELCMD < }%%% *de \bigO ^2n) operations in F \F , where our black box is assumed to require O( \mu \bigO ) operations in F \F to compute a matrix-vector product by a vector over F %DIFDELCMD < ]%%% /(f^e) (and \mu \Fzfe is assumed greater than den \Pden .
Edit intent: fluency