wanyu/IteraTeR_full_sent huggingface dataset:
Original: In this brief exploratory paper, we present a novel free group based method for error correction, making use of the universal property of free groups (UPFG). Through example, we show that , in certain scenarios, our method of error correction is more efficient and more deterministic than minimum distance error correction. We argue that our method works well under specific conditions that have been shown to have been met by certain neural systems. We hope that this might interest others in the role free groups may play in understanding how error correction of neural codes works .
Revised: Recent experimental and theoretical work on neural populations belonging to two separate early sensory systems, olfaction and vision, has challenged the notion that the two operate under different computational paradigms by providing evidence for the respective neural population codes having three central, common features: they are highly redundant; they URLanized such that information is carried in the identity, and not the relative timing, of the active neurons; they are capable of error correction. We present the first model that captures these three properties in a general manner, making it possible to investigate whether similar structure is present in other population codes. Our model also makes specific predictions about additional, as yet unseen, structure in such codes. If these predictions are found in real data, this would provide new evidence that such population codes are operating under more general computational principles .
Edit intent: meaning-changed
Original:  Here, we reinterpret the themodynamic uncertainty relation to define the efficiency of a motor in cargo transport . By analyzing the existing motility data of several types of molecular motors , we calculate the energetic cost (or efficiency) of generating time traces of a given precision.
Revised:  Here, we reinterpret the themodynamic uncertainty relation to define the efficiency of a motor in cargo transport is characterized with a small \mathcal{Q several types of molecular motors , we calculate the energetic cost (or efficiency) of generating time traces of a given precision.
Edit intent: meaning-changed
Original:  By analyzing the existing motility data of several types of molecular motors , we calculate the energetic cost (or efficiency) of generating time traces of a given precision. The energetic cost for kinesin-1 varies nonmonotonically with ATP concentration and load (f) , and is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably,  this local minimum vanishes and the cost increases significantly for a mutant that has a longer neck-linker , which underscores the importance of molecular structure in transport properties.
Revised:  By analyzing the existing motility data of several types of molecular motors show that \mathcal{Q ATP concentration and load (f) , and is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably, this local minimum vanishes and the cost increases significantly for a mutant that has a longer neck-linker , which underscores the importance of molecular structure in transport properties.
Edit intent: clarity
Original:  The energetic cost for kinesin-1 varies nonmonotonically with ATP concentration and load (f) , and is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably,  this local minimum vanishes and the cost increases significantly for a mutant that has a longer neck-linker , which underscores the importance of molecular structure in transport properties.
Revised:  The energetic cost for kinesin-1 varies nonmonotonically with ATP concentration and load (f) . For kinesin-1, \mathcal{Q is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably, this local minimum vanishes and the cost increases significantly for a mutant that has a longer neck-linker , which underscores the importance of molecular structure in transport properties.
Edit intent: meaning-changed
Original:  The energetic cost for kinesin-1 varies nonmonotonically with ATP concentration and load (f) , and is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably,  this local minimum vanishes and the cost increases significantly for a mutant that has a longer neck-linker , which underscores the importance of molecular structure in transport properties.
Revised:  The energetic cost for kinesin-1 varies nonmonotonically with ATP concentration and load (f) , and is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably, for the mutant with a longer neck-linker this local minimum vanishes and the cost increases significantly for a mutant that has a longer neck-linker , which underscores the importance of molecular structure in transport properties.
Edit intent: clarity
Original:  The energetic cost for kinesin-1 varies nonmonotonically with ATP concentration and load (f) , and is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably,  this local minimum vanishes and the cost increases significantly for a mutant that has a longer neck-linker , which underscores the importance of molecular structure in transport properties.
Revised:  The energetic cost for kinesin-1 varies nonmonotonically with ATP concentration and load (f) , and is locally minimized at [ATP] \approx 200 \muM and f\approx 4 pN. Remarkably, this local minimum vanishes , and the energetic cost to achieve the same precision as the wild-type increases significantly , which underscores the importance of molecular structure in transport properties.
Edit intent: meaning-changed
Original:  We compute in particular the finite memory corrections to the square root impact law, as well as the impact decay and the permanent impact of a meta-order  .
Revised:  We compute in particular the finite memory corrections to the square root impact law, as well as the impact decay and the permanent impact of a meta-order . The latter is found to be linear in the traded volume and independent of the trading rate, as dictated by no-arbitrage arguments .
