wanyu/IteraTeR_full_sent huggingface dataset:
Original: We consider the Barab\'{a}si-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons.
Revised: We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barab\'{a}si-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons.
Edit intent: meaning-changed
Original:  A Matthew effect in inhibitory synaptic plasticity is thus found to occur; good BS (with higher bursting measure) gets better via long-term depression (LTD) , while bad BS (with lower bursting measure) gets worse via long-term potentiation (LTP) .
Revised:  A Matthew effect in inhibitory synaptic plasticity : good BS gets better via long-term depression (LTD) , while bad BS (with lower bursting measure) gets worse via long-term potentiation (LTP) .
Edit intent: coherence
Original:  Furthermore, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic burst onset times.
Revised:  We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic burst onset times.
Edit intent: meaning-changed
Original:  Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l^* and the asymmetry parameter \Delta l in the SFN , and Matthew effects in inhibitory synaptic plasticity are also found to occur by varying l^* and \Delta l .
Revised:  Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l^* and the asymmetry parameter \Delta l in the SFN .
Edit intent: coherence
Original:  To vary top-down influences, we evaluate the models on a large Corpus dataset with a memorization task andon a large visual search dataset. We can separate the exploration of a natural scene into three phases: The first saccade, which follows a different narrower distribution, an initial guided exploration characterized by a gradual broadening of the fixation density and an equilibrium state which is reached after roughly 10 fixations.
Revised:  To vary top-down influences, we evaluate the models on two large datasets with different tasks: first, a memorisation task and, second, a search task. Our results lend support to a separation of visual scene exploration into three phases: The first saccade, which follows a different narrower distribution, an initial guided exploration characterized by a gradual broadening of the fixation density and an equilibrium state which is reached after roughly 10 fixations.
Edit intent: clarity
Original:  We can separate the exploration of a natural scene into three phases: The first saccade, which follows a different narrower distribution, an initial guided exploration characterized by a gradual broadening of the fixation density and an equilibrium state which is reached after roughly 10 fixations.
Revised:  We can separate the exploration of a natural scene into three phases: The first saccade, an initial guided exploration characterized by a gradual broadening of the fixation density and an equilibrium state which is reached after roughly 10 fixations.
Edit intent: clarity
Original:  We can separate the exploration of a natural scene into three phases: The first saccade, which follows a different narrower distribution, an initial guided exploration characterized by a gradual broadening of the fixation density and an equilibrium state which is reached after roughly 10 fixations.
Revised:  We can separate the exploration of a natural scene into three phases: The first saccade, which follows a different narrower distribution, an initial guided exploration characterised by a gradual broadening of the fixation density and an equilibrium state which is reached after roughly 10 fixations.
Edit intent: fluency
Original:  We can separate the exploration of a natural scene into three phases: The first saccade, which follows a different narrower distribution, an initial guided exploration characterized by a gradual broadening of the fixation density and an equilibrium state which is reached after roughly 10 fixations.
Revised:  We can separate the exploration of a natural scene into three phases: The first saccade, which follows a different narrower distribution, an initial guided exploration characterized by a gradual broadening of the fixation density , and an steady state which is reached after roughly 10 fixations.
Edit intent: clarity
Original:  We conclude that any low-level bottom-up factors are restricted to the first saccade, possibly caused by the image onset. Later eye movements are better explained by including high-level features , but this high-level bottom-up control can be overruled by top-down influences.
Revised:  We conclude that any low-level bottom-up factors are mainly limited to the generation of the first saccade. All saccades are better explained by including high-level features , but this high-level bottom-up control can be overruled by top-down influences.
Edit intent: clarity
Original:  We present an O(n \log n) algorithm for trajectories in R^1 with n vertices and a (1 + \epsilon)-approximation algorithm for trajectories in R^2 for identifying all such stay points.
Revised:  We present an O(n \log n) algorithm for trajectories in R^1 with n vertices and a (1 + \epsilon)-approximation algorithm for trajectories in R^2 to identify all such stay points.
Edit intent: fluency
Original:  Thus, while traditional asymptotics in RDD require a growing number of observations local to the cut-off as n grows, our framework allows for the number q of observations local to the cut-off to be fixed as n grows.
Revised:  Thus, while traditional asymptotic analysis in RDD requires a growing number of observations local to the cut-off as n grows, our framework allows for the number q of observations local to the cut-off to be fixed as n grows.
Edit intent: clarity
Original:  In this paper we propose a new test for continuity of a density at a point based on the so-called g-order statistics, and study its properties under a novel asymptotic framework. The asymptotic frameworkis intended to approximate a small sample phenomenon: even though the total number n of observations may be large , the number of effective observations local to the cut-off is often small.
Revised:  In this paper we propose a new test for continuity of a density at a point based on the so-called g-order statistics, and study its properties under two complementary asymptotic frameworks. In the first asymptotic framework , the number of effective observations local to the cut-off is often small.
