wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this setting of informational market, the first principal goal resides in describing as explicit as possible the set of all deflators for (S^{\tau}, \mathbb G), while the second principal goal lies in addressing the No-Free-Lunch-with-Vanishing-Risk concept (NFLVR hereafter) for (S^{\tau}, \mathbb G).
Revised:  In this setting of informational market, the first principal goal resides in describing as explicitly as possible the set of all deflators for (S^{\tau}, \mathbb G), while the second principal goal lies in addressing the No-Free-Lunch-with-Vanishing-Risk concept (NFLVR hereafter) for (S^{\tau}, \mathbb G).
Edit intent: fluency
Original:  We study the problem of computing the visibility polygon from a given viewpoint q in \({\mathcal {P}\) where only O(1) workspace is available}  }  . The problem was previously solved in O( cn) time , where c denotes the number of critical vertices for q which are the vertices of \({\mathcal {P}\) where the visibility region changes.
Revised:  We study the problem of computing the visibility polygon from a given viewpoint q }\) where only O(1) workspace is available} } . The problem was previously solved in O( cn) time , where c denotes the number of critical vertices for q which are the vertices of \({\mathcal {P}\) where the visibility region changes.
Edit intent: meaning-changed
Original:  We study the problem of computing the visibility polygon from a given viewpoint q in \({\mathcal {P}\) where only O(1) workspace is available}  }  . The problem was previously solved in O( cn) time , where c denotes the number of critical vertices for q which are the vertices of \({\mathcal {P}\) where the visibility region changes.
Revised:  We study the problem of computing the visibility polygon from a given viewpoint q in \({\mathcal {P}\) where only O(1) workspace is available} inside \mathcal } . The problem was previously solved in O( cn) time , where c denotes the number of critical vertices for q which are the vertices of \({\mathcal {P}\) where the visibility region changes.
Edit intent: meaning-changed
Original:  We study the problem of computing the visibility polygon from a given viewpoint q in \({\mathcal {P}\) where only O(1) workspace is available}  }  . The problem was previously solved in O( cn) time , where c denotes the number of critical vertices for q which are the vertices of \({\mathcal {P}\) where the visibility region changes.
Revised:  We study the problem of computing the visibility polygon from a given viewpoint q in \({\mathcal {P}\) where only O(1) workspace is available} P} . The problem was previously solved in O( cn) time , where c denotes the number of critical vertices for q which are the vertices of \({\mathcal {P}\) where the visibility region changes.
Edit intent: meaning-changed
Original:  We study the problem of computing the visibility polygon from a given viewpoint q in \({\mathcal {P}\) where only O(1) workspace is available}  }  . The problem was previously solved in O( cn) time , where c denotes the number of critical vertices for q which are the vertices of \({\mathcal {P}\) where the visibility region changes.
Revised:  We study the problem of computing the visibility polygon from a given viewpoint q in \({\mathcal {P}\) where only O(1) workspace is available} } where only few working variables can be used . The problem was previously solved in O( cn) time , where c denotes the number of critical vertices for q which are the vertices of \({\mathcal {P}\) where the visibility region changes.
Edit intent: meaning-changed
Original:  We study the problem of computing the visibility polygon from a given viewpoint q in \({\mathcal {P}\) where only O(1) workspace is available}  }  . The problem was previously solved in O( cn) time , where c denotes the number of critical vertices for q which are the vertices of \({\mathcal {P}\) where the visibility region changes.
Revised:  We study the problem of computing the visibility polygon from a given viewpoint q in \({\mathcal {P}\) where only O(1) workspace is available} } . The problem was previously solved in O( }\) where the visibility region changes.
Edit intent: coherence
Original:  In another research, the visibility area of a point be computed in O(} n ) time , where r denotes the cardinality of reflex vertices of \({\mathcal {P}\) }   in the output.
Revised:  In another research, the visibility area of a point be computed in O(} n ) time using O(1) variables for working space , where r denotes the cardinality of reflex vertices of \({\mathcal {P}\) } in the output.
Edit intent: meaning-changed
Original:  In another research, the visibility area of a point be computed in O(} n ) time , where r denotes the cardinality of reflex vertices of \({\mathcal {P}\) }   in the output.
