wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our approach consists in dividing a program into basic ( "branchless") blocks , establishing the maximal (resp. minimal )  energy consumption for each block using an evolutionary algorithm , and combining the obtained values according to the program control flow, using static analysis, to produce energy bound functions .
Revised:  Our approach consists in dividing a program into basic ( "branchless") blocks , establishing the maximal (resp. minimal ) energy consumption for each block using an evolutionary algorithm . Then it combines the obtained values according to the program control flow, using static analysis, to produce energy bound functions .
Edit intent: clarity
Original:  Our experimental results show that the bounds obtained by our prototype tool can be tight while remaining on the safe side of budgets in practice  .
Revised:  Our experimental results show that the bounds obtained by our prototype implementation can be tight while remaining on the safe side of budgets in practice .
Edit intent: clarity
Original:  This circumstance requires clinical intervention and becomes challenging when patients are sedated or verbal communication is difficult.
Revised:  This circumstance requires clinical intervention and becomes challenging when verbal communication is difficult.
Edit intent: clarity
Original:  Our framework exploits the cortical activation provoked by the inspiratory compensation when the patient and the ventilator are desynchronized.
Revised:  Our framework exploits the cortical activation provoked by the inspiratory compensation when the subject and the ventilator are desynchronized.
Edit intent: clarity
Original:  Use of a semi-supervised approach and Riemannian geometry of EEG covariance matrices allows effective classification of respiratory state .
Revised:  Use of a semi-supervised approach and Riemannian geometry of EEG covariance matrices allows effective classification of respiratory states .
Edit intent: fluency
Original:  Results evidence that performance , in terms of areas under ROC curves (AUC) , are significantly improved using EEG signals (AUC=0.91) compared to traditional detection based on air flow (AUC=0.76) . Reduction in the number of electrodes that can achieve discrimination can often be desirable (e.g. for portable BCI systems).
Revised:  Classification performances , in terms of areas under ROC curves (AUC) , are significantly improved using EEG signals (AUC=0.91) compared to traditional detection based on air flow (AUC=0.76) . Reduction in the number of electrodes that can achieve discrimination can often be desirable (e.g. for portable BCI systems).
Edit intent: clarity
Original:  Results evidence that performance , in terms of areas under ROC curves (AUC) , are significantly improved using EEG signals (AUC=0.91) compared to traditional detection based on air flow (AUC=0.76) . Reduction in the number of electrodes that can achieve discrimination can often be desirable (e.g. for portable BCI systems).
Revised:  Results evidence that performance , in terms of areas under ROC curves , are significantly improved using EEG signals (AUC=0.91) compared to traditional detection based on air flow (AUC=0.76) . Reduction in the number of electrodes that can achieve discrimination can often be desirable (e.g. for portable BCI systems).
Edit intent: clarity
Original:  Results evidence that performance , in terms of areas under ROC curves (AUC) , are significantly improved using EEG signals (AUC=0.91) compared to traditional detection based on air flow (AUC=0.76) . Reduction in the number of electrodes that can achieve discrimination can often be desirable (e.g. for portable BCI systems).
Revised:  Results evidence that performance , in terms of areas under ROC curves (AUC) , are significantly improved using EEG signals compared to detection based on air flow (AUC=0.76) . Reduction in the number of electrodes that can achieve discrimination can often be desirable (e.g. for portable BCI systems).
Edit intent: clarity
Original:  By using an iterative channel selection technique, the Common Highest Order Ranking (CHOrRa), we find that a reduced set of electrodes (n=6) can slightly improve AUC to 0.95 for an intra-subject configuration, and it still provides fairly good performances (AUC%DIFDELCMD < \geqslant %%% 0.82) for a general inter-subject setting.
Revised:  By using an iterative channel selection technique, the Common Highest Order Ranking (CHOrRa), we find that a reduced set of electrodes (n=6) can slightly improve for an intra-subject configuration, and it still provides fairly good performances (AUC%DIFDELCMD < \geqslant %%% 0.82) for a general inter-subject setting.
Edit intent: clarity
Original:  By using an iterative channel selection technique, the Common Highest Order Ranking (CHOrRa), we find that a reduced set of electrodes (n=6) can slightly improve AUC to 0.95 for an intra-subject configuration, and it still provides fairly good performances (AUC%DIFDELCMD < \geqslant %%% 0.82) for a general inter-subject setting.
Revised:  By using an iterative channel selection technique, the Common Highest Order Ranking (CHOrRa), we find that a reduced set of electrodes (n=6) can slightly improve AUC to 0.95 for an intra-subject configuration, and it still provides fairly good performances %DIFDELCMD < \geqslant %%% 0.82) for a general inter-subject setting.
