wanyu/IteraTeR_v2 huggingface dataset:
Original: For a market with m assets and T discrete trading sessions, Cover and Ordentlich (1998) found that the " Cost of Achieving the Best Rebalancing Rule in Hindsight " is p(T,m)=\sumn_1+\cdot\cdot\cdot+n_m=TT{n_1,...,n_m}(n_1/T)^{n_1}\cdot\cdot\cdot(n_m/T)^{n_m}.
Revised: For a market with m assets and T discrete trading sessions, Cover and Ordentlich (1998) found that the ` Cost of Achieving the Best Rebalancing Rule in Hindsight " is p(T,m)=\sumn_1+\cdot\cdot\cdot+n_m=TT{n_1,...,n_m}(n_1/T)^{n_1}\cdot\cdot\cdot(n_m/T)^{n_m}.
Edit intent: fluency
Original: For a market with m assets and T discrete trading sessions, Cover and Ordentlich (1998) found that the " Cost of Achieving the Best Rebalancing Rule in Hindsight " is p(T,m)=\sumn_1+\cdot\cdot\cdot+n_m=TT{n_1,...,n_m}(n_1/T)^{n_1}\cdot\cdot\cdot(n_m/T)^{n_m}.
Revised: For a market with m assets and T discrete trading sessions, Cover and Ordentlich (1998) found that the " Cost of Achieving the Best Rebalancing Rule in Hindsight ' is p(T,m)=\sumn_1+\cdot\cdot\cdot+n_m=TT{n_1,...,n_m}(n_1/T)^{n_1}\cdot\cdot\cdot(n_m/T)^{n_m}.
Edit intent: fluency
Original:  To put this in perspective , for the Dow Jones (30) stocks, the Cover and Ordentlich (1998) strategy needs a 10, 000-year horizon in order to guarantee to get within 1\\%DIF < of the compound-annual growth rate of the best (30-stock) %DIF > of the compound-annual  rebalancing rule in hindsight.
Revised:  To put this in perspective , for the Dow Jones (30) stocks, the Ordentlich and Cover (1998) strategy needs a 10, 000-year horizon in order to guarantee to get within 1\\%DIF < of the compound-annual growth rate of the best (30-stock) %DIF > of the compound-annual rebalancing rule in hindsight.
Edit intent: clarity
Original:  To put this in perspective , for the Dow Jones (30) stocks, the Cover and Ordentlich (1998) strategy needs a 10, 000-year horizon in order to guarantee to get within 1\\%DIF < of the compound-annual growth rate of the best (30-stock) %DIF > of the compound-annual  rebalancing rule in hindsight.
Revised:  To put this in perspective , for the Dow Jones (30) stocks, the Cover and Ordentlich (1998) strategy needs a 10, 000-year horizon in order to guarantee to get within 1\\%DIF < of the compound-annual growth rate of the best (30-stock) %DIF > of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Edit intent: fluency
Original:  By contrast, it takes 1,000 years (in the worst case) to enforce a growth rate that is within 1\\%DIF < of the best pairs %DIF > of the  rebalancing rule in hindsight.
Revised:  By contrast, it takes 1,000 years (in the worst case) to enforce a growth rate that is within 1\\%DIF < of the best pairs %DIF > of the best pairs rebalancing rule in hindsight.
Edit intent: fluency
Original:  For any preselected pair (i,j) of stocks it takes 320 years.
Revised:  For any fixed pair of stocks it takes 320 years.
Edit intent: clarity
Original:  Thus, the more modest goal of growth at the same asymptotic rate as the best pairs rebalancing rule in hindsight leads to a  practical trading strategy that still beats the market asymptotically, albeit with a lower asymptotic growth rate than the full-support universal portfolio.
Revised:  Thus, the more modest goal of growth at the same asymptotic rate as the best pairs rebalancing rule in hindsight leads to a more practical trading strategy that still beats the market asymptotically, albeit with a lower asymptotic growth rate than the full-support universal portfolio.
Edit intent: fluency
Original: For a market with m assets and T discrete trading sessions, Ordentlich and Cover (1998) found that the `Cost of Achieving the Best Rebalancing Rule in Hindsight' is p(T,m)=\sum%DIFDELCMD < \limits%%% _{n_1+\cdot\cdot\cdot+n_m=T (n_1/T)^{n_1\cdot\cdot\cdot(n_m/T)^{n_m}.
