wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We first obtain the collection of all possible factors and then provide a simultaneous test, security by security, of which factors are significant . Since the collection of risk factors is large and highly correlated, high-dimension methods (including the LASSO and prototype clustering) have to be used.
Revised:  We first obtain the collection of all possible factors and then provide a simultaneous test, security by security, of which factors are significant . Since the collection of risk factors is large and highly correlated, high-dimension methods (including the LASSO and prototype clustering) are used.
Edit intent: coherence
Original: The purpose of this paper is to test a multi-factor modelfor realized returns implied by the generalized arbitrage pricing theory (APT) recently developed by Jarrow and Protter (2016) and Jarrow (2016).
Revised: The paper proposes a new high-dimensional algorithm, the Groupwise Interpretable Basis Selection (GIBS) algorithm to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the generalized arbitrage pricing theory (APT) recently developed by Jarrow and Protter (2016) and Jarrow (2016).
Edit intent: meaning-changed
Original: The purpose of this paper is to test a multi-factor modelfor realized returns implied by the generalized arbitrage pricing theory (APT) recently developed by Jarrow and Protter (2016) and Jarrow (2016). This model relaxes the convention that the number of risk-factors is small.
Revised: The purpose of this paper is to test a multi-factor modelfor realized returns implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small.
Edit intent: clarity
Original: The paper proposes a new high-dimensional algorithm , the Groupwise Interpretable Basis Selection (GIBS) algorithm  to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small.
Revised: The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small.
Edit intent: meaning-changed
Original: The paper proposes a new high-dimensional algorithm , the Groupwise Interpretable Basis Selection (GIBS) algorithm  to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small.
Revised: The paper proposes a new high-dimensional algorithm , the Groupwise Interpretable Basis Selection (GIBS) algorithm , to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small.
Edit intent: fluency
Original: This paper studies the effects of price impact upon optimal investment , as well as the pricing of, and demand for, derivative contracts. Assuming market makers have exponential preferences, we show for general utility functions that a large investor's optimal investment problem with price impactcan be re-expressed as a constrained optimization problem in fictitious market without price impact.
Revised: This paper studies the effects of price impact upon optimal investment , as well as both pricing rules and demand schedules for contingent claims. For exponential market makers preferences, we show for general utility functions that a large investor's optimal investment problem with price impactcan be re-expressed as a constrained optimization problem in fictitious market without price impact.
Edit intent: clarity
Original:  Assuming market makers have exponential preferences, we show for general utility functions that a large investor's optimal investment problem with price impactcan be re-expressed as a constrained optimization problem in fictitious market without price impact. While typically the (random) constraint set is  neither closed nor convex , in several important cases of interest, the constraint is non-binding.
Revised:  Assuming market makers have exponential preferences, we establish two effects due to price impact: constrained trading, and non-linear hedging costs. To the former, wealth processes in the impact model are identified with those in a model without impact, but with constrained trading, where the (random) constraint set is neither closed nor convex , in several important cases of interest, the constraint is non-binding.
Edit intent: meaning-changed
Original:  While typically the (random) constraint set is  neither closed nor convex , in several important cases of interest, the constraint is non-binding. In these instances, we explicitly identify optimal demands for derivative contracts, and state three notions of an arbitrage free price . Due to price impact, even if a price is not arbitrage free, the resulting arbitrage opportunity only exists for limited position sizes, and might be ignored because of hedging considerations.
Revised:  While typically the (random) constraint set is neither closed nor convex . Regarding hedging, non-linear hedging costs motivate the study of arbitrage free prices for the claim. We provide three such notions, which coincide in the frictionless case, but which dramatically differ in the presence of price impact. Additionally, we show arbitrage opportunities, should they arise from claim prices, can be exploited only for limited position sizes, and might be ignored because of hedging considerations.
Edit intent: meaning-changed
Original:  We also provide a discrete algorithm for constructing it in O(n^3  +n^2f(n)) time in the real-RAM model of computation, where f(n) is the runtime of a geometric primitive (which we identify ) that can be performed in the real-RAM model or can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation  .
Revised:  We also provide a discrete algorithm for constructing it in O(n^3 \log n +n^2f(n)) time in the real-RAM model of computation, where f(n) is the runtime of a geometric primitive (which we identify ) that can be performed in the real-RAM model or can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation .
Edit intent: meaning-changed
Original:  We also provide a discrete algorithm for constructing it in O(n^3  +n^2f(n)) time in the real-RAM model of computation, where f(n) is the runtime of a geometric primitive (which we identify ) that can be performed in the real-RAM model or can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation  .