Edit intent: clarity
Original: Humans and other animals have been shown to perform near-optimal probabilistic inference in a wide range of psychophysical tasks.
Revised: Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks.
Edit intent: clarity
Original:  On the face of it, this is surprising because optimal probabilistic inference in each case is associated with highly non-trivial behavioral strategies .
Revised:  Such computation requires trial-to-trial representation of the uncertainty associated with task variables .
Edit intent: clarity
Original:  Yet, typically subjects receive little to no feedback during most of these tasks and the received feedback is not explicitly probabilistic in nature .
Revised:  Yet, subjects do not typically receive explicitly probabilistic feedback .
Edit intent: clarity
Original:  How can subjects learn such non-trivial behavioral strategies from scarce non-probabilistic feedback?
Revised:  How can subjects learn probabilistic inference from non-probabilistic feedback?
Edit intent: clarity
Original:  We show that generic feed-forward and recurrent neural networks trained with a relatively small number of non-probabilistic examples using  simple error-based learning rules can perform near-optimal probabilistic inference in standard psychophysical tasks.
Revised:  We show that generic neural networks trained with a relatively small number of non-probabilistic examples using simple error-based learning rules can perform near-optimal probabilistic inference in standard psychophysical tasks.
Edit intent: clarity
Original:  We show that generic feed-forward and recurrent neural networks trained with a relatively small number of non-probabilistic examples using  simple error-based learning rules can perform near-optimal probabilistic inference in standard psychophysical tasks.
Revised:  We show that generic feed-forward and recurrent neural networks trained with non-probabilistic examples using simple error-based learning rules can perform near-optimal probabilistic inference in standard psychophysical tasks.
Edit intent: clarity
Original:  We show that generic feed-forward and recurrent neural networks trained with a relatively small number of non-probabilistic examples using  simple error-based learning rules can perform near-optimal probabilistic inference in standard psychophysical tasks.
Revised:  We show that generic feed-forward and recurrent neural networks trained with a relatively small number of non-probabilistic examples using a simple error-based learning rules can perform near-optimal probabilistic inference in standard psychophysical tasks.
Edit intent: fluency
Original:  We show that generic feed-forward and recurrent neural networks trained with a relatively small number of non-probabilistic examples using  simple error-based learning rules can perform near-optimal probabilistic inference in standard psychophysical tasks.
Revised:  We show that generic feed-forward and recurrent neural networks trained with a relatively small number of non-probabilistic examples using simple error-based learning rule perform near-optimal probabilistic inference in standard psychophysical tasks.
Edit intent: clarity
Original:  Our results suggest that near-optimal probabilistic inference in standard psychophysical tasks emerges naturally and robustly in generic neural networks trained with error-based learning rules , even when neither the training objective nor the training examples are explicitly probabilistic, and that these types of networks can be used as simple plausible neural models of probabilistic inference.
Revised:  Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules , even when neither the training objective nor the training examples are explicitly probabilistic, and that these types of networks can be used as simple plausible neural models of probabilistic inference.
Edit intent: clarity
Original:  Our results suggest that near-optimal probabilistic inference in standard psychophysical tasks emerges naturally and robustly in generic neural networks trained with error-based learning rules , even when neither the training objective nor the training examples are explicitly probabilistic, and that these types of networks can be used as simple plausible neural models of probabilistic inference.
Revised:  Our results suggest that near-optimal probabilistic inference in standard psychophysical tasks emerges naturally and robustly in generic neural networks trained with error-based learning rules and that these types of networks can be used as simple plausible neural models of probabilistic inference.
Edit intent: coherence
Original:  Such computation requires trial-to-trial representation of the uncertainty associated with task variables .
Revised:  Probabilistic inference requires trial-to-trial representation of the uncertainty associated with task variables .
Edit intent: clarity
Original:  Such computation requires trial-to-trial representation of the uncertainty associated with task variables .
Revised:  Such computation requires trial-to-trial representation of the uncertainties associated with task variables .
Edit intent: fluency
Original:  We show that generic neural networks trained with non-probabilistic examples using a simple error-based learning rule perform near-optimal probabilistic inference in eight common psychophysical tasks.
Revised:  We show that generic neural networks trained with non-probabilistic feedback using a simple error-based learning rule perform near-optimal probabilistic inference in eight common psychophysical tasks.
Edit intent: clarity
Original:  In all tasks, the number of neurons required for a given level of performance scales sub-linearly with the size of the input population, a substantial improvement on previous implementations of probabilistic inference using hand-crafted neurons.