Revised: This paper derives a robust on-line equity trading algorithm that achieves the greatest possible percentage of the `Cost of Achieving the Best Rebalancing Rule in Hindsight' is p(T,m)=\sum%DIFDELCMD < \limits%%% _{n_1+\cdot\cdot\cdot+n_m=T (n_1/T)^{n_1\cdot\cdot\cdot(n_m/T)^{n_m}.
Edit intent: clarity
Original: For a market with m assets and T discrete trading sessions, Ordentlich and Cover (1998) found that the `Cost of Achieving the Best Rebalancing Rule in Hindsight' is p(T,m)=\sum%DIFDELCMD < \limits%%% _{n_1+\cdot\cdot\cdot+n_m=T (n_1/T)^{n_1\cdot\cdot\cdot(n_m/T)^{n_m}.
Revised: For a market with m assets and T discrete trading sessions, Ordentlich and Cover (1998) found that the %DIFDELCMD < \limits%%% _{n_1+\cdot\cdot\cdot+n_m=T (n_1/T)^{n_1\cdot\cdot\cdot(n_m/T)^{n_m}.
Edit intent: coherence
Original: For a market with m assets and T discrete trading sessions, Ordentlich and Cover (1998) found that the `Cost of Achieving the Best Rebalancing Rule in Hindsight' is p(T,m)=\sum%DIFDELCMD < \limits%%% _{n_1+\cdot\cdot\cdot+n_m=T (n_1/T)^{n_1\cdot\cdot\cdot(n_m/T)^{n_m}.
Revised: For a market with m assets and T discrete trading sessions, Ordentlich and Cover (1998) found that the `Cost of Achieving the Best Rebalancing Rule in Hindsight' is p(T,m)=\sum%DIFDELCMD < \limits%%% _{n_1+\cdot\cdot\cdot+n_m=T \cdot\cdot\cdot(n_m/T)^{n_m}.
Edit intent: fluency
Original:  This paper gives a workable generalization: the cost (read: super-replicating price) of achieving the best s-stock }  rebalancing rule in hindsight is p(T,s).
Revised:  This paper gives a workable generalization: the cost (read: super-replicating price) of achieving the best s-stock } rebalancing rule in hindsight p(T,s).
Edit intent: clarity
Original:  In particular, the cost of achieving the best pairs rebalancing rule in hindsight is \sum%DIFDELCMD < \limits%%% _{n=0 (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover  (1998) strategy needs a 10,049-year horizon (under annual rebalancing) in order to guarantee to get within 1%DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Revised:  In particular, the cost of achieving the best pairs rebalancing rule \sum%DIFDELCMD < \limits%%% _{n=0 (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover (1998) strategy needs a 10,049-year horizon (under annual rebalancing) in order to guarantee to get within 1%DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Edit intent: fluency
Original:  In particular, the cost of achieving the best pairs rebalancing rule in hindsight is \sum%DIFDELCMD < \limits%%% _{n=0 (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover  (1998) strategy needs a 10,049-year horizon (under annual rebalancing) in order to guarantee to get within 1%DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Revised:  In particular, the cost of achieving the best pairs rebalancing rule in hindsight is %DIFDELCMD < \limits%%% _{n=0 (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover (1998) strategy needs a 10,049-year horizon (under annual rebalancing) in order to guarantee to get within 1%DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Edit intent: clarity
Original:  In particular, the cost of achieving the best pairs rebalancing rule in hindsight is \sum%DIFDELCMD < \limits%%% _{n=0 (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover  (1998) strategy needs a 10,049-year horizon (under annual rebalancing) in order to guarantee to get within 1%DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Revised:  In particular, the cost of achieving the best pairs rebalancing rule in hindsight is \sum%DIFDELCMD < \limits%%% (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover (1998) strategy needs a 10,049-year horizon (under annual rebalancing) in order to guarantee to get within 1%DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Edit intent: coherence
Original:  In particular, the cost of achieving the best pairs rebalancing rule in hindsight is \sum%DIFDELCMD < \limits%%% _{n=0 (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover  (1998) strategy needs a 10,049-year horizon (under annual rebalancing) in order to guarantee to get within 1%DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Revised:  In particular, the cost of achieving the best pairs rebalancing rule in hindsight is \sum%DIFDELCMD < \limits%%% _{n=0 (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover 's (1998) strategy needs a 10,049-year horizon (under annual rebalancing) in order to guarantee to get within 1%DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Edit intent: fluency
Original:  In particular, the cost of achieving the best pairs rebalancing rule in hindsight is \sum%DIFDELCMD < \limits%%% _{n=0 (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover  (1998) strategy needs a 10,049-year horizon (under annual rebalancing) in order to guarantee to get within 1%DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Revised:  In particular, the cost of achieving the best pairs rebalancing rule in hindsight is \sum%DIFDELCMD < \limits%%% _{n=0 (n/T)^n((T-n)/T)^{T-n Ordentlich and Cover (1998) %DIFDELCMD < \\%%% %DIF < of the compound-annual growth rate of the best (30-stock) rebalancing rule in hindsight.