Edit intent: meaning-changed
Original:  We leverage recent contributions in the robust mechanism design literature that exploit the link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in incomplete information games to construct an econometrics framework for learning about auction fundamentals using observed data on bids.
Revised:  We leverage the recent contributions of \mbox{%DIFAUXCMD Bergemann2013 in the robust mechanism design literature that exploit the link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in incomplete information games to construct an econometrics framework for learning about auction fundamentals using observed data on bids.
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Original:  Our approach for constructing these sets inherits the computational simplicity of solving for correlated equilibria: checking whether a particular valuation distribution belongs to the identified set is as simple as determining whether a linear {\it  program is feasible.
Revised:  Our approach for constructing these sets inherits the computational simplicity of solving for correlated equilibria: checking whether a particular valuation distribution belongs to the identified set is as simple as determining whether a {\it program is feasible.
Edit intent: clarity
Original:  Our approach for constructing these sets inherits the computational simplicity of solving for correlated equilibria: checking whether a particular valuation distribution belongs to the identified set is as simple as determining whether a linear {\it  program is feasible.
Revised:  Our approach for constructing these sets inherits the computational simplicity of solving for correlated equilibria: checking whether a particular valuation distribution belongs to the identified set is as simple as determining whether a linear {\it linear program is feasible.
Edit intent: meaning-changed
Original:  Extending the concept of  confusion matrix, we present a 'trade information matrix' to attribute the expected profit and loss of tick level predictive classifiers under execution constraints, such as fill probabilities and position dependent trade rules, to correct and incorrect predictions.
Revised:  Extending the concept of a confusion matrix, we present a 'trade information matrix' to attribute the expected profit and loss of tick level predictive classifiers under execution constraints, such as fill probabilities and position dependent trade rules, to correct and incorrect predictions.
Edit intent: fluency
Original:  We describe the training of a recurrent neural network (RNN) and show (i) there is little gain from re-training the model on a frequent basis; (ii) that there are distinct intra-day classifier performance trends; and (iii) classifier accuracy quickly erodes with the length of prediction horizon. Our findings suggest that our computationally tractable approach can be used to directly evaluate the performance sensitivity of a market making strategy to classifier error and can augment traditional market simulation based testing.
Revised:  Our approach directly evaluates the performance sensitivity of a market making strategy to classifier error and can augment traditional market simulation based testing.
Edit intent: coherence
Original: High frequency trading has been characterized as an arms race with 'Red Queen' characteristics Farmer,2012%DIFDELCMD < ]%%% . It is improbable, even impossible, that many market participants can sustain a competitive advantage through the sole reliance on low latency trade execution systems.
Revised:  Farmer,2012%DIFDELCMD < ]%%% . It is improbable, even impossible, that many market participants can sustain a competitive advantage through the sole reliance on low latency trade execution systems.
Edit intent: coherence
Original: High frequency trading has been characterized as an arms race with 'Red Queen' characteristics Farmer,2012%DIFDELCMD < ]%%% . It is improbable, even impossible, that many market participants can sustain a competitive advantage through the sole reliance on low latency trade execution systems.
Revised: High frequency trading has been characterized as an arms race with 'Red Queen' characteristics %DIFDELCMD < ]%%% . It is improbable, even impossible, that many market participants can sustain a competitive advantage through the sole reliance on low latency trade execution systems.
Edit intent: coherence
Original: High frequency trading has been characterized as an arms race with 'Red Queen' characteristics Farmer,2012%DIFDELCMD < ]%%% . It is improbable, even impossible, that many market participants can sustain a competitive advantage through the sole reliance on low latency trade execution systems. The growth in volume of market data, advances in computer hardware and commensurate prominence of machine learning in other disciplines, have spurred the exploration of machine learning for price discovery. Even though the application of machine learning to price prediction has been extensively researched, the merit of this approach for high frequency market making has received little attention.
Revised: High frequency trading has been characterized as an arms race with 'Red Queen' characteristics Farmer,2012%DIFDELCMD < ]%%% 
Edit intent: coherence
Original:  Extending the concept of a confusion matrix, we present a 'trade information matrix' to attribute the expected profit and loss of tick level predictive classifiers under execution constraints, such as fill probabilities and position dependent trade rules, to correct and incorrect predictions.
Revised:  Extending the concept of a confusion matrix, we present a 'trade information matrix' to attribute the expected profit and loss of the high frequency strategy under execution constraints, such as fill probabilities and position dependent trade rules, to correct and incorrect predictions.
Edit intent: meaning-changed
Original:  We apply the  execution model and trade information matrix to Level II E-mini S&P 500 futures history and demonstrate an estimation approach for measuring the sensitivity of the P&L to classification error .