Revised:  In all tasks, the number of neurons required for a given level of performance grows sub-linearly with the size of the input population, a substantial improvement on previous implementations of probabilistic inference using hand-crafted neurons.
Edit intent: clarity
Original:  Previous work has implemented such computation using neural networks with hand-crafted and task-dependent operations.
Revised:  Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations.
Edit intent: fluency
Original:  We show that generic neural networks trained with non-probabilistic feedback using a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks.
Revised:  We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks.
Edit intent: clarity
Original:  In a probabilistic categorization task, error-based learning in a generic network simultaneously accounts for a monkey's learning curve and for the evolution of qualitative aspects of its choice behavior.
Revised:  In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and for the evolution of qualitative aspects of its choice behavior.
Edit intent: clarity
Original:  In a probabilistic categorization task, error-based learning in a generic network simultaneously accounts for a monkey's learning curve and for the evolution of qualitative aspects of its choice behavior.
Revised:  In a probabilistic categorization task, error-based learning in a generic network simultaneously accounts for a monkey's learning curve and the evolution of qualitative aspects of its choice behavior.
Edit intent: fluency
Original:  In all tasks, the number of neurons required for a given level of performance grows sub-linearly with the size of the input population , a substantial improvement on previous implementations of probabilistic inference using hand-crafted neurons .
Revised:  In all tasks, the number of neurons required for a given level of performance grows sub-linearly with the input population size , a substantial improvement on previous implementations of probabilistic inference using hand-crafted neurons .
Edit intent: clarity
Original:  In all tasks, the number of neurons required for a given level of performance grows sub-linearly with the size of the input population , a substantial improvement on previous implementations of probabilistic inference using hand-crafted neurons .
Revised:  In all tasks, the number of neurons required for a given level of performance grows sub-linearly with the size of the input population , a substantial improvement on previous implementations of probabilistic inference .
Edit intent: clarity
Original:  Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules and that these types of networks can be used as simple plausible neural models of probabilistic inference .
Revised:  Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules .
Edit intent: coherence
Original: The  Machine-to-Machine (M2M) traffic exhibit heterogeneity in several dimensions such as packet arrival rate, delay tolerance requirementsetc .
Revised: The Machine-to-Machine (M2M) communications has fairly heterogeneous service delay requirements .
Edit intent: clarity
Original:  Therefore, motivated by this, we study the delay-performance of a heterogeneous M2M uplink channel from the sensors to the Programmable Logic Controller (PLC) in an industrial automation scenario.
Revised:  Therefore, we study the delay-performance of a heterogeneous M2M uplink channel from the sensors to the Programmable Logic Controller (PLC) in an industrial automation scenario.
Edit intent: coherence
Original:  Therefore, motivated by this, we study the delay-performance of a heterogeneous M2M uplink channel from the sensors to the Programmable Logic Controller (PLC) in an industrial automation scenario.
Revised:  Therefore, motivated by this, we study the delay-performance of a heterogeneous M2M uplink from the sensors to the Programmable Logic Controller (PLC) in an industrial automation scenario.
Edit intent: clarity
Original:  For the classic geometric version of Tverberg's theorem, we obtain probabilistic algorithms that are weakly polynomial in both the dimension and the number of points .
Revised:  For the classic geometric version of Tverberg's theorem, we obtain probabilistic algorithms that are weakly polynomial in the number of points .
Edit intent: clarity
Original:  For geodetic convexity on graphs, we  show that the general problem of finding Radon partitions is NP-hard , and present efficient algorithms for certain special classes of graphs .
Revised:  For geodetic convexity in graphs, we show that the general problem of finding Radon partitions is NP-hard , and present efficient algorithms for certain special classes of graphs .
Edit intent: clarity
Original:  For geodetic convexity on graphs, we  show that the general problem of finding Radon partitions is NP-hard , and present efficient algorithms for certain special classes of graphs .
Revised:  For geodetic convexity on graphs, we show that the general problem of finding the Radon number is NP-hard , and present efficient algorithms for certain special classes of graphs .
Edit intent: clarity
Original:  For geodetic convexity on graphs, we  show that the general problem of finding Radon partitions is NP-hard , and present efficient algorithms for certain special classes of graphs .
Revised:  For geodetic convexity on graphs, we show that the general problem of finding Radon partitions is NP-hard .
Edit intent: clarity
Original:  Our problem captures a wide range of applications, such as sensor networks , anycast acknowledgement and distributed caching.
Revised:  Our problem captures a wide range of applications, such as in-situ sensing , anycast acknowledgement and distributed caching.
Edit intent: clarity
Original: Crossing syntactic dependencies have been observed to be infrequent in natural language, to the point that some syntactic theories and formalisms disregard them entirely .
