wanyu/IteraTeR_full_sent huggingface dataset:
Original: By numerical and empirical means a dynamic transition of a complex network was found from a hierarchical scale-free Minimal Spanning Tree (MST ), representing the stock market before the recent worldwide financial crash, to a superstar-like MST decorated by a scale-free hierarchy of trees representing the market's state for the period containing the crash.
Revised: By numerical and empirical means a dynamic transition of a complex network representing FSE were found. First transition is from a hierarchical scale-free Minimal Spanning Tree (MST ), representing the stock market before the recent worldwide financial crash, to a superstar-like MST decorated by a scale-free hierarchy of trees representing the market's state for the period containing the crash.
Edit intent: meaning-changed
Original: By numerical and empirical means a dynamic transition of a complex network was found from a hierarchical scale-free Minimal Spanning Tree (MST ), representing the stock market before the recent worldwide financial crash, to a superstar-like MST decorated by a scale-free hierarchy of trees representing the market's state for the period containing the crash.
Revised: By numerical and empirical means a dynamic transition of a complex network was found from a hierarchical scale-free MST representing the stock market before the recent worldwide financial crash, to a superstar-like MST decorated by a scale-free hierarchy of trees representing the market's state for the period containing the crash.
Edit intent: clarity
Original:  Analogous results we obtained earlier for the Warsaw Stock Exchange . Our results can serve as  an empirical foundation for the theory of dynamic structural and topological phase transitions on financial markets.
Revised:  Analogous results we obtained earlier for the Warsaw Stock Exchange and more pronounced than those found by Onnela-Chakraborti-Kaski-Kert\'esz for S an empirical foundation for the theory of dynamic structural and topological phase transitions on financial markets.
Edit intent: meaning-changed
Original:  Our results can serve as  an empirical foundation for the theory of dynamic structural and topological phase transitions on financial markets.
Revised:  Our results can serve as P 500 index in the vicinity of Black Monday (October 19, 1987) and also in the vicinity of January 1, 1998. Our results provide an empirical foundation for the theory of dynamic structural and topological phase transitions on financial markets.
Edit intent: meaning-changed
Original: Recently, it has been shown that CSMA-type random access algorithms are throughput optimal since they achieve the maximum throughput while maintaining the network stability. However, the optimality is established with the following unrealistic assumptions; i-) the underlaying Markov chain reaches a stationary distribution immediately, which causes large delay in practice; ii-) the channel is static and does not change over time .
Revised: It has been known that load unaware channel selection in 802.11 networks results in high level interference, and can significantly reduce the network throughput. In current implementation, the only way to determine the traffic load on a channel is to measure that channel for a certain duration of time. Therefore, in order to find the best channel with the minimum load all channels have to be measured, which is costly and can cause unacceptable communication interruptions between the AP and the stations .
Edit intent: meaning-changed
Original:  In this paper, we design fully distributed scheduling algorithms which are provably throughput optimal for general fading channels. When arbitrary backoff time is allowed, the proposed distributed algorithm achieves the same performance in terms of rate region and delay as that of a centralized system without requiring any message passing. For the case where backoff time is discrete, we show that our algorithm still maintains throughput-optimality and achieves good delay performance at the expense of low overhead for collision resolution .
Revised:  In this paper, we propose a learning based approach which aims to find the channel with the minimum load by measuring only limited number of channels. Our method uses Gaussian Process Regressing to accurately track the traffic load on each channel based on the previous measured load. We confirm the performance of our algorithm by using experimental data, and show that the time consumed for the load measurement can be reduced up to 46\% compared to the case where all channels are monitored .
Edit intent: meaning-changed
Original:  In the case of geometric Brownian motion based models, we can represent such heterogeneous attitudes by letting the market price of risk be a random variable, the distribution of which corresponds to the variability of attitude across the market.
Revised:  In Brownian-based models, we can represent such heterogeneous attitudes by letting the market price of risk be a random variable, the distribution of which corresponds to the variability of attitude across the market.
Edit intent: coherence
Original:  If the flow of market information is determined by the movements of the pricesof assets , then neither the Brownian driver nor the market price of risk are directly visible: the filtration is generated by an "information process" given by a combination of the two that takes the form of a Brownian motion with random drift .
Revised:  If the flow of market information is determined by the movements of prices , then neither the Brownian driver nor the market price of risk are directly visible: the filtration is generated by an "information process" given by a combination of the two that takes the form of a Brownian motion with random drift .
Edit intent: clarity
Original:  If the flow of market information is determined by the movements of the pricesof assets , then neither the Brownian driver nor the market price of risk are directly visible: the filtration is generated by an "information process" given by a combination of the two that takes the form of a Brownian motion with random drift .
Revised:  If the flow of market information is determined by the movements of the pricesof assets , then neither the Brownian driver nor the market price of risk are directly visible: the filtration is generated by an "information process" given by a combination of the two .