Revised:  In another research, the visibility area of a point be computed in O(} n ) time , where r denotes the cardinality of reflex vertices of }\) } in the output.
Edit intent: meaning-changed
Original:  In another research, the visibility area of a point be computed in O(} n ) time , where r denotes the cardinality of reflex vertices of \({\mathcal {P}\) }   in the output.
Revised:  In another research, the visibility area of a point be computed in O(} n ) time , where r denotes the cardinality of reflex vertices of \({\mathcal {P}\) } \mathcal in the output.
Edit intent: meaning-changed
Original:  In another research, the visibility area of a point be computed in O(} n ) time , where r denotes the cardinality of reflex vertices of \({\mathcal {P}\) }   in the output.
Revised:  In another research, the visibility area of a point be computed in O(} n ) time , where r denotes the cardinality of reflex vertices of \({\mathcal {P}\) } P in the output.
Edit intent: meaning-changed
Original:  In the constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Revised:  If reporting the parts of \mathcal{P constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Edit intent: meaning-changed
Original:  In the constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Revised:  In the constant-memory for computing visibility area. We improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Edit intent: clarity
Original:  In the constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Revised:  In the constant-memory model, so, we improve time-complexity such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Edit intent: clarity
Original:  In the constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Revised:  In the constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time and in-place algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Edit intent: coherence
Original:  In the constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Revised:  In the constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a viewpoint inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Edit intent: clarity
Original:  In the constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon , set of non-crossed line-segments and set of non-intersected simple polygons .
Revised:  In the constant-memory model, so, we improve time-complexity of previous works such that it just depends on n . Actually, we provide a linear-time , constant-space, strict in-place and 2-pass algorithm for computing visibility area of a point inside a simple polygon .
Edit intent: coherence
Original:  We study the problem of computing the visibility polygon from a given viewpoint q inside \mathcal {P} where only few working variables can be used. The problem was previously solved in O(n ) time using O(1) variables for working space, where denotes the cardinality of reflex vertices of \mathcal%DIFDELCMD < {%%% P in the output .
Revised:  We study the problem of computing the visibility polygon from a given viewpoint q inside \mathcal {P} where only sub-linear variables are allowed for working space. Without any memory-constrained, this problem was previously solved in O(n ) time using O(1) variables for working space, where denotes the cardinality of reflex vertices of \mathcal%DIFDELCMD < {%%% P in the output .
Edit intent: meaning-changed
Original:  The problem was previously solved in O(n ) time using O(1) variables for working space, where denotes the cardinality of reflex vertices of \mathcal%DIFDELCMD < {%%% P in the output .
Revised:  The problem was previously solved in O(n denotes the cardinality of reflex vertices of \mathcal%DIFDELCMD < {%%% P in the output .
Edit intent: clarity
Original:  The problem was previously solved in O(n ) time using O(1) variables for working space, where denotes the cardinality of reflex vertices of \mathcal%DIFDELCMD < {%%% P in the output .
Revised:  The problem was previously solved in O(n ) time using O(1) variables for working space, where %DIFDELCMD < {%%% P in the output .
Edit intent: clarity
Original:  The problem was previously solved in O(n ) time using O(1) variables for working space, where denotes the cardinality of reflex vertices of \mathcal%DIFDELCMD < {%%% P in the output .
Revised:  The problem was previously solved in O(n ) time using O(1) variables for working space, where denotes the cardinality of reflex vertices of \mathcal%DIFDELCMD < {%%% in the output .
Edit intent: clarity
Original:  The problem was previously solved in O(n ) time using O(1) variables for working space, where denotes the cardinality of reflex vertices of \mathcal%DIFDELCMD < {%%% P in the output .
Revised:  The problem was previously solved in O(n ) time using O(1) variables for working space, where denotes the cardinality of reflex vertices of \mathcal%DIFDELCMD < {%%% P ) -time and O(n) -variables space. In a newer research, the visibility area of a point be computed in O(n) -time, using O(\sqrt{n .
Edit intent: meaning-changed
Original:  In this paper, we present an optimal in-place algorithmof O(n) time, and O(1) extra variables (in addition to the being in-place) for computing visibility area . If reporting the parts of \mathcal{P linear-time and in-place algorithm for computing visibility area of a viewpoint inside a simple polygon .