Revised:  We apply the execution model and trade information matrix to Level II E-mini S&P 500 futures history and demonstrate an estimation approach for measuring the sensitivity of the P&L to the error of a Recurrent Neural Network .
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Original:  Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of cheminformatic , metabolomic, transcriptomic, proteomic, and thermochemical data.
Revised:  Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic , metabolomic, transcriptomic, proteomic, and thermochemical data.
Edit intent: fluency
Original:  This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios  .
Revised:  This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios . This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods .
Edit intent: meaning-changed
Original:  But the physical correspondence between the cosmic web and structural-engineering or textile 'spiderwebs' is even deeper than previously known  . Here we explain that in a good structure-formation approximation known as the adhesion model, threads of the cosmic web form a spiderweb, i.e. can be strung up to be entirely in tension  .
Revised:  But the physical correspondence between the cosmic web and structural-engineering or textile 'spiderwebs' is even deeper than previously known , and extends to origami tessellations as well . Here we explain that in a good structure-formation approximation known as the adhesion model, threads of the cosmic web form a spiderweb, i.e. can be strung up to be entirely in tension .
Edit intent: meaning-changed
Original:  But the physical correspondence between the cosmic web and structural-engineering or textile 'spiderwebs' is even deeper than previously known  . Here we explain that in a good structure-formation approximation known as the adhesion model, threads of the cosmic web form a spiderweb, i.e. can be strung up to be entirely in tension  .
Revised:  But the physical correspondence between the cosmic web and structural-engineering or textile 'spiderwebs' is even deeper than previously known . Here we explain that in a good structure-formation approximation known as the adhesion model, threads of the cosmic web form a spiderweb, i.e. can be strung up to be entirely in tension . The correspondence is exact if nodes sampling voids are included, and if structure is excluded within collapsed regions (walls, filaments and haloes), where dark-matter multistreaming and baryonic physics affect the structure .
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Original:  Our main contributions include both a novel multi-scale variant of a recently proposed PointNet architecture with emphasis on local shape information, and a series of novel applications in which we demonstrate how learning from training data arising from well-structured triangle meshes, and applying the trained model to noisy point clouds can produce superior results compared to specialized state-of-the-art techniques.
Revised:  Our main contributions include both a novel multi-scale variant of the recently proposed PointNet architecture with emphasis on local shape information, and a series of novel applications in which we demonstrate how learning from training data arising from well-structured triangle meshes, and applying the trained model to noisy point clouds can produce superior results compared to specialized state-of-the-art techniques.
Edit intent: fluency
Original: We introduce  a path-dependent geometric framework which generalizes the HJM modeling approach to a wide variety of other asset classes. A machine learning regularization frameworkis developed with the objective of removing arbitrage opportunities from models within this general framework.
Revised: We introduce a path-dependent geometric framework which generalizes the HJM modeling approach to a wide variety models. In this framework, fundamental results and techniques from risk-neutral pricing theory such as NFLVR, market completeness, and changes of measure are given an equivalent formulation and extended to models which are deformable into arbitrage-free models. We use this scheme to construct a meta-algorithm which ensures that a wide range of factor estimation schemes return arbitrage-free estimates and incorporate this additional information into their estimation procedure. We show that using our meta-algorithm we are able to produce more accurate estimates of forward-rate curves, specifically at the long-end. The spread between a model and its arbitrage-free regularization is then used to construct a mis-pricing detection or classification algorithm, which is in turn used to develop a pairs trading strategy. Our theory provides a sound theoretical foundation for a risk-neutral pricing theory capable of handling models which potentially admit arbitrage but which can which can be deformed into arbitrage-free models within this general framework.
Edit intent: meaning-changed
Original:  A machine learning regularization frameworkis developed with the objective of removing arbitrage opportunities from models within this general framework. The regularization method relies on minimal deformations of a model subject to a path-dependent penalty that detects arbitrage opportunities. We prove that the solution of this regularization problem is independent of the arbitrage-penalty chosen, subject to a fixed information loss functional. In addition to the general properties of the minimal deformation, we also consider several explicit examples. This paper is focused on placing machine learning methods in finance on a sound theoretical basis and the techniques developed to achieve this objective may be of interest in other areas of application .
Revised:  A machine learning regularization frameworkis developed with the objective of removing arbitrage opportunities from models .