Edit intent: coherence
Original:  By contrast, it takes at most 980 years to enforce a growth rate that is within 1%DIFDELCMD < \\%%% %DIF < of the  best pairs rebalancing rule in hindsight.
Revised:  By contrast, it takes at most 980 years to enforce a growth rate that is %DIFDELCMD < \\%%% %DIF < of the best pairs rebalancing rule in hindsight.
Edit intent: clarity
Original:  Thus, the more modest goal of growth at the same asymptotic rate as the best pairs rebalancing rule in hindsight leads to a more practical trading strategy that still beats the market asymptotically, albeit with a lower asymptotic growth ratethan the full-support universal portfolio .
Revised:  Thus, the more modest benchmark (instead of the best all-stock rebalancing rule in hindsight leads to a more practical trading strategy that still beats the market asymptotically, albeit with a lower asymptotic growth ratethan the full-support universal portfolio .
Edit intent: clarity
Original:  Thus, the more modest goal of growth at the same asymptotic rate as the best pairs rebalancing rule in hindsight leads to a more practical trading strategy that still beats the market asymptotically, albeit with a lower asymptotic growth ratethan the full-support universal portfolio .
Revised:  Thus, the more modest goal of growth at the same asymptotic rate as the best pairs rebalancing rule in hindsight ), we reduce the "cost of universality" and achieve a higher learning rate .
Edit intent: coherence
Original:  Our result shows that the problem of recognizing these graphs is fixed-parameter tractable when parameterized both by k and by the degeneracy of the given graph.
Revised:  Our results shows that the problem of recognizing these graphs is fixed-parameter tractable when parameterized both by k and by the degeneracy of the given graph.
Edit intent: fluency
Original:  To prove this, we  describe how to embed the leaf root of a leaf power graph into a product of the graph with a cycle graph.
Revised:  To prove this, we first describe how to embed the leaf root of a leaf power graph into a product of the graph with a cycle graph.
Edit intent: coherence
Original:  I complete the Cover-Ordentlich (1998) analysis by deriving the price at any time t.
Revised:  I complete the Ordentlich-Cover (1998) analysis by deriving the price at any time t.
Edit intent: clarity
Original:  Researchers will often have several models, i.e. \ hypotheses, about an underlying system mechanism, but insufficient experimental data to discriminate between the models, i.e. \ discard inaccurate models.
Revised:  Researchers will often have several models, i.e. hypotheses, about an underlying system mechanism, but insufficient experimental data to discriminate between the models, i.e. \ discard inaccurate models.
Edit intent: fluency
Original:  Researchers will often have several models, i.e. \ hypotheses, about an underlying system mechanism, but insufficient experimental data to discriminate between the models, i.e. \ discard inaccurate models.
Revised:  Researchers will often have several models, i.e. \ hypotheses, about an underlying system mechanism, but insufficient experimental data to discriminate between the models, i.e. discard inaccurate models.
Edit intent: fluency
Original:  The refugee crisis of 2015 has resulted in critical levels of opinion polarization on the question of whether  welcome migrants, causing clashes in the receiving countries.
Revised:  The refugee crisis of 2015 has resulted in critical levels of opinion polarization on the question of whether to welcome migrants, causing clashes in the receiving countries.
Edit intent: fluency
Original:  The refugee crisis of 2015 has resulted in critical levels of opinion polarization on the question of whether  welcome migrants, causing clashes in the receiving countries.
Revised:  The refugee crisis of 2015 has resulted in critical levels of opinion polarization on the question of whether welcome migrants, causing clashes in receiving countries.