Edit intent: clarity
Original: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between  \eps and a fixed horizon T>%DIFDELCMD < \eps%%%  . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Revised: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% the optimal problem \sup_{\tau\in\mathcal{T_{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between \eps and a fixed horizon T>%DIFDELCMD < \eps%%% . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Edit intent: meaning-changed
Original: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between  \eps and a fixed horizon T>%DIFDELCMD < \eps%%%  . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Revised: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed time horizon, (\phi_t^i)_{0\leq t\leq T is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between \eps and a fixed horizon T>%DIFDELCMD < \eps%%% . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Edit intent: meaning-changed
Original: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between  \eps and a fixed horizon T>%DIFDELCMD < \eps%%%  . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Revised: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps^i\in[0,T] is a constant, i=1,...o,n, and T_{\eps,T} is the set of Brownian stopping times that lie between \eps and a fixed horizon T>%DIFDELCMD < \eps%%% . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Edit intent: meaning-changed
Original: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between  \eps and a fixed horizon T>%DIFDELCMD < \eps%%%  . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Revised: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of stopping times that lie between \eps and a fixed horizon T>%DIFDELCMD < \eps%%% . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Edit intent: fluency
Original: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between  \eps and a fixed horizon T>%DIFDELCMD < \eps%%%  . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Revised: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between a constant \eps and a fixed horizon T>%DIFDELCMD < \eps%%% . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Edit intent: clarity
Original: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between  \eps and a fixed horizon T>%DIFDELCMD < \eps%%%  . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Revised: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between \eps %DIFDELCMD < \eps%%% . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Edit intent: clarity
Original: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between  \eps and a fixed horizon T>%DIFDELCMD < \eps%%%  . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Revised: T}}EB_{\tau-\eps}, where }%DIFDELCMD < \eps %%% _{\eps,T}}E[i=1^n (\tau-\eps^i)^+^i], where T>0 } is a fixed strictly positive constant, B is a Brownian motion, is progressively measurable with respect to the Brownian filtration, }\eps and T_{\eps,T} is the set of Brownian stopping times that lie between \eps and a fixed horizon T>%DIFDELCMD < \eps%%% \in[0,T] and T . We solve this problem by conditioning and then using the theory of reflected backward stochastic differential equations (RBSDEs).
Edit intent: meaning-changed
Original:  _{0,T}}EB_{(\tau-\eps)^+} recently posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.}
Revised:  As a corollary, we provide the solution to the optimal stopping problem \sup_{\tau\in\mathcal{T_{0,T}}EB_{(\tau-\eps)^+} recently posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012.}
Edit intent: meaning-changed
Original:  As a corollary, we provide the solution to the optimal stopping problem \tau\in\mathcal{T_{0,T}}EB_{(\tau-\eps)^+} recently posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012. \eps
Revised:  As a corollary, we provide the solution to the optimal stopping problem \tau\in\mathcal{T_{0,T}}EB_{(\tau-\eps)^+} recently posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012. We also provide its asymptotic order as\eps
Edit intent: meaning-changed
Original:  As a corollary, we provide the solution to the optimal stopping problem \tau\in\mathcal{T_{0,T}}EB_{(\tau-\eps)^+} recently posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012. \eps
Revised:  As a corollary, we provide the solution to the optimal stopping problem \tau\in\mathcal{T_{0,T}}EB_{(\tau-\eps)^+} recently posed by Shiryaev at the International Conference on Advanced Stochastic Optimization URLanized by the Steklov Institute of Mathematics in September 2012. \eps\searrow 0.
Edit intent: meaning-changed
Original: In yeast, phenotypic adaptations can evolve by natural selection of conformational variant prions and their variant amyloid fibers. This system requires the Hsp104 disaggregase, which fragments amyloid fibers into smaller seed prions that are passed on to mitotic descendants and meiotic spores. Interestingly, Hsp104 is found in diverse eukaryotes except metazoans. To investigate whether a prion-based transmission "genetics" was incompatible with the evolution of Metazoa , we identify genes conserved in fungi and choanoflagellates but lost in animals.
Revised: The evolution of animals involved a transition from a unicellular transcriptional regime to one supporting developmentally programmed somatic multicellularity. This transition was facilitated by an emergent developmental gene repertoire via gene family expansion and new gene innovation. Unknown, however, is whether this genomic-rewiring was constrained by genetic functions incompatible with key features of animals. Here , we identify genes conserved in fungi and choanoflagellates but lost in animals.
Edit intent: clarity
Original:  To investigate whether a prion-based transmission "genetics" was incompatible with the evolution of Metazoa , we identify genes conserved in fungi and choanoflagellates but lost in animals. We show that both eukaryotic clpB amyloid disaggregases , HSP104 and its nuclear-encoded mitochondrial endo-ortholog HSP78, were lost in the stem-metazoan lineage along with only a small number of other relevant genes.
Revised:  To investigate whether a prion-based transmission "genetics" was incompatible with the evolution of Metazoa , we identify conserved eukaryotic genes that were lost in stem-metazoans. Among the few genes lost are both eukaryotic clpB amyloid disaggregases , HSP104 and its nuclear-encoded mitochondrial endo-ortholog HSP78, were lost in the stem-metazoan lineage along with only a small number of other relevant genes.
