wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Conducting an extensive empirical analysis   , we show how the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio  .
Revised:  Conducting an extensive empirical analysis with four datasets based on the S , we show how the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio .
Edit intent: meaning-changed
Original:  Conducting an extensive empirical analysis   , we show how the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio  .
Revised:  Conducting an extensive empirical analysis P 500 , we show how the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio .
Edit intent: meaning-changed
Original:  Conducting an extensive empirical analysis   , we show how the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio  .
Revised:  Conducting an extensive empirical analysis , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio .
Edit intent: coherence
Original:  Conducting an extensive empirical analysis   , we show how the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio  .
Revised:  Conducting an extensive empirical analysis , we show how the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio . In addition, we identify estimators that are strongly influenced by the choice of the tuning parameter and detect a clear relationship between the selection criterion within the cross-validation and the evaluated performance measure .
Edit intent: meaning-changed
Original:  Conducting an extensive empirical analysis with four datasets based on the S P 500 , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Revised:  Conducting an extensive empirical analysis with three datasets based on the S P 500 , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Edit intent: meaning-changed
Original:  Conducting an extensive empirical analysis with four datasets based on the S P 500 , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Revised:  Conducting an extensive empirical analysis with four datasets based on the P 500 , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Edit intent: clarity
Original:  Conducting an extensive empirical analysis with four datasets based on the S P 500 , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Revised:  Conducting an extensive empirical analysis with four datasets based on the S Russell 3000 , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Edit intent: meaning-changed
Original:  Conducting an extensive empirical analysis with three datasets based on the Russell 3000  , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Revised:  Conducting an extensive empirical analysis with four datasets based on the Russell 3000 , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Edit intent: meaning-changed
Original:  Conducting an extensive empirical analysis with three datasets based on the Russell 3000  , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Revised:  Conducting an extensive empirical analysis with three datasets based on the S , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Edit intent: meaning-changed
Original:  Conducting an extensive empirical analysis with three datasets based on the Russell 3000  , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Revised:  Conducting an extensive empirical analysis with three datasets based on the Russell 3000 P 500 , we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.
Edit intent: meaning-changed
Original:  By constructing a simple yet flexible model class, with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) , intensive care unit (ICU) and type 1 diabetes mellitus (T1DM) ;
Revised:  By constructing a simple yet flexible model class, with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) and intensive care unit (ICU) and type 1 diabetes mellitus (T1DM) ;
Edit intent: fluency
Original:  By constructing a simple yet flexible model class, with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) , intensive care unit (ICU) and type 1 diabetes mellitus (T1DM) ;
Revised:  By constructing a simple yet flexible model class, with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) , intensive care unit (ICU) ;
Edit intent: coherence
Original:  In addition to data-driven decision-making and control, we believe the model is also useful for basic quantification of endocrine physiology. In both cases the available data is sparse and collected in clinical settings, major factors that have constrained our model choice to the simple form adopted.
Revised:  In both cases, the available data is sparse and collected in clinical settings, major factors that have constrained our model choice to the simple form adopted.
Edit intent: coherence
Original:  The resulting model leads to  prediction of the BG level as an expected value accompanied by a band around this value which accounts for uncertainties in prediction .
Revised:  The resulting model leads to prediction of the BG level as an expected value accompanied by a band around this value which accounts for uncertainties in the prediction. Such predictions, then, have the potential for use as part of control systems which are robust to model imperfections and noisy data. Finally, a comparison of the predictive capability of the model with two different models specifically built for T2DM and ICU contexts is also performed .
Edit intent: meaning-changed
Original:  With conserved total particle number, dispersal has no effect on survival probability , while with birth-death fluctuations present, moving slower is the optimal strategy for the weaker species : it is imperative to include fluctuations to properly formulate survival strategies.
Revised:  With conserved total particle number, dispersal has no effect on survival probability . With a fluctuating number, faster dispersal is advantageous if intra-species competition is present, while moving slower is the optimal strategy for the weaker species : it is imperative to include fluctuations to properly formulate survival strategies.
Edit intent: meaning-changed
Original:  With conserved total particle number, dispersal has no effect on survival probability , while with birth-death fluctuations present, moving slower is the optimal strategy for the weaker species : it is imperative to include fluctuations to properly formulate survival strategies.
Revised:  With conserved total particle number, dispersal has no effect on survival probability , while with birth-death fluctuations present, moving slower is the optimal strategy for the disadvantaged species if there is no intra-species competition : it is imperative to include fluctuations to properly formulate survival strategies.
Edit intent: meaning-changed
Original: An increasing number of software applications incorporate runtime Deep Neural Network (DNN) inference for its great accuracy in many problem domains. While much prior work has separately tackled the problems of improving DNN-inference accuracy and improving DNN-inference efficiency, an important problem is under-explored: disciplined methods for dynamically managing application-specific latency, accuracy, and energy tradeoffsand constraints at run time. To address this need, we propose ALERT, a co-designed combination of runtime system and DNN nesting technique. The runtime takes latency, accuracy, and energy constraints , and uses dynamic feedback to predict the best DNN-model and system power-limit setting.
