wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This ability  is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills  . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world  .
Revised:  This ability , referred to as lifelong learning, is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world .
Edit intent: meaning-changed
Original:  This ability  is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills  . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world  .
Revised:  This ability is mediated by a rich set of neurocognitive mechanisms that together contribute to the early development and experience-driven specialization of our sensorimotor skills . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world .
Edit intent: clarity
Original:  This ability  is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills  . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world  .
Revised:  This ability is mediated by a rich set of neurocognitive functions that together contribute to the development and specialization of our sensorimotor skills . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world .
Edit intent: clarity
Original:  This ability  is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills  . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world  .
Revised:  This ability is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills as well as to the long-term memory consolidation and retrieval without catastrophic URLetting . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world .
Edit intent: meaning-changed
Original:  This ability  is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills  . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world  .
Revised:  This ability is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills . Consequently, lifelong learning capabilities are crucial for computational learning systems and autonomous agents (inter)acting in the real world .
Edit intent: clarity
Original:  This ability  is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills  . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world  .
Revised:  This ability is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents interacting in the real world .
Edit intent: fluency
Original:  This ability  is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills  . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world  .
Revised:  This ability is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world and processing continuous streams of information .
Edit intent: meaning-changed
Original:  However, continual lifelong learning remains a long-standing challenge for machine learning and neural network models since the incremental acquisition of new skills from non-stationary data distributions generally leads to catastrophic URLetting or interference.
Revised:  However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the incremental acquisition of new skills from non-stationary data distributions generally leads to catastrophic URLetting or interference.
Edit intent: clarity
Original:  However, continual lifelong learning remains a long-standing challenge for machine learning and neural network models since the incremental acquisition of new skills from non-stationary data distributions generally leads to catastrophic URLetting or interference.
Revised:  However, continual lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic URLetting or interference.
Edit intent: clarity
Original:  This limitation represents a major drawback also for state-of-the-art deep  neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time.
Revised:  This limitation represents a major drawback also for state-of-the-art deep and shallow neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time.
Edit intent: meaning-changed
Original:  We discuss well-established research and recent methodological trends motivated by experimentally observed lifelong learning factors in biological systems . Such factors include principles of neurosynaptic stability-plasticity, critical developmental stages , intrinsically motivated exploration, transfer learning, and crossmodal integration .
Revised:  We discuss well-established research and recent methodological trends motivated by experimentally observed lifelong learning factors in biological systems such as neurosynaptic plasticity, multi-task transfer learning , intrinsically motivated exploration, transfer learning, and crossmodal integration .
Edit intent: clarity
Original: As architecture, system, data management, and machine learning communities pay greater attention to innovative big data and data-driven artificial intelligence (in short, AI ) algorithms, architecture, and systems , the pressure of benchmarking rises.
Revised: Several fundamental changes in technology indicate domain-specific hardware and software co-design is the only path left. In this context, architecture, system, data management, and machine learning communities pay greater attention to innovative big data and data-driven artificial intelligence (in short, AI ) algorithms, architecture, and systems , the pressure of benchmarking rises.
Edit intent: meaning-changed
Original: As architecture, system, data management, and machine learning communities pay greater attention to innovative big data and data-driven artificial intelligence (in short, AI ) algorithms, architecture, and systems , the pressure of benchmarking rises. However , complexity, diversity, frequently changed workloads, and rapid evolution of big data , especially AI systems raise great challenges in benchmarking .
Revised: As architecture, system, data management, and machine learning communities pay greater attention to innovative big data and data-driven artificial intelligence (in short, AI ) algorithms, architecture, and systems . Unfortunately , complexity, diversity, frequently changed workloads, and rapid evolution of big data , especially AI systems raise great challenges in benchmarking .
Edit intent: coherence
Original:  First, for the sake of conciseness, benchmarking scalability, portability cost, reproducibility, and better interpretation of performance data, we need understand what are the abstractions of frequently-appearing units of computation , which we call dwarfs, among big data and AI workloads.
Revised:  First, the traditional benchmarking methodology that creates a new benchmark or proxy for every possible workload is not scalable, or even impossible for Big Data and AI benchmarking. Second, it is prohibitively expensive to tailor the architecture to characteristics of one or more application or even a domain of applications. We consider each big data and AI workload as a pipeline of one or more classes of units of computation , which we call dwarfs, among big data and AI workloads.