Revised:  In this paper, we present an optimal in-place algorithmof O(n) time, and O(1) extra variables (in addition to the being in-place) for computing visibility area , where c<n is the number of critical vertices. We keep the algorithm in the linear-time and in-place algorithm for computing visibility area of a viewpoint inside a simple polygon .
Edit intent: meaning-changed
Original:  In addition, we will compare the objectives and learning signals of brains and machines, leading us to conclude that simply scaling up the current DL algorithms will  not lead to human level mindreading skills. We then provide some insights about how to fairly compare human and DL performance. In the end we find that DL can contribute to our understanding of biological computations by providing an example of an end-to-end algorithm that solves the same problems the biological agents face .
Revised:  In addition, we will compare the objectives and learning signals of brains and machines, leading us to conclude that simply scaling up the current DL algorithms will not lead to human level Theory of Mind .
Edit intent: clarity
Original: Long before the mathematical developments that led inexorably to the development of systems factorial technology, the somewhat arduous, but arguably requisite labors which precisely defined parallel and serial architectures had begun (e.g., Townsend, 1969, 1972). Both then and now, what are now referred to as standard serial models and standard parallel models not only play an important role in psychological science, they are often what non-mathematical psychologists are (sometimes in an unschooled fashion) referring to when they up architectural concepts.
Revised: Critical parts of the definitions of standard serial and standard parallel models not only play an important role in psychological science, they are often what non-mathematical psychologists are (sometimes in an unschooled fashion) referring to when they up architectural concepts.
Edit intent: clarity
Original:  Both then and now, what are now referred to as standard serial models and standard parallel models not only play an important role in psychological science, they are often what non-mathematical psychologists are (sometimes in an unschooled fashion) referring to when they up architectural concepts. Interestingly, two strategic and critical properties, and therefore implicit predictions of the canonical serial and parallel models have witnessed little analysis. Thus , standard parallel models predict stochastically independent actual processing timesand therefore total completion times.
Revised:  Both then and now, what are now referred to as standard serial models and standard parallel modes refer to stochastic independence. Standard serial models are defined by stochastic independence and identical distributions of their processing times. Processing times in the serial models are identical to the intercompletion time statistics. Similarly , standard parallel models predict stochastically independent actual processing timesand therefore total completion times.
Edit intent: clarity
Original:  What do standard serial models predict about this statistic? Similarly, standard serial models predict independent actual processing times and therefore intercompletion times . What do standard parallel models predict about this statistic?
Revised:  What do standard serial models can predict for the total completion time or what standard parallel models can predict for the intercompletion times. Here we demonstrate that standard serial models possess a tendency to predict a positive dependence for the total completion times with that always being true in the case of a single processing order. However, with mixtures of processing orders, standard serial models may predict negative dependence of the total completion times. Comparably, standard parallel models typically predict neither independence of the intercompletion times nor identical distributions. In fact, standard parallel models predict about this statistic?
Edit intent: meaning-changed
Original:  What do standard parallel models predict about this statistic? Finally, are there psychological questions that pertain to these distinctions and are there any experiments that have emerged to assess the latter. All these matters are targeted for our exposition .
Revised:  What do standard parallel models predict increasing intercompletion times as the individual channels continue to finish. Nevertheless, dramatically increasing hazard functions of the channels can defeat that tendency. And, standard parallel models can predict intercompletion time independence but only when individual channel distributions are exponential. Finally, we use these and ancillary mathematical results to conclude that standard serial and standard parallel models can never perfectly mimic one another. Therefore, our findings set the stage for explicit model testing between these classes .
Edit intent: meaning-changed
Original:  In this context, we consider the  M/G/1-FCFS queue endowed with an arbitrarily-designed cost function for the waiting times of the incoming jobs , and we study an undiscounted value function integrating the total cost surplus expected from each state relative to the steady-state costs.
Revised:  In this context, we consider the continuous-space M/G/1-FCFS queue endowed with an arbitrarily-designed cost function for the waiting times of the incoming jobs , and we study an undiscounted value function integrating the total cost surplus expected from each state relative to the steady-state costs.