Edit intent: meaning-changed
Original:  The asymptotic frameworkis intended to approximate a small sample phenomenon: even though the total number n of observations may be large , the number of effective observations local to the cut-off is often small. Thus, while traditional asymptotic analysis in RDD requires a growing number of observations local to the cut-off as n grows, our framework allows for the number q of observations local to the cut-off to be fixed as n grows.
Revised:  The asymptotic frameworkis intended to approximate a small sample phenomenon: even though the total number n of observations may be large , the number of effective observations local to the cut-off is fixed as the sample size n diverges to infinity, while in the cut-off to be fixed as n grows.
Edit intent: meaning-changed
Original:  Thus, while traditional asymptotic analysis in RDD requires a growing number of observations local to the cut-off as n grows, our framework allows for the number q of observations local to the cut-off to be fixed as n grows. The new test is easy to implement, asymptotically valid under weaker conditions than those used by competing methods,  exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity , and has favorable power properties against certain alternatives .
Revised:  Thus, while traditional asymptotic analysis in RDD requires a growing number of observations local to the cut-off as n grows, our framework allows for the number q of observations local to the second framework q diverges to infinity slowly as n diverges to infinity. Under both of these frameworks, we show that the test we propose is asymptotically valid in the sense that it has limiting rejection probability under the null hypothesis not exceeding the nominal level. More importantly, the test is easy to implement, asymptotically valid under weaker conditions than those used by competing methods, exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity , and has favorable power properties against certain alternatives .
Edit intent: meaning-changed
Original:  The new test is easy to implement, asymptotically valid under weaker conditions than those used by competing methods,  exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity , and has favorable power properties against certain alternatives .
Revised:  The new test is easy to implement, asymptotically valid under weaker conditions than those used by competing methods, and exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity , and has favorable power properties against certain alternatives .
Edit intent: coherence
Original:  The new test is easy to implement, asymptotically valid under weaker conditions than those used by competing methods,  exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity , and has favorable power properties against certain alternatives .
Revised:  The new test is easy to implement, asymptotically valid under weaker conditions than those used by competing methods, exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity .
Edit intent: coherence
Original: We extend analytic large network results on default contagion in random graphs to capture a pronounced Block Model structure. This includes as a special case the Core-Periphery network structure , which plays a prominent role in recent research on systemic risk.
Revised: One of the most defining features of modern financial networks is their inherent complex and intertwined structure. In particular the often observed core-periphery structure plays a prominent role in recent research on systemic risk.
Edit intent: clarity
Original:  This includes as a special case the Core-Periphery network structure , which plays a prominent role in recent research on systemic risk. Further, in the existing literature on systemic risk using random graph methods the problematic assumption that the distribution of liabilities solely depends on the creditor type seems to persist. Under this assumption a straightforward application of the law of large numbers allows to turn edge related random elements into deterministic vertex properties. Here we study a general setting in which the liabilities may depend on both the creditor and the debtor where this argument breaks down and a direct asymptotic analysis of the edge weighted random graph becomes necessary. Among several other applications our results allow us to obtain resilience conditions for the entire network (for example the global financial network ) based only on subnetwork conditions. Contrasting earlier research we also give an example that demonstrates how reshuffling edge weights to form blocks can in fact impact resilience even for otherwise very homogeneous networks .
Revised:  This includes as a special case the Core-Periphery network structure , which plays a prominent role . Here we study and quantify the impact that the complexity of networks has on contagion effects and system stability, and our focus is on the channel of default contagion that describes the spread of initial distress via direct balance sheet exposures. We present a general approach describing the financial network by a random graph, where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and weights (exposures) depend on the types of both the receiving and the sending vertex. Our main result allows to compute explicitly the systemic damage caused by some initial local shock event, and we derive a complete characterization of resilient respectively non-resilient financial systems. Due to the random graphs approach these results bear a considerable robustness to local uncertainties and small changes of the network structure over time. In particular, it is possible to condense the precise micro-structure of the network to macroscopic statistics. Applications of our theory demonstrate that indeed the features captured by our model can have significant impact on system stability; we derive resilience conditions for the global network based on subnetwork conditions only .
Edit intent: meaning-changed
Original:  In particular the often observed core-periphery structure plays a prominent role. Here we study and quantify the impact that the complexity of networks has on contagion effects and system stability, and our focus is on the channel of default contagion that describes the spread of initial distress via direct balance sheet exposures{\em   .
Revised:  From the perspective of systemic risk it is important to understand the influence of this network structure on default contagion. Using sparse random graphs to model the financial network, asymptotic methods turned out powerful to analytically describe the contagion process and to make statements about resilience. So far, however, they have been limited to so-called{\em .
Edit intent: coherence
Original:  Here we study and quantify the impact that the complexity of networks has on contagion effects and system stability, and our focus is on the channel of default contagion that describes the spread of initial distress via direct balance sheet exposures{\em   .
Revised:  Here we study and quantify the impact that the complexity of networks has on contagion effects and system stability, and our focus is on the channel of default contagion that describes the spread of initial distress via direct balance sheet exposures{\em rank one .