Edit intent: meaning-changed
Original:  First, for the sake of conciseness, benchmarking scalability, portability cost, reproducibility, and better interpretation of performance data, we need understand what are the abstractions of frequently-appearing units of computation , which we call dwarfs, among big data and AI workloads.
Revised:  First, for the sake of conciseness, benchmarking scalability, portability cost, reproducibility, and better interpretation of performance data, we need understand what are the abstractions of frequently-appearing units of computation performed on different initial or intermediate data inputs, each class of which we call dwarfs, among big data and AI workloads.
Edit intent: meaning-changed
Original:  First, for the sake of conciseness, benchmarking scalability, portability cost, reproducibility, and better interpretation of performance data, we need understand what are the abstractions of frequently-appearing units of computation , which we call dwarfs, among big data and AI workloads. Second, for the sake of fairness, the benchmarks must include diversity of data and workloads. Third, for co-design of software and hardware, the benchmarks should be consistent across different communities. Other than creating a new benchmark or proxy for every possible workload, we propose using dwarf-based benchmarks--the combination of eight dwarfs--to represent diversity of big data and AI workloads.
Revised:  First, for the sake of conciseness, benchmarking scalability, portability cost, reproducibility, and better interpretation of performance data, we need understand what are the abstractions of frequently-appearing units of computation , which we call a data motif. On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.
Edit intent: meaning-changed
Original:  The current version--BigDataBench 4.0 provides 13 representative real-world data sets and 47 big data and AI benchmarks, including seven workload types: online service, offline analytics, graph analytics, AI, data warehouse, NoSQL, and streaming.
Revised:  Following this methodology, we present a unified big data and AI benchmarks, including seven workload types: online service, offline analytics, graph analytics, AI, data warehouse, NoSQL, and streaming.
Edit intent: clarity
Original:  The current version--BigDataBench 4.0 provides 13 representative real-world data sets and 47 big data and AI benchmarks, including seven workload types: online service, offline analytics, graph analytics, AI, data warehouse, NoSQL, and streaming. BigDataBench 4.0 is publicly available from URL Also, for the first time, we comprehensively characterize the  benchmarks of seven workload types in BigDataBench 4.0 in addition to traditional benchmarks like SPECCPU, PARSEC and HPCC in a hierarchical manner and drill down on five levels, using the Top-Down analysis from an architecture perspective .
Revised:  The current version--BigDataBench 4.0 provides 13 representative real-world data sets and 47 big data and AI benchmark suite---BigDataBench 4.0 is publicly available from URL Also, for the first time, we comprehensively characterize the benchmarks of seven workload types in BigDataBench 4.0 in addition to traditional benchmarks like SPECCPU, PARSEC and HPCC in a hierarchical manner and drill down on five levels, using the Top-Down analysis from an architecture perspective .
Edit intent: clarity
Original:  BigDataBench 4.0 is publicly available from URL Also, for the first time, we comprehensively characterize the  benchmarks of seven workload types in BigDataBench 4.0 in addition to traditional benchmarks like SPECCPU, PARSEC and HPCC in a hierarchical manner and drill down on five levels, using the Top-Down analysis from an architecture perspective .
Revised:  BigDataBench 4.0 , publicly available URL This unified benchmark suite sheds new light on domain-specific hardware and software co-design: tailoring the system and architecture to characteristics of the unified eight data motifs other than one or more application case by case. Also, for the first time, we comprehensively characterize the benchmarks of seven workload types in BigDataBench 4.0 in addition to traditional benchmarks like SPECCPU, PARSEC and HPCC in a hierarchical manner and drill down on five levels, using the Top-Down analysis from an architecture perspective .
Edit intent: meaning-changed
Original:  BigDataBench 4.0 is publicly available from URL Also, for the first time, we comprehensively characterize the  benchmarks of seven workload types in BigDataBench 4.0 in addition to traditional benchmarks like SPECCPU, PARSEC and HPCC in a hierarchical manner and drill down on five levels, using the Top-Down analysis from an architecture perspective .
Revised:  BigDataBench 4.0 is publicly available from URL Also, for the first time, we comprehensively characterize the CPU pipeline efficiency using the benchmarks of seven workload types in BigDataBench 4.0 in addition to traditional benchmarks like SPECCPU, PARSEC and HPCC in a hierarchical manner and drill down on five levels, using the Top-Down analysis from an