Revised:  We also provide a discrete algorithm for constructing it in O(n^3 +n^2f(n)) time in the real-RAM model of computation, where f(n) is the runtime of a geometric primitive (which we define ) that can be performed in the real-RAM model or can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation .
Edit intent: clarity
Original:  We also provide a discrete algorithm for constructing it in O(n^3  +n^2f(n)) time in the real-RAM model of computation, where f(n) is the runtime of a geometric primitive (which we identify ) that can be performed in the real-RAM model or can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation  .
Revised:  We also provide a discrete algorithm for constructing it in O(n^3 +n^2f(n)) time in the real-RAM model of computation, where f(n) is the runtime of a geometric primitive (which we identify ) that can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation .
Edit intent: coherence
Original:  We also provide a discrete algorithm for constructing it in O(n^3  +n^2f(n)) time in the real-RAM model of computation, where f(n) is the runtime of a geometric primitive (which we identify ) that can be performed in the real-RAM model or can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation  .
Revised:  We also provide a discrete algorithm for constructing it in O(n^3 +n^2f(n)) time in the real-RAM model of computation, where f(n) is the runtime of a geometric primitive (which we identify ) that can be performed in the real-RAM model or can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation . We show, however, how to compute the geometric primitive exactly for polygonal convex distance functions .
Edit intent: meaning-changed
Original:  \mbox{%DIFAUXCMD DBLP:journals/jea/KumarMY03 proposed the open problem " Are there practical methods for computing an MEB with outliers?
Revised:  \mbox{%DIFAUXCMD DBLP:journals/jea/KumarMY03 proposed the open problem ` Are there practical methods for computing an MEB with outliers?
Edit intent: fluency
Original: Motivated by the arising realistic issues in big data , the problem of Minimum Enclosing Ball (MEB) with outliers has attracted a great deal of attention in recent years. Though several methods have been proposed in both theory and practice, most of them still suffer from the issues such as high time  complexities or unstable performances for different datasets.
Revised: Motivated by the arising realistic issues in big data , the problem of Minimum Enclosing Ball (MEB) with outliers has attracted a great deal of attention in the past decades. However, most existing methods still suffer from the issues such as high time complexities or unstable performances for different datasets.
Edit intent: clarity
Original:  To answer this question, we extend the idea of core-set and present a randomized algorithm for MEB with outliers in high dimension . In particular, we provide a more `robust' analysis for the core-set construction of MEB, and propose a couple of novel improvements to further reduce the time complexity.
Revised:  To answer this question, we extend the idea of core-set and present a randomized algorithm for MEB with outliers in high dimension based on the elegant geometric technique `` core-set construction of MEB, and propose a couple of novel improvements to further reduce the time complexity.
Edit intent: meaning-changed
Original:  In particular, we provide a more `robust' analysis for the core-set construction of MEB, and propose a couple of novel improvements to further reduce the time complexity. The ideas behind are interesting in their own right and we expect to apply them to solve more high-dimensional problems. To show the efficiency and practicality, we test our algorithm on random datasets and also apply it to handle outlier recognition on benchmark datasets. To our best knowledge, this is the first algorithm yielding both solid theoretical quality guarantee and promising practical performance for the problem of MEB with outliers .
Revised:  In particular, we provide a more `robust' analysis for the core-set ". The algorithm needs only linear time and space complexities and achieves a solid theoretical quality guarantee. Another advantage over the existing methods is that our algorithm can be naturally extended to handle multi-class inliers. Our experimental results show that our algorithm outperforms existing algorithms on both random and benchmark datasets .
Edit intent: clarity
Original:  We apply the Berger-Levy theory of energy efficient information transmission to interpret simulations of a Hodgkin-Huxley-type postsynaptic neuron model, where we varied independently the firing rate and synchronization level in the presynaptic population  .
Revised:  We apply the Berger-Levy theory of energy efficient information transmission to interpret simulations of a Hodgkin-Huxley-type postsynaptic neuron model, where we varied the firing rate and synchronization level in the presynaptic population .
Edit intent: clarity
Original:  We apply the Berger-Levy theory of energy efficient information transmission to interpret simulations of a Hodgkin-Huxley-type postsynaptic neuron model, where we varied independently the firing rate and synchronization level in the presynaptic population  .