Revised: An increasing number of software applications incorporate runtime Deep Neural Networks (DNNs) to process sensor data and return inference results to humans. Effective deployment of DNNs in these interactive scenarios requires meeting latency and accuracy constraints while minimizing energy, a problem exacerbated by common system dynamics. Prior approaches handle dynamics through either (1) system-oblivious DNN adaptation, which adjusts DNN latency/accuracy tradeoffs, or (2) application-oblivious system adaptation, which adjusts resources to change latency/energy tradeoffs. In contrast, this paper improves on the state-of-the-art by coordinating application- and system-level adaptation. ALERT, our runtime scheduler, uses a probabilistic model to detect environmental volatility and then simultaneously select both a DNN and a system resource configuration to meet latency, accuracy, and energy constraints , and uses dynamic feedback to predict the best DNN-model and system power-limit setting.
Edit intent: clarity
Original:  The runtime takes latency, accuracy, and energy constraints , and uses dynamic feedback to predict the best DNN-model and system power-limit setting. The DNN nesting creates a type of flexible network that efficiently delivers a series of results with increasing accuracy as time goes on . These two parts well complement each other: the runtime is aware of the tradeoffs of different DNN settings, and the nested DNNs' flexibility allows the runtime prediction to satisfy application requirements even in unpredictable, changing environments. On real systems for both image and speech , ALERT achieves close-to-optimal results.
Revised:  The runtime takes latency, accuracy, and energy constraints . We evaluate ALERT on CPU and GPU platforms for image and speech , ALERT achieves close-to-optimal results.
Edit intent: clarity
Original:  On real systems for both image and speech , ALERT achieves close-to-optimal results. Comparing with the optimal static DNN-model and power-limit setting, which is impractical to predict, ALERTachieves a harmonic mean 33\% of energy savings while satisfying accuracy constraints, and reduces image-classification error rate by 58\% and sentence-prediction perplexity by 52\% while satisfying energy constraints .
Revised:  On real systems for both image and speech tasks in dynamic environments. ALERT's holistic approach achieves more than 13\% energy reduction, and 27\% error reduction over prior approaches that adapt solely at the application or system level. Furthermore, ALERT incurs only 3\% more energy consumption and 2\% higher DNN-inference error than an oracle scheme with perfect application and system knowledge .
Edit intent: clarity
Original:  Our newly developed model features not only in terms with a controlling shareholder who diverts a fraction of the output, but also the good (or bad) memory in his budget dynamics which can be well-calibrated by a pathwise way from the historical data.
Revised:  Our newly developed model features not only in terms with a controlling shareholder who diverts a fraction of the output, but also good (or bad) memory in his budget dynamics which can be well-calibrated by a pathwise way from the historical data.
Edit intent: fluency
Original:  More importantly, by establishing an approximation scheme for the good (or bad) memory of investors on the historical market information, we conclude that the good /bad memory would increase /decreaseboth real stock returns and interest rates while the equilibrium balances the economy by preventing investors from benefiting the memory .
Revised:  More importantly, by establishing an approximation scheme for good ( bad) memory of investors on the historical market information, we conclude that the good /bad memory would increase /decreaseboth real stock returns and interest rates while the equilibrium balances the economy by preventing investors from benefiting the memory .
Edit intent: fluency
Original:  More importantly, by establishing an approximation scheme for the good (or bad) memory of investors on the historical market information, we conclude that the good /bad memory would increase /decreaseboth real stock returns and interest rates while the equilibrium balances the economy by preventing investors from benefiting the memory .
Revised:  More importantly, by establishing an approximation scheme for the good (or bad) memory of investors on the historical market information, we conclude that good (bad) memory would increase /decreaseboth real stock returns and interest rates while the equilibrium balances the economy by preventing investors from benefiting the memory .
Edit intent: fluency
Original:  More importantly, by establishing an approximation scheme for the good (or bad) memory of investors on the historical market information, we conclude that the good /bad memory would increase /decreaseboth real stock returns and interest rates while the equilibrium balances the economy by preventing investors from benefiting the memory .
Revised:  More importantly, by establishing an approximation scheme for the good (or bad) memory of investors on the historical market information, we conclude that the good /bad memory would increase (decrease) aforementioned dynamics and reveal that good (bad) memory strengthens (weakens) investor protection for minority shareholder when the ownership concentration is sufficiently high, while good (bad) memory inversely weakens (strengthens) investor protection for minority shareholder when the ownership concentration is sufficiently low .
Edit intent: meaning-changed
Original: Automatic analysis of emotions and affects from speech is an inherently challenging problem with a broad range of applications in Human-Computer Interaction (HCI), health informatics , assistive technologies and multimedia retrieval. Understanding human's specific and basic emotions and reacting accordingly can improve HCI. Besides, giving machines skills to understand human' s emotions when interacting with other humans can help humans with a socio-affective intelligence .
