wanyu/IteraTeR_full_sent huggingface dataset:
Original:  For a rigorous analysis of the KiNetX algorithm , we developed a random generator of artificial reaction networks , AutoNetGen, which encodes chemical logic into their underlying graph structure.
Revised:  The novelty of KiNetX consists in the rigorous propagation of correlated free-energy uncertainty through all steps of our kinetic analyis. To examine the performance of KiNetX , we developed a random generator of artificial reaction networks , AutoNetGen, which encodes chemical logic into their underlying graph structure.
Edit intent: meaning-changed
Original:  Whereas the FID uses one of the hidden layers of InceptionNet, the FCD utilizes the penultimate layer of a deep neural network called "ChemblNet" , which was trained to predict drug activities.
Revised:  Whereas the FID uses one of the hidden layers of InceptionNet, the FCD utilizes the penultimate layer of a deep neural network called ChemNet , which was trained to predict drug activities.
Edit intent: fluency
Original: Networks of spiking neurons ( SNNs)are frequently studied as models for networks of neurons in the brain, but also as paradigm for novel energy efficient computing hardware. In principle they are especially suitable for computations in the temporal domain , such as speech processing, because their computations are carried out via events in time and space.
Revised: Networks of spiking neurons ( RSNNs). But computing and learning capabilities of currently available RSNN models have remained poor, especially in comparison with the performance of recurrent networks of artificial neurons , such as speech processing, because their computations are carried out via events in time and space.
Edit intent: meaning-changed
Original:  In principle they are especially suitable for computations in the temporal domain , such as speech processing, because their computations are carried out via events in time and space. But so far they have been lacking the capability to preserve information for longer time spans during a computation, until it is updated or needed - like a register of a digital computer. This function is provided to artificial neural networks through Long Short-Term Memory (LSTM) units.
Revised:  In principle they are especially suitable for computations in the temporal domain , such as Long Short-Term Memory (LSTM) units.
Edit intent: coherence
Original:  This function is provided to artificial neural networks through Long Short-Term Memory (LSTM) units. We show here that SNNs attain similar capabilities if one includes adapting neurons in the network. Adaptation denotes an increase of the firing threshold of a neuron after preceding firing. A substantial fraction of neurons in the neocortex of rodents and humans has been found to be adapting. It turns out that if adapting neurons are integrated in a suitable manner into the architecture of SNNs, the performance of these enhanced SNNs, which we call LSNNs, for computation in the temporal domain approaches that of artificial neural networks with LSTM-units. In addition, the computing and learning capabilities of LSNNs can be substantially enhanced through learning-to-learn (L2L ) methods from machine learning , that have so far been applied primarily to LSTM networks and apparently never to SSNs. This preliminary report on arXiv will be replaced by a more detailed version in about a month .
Revised:  This function is provided to artificial neural networks through Long Short-Term Memory (LSTM) networks. In this article, we investigate whether deep learning can improve RSNN performance. We applied backpropagation through time (BPTT), augmented by biologically inspired heuristics for synaptic rewiring, to RSNNs whose inherent time constants were enriched through simple models for adapting spiking neurons. We found that the resulting RSNNs approximate, for the first time, the computational power of LSTM networks on two common benchmark tasks. Furthermore, our results show that recent successes with applications of Learning-to-Learn (L2L ) methods from machine learning , that have so far been applied primarily to LSTM networks and apparently never to SSNs. This preliminary report on arXiv will be replaced by a more detailed version in about a month .
Edit intent: coherence
Original:  In addition, the computing and learning capabilities of LSNNs can be substantially enhanced through learning-to-learn (L2L ) methods from machine learning , that have so far been applied primarily to LSTM networks and apparently never to SSNs. This preliminary report on arXiv will be replaced by a more detailed version in about a month .
Revised:  In addition, the computing and learning capabilities of LSNNs can be substantially enhanced through learning-to-learn (L2L ) to LSTM networks can be ported to RSNNs. This opens the door to the investigation of L2L in data-based models for neural networks of the brain, whose activity can -- unlike that of LSTM networks -- be compared directly with recordings from neurons in the brain. In particular, L2L shows that RSNNs can learn large families of non-linear transformations from very few examples, using previously unknown network learning mechanisms. Furthermore, meta-reinforcement learning (meta-RL) shows that LSNNs can learn and execute complex exploration and exploitation strategies .