Revised:  We apply the Berger-Levy theory of energy efficient information transmission to interpret simulations of a Hodgkin-Huxley-type postsynaptic neuron model, where we varied independently the firing rate and synchronization level in the presynaptic population independently .
Edit intent: meaning-changed
Original: Data-driven spatial filtering approaches are commonly used to assess rhythmic brain activity from multichannel recordings such as electroencephalography (EEG). As spatial filter estimationis prone to noise, non-stationarity effects and limited data, a high model variability induced by slight changes of, e.g., involved hyperparametersis generally encountered. These aspects challenge the assessment of functionally relevant features which are of special importance in closed-loop applications as ,  e.g., in the field of rehabilitation.
Revised: Data-driven spatial filtering algorithms optimize scores such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for filter estimation , e.g., in the field of rehabilitation.
Edit intent: clarity
Original:  These aspects challenge the assessment of functionally relevant features which are of special importance in closed-loop applications as ,  e.g., in the field of rehabilitation.
Revised:  These aspects challenge the assessment of functionally relevant features which are of special importance in closed-loop applications as , however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters. This leads to highly variable solutions and impedes the selection of a suitable candidate for, e.g., in the field of rehabilitation.
Edit intent: meaning-changed
Original:  These aspects challenge the assessment of functionally relevant features which are of special importance in closed-loop applications as ,  e.g., in the field of rehabilitation. We propose a data-driven method to identify groups of reliable and functionally relevant oscillatory components computed by a spatial filtering approach. Therefore, we initially embrace the variability of decoding models in a large configuration space before condensing information by density-based clustering of components' functional signatures. Exemplified for a hand force task with  rich within-trial structure , the approach was evaluated on EEG data of 18 healthy subjects .
Revised:  These aspects challenge the assessment of functionally relevant features which are of special importance in closed-loop applications as , e.g., ~neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory components into homogeneous clusters, each representing specific within-trial envelope dynamics. The proposed method is exemplified by and evaluated on a complex hand force task with rich within-trial structure , the approach was evaluated on EEG data of 18 healthy subjects .
Edit intent: clarity
Original:  Exemplified for a hand force task with  rich within-trial structure , the approach was evaluated on EEG data of 18 healthy subjects . We found that functional characteristics of single componentsare revealed by distinct temporal dynamics of their event-related power changes. Based on a within-subject analysis, our clustering revealed seven groups of homogeneous envelope dynamics on average. To support introspection by practitioners, we provide a set of metrics to characterize and validate single clusterings. We show that identified clusters contain components of strictly confined frequency ranges, dominated by the alpha and beta band. Our method is applicable to any spatial filtering algorithm .
Revised:  Exemplified for a hand force task with rich within-trial structure , the approach was evaluated on EEG data of 18 healthy subjects , we found that the components' distinct temporal envelope dynamics are highly subject-specific. On average, we obtained seven clusters per subject, which were strictly confined regarding their underlying frequency bands. As the analysis method is not limited to a specific spatial filtering algorithm .
Edit intent: clarity
Original:  Our method is applicable to any spatial filtering algorithm . Despite high model variability, it allows capturing and monitoring relevant oscillatory features . We foresee its application in closed-loop applications such as brain-computer interface based protocols in stroke rehabilitation.
Revised:  Our method is applicable to any spatial filtering algorithm , it could be utilized for a wide range of neurotechnological applications, e.g., to select and monitor functionally relevant features for brain-computer interface based protocols in stroke rehabilitation.
Edit intent: clarity
Original:  Many contemporary neuroscientists regard the brain as an information processor. However, evidence from brain imaging experiments demonstrates that the brain is actually a voracious consumer of energy, and that functionality is intimately tied to metabolism. Maintaining a critical level of energy in the brain is required to sustain consciousness, and URLanisation of this energy distinguishes conscious from unconscious states.
Revised:  Energetic activity is fundamental to all physical processes and causally drives biological behaviour. Recent neuroscientific evidence can be interpreted in a way that sug-gests consciousness is a product of URLanization of energetic activity in the brain is required to sustain consciousness, and URLanisation of this energy distinguishes conscious from unconscious states.
Edit intent: clarity
Original:  Maintaining a critical level of energy in the brain is required to sustain consciousness, and URLanisation of this energy distinguishes conscious from unconscious states. Meanwhile, contemporary physicists often regard energy as an abstract mathematical property. But this view neglects energy's causal efficacy and actuality, as identified by Aristotle and later appreciated by many important biologists, psychologists and physicists. By reconsidering the nature of energy and recasting its role in neural activity, we arrive at a theory of consciousness that is consistent with the laws of physics, chemistry and biology.