Revised: Crossing syntactic dependencies have been observed to be infrequent in human languages .
Edit intent: clarity
Original:  We provide statistical evidence suggesting that this is not the case, as the proportion of dependency crossings in a wide range of natural language treebanks can be accurately estimated by a simple predictor based on  the local probability that two dependencies cross given their lengths.
Revised:  We provide statistical evidence suggesting that this is not the case, as the proportion of dependency crossings in a wide range of languages can be accurately estimated by a simple predictor based on the local probability that two dependencies cross given their lengths.
Edit intent: clarity
Original:  The relative error of this predictor never exceeds 5\% on average, whereas  a baseline predictor assuming a random ordering of the words of a sentence incurs a relative error that is at least 6 times greater.
Revised:  The relative error of this predictor never exceeds 5\% on average, whereas the error of a baseline predictor assuming a random ordering of the words of a sentence incurs a relative error that is at least 6 times greater.
Edit intent: clarity
Original:  The relative error of this predictor never exceeds 5\% on average, whereas  a baseline predictor assuming a random ordering of the words of a sentence incurs a relative error that is at least 6 times greater.
Revised:  The relative error of this predictor never exceeds 5\% on average, whereas a baseline predictor assuming a random ordering of the words of a sentence is at least 6 times greater.
Edit intent: clarity
Original:  In this work, we use a hybrid spatial model of tumor response to a DNA-damaging drug to study how the development of chemoresistance in micrometastases depends on the drug dosing schedule.
Revised:  In this work, we use a hybrid spatial model of tumor response to a DNA damaging drug to study how the development of chemoresistance in micrometastases depends on the drug dosing schedule.
Edit intent: fluency
Original:  Simulations reveal a small number of fractionated-dose protocols that , for the case of acquired resistance, can eradicate micrometastases that are unresponsive to metronomic therapy while also being more efficient at eradicating cell lines that are responsive to metronomic therapy.
Revised:  Simulations reveal a small number of fractionated-dose protocols that are at least as effective as metronomic therapy in eradicating micrometastases with acquired resistance (weak or strong), while also being more efficient at eradicating cell lines that are responsive to metronomic therapy.
Edit intent: clarity
Original:  A subset of those fractionated-dose protocols can also eradicate some pre-existing resistant micrometastases more efficiently than metronomic therapy . Given the responsiveness of very different theoretical cell lines to these few fractionated-dose protocols, these may represent more effective ways to schedule chemotherapy with the goal of limiting metastatic tumor progression.
Revised:  A subset of those fractionated-dose protocols can also eradicate some pre-existing resistant cells . Given the responsiveness of very different theoretical cell lines to these few fractionated-dose protocols, these may represent more effective ways to schedule chemotherapy with the goal of limiting metastatic tumor progression.
Edit intent: clarity
Original:  However, despite significant progress in this area, there still lack rules that are theoretically justified , and yet can be considered biologically relevant.
Revised:  However, despite significant progress in this area, there still lack rules that have a theoretical basis , and yet can be considered biologically relevant.
Edit intent: clarity
Original:  Here we examine the general conditions under which optimal synaptic plasticity takes place to support the supervised learning of a precise temporal code.
Revised:  Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code.
Edit intent: clarity
Original:  As part of our analysis we introduce two analytically derived learning rules, one of which relies on an instantaneous error signal to optimise synaptic weights in a network (INST rule), and the other one relying on a filtered error signal to minimise the variance of synaptic weight modifications (FILT rule).
Revised:  As part of our analysis we introduce two analytically derived learning rules, one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal to minimise the variance of synaptic weight modifications (FILT rule).
Edit intent: clarity
Original:  As part of our analysis we introduce two analytically derived learning rules, one of which relies on an instantaneous error signal to optimise synaptic weights in a network (INST rule), and the other one relying on a filtered error signal to minimise the variance of synaptic weight modifications (FILT rule).
Revised:  As part of our analysis we introduce two analytically derived learning rules, one of which relies on an instantaneous error signal to optimise synaptic weights in a network (INST rule), and the other one on a filtered error signal to minimise the variance of synaptic weight modifications (FILT rule).
Edit intent: fluency
Original:  As part of our analysis we introduce two analytically derived learning rules, one of which relies on an instantaneous error signal to optimise synaptic weights in a network (INST rule), and the other one relying on a filtered error signal to minimise the variance of synaptic weight modifications (FILT rule).
Revised:  As part of our analysis we introduce two analytically derived learning rules, one of which relies on an instantaneous error signal to optimise synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule).
Edit intent: clarity
Original:  We test the optimality of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes  .
Revised:  We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes .
Edit intent: clarity