Edit intent: clarity
Original:  We show that both eukaryotic clpB amyloid disaggregases , HSP104 and its nuclear-encoded mitochondrial endo-ortholog HSP78, were lost in the stem-metazoan lineage along with only a small number of other relevant genes.
Revised:  We show that both eukaryotic clpB orthologs , HSP104 and its nuclear-encoded mitochondrial endo-ortholog HSP78, were lost in the stem-metazoan lineage along with only a small number of other relevant genes.
Edit intent: clarity
Original:  We show that both eukaryotic clpB amyloid disaggregases , HSP104 and its nuclear-encoded mitochondrial endo-ortholog HSP78, were lost in the stem-metazoan lineage along with only a small number of other relevant genes.
Revised:  We show that both eukaryotic clpB amyloid disaggregases , HSP104 and HSP78, were lost in the stem-metazoan lineage along with only a small number of other relevant genes.
Edit intent: clarity
Original:  We show that both eukaryotic clpB amyloid disaggregases , HSP104 and its nuclear-encoded mitochondrial endo-ortholog HSP78, were lost in the stem-metazoan lineage along with only a small number of other relevant genes. We show that these gene losses are not unrelated historical accidents because these loci comprise a very small regulon devoted to prion transmission in yeast. We propose that evolution of developmental asymmetric cell-specifications necessitated the evolutionary deprecation of the ancient clpB system .
Revised:  We show that both eukaryotic clpB amyloid disaggregases , HSP104 and its nuclear-encoded mitochondrial endo-ortholog HSP78, which encode amyloid disaggregases. Despite their ancient origins in the eukaryotic and mitochondrial primogenitors, we find that both clpB loci of Saccharomyces share a promoter signature that is specific to 7 genes involved in prion homeostasis. We suggest that the loss of these amyloid disaggregases was a necessary step in the evolutionary origin of the asymmetric cell specifications of somatic development .
Edit intent: coherence
Original:  Among the few genes lost are both eukaryotic clpB orthologs, HSP104 and HSP78  , which encode amyloid disaggregases.
Revised:  These genes suggest a coherent pattern of gene loss from which the biological and ecological context of animal origins can be inferred. First, we find that a large number of genes for multiple biosynthetic pathways were lost in the stem-metazoan lineage. Second, we find that of all the essential amino acid pathway genes absent in animals, only the biosynthetic pathways for the branched chain amino acids are also absent in choanoflagellates, the sister group to animals. Third, we find only a few conserved genes lost in the stem-metazoan lineage that do not encode metabolic enzymes, and these include the eukaryotic clpB genes, HSP78 , which encode amyloid disaggregases.
Edit intent: meaning-changed
Original:  Among the few genes lost are both eukaryotic clpB orthologs, HSP104 and HSP78  , which encode amyloid disaggregases. Despite their ancient origins in the eukaryotic and mitochondrial primogenitors, we find that both clpB loci of Saccharomyces share a promoter signature that is specific to 7 genes involved in prion homeostasis.
Revised:  Among the few genes lost are both eukaryotic clpB orthologs, HSP104 and HSP78 , which encode protein disaggregase type chaperones. Finally, we find that in choanoflagellates and fungi the clpB genes and a small repertoire of refolding chaperones are co-regulated by the heat shock element HSE4 that can be bound by two Hsf1 trimers. Based on these findings, we propose that loss of branched chain biosynthetic genes in the holozoan radiation represents an initial pattern of loss that foreshadows the wholesale loss of metabolic genes in the eukaryotic and mitochondrial primogenitors, we find that both clpB loci of Saccharomyces share a promoter signature that is specific to 7 genes involved in prion homeostasis.
Edit intent: meaning-changed
Original:  Despite their ancient origins in the eukaryotic and mitochondrial primogenitors, we find that both clpB loci of Saccharomyces share a promoter signature that is specific to 7 genes involved in prion homeostasis. We suggest that the loss of these amyloid disaggregases was a necessary step in the evolutionary origin of the asymmetric cell specifications of somatic development .
Revised:  Despite their ancient origins in the stem-metazoan lineage. We further propose that key components of metabolic pathways may have required chaperone-mediated folding, and that loss of these amyloid disaggregases was a necessary step in the evolutionary origin of the asymmetric cell specifications of somatic development .
Edit intent: meaning-changed
Original: The evolution of animals involved a transition from a unicellular transcriptional regime to a transcriptional program supporting developmentally programmed somatic multicellularity. This transition was facilitated by an emergent gene repertoire devoted to developmental regulation, which involved gene family expansion and diversification as well as gene innovation.
Revised: The evolution of animals involved acquisition of an emergent gene repertoire devoted to developmental regulation, which involved gene family expansion and diversification as well as gene innovation.
Edit intent: clarity
Original:  This transition was facilitated by an emergent gene repertoire devoted to developmental regulation, which involved gene family expansion and diversification as well as gene innovation. Unknown, however, is whether the loss of key genes co-evolved with and/or facilitated this genomic rewiring .
Revised:  This transition was facilitated by an emergent gene repertoire for gastrulation. Whether loss of genes also co-evolved with and/or facilitated this genomic rewiring .
Edit intent: meaning-changed