Revised:  Maintaining a critical level of energy in the brain . The nature of energy and recasting its role in neural activity, we arrive at a theory of consciousness that is consistent with the laws of physics, chemistry and biology.
Edit intent: coherence
Original:  By reconsidering the nature of energy and recasting its role in neural activity, we arrive at a theory of consciousness that is consistent with the laws of physics, chemistry and biology. The argument draws on the integrated information theory (IIT) developed by Tononi et al. but reinterprets their findings from the perspective of energy exchange. In IIT, the conscious state in a system, such as a brain , is defined by the quantity of integrated differences, or information, it contains .
Revised:  By reconsidering the nature of energy itself, though, remains largely mysterious, and we do not fully under-stand how it contributes to brain function or consciousness .
Edit intent: coherence
Original:  An approximate answer to an SINR query is such that one gets a definite yes or definite no , when the ratio between the strongest signal and all other signals combined is well above or well below the reception threshold, while the answer in the intermediate range is allowed to be either yes or no .
Revised:  An approximate answer to an SINR query is such that one gets a definite YES or definite NO , when the ratio between the strongest signal and all other signals combined is well above or well below the reception threshold, while the answer in the intermediate range is allowed to be either yes or no .
Edit intent: clarity
Original:  An approximate answer to an SINR query is such that one gets a definite yes or definite no , when the ratio between the strongest signal and all other signals combined is well above or well below the reception threshold, while the answer in the intermediate range is allowed to be either yes or no .
Revised:  An approximate answer to an SINR query is such that one gets a definite yes or definite no , when the ratio between the strongest signal and all other signals combined is well above or well below the reception threshold, while the answer in the intermediate range is allowed to be either YES or NO .
Edit intent: clarity
Original:  We describe several compact data structures that support approximate SINR queries in the plane in a dynamic context, i.e., where both queries and updates (insertion or deletion of a transmitter)can be performed efficiently} n) and the amortized update time is O(\textrm{polylog} n), while the query time is O(\textrm{polylog} n) for uniform power, and randomized time O(n \mathop{\textrm{polylog}} n) with high probability for non-uniform power.
Revised:  We describe compact data structures that support approximate SINR queries in the plane in a dynamic context, i.e., where both queries and updates (insertion or deletion of a transmitter)can be performed efficiently} n) and the amortized update time is O(\textrm{polylog} n), while the query time is O(\textrm{polylog} n) for uniform power, and randomized time O(n \mathop{\textrm{polylog}} n) with high probability for non-uniform power.
Edit intent: clarity
Original:  We describe several compact data structures that support approximate SINR queries in the plane in a dynamic context, i.e., where both queries and updates (insertion or deletion of a transmitter)can be performed efficiently} n) and the amortized update time is O(\textrm{polylog} n), while the query time is O(\textrm{polylog} n) for uniform power, and randomized time O(n \mathop{\textrm{polylog}} n) with high probability for non-uniform power.
Revised:  We describe several compact data structures that support approximate SINR queries in the plane in a dynamic context, i.e., where transmitters may be inserted and deleted over time. We distinguish between two main variants --- uniform power and non-uniform power. In both variants the preprocessing time is O(n \mathop{\textrm{polylog} n) and the amortized update time is O(\textrm{polylog} n), while the query time is O(\textrm{polylog} n) for uniform power, and randomized time O(n \mathop{\textrm{polylog}} n) with high probability for non-uniform power.
Edit intent: clarity
Original: This paper introduces Tiramisu, an optimization framework designed to generate efficient code for high-performance systems such as multicores, GPUs, FPGAs, distributed machines, or any combination of these .
Revised: This paper introduces Tiramisu, a polyhedral framework designed to generate efficient code for high-performance systems such as multicores, GPUs, FPGAs, distributed machines, or any combination of these .
Edit intent: meaning-changed
Original: This paper introduces Tiramisu, an optimization framework designed to generate efficient code for high-performance systems such as multicores, GPUs, FPGAs, distributed machines, or any combination of these .
Revised: This paper introduces Tiramisu, an optimization framework designed to generate efficient code for high-performance systems such as multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel extensions to explicitly manage the complexities that arise when targeting these systems. The extensions include explicit communication, synchronization, and mapping buffers to different memory hierarchies.
Edit intent: meaning-changed