Edit intent: fluency
Original:  Agent-based simulations can be useful to this goal.
Revised:  Agent-based simulations can help achieve this goal.
Edit intent: clarity
Original:  In this work, we introduce our agent-based model MigrAgent and  preliminary results.
Revised:  In this work, we introduce our model MigrAgent and preliminary results.
Edit intent: clarity
Original:  In this work, we introduce our agent-based model MigrAgent and  preliminary results.
Revised:  In this work, we introduce our agent-based model MigrAgent and our preliminary results.
Edit intent: fluency
Original:  The model synthetizes the dynamics of migration intake and post-migration adaptation.
Revised:  The model synthesizes the dynamics of migration intake and post-migration adaptation.
Edit intent: fluency
Original:  In detail, it explores the different acculturation outcomes that can emerge from the mutual adaptation of a migrant  and a local population depending on their degree of tolerance.
Revised:  It explores the different acculturation outcomes that can emerge from the mutual adaptation of a migrant and a local population depending on their degree of tolerance.
Edit intent: coherence
Original:  In detail, it explores the different acculturation outcomes that can emerge from the mutual adaptation of a migrant  and a local population depending on their degree of tolerance.
Revised:  In detail, it explores the different acculturation outcomes that can emerge from the mutual adaptation of a migrant population and a local population depending on their degree of tolerance.
Edit intent: fluency
Original:  With parameter sweeping, we detect how different acculturation strategies can coexist in a society and at different degree for various subgroups.
Revised:  With parameter sweeping, we detect how different acculturation strategies can coexist in a society and in different degrees among various subgroups.
Edit intent: clarity
Original:  Results show higher polarization effects between a local population and migrants for fast intake conditions.
Revised:  Results show higher polarization effects between a local population and a migrant population for fast intake conditions.
Edit intent: fluency
Original:  In slow intake of migrants , transitory conditions between acculturation outcomes emerge for subgroups, e.g.  from assimilation to integration for liberal migrants and from marginalization to separation for conservative migrants.
Revised:  When migrant intake is slow , transitory conditions between acculturation outcomes emerge for subgroups, e.g. from assimilation to integration for liberal migrants and from marginalization to separation for conservative migrants.
Edit intent: clarity
Original:  In slow intake of migrants , transitory conditions between acculturation outcomes emerge for subgroups, e.g.  from assimilation to integration for liberal migrants and from marginalization to separation for conservative migrants.
Revised:  In slow intake of migrants , transitory conditions between acculturation outcomes emerge for subgroups, e.g. , from assimilation to integration for liberal migrants and from marginalization to separation for conservative migrants.
Edit intent: fluency
Original:  Additionally, relative groups sizes due to speed of intake illustrate counterintuitive scenariosas separation of liberal locals.
Revised:  Relative group sizes due to speed of intake illustrate counterintuitive scenariosas separation of liberal locals.
Edit intent: coherence
Original:  Additionally, relative groups sizes due to speed of intake illustrate counterintuitive scenariosas separation of liberal locals.
Revised:  Additionally, relative groups sizes due to speed of intake cause counterintuitive scenarios, such as the separation of liberal locals.
Edit intent: clarity
Original: Steady state visual evoked potential (SSVEP)-based brain computer interfaces have shown its robustness in achieving high information transfer rate.
Revised: Steady-state visual evoked potential (SSVEP)-based brain computer interfaces have shown its robustness in achieving high information transfer rate.
Edit intent: fluency
Original: Steady state visual evoked potential (SSVEP)-based brain computer interfaces have shown its robustness in achieving high information transfer rate.
Revised: Steady state visual evoked potential (SSVEP)-based brain computer-interfaces (BCIs) have shown its robustness in achieving high information transfer rate.
Edit intent: clarity
Original:  However, the calibration may be time-consuming in order to collect sufficient training templates, deteriorating the practicality in  real-world context.
Revised:  However, collecting sufficient calibration (e.g. training templates) data could be laborious and time-consuming, hindering the practicality in real-world context.
Edit intent: clarity
Original:  However, the calibration may be time-consuming in order to collect sufficient training templates, deteriorating the practicality in  real-world context.
Revised:  However, the calibration may be time-consuming in order to collect sufficient training templates, deteriorating the practicality in a real-world context.