Edit intent: coherence
Original: We introduce an unsupervised and non-anticipative machine learning algorithm which is able to detect and remove arbitrage from a wide variety models. In this framework, fundamental results and techniques from risk-neutral pricing theory such as NFLVR, market completeness, and changes of measure are given an equivalent formulation and extended to models which are deformable into arbitrage-free models.
Revised: We introduce a regularization approach to arbitrage-free models.
Edit intent: clarity
Original:  In this framework, fundamental results and techniques from risk-neutral pricing theory such as NFLVR, market completeness, and changes of measure are given an equivalent formulation and extended to models which are deformable into arbitrage-free models. We use this scheme to construct a meta-algorithm which ensures that a wide range of factor estimation schemes return arbitrage-free estimates and incorporate this additional information into their estimation procedure.
Revised:  In this framework, fundamental results and techniques from risk-neutral pricing theory such as NFLVR, market completeness, and changes of measure are given an equivalent formulation and extended to models which are deformable into arbitrage-free factor-model selection. The considered model selection problem seeks to learn the closest arbitrage-free estimates and incorporate this additional information into their estimation procedure.
Edit intent: meaning-changed
Original:  We use this scheme to construct a meta-algorithm which ensures that a wide range of factor estimation schemes return arbitrage-free estimates and incorporate this additional information into their estimation procedure. We show that using our meta-algorithm we are able to produce more accurate estimates of forward-rate curves, specifically at the long-end. The spread between a model and its arbitrage-free regularization is then used to construct a mis-pricing detection or classification algorithm, which is in turn used to develop a pairs trading strategy.
Revised:  We use this scheme to construct a meta-algorithm which ensures that a wide range of factor estimation schemes return arbitrage-free HJM-type model to any prespecified factor-model. An asymptotic solution to this, a priori computationally intractable, problem is represented as the limit of a 1-parameter family of optimizers to computationally tractable model selection tasks. Each of these simplified model-selection tasks seeks to learn the most similar model, to the prescribed factor-model, subject to a penalty detecting when the reference measure is a local martingale-measure for the entire underlying financial market. A simple expression for the penalty terms is obtained in the bond market withing the affine-term structure setting, and it is used to formulate a deep-learning approach to arbitrage-free regularization is then used to construct a mis-pricing detection or classification algorithm, which is in turn used to develop a pairs trading strategy.
Edit intent: meaning-changed
Original:  The spread between a model and its arbitrage-free regularization is then used to construct a mis-pricing detection or classification algorithm, which is in turn used to develop a pairs trading strategy. Our theory provides a sound theoretical foundation for a risk-neutral pricing theory capable of handling models which potentially admit arbitrage but which can which can be deformed into arbitrage-free models .
Revised:  The spread between a model and its arbitrage-free affine term-structure modelling. Numerical implementations are also performed to evaluate the performance in the bond market .
Edit intent: meaning-changed
Original:  We also  compare and analyse the statistical distributions of  terminal wealth of log utility  based on two optimal strategies, one using the full information of intensity process , the other a proxy constant intensity process . These two strategies may be considered respectively the active and passive optimal portfolio investment. Our simulation results show that, statistically, active portfolio investment is more volatile and performs either much better or much worse than the passive portfolio investment in extreme scenarios .
Revised:  We also compare and analyse the statistical distributions of terminal wealth of log utility based on two optimal strategies, one using the full information of intensity process , the other a proxy constant intensity process .
Edit intent: coherence
Original:  The best optimal algorithm, due to Gudmundsson, van Kreveld, and Staals (2013), finds an axis-parallel square hotspot with fixed side length in O(n^2).
Revised:  The fastest optimal algorithm, due to Gudmundsson, van Kreveld, and Staals (2013), finds an axis-parallel square hotspot with fixed side length in O(n^2).
Edit intent: meaning-changed
Original:  The best optimal algorithm, due to Gudmundsson, van Kreveld, and Staals (2013), finds an axis-parallel square hotspot with fixed side length in O(n^2).
Revised:  The best optimal algorithm, due to Gudmundsson, van Kreveld, and Staals (2013), finds an axis-parallel square hotspot of fixed side length in O(n^2).
Edit intent: clarity
Original:  We present an approximation algorithm with  time complexity O(n \log n) and approximation factor 1/4 for orthogonal trajectories, in which the entity moves in a direction parallel either to the x or to the y-axis  .
Revised:  We present an approximation algorithm with the time complexity O(n \log n) and approximation factor 1/4 for orthogonal trajectories, in which the entity moves in a direction parallel either to the x or to the y-axis .
Edit intent: fluency