Edit intent: clarity
Original:  Here we study and quantify the impact that the complexity of networks has on contagion effects and system stability, and our focus is on the channel of default contagion that describes the spread of initial distress via direct balance sheet exposures{\em   .
Revised:  Here we study and quantify the impact that the complexity of networks has on contagion effects and system stability, and our focus is on the channel of default contagion that describes the spread of initial distress via direct balance sheet exposures{\em models in which informally the only network parameter is the degree sequence (see (Amini et. al. 2016) and (Detering et. al. 2019) for example) and the contagion process can be described by a one dimensional fix-point equation. These networks fail to account for a pronounced block structure such as core/periphery or a network composed of different connected blocks for different countries .
Edit intent: meaning-changed
Original:  We present a general approach describing the financial network by a random graph , where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and weights (exposures ) depend on the types of both  the receiving and the sending vertex  .
Revised:  We present a much more general model here , where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and weights (exposures ) depend on the types of both the receiving and the sending vertex .
Edit intent: clarity
Original:  We present a general approach describing the financial network by a random graph , where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and weights (exposures ) depend on the types of both  the receiving and the sending vertex  .
Revised:  We present a general approach describing the financial network by a random graph , where we distinguish vertices (institutions) of different types and let edge probabilities and weights (exposures ) depend on the types of both the receiving and the sending vertex .
Edit intent: coherence
Original:  We present a general approach describing the financial network by a random graph , where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and weights (exposures ) depend on the types of both  the receiving and the sending vertex  .
Revised:  We present a general approach describing the financial network by a random graph , where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and exposures depend on the types of both the receiving and the sending vertex .
Edit intent: clarity
Original:  We present a general approach describing the financial network by a random graph , where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and weights (exposures ) depend on the types of both  the receiving and the sending vertex  .
Revised:  We present a general approach describing the financial network by a random graph , where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and weights (exposures ) depend on the types of both , the receiving and the sending vertex .
Edit intent: fluency
Original:  We present a general approach describing the financial network by a random graph , where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and weights (exposures ) depend on the types of both  the receiving and the sending vertex  .
Revised:  We present a general approach describing the financial network by a random graph , where we distinguish vertices (institutions) of different types - for example core/periphery - and let edge probabilities and weights (exposures ) depend on the types of both the receiving and the sending vertex plus additional parameters .
Edit intent: meaning-changed
Original:  Due to the random graphs approach these results bear a considerable robustness to local uncertainties and small changes of the network structure over time. In particular, it is possible to condense the precise micro-structure of the network to macroscopic statistics. Applications of our theory demonstrate that indeed the features captured by our model can have significant impact on system stability;
Revised:  This is the first instance that default contagion is rigorously studied in a model outside the class of rank one models and several technical challenges arise. Moreover, in contrast to previous work, in which networks could be classified as resilient or non resilient, independent of the distribution of the shock, information about the shock becomes important in our model and a more refined resilience condition arises. Among other applications of our theory demonstrate that indeed the features captured by our model can have significant impact on system stability;
Edit intent: meaning-changed
Original: In the context of a sequential search problem, I explore large-generations learning dynamics for agents who suffer from the "gambler's fallacy  " - the statistical bias of anticipating too much regression to the mean for realizations of independent random events.
Revised: I study endogenous learning dynamics for agents who suffer from the "gambler's fallacy " - the statistical bias of anticipating too much regression to the mean for realizations of independent random events.
Edit intent: meaning-changed
Original: In the context of a sequential search problem, I explore large-generations learning dynamics for agents who suffer from the "gambler's fallacy  " - the statistical bias of anticipating too much regression to the mean for realizations of independent random events.
Revised: In the context of a sequential search problem, I explore large-generations learning dynamics for people expecting systematic reversals from random sequences -- the "gambler's fallacy " - the statistical bias of anticipating too much regression to the mean for realizations of independent random events.
Edit intent: meaning-changed
Original: In the context of a sequential search problem, I explore large-generations learning dynamics for agents who suffer from the "gambler's fallacy  " - the statistical bias of anticipating too much regression to the mean for realizations of independent random events.
Revised: In the context of a sequential search problem, I explore large-generations learning dynamics for agents who suffer from the "gambler's fallacy . " - the statistical bias of anticipating too much regression to the mean for realizations of independent random events.
Edit intent: fluency
Original: In the context of a sequential search problem, I explore large-generations learning dynamics for agents who suffer from the "gambler's fallacy  " - the statistical bias of anticipating too much regression to the mean for realizations of independent random events. Searchers are uncertain about search poolqualities of different periods but infer these fundamentals from search outcomes of the previous generation.
Revised: In the context of a sequential search problem, I explore large-generations learning dynamics for agents who suffer from the "gambler's fallacy " Biased agents face an optimal-stopping problem, such as managers conducting sequential interviews. They are uncertain about search poolqualities of different periods but infer these fundamentals from search outcomes of the previous generation.
Edit intent: meaning-changed