Edit intent: meaning-changed
Original:  In this context, we consider the  M/G/1-FCFS queue endowed with an arbitrarily-designed cost function for the waiting times of the incoming jobs , and we study an undiscounted value function integrating the total cost surplus expected from each state relative to the steady-state costs. When coupled with random initial policies, this value function takes closed-form expressions for polynomial and exponential costs, or for piecewise compositions of the latter, thus hinting in the most general case at the derivation of interval bounds for the value function in the form of power series or trigonometric sums.
Revised:  In this context, we consider the M/G/1-FCFS queue endowed with an arbitrarily-designed cost function for the waiting times of the incoming jobs . The associated value function is a solution of Poisson's equation for Markov chains, which in this work we solve in the Laplace transform domain by considering an ancillary, underlying stochastic process extended to (imaginary) negative backlog states. This construction enables us to issue closed-form expressions for polynomial and exponential costs, or for piecewise compositions of the latter, thus hinting in the most general case at the derivation of interval bounds for the value function in the form of power series or trigonometric sums.
Edit intent: meaning-changed
Original:  When coupled with random initial policies, this value function takes closed-form expressions for polynomial and exponential costs, or for piecewise compositions of the latter, thus hinting in the most general case at the derivation of interval bounds for the value function in the form of power series or trigonometric sums.
Revised:  When coupled with random initial policies, this value function takes closed-form value functions for polynomial and exponential costs, or for piecewise compositions of the latter, thus hinting in the most general case at the derivation of interval bounds for the value function in the form of power series or trigonometric sums.
Edit intent: clarity
Original:  When coupled with random initial policies, this value function takes closed-form expressions for polynomial and exponential costs, or for piecewise compositions of the latter, thus hinting in the most general case at the derivation of interval bounds for the value function in the form of power series or trigonometric sums.
Revised:  When coupled with random initial policies, this value function takes closed-form expressions for polynomial and exponential cost functions and for piecewise compositions of the latter, thus hinting in the most general case at the derivation of interval bounds for the value function in the form of power series or trigonometric sums.
Edit intent: clarity
Original:  When coupled with random initial policies, this value function takes closed-form expressions for polynomial and exponential costs, or for piecewise compositions of the latter, thus hinting in the most general case at the derivation of interval bounds for the value function in the form of power series or trigonometric sums.
Revised:  When coupled with random initial policies, this value function takes closed-form expressions for polynomial and exponential costs, or for piecewise compositions of the latter, in turn permitting the derivation of interval bounds for the value function in the form of power series or trigonometric sums.
Edit intent: clarity
Original:  The value functionapproximations induced by Taylor polynomial expansions of the cost function prove however to converge only for entire cost functions with low growth orders , and to diverge otherwise.
Revised:  We review various cost approximation schemes and assess the convergence of the interval bounds these induce on the value function. Namely: Taylor expansions (divergent, except for a narrow class of entire functions with low growth orders , and to diverge otherwise.
Edit intent: clarity
Original:  The value functionapproximations induced by Taylor polynomial expansions of the cost function prove however to converge only for entire cost functions with low growth orders , and to diverge otherwise.
Revised:  The value functionapproximations induced by Taylor polynomial expansions of the cost function prove however to converge only for entire cost functions with low orders of growth), and uniform approximation schemes (polynomials , and to diverge otherwise.
Edit intent: meaning-changed
Original:  The value functionapproximations induced by Taylor polynomial expansions of the cost function prove however to converge only for entire cost functions with low growth orders , and to diverge otherwise. A more suitable approach for assessing convergent interval bounds is found in the uniform approximation framework. Bernstein polynomialsconstitute straightforward, yet slowly convergent, cost function approximators over intervals. The best convergence rate in the sense of D. Jackson's theorem is achieved by more sophisticated polynomial solutions derived from trigonometric sums. This study URLanized as a guide to implementing multiple-server dispatching policies , from the specification of  cost functions towards the computation of interval bounds for the value functions and the  implementation of the policy improvement step.
Revised:  The value functionapproximations induced by Taylor polynomial expansions of the cost function prove however to converge only for entire cost functions with low growth orders , trigonometric), which achieve optimal convergence rates over finite intervals. This study addresses all the steps to implementing dispatching policies for systems of parallel servers , from the specification of cost functions towards the computation of interval bounds for the value functions and the implementation of the policy improvement step.
Edit intent: clarity