Revised: Automatic emotions recognition and Major Depressive Disorder (MDD) diagnosis are inherently challenging problems in health informatics applications. According to the World Health Organization, 300M people are affected by depression in 2017 with only the third of them correctly identified. MDD is a permanent mood disorder where the patient constantly feels negative emotions (low Valence) and lacks excitement and interest (low Arousal). Therefore, developing emotionally intelligent systems enhances depression assessment in the early stages and improves the quality of service in depressed patients' care .
Edit intent: meaning-changed
Original:  In this paper, we present a deep Neural Network-based architecture called EmoAudioNetwhich studies the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies.
Revised:  In this paper, we present a framework for emotions recognition and depression diagnosis from speech called EmoAudioNet. To preserve the high privacy of patient data, only speech is used. The deep EmoAudioNet studies the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies.
Edit intent: meaning-changed
Original: Automatic emotions recognition and Major Depressive Disorder (MDD) diagnosis are inherently challenging problems in health informatics applications. According to the World Health Organization, 300M people are affected by depression in 2017 with only the third of them correctly identified. MDD is a permanent mood disorder where the patient constantly feels negative emotions (low Valence) and lacks excitement and interest (low Arousal).
Revised: Automatic emotions recognition and Major Depressive Disorder (MDD) . MDD describes the constant expression of certain emotions: negative emotions (low Valence) and lacks excitement and interest (low Arousal).
Edit intent: clarity
Original:  In this paper, we present a framework for emotions recognition and depression diagnosis from speechcalled EmoAudioNet. To preserve the high privacy of patient data, only speech is used. The deep EmoAudioNet studies the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies.
Revised:  In this paper, we present a new deep neural network architecture, called EmoAudioNet, for emotion and depression recognition from speech. Deep EmoAudioNet learns from the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies.
Edit intent: clarity
Original:  In the experimental results, two datasets are used: RECOLA for continuous dimensional emotion recognition and DAIC-WOZ for automatic depression diagnosis. The extensive experiments showed that the proposed approach significantly outperforms previous works in the literature in both datasets .
Revised:  Our model outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression, respectively .
Edit intent: meaning-changed
Original:  Our model  outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression , respectively.
Revised:  Our model shows very promising results in predicting affect and depression. It works similarly or outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression , respectively.
Edit intent: meaning-changed
Original:  Our model  outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression , respectively.
Revised:  Our model outperforms the state-of-the-art methods according to several evaluation metrics on RECOLA and on DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression , respectively.
Edit intent: meaning-changed
Original:  Our model  outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression , respectively.
Revised:  Our model outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets in predicting arousal, valence, and depression , respectively.
Edit intent: coherence
Original:  Our model  outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression , respectively.
Revised:  Our model outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression . Code of EmoAudioNet is publicly available on GitHub: URL
Edit intent: meaning-changed
Original:  Measures of variable importance usually work by changing the value of one or more variables with the others held fixed and then recomputing the function of interest. That approach is problematic because it can create very unrealistic combinationsof predictors that never appear in practice or that were never present when the prediction function was being created. Our cohort refinement Shapley approach measures variable importance without using any data points that were not actually observed .
Revised:  Measures of variable importance usually work by changing the value of one or more variables with the others held fixed , potentially creating unlikely or even logically impossible combinations. Our cohort Shapley measure uses only observed data points. Instead of changing the value of a predictor we include or exclude subjects similar to the target subject on that predictor to form a similarity cohort. Then we apply Shapley value to the cohort averages. We connect variable importance measures from explainable AI to function decompositions from global sensitivity analysis. We introduce a squared cohort Shapley value that splits previously studied Shapley effects over subjects, consistent with a Shapley axiom .
Edit intent: meaning-changed
Original:  This suggests that font size information is used when programming return-sweeps and corrective saccades . Return-sweeps in the longer line condition landed further to the right of the line start and the proportion of corrective saccades increased compared to the short line condition.
Revised:  This suggests that font size information is used when programming return-sweeps . Return-sweeps in the longer line condition landed further to the right of the line start and the proportion of corrective saccades increased compared to the short line condition.
Edit intent: clarity
Original:  This suggests that font size information is used when programming return-sweeps and corrective saccades . Return-sweeps in the longer line condition landed further to the right of the line start and the proportion of corrective saccades increased compared to the short line condition.
Revised:  This suggests that font size information is used when programming return-sweeps and corrective saccades . Return-sweeps in the longer line condition landed further to the right of the line start and the proportion of under-sweeps increased compared to the short line condition.
Edit intent: clarity
Original:  Exact time-dependent solutions of the CME---which typically consists of infinitely many coupled differential equations---are rare, and are valuable for numerical benchmarking and getting intuition for the behavior of more complicated systems.
Revised:  Exact time-dependent solutions of the CME -- which typically consists of infinitely many coupled differential equations---are rare, and are valuable for numerical benchmarking and getting intuition for the behavior of more complicated systems.
Edit intent: fluency