Edit intent: meaning-changed
Original:  But computing and learning capabilities of currently available RSNN models have remained poor, especially in comparison with the performance of recurrent networks of artificial neurons, such as Long Short-Term Memory (LSTM) networks. In this article, we investigate whether deep learning can improve RSNN performance. We applied backpropagation through time (BPTT) , augmented by biologically inspired heuristics for synaptic rewiring, to RSNNs whose inherent time constants were enriched through simple models for adapting spiking neurons .
Revised:  But computing and learning capabilities of currently available RSNN models have remained poor, especially in comparison with ANNs. We address two possible reasons for that. One is that RSNNs in the brain are not randomly connected or designed according to simple rules, and they do not start learning as a tabula rasa network. Rather, RSNNs in the brain were optimized for their tasks through evolution, development, and prior experience. Details of these optimization processes are largely unknown. But their functional contribution can be approximated through powerful optimization methods, such as backpropagation through time (BPTT) , augmented by biologically inspired heuristics for synaptic rewiring, to RSNNs whose inherent time constants were enriched through simple models for adapting spiking neurons .
Edit intent: meaning-changed
Original:  We applied backpropagation through time (BPTT) , augmented by biologically inspired heuristics for synaptic rewiring, to RSNNs whose inherent time constants were enriched through simple models for adapting spiking neurons .
Revised:  We applied backpropagation through time (BPTT) . A second major mismatch between RSNNs in the brain and models is that the latter only show a small fraction of the dynamics of neurons and synapses in the brain. We include neurons in our RSNN model that reproduce one prominent dynamical process of biological neurons that takes place at the behaviourally relevant time scale of seconds: neuronal adaptation. We denote these networks as LSNNs because of their Long short-term memory. The inclusion of adapting neurons drastically increases the computing and learning capability of RSNNs if they are trained and configured by deep learning (BPTT combined with a rewiring algorithm that optimizes the network architecture) .
Edit intent: meaning-changed
Original:  We found that the resulting RSNNs approximate, for the first time , the computational power of LSTM networks on two common benchmark tasks. Furthermore, our results show that recent successes with applications of Learning-to-Learn (L2L)to LSTM networks can be ported to RSNNs. This opens the door to the investigation of L2L in data-based models for neural networks of the brain, whose activity can -- unlike that of LSTM networks -- be compared directly with recordings from neurons in the brain.
Revised:  We found that the first time that of LSTM networks -- be compared directly with recordings from neurons in the brain.
Edit intent: clarity
Original:  Furthermore, our results show that recent successes with applications of Learning-to-Learn (L2L)to LSTM networks can be ported to RSNNs. This opens the door to the investigation of L2L in data-based models for neural networks of the brain, whose activity can -- unlike that of LSTM networks -- be compared directly with recordings from neurons in the brain. In particular, L2L shows that RSNNs can learn large families of non-linear transformations from very few examples , using previously unknown network learning mechanisms.
Revised:  Furthermore, our results show that recent successes with applications of Learning-to-Learn (L2L)to LSTM networks can be ported to RSNNs. This opens the door to the investigation of L2L in data-based models for neural networks of the brain, whose activity can -- unlike that of LSTM networks . In addition RSNNs with adapting neurons can acquire abstract knowledge from prior learning in a Learning-to-Learn ( L2L shows that RSNNs can learn large families of non-linear transformations from very few examples , using previously unknown network learning mechanisms.
Edit intent: meaning-changed
Original:  In particular, L2L shows that RSNNs can learn large families of non-linear transformations from very few examples , using previously unknown network learning mechanisms. Furthermore, meta-reinforcement learning (meta-RL) shows that LSNNs can learn and execute complex exploration and exploitation strategies .
Revised:  In particular, L2L shows that RSNNs can learn large families of non-linear transformations from very few examples . We demonstrate this for supervised learning and reinforcement learning .
Edit intent: clarity
Original: In our previous paper \mbox{%DIFAUXCMD BFFMB , we have introduced a general class of systemic risk measures that allow random allocations to individual banks before aggregation of their risks.
Revised: In our previous paper "A Unified Approach to Systemic Risk Measures via Acceptance Set" , we have introduced a general class of systemic risk measures that allow random allocations to individual banks before aggregation of their risks.