Edit intent: fluency
Original:  This study aims to develop a cross-subject transferring approach to reduce the need of training data from a new user.
Revised:  This study aims to develop a cross-subject transferring approach to reduce the need for training data from a new user.
Edit intent: fluency
Original:  The proposed least-squares transformation (LST) method was able to significantly reduce the training templates required for TRCA-based SSVEP detection on a 40-class SSVEP dataset from 8 subjects.
Revised:  The proposed least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class SSVEP dataset from 8 subjects.
Edit intent: clarity
Original:  This study shed light on plug-and-play high speed SSVEP-based BCIfor further practical applications .
Revised:  This study shed light on plug-and-play high-speed SSVEP-based BCIs .
Edit intent: clarity
Original:  However, collecting sufficient calibration (e.g. trainingtemplates) data could be laborious and time-consuming, hindering the practicality in a real-world context.
Revised:  However, collecting sufficient calibration (e.g. trainingtemplates) data could be laborious and time-consuming, deteriorating its practicality in a real-world context.
Edit intent: clarity
Original:  This study aims to develop a cross-subject transferring approach to reduce the need for training data from a new user.
Revised:  This study aims to develop a cross-subject transferring approach to reduce the need for training data from a test user.
Edit intent: clarity
Original:  Study results showed that a new least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class TRCA-based SSVEP BCI.
Revised:  Study results showed that a new least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class SSVEP BCI.
Edit intent: clarity
Original:  The LST method may lead to numerous practical applications of plug-and-play high-speed SSVEP-based BCIs .
Revised:  The LST method may lead to numerous real-world applications of a plug-and-play high-speed SSVEP-based BCIs .
Edit intent: clarity
Original:  The LST method may lead to numerous practical applications of plug-and-play high-speed SSVEP-based BCIs .
Revised:  The LST method may lead to numerous practical applications of plug-and-play high-speed SSVEP-based BCI .
Edit intent: fluency
Original: Steady-state visual evoked potential (SSVEP)-based brain computer-interfaces (BCIs) have shown its robustness in achieving high information transfer rate .
Revised: Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in achieving high information transfer rate .
Edit intent: fluency
Original: Steady-state visual evoked potential (SSVEP)-based brain computer-interfaces (BCIs) have shown its robustness in achieving high information transfer rate .
Revised: Steady-state visual evoked potential (SSVEP)-based brain computer-interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication .
Edit intent: clarity
Original:  The LST method may lead to numerous real-world applications of a plug-and-play high-speed SSVEP-based BCI .
Revised:  The LST method may lead to numerous real-world applications of a plug-and-play high-speed SSVEP BCIs .
Edit intent: clarity
Original:  While the completion time of a long- running ML job is determined by the time required to reach model convergence, practically that is also largely influenced by the values of various system settings.
Revised:  While the completion time of a long-running ML job is determined by the time required to reach model convergence, practically that is also largely influenced by the values of various system settings.
Edit intent: fluency
Original:  While our techniques are general enough to various PS- style ML systems, we have prototyped our techniques on top of TensorFlow.
Revised:  While our techniques are general enough to various PS-style ML systems, we have prototyped our techniques on top of TensorFlow.
Edit intent: fluency
Original: In this short note we focus on two recently introduced parameters in the literature, namely `local dimension ' (a parameter on partially ordered sets ) and `local boxicity ' (a parameter on graphs).
Revised: In this paper, we focus on two recently introduced parameters in the literature, namely `local dimension ' (a parameter on partially ordered sets ) and `local boxicity ' (a parameter on graphs).
Edit intent: clarity
Original: In this short note we focus on two recently introduced parameters in the literature, namely `local dimension ' (a parameter on partially ordered sets ) and `local boxicity ' (a parameter on graphs).
Revised: In this short note we focus on two recently introduced parameters in the literature, namely `local boxicity ' (a parameter on partially ordered sets ) and `local boxicity ' (a parameter on graphs).
Edit intent: clarity
Original: In this short note we focus on two recently introduced parameters in the literature, namely `local dimension ' (a parameter on partially ordered sets ) and `local boxicity ' (a parameter on graphs).
Revised: In this short note we focus on two recently introduced parameters in the literature, namely `local dimension ' (a parameter on graphs ) and `local boxicity ' (a parameter on graphs).
Edit intent: clarity