Edit intent: meaning-changed
Original:  We show that the dual problem of the minimization problem which identify the systemic risk measure , provides a valuation of the random allocations  which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Revised:  We show that the dual formulation of the minimization problem which identify the systemic risk measure , provides a valuation of the random allocations which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Edit intent: clarity
Original:  We show that the dual problem of the minimization problem which identify the systemic risk measure , provides a valuation of the random allocations  which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Revised:  We show that the dual problem of the minimization problem identifying the systemic risk measure , provides a valuation of the random allocations which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Edit intent: fluency
Original:  We show that the dual problem of the minimization problem which identify the systemic risk measure , provides a valuation of the random allocations  which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Revised:  We show that the dual problem of the minimization problem which identify the systemic risk measure provides a valuation of the random allocations which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Edit intent: fluency
Original:  We show that the dual problem of the minimization problem which identify the systemic risk measure , provides a valuation of the random allocations  which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Revised:  We show that the dual problem of the minimization problem which identify the systemic risk measure , provides a valuation of the random allocations , which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Edit intent: fluency
Original: In our previous paper  "A Unified Approach to Systemic Risk Measures via Acceptance Set"  , we have introduced a general class of systemic risk measures that allow  random allocations to individual banks before aggregation of their risks.
Revised: In our previous paper , "A Unified Approach to Systemic Risk Measures via Acceptance Set" , we have introduced a general class of systemic risk measures that allow random allocations to individual banks before aggregation of their risks.
Edit intent: fluency
Original: In our previous paper  "A Unified Approach to Systemic Risk Measures via Acceptance Set"  , we have introduced a general class of systemic risk measures that allow  random allocations to individual banks before aggregation of their risks.
Revised: In our previous paper "A Unified Approach to Systemic Risk Measures via Acceptance Set" ( , we have introduced a general class of systemic risk measures that allow random allocations to individual banks before aggregation of their risks.
Edit intent: fluency
Original: In our previous paper  "A Unified Approach to Systemic Risk Measures via Acceptance Set"  , we have introduced a general class of systemic risk measures that allow  random allocations to individual banks before aggregation of their risks.
Revised: In our previous paper "A Unified Approach to Systemic Risk Measures via Acceptance Set" Mathematical Finance, 2018 , we have introduced a general class of systemic risk measures that allow random allocations to individual banks before aggregation of their risks.
Edit intent: meaning-changed
Original: In our previous paper  "A Unified Approach to Systemic Risk Measures via Acceptance Set"  , we have introduced a general class of systemic risk measures that allow  random allocations to individual banks before aggregation of their risks.
Revised: In our previous paper "A Unified Approach to Systemic Risk Measures via Acceptance Set" ) , we have introduced a general class of systemic risk measures that allow random allocations to individual banks before aggregation of their risks.
Edit intent: fluency
Original: In our previous paper  "A Unified Approach to Systemic Risk Measures via Acceptance Set"  , we have introduced a general class of systemic risk measures that allow  random allocations to individual banks before aggregation of their risks.
Revised: In our previous paper "A Unified Approach to Systemic Risk Measures via Acceptance Set" , we have introduced a general class of systemic risk measures that allow for random allocations to individual banks before aggregation of their risks.
Edit intent: fluency
Original:  In the present paper, we address the question of fairness of these allocations and propose a fair allocation of the total risk to individual banks. We show that the dual formulation of the minimization problem identifying the systemic risk measure provides a valuation of the random allocations, which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Revised:  In the present paper, we prove the dual representation of a particular subclass of such systemic risk measures and the existence and uniqueness of the optimal allocation related to them. We also introduce an associated utility maximization problem which has the same optimal solution as the systemic risk measure provides a valuation of the random allocations, which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Edit intent: meaning-changed
Original:  We show that the dual formulation of the minimization problem identifying the systemic risk measure provides a valuation of the random allocations, which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Revised:  We show that the dual formulation of the minimization problem identifying the systemic risk measure . In addition, the optimizer in the dual formulation provides a which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Edit intent: meaning-changed
Original:  We show that the dual formulation of the minimization problem identifying the systemic risk measure provides a valuation of the random allocations, which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Revised:  We show that the dual formulation of the minimization problem identifying the systemic risk measure provides a valuation of the random allocations,risk allocation which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Edit intent: meaning-changed
Original:  We show that the dual formulation of the minimization problem identifying the systemic risk measure provides a valuation of the random allocations, which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Revised:  We show that the dual formulation of the minimization problem identifying the systemic risk measure provides a valuation of the random allocations, which is fair from the point of view of the society/regulator and from the individual financial institutions.
Edit intent: clarity
Original:  We show that the dual formulation of the minimization problem identifying the systemic risk measure provides a valuation of the random allocations, which is fair both from the point of view of the society/regulator and from the individual financial institutions.
Revised:  We show that the dual formulation of the minimization problem identifying the systemic risk measure provides a valuation of the random allocations, which is fair both from the point of view of the individual financial institutions.
Edit intent: coherence
Original:  First,  unexpected formation of micron-sized dye-depleted fluid domains was observed upon cooling of GUVs to about 10 K above the lipid main phase transition temperature, TM.
Revised:  First, an unexpected formation of micron-sized dye-depleted fluid domains was observed upon cooling of GUVs to about 10 K above the lipid main phase transition temperature, TM.
Edit intent: fluency
Original:  First,  unexpected formation of micron-sized dye-depleted fluid domains was observed upon cooling of GUVs to about 10 K above the lipid main phase transition temperature, TM.
Revised:  First, unexpected formation of micron-sized dye-depleted fluid domains was observed upon cooling . These domains nucleated about 10 K above the lipid main phase transition temperature, TM.
Edit intent: meaning-changed
Original:  Whereas the stripe structures are related to the phase transition of the lipid, the dye-excluding domains seem to be caused by traces of impurities present in the sugar solutions. Supplementing the solutions with nm-sized liposomes at millimolar lipid concentration suppresses the formation of the micron-sized domains, presumably by providing competitive binding of the impurities to the liposome membrane in excess.
Revised:  Whereas the stripe structures are related to the phase transition of the lipid, the dye-excluding domains seem to be caused by traces of impurities present in the glucose. Supplementing glucose solutions with nm-sized liposomes at millimolar lipid concentration suppresses the formation of the micron-sized domains, presumably by providing competitive binding of the impurities to the liposome membrane in excess.
Edit intent: clarity
Original:  In this setting, our principal goal resides in describing as explicit as possible the set of all deflators, which constitutes the dual set of all "admissible" wealth processes, for the stopped model S^{\tau death time and the default time (that \tau might represent) can be seen when they occur only, the progressive enlargement of \mathbb F with \tau sounds tailor-fit for modelling the new flow of information  that incorporates both \mathbb F and \tau.
Revised:  As the death time and the default time (that \tau might represent) can be seen when they occur only, the progressive enlargement of \mathbb F with \tau sounds tailor-fit for modelling the new flow of information that incorporates both \mathbb F and \tau.
Edit intent: clarity
Original:  In this setting, our principal goal resides in describing as explicit as possible the set of all deflators, which constitutes the dual set of all "admissible" wealth processes, for the stopped model S^{\tau death time and the default time (that \tau might represent) can be seen when they occur only, the progressive enlargement of \mathbb F with \tau sounds tailor-fit for modelling the new flow of information  that incorporates both \mathbb F and \tau.
Revised:  In this setting, our principal goal resides in describing as explicit as possible the set of all deflators, which constitutes the dual set of all "admissible" wealth processes, for the stopped model S^{\tau death time and the default time (that \tau might represent) can be seen when they occur only, the progressive enlargement of \mathbb F with \tau sounds tailor-fit for modelling the new flow of information \mathbb G that incorporates both \mathbb F and \tau.
Edit intent: meaning-changed
Original:  , \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 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).
Edit intent: meaning-changed
Original:  [ 8 ], on martingales classification and representation for progressive enlarged filtration, our aim is fully achieved for both cases of local martingale deflators and general supermartingale delators .
Revised:  [ 7 ], on martingales classification and representation for progressive enlarged filtration, our aim is fully achieved for both cases of local martingale deflators and general supermartingale delators .
Edit intent: clarity
Original:  The results are illustrated on several particular models for (\tau, S,\mathbb F) such as the discrete-time and the jump-diffusion settings for (S,\mathbb F), and the case when \tau avoids \mathbb F-stopping times .
Revised:  The results are illustrated on the two particular cases when ( S,\mathbb F) such as the discrete-time and the jump-diffusion settings for (S,\mathbb F), and the case when \tau avoids \mathbb F-stopping times .
Edit intent: clarity
Original:  The results are illustrated on several particular models for (\tau, S,\mathbb F) such as the discrete-time and the jump-diffusion settings for (S,\mathbb F), and the case when \tau avoids \mathbb F-stopping times .
Revised:  The results are illustrated on several particular models for (\tau, S,\mathbb F) follows the jump-diffusion settings for (S,\mathbb F), and the case when \tau avoids \mathbb F-stopping times .
Edit intent: clarity
Original:  The results are illustrated on several particular models for (\tau, S,\mathbb F) such as the discrete-time and the jump-diffusion settings for (S,\mathbb F), and the case when \tau avoids \mathbb F-stopping times .
Revised:  The results are illustrated on several particular models for (\tau, S,\mathbb F) such as the discrete-time and the jump-diffusion model and the discrete-time model .
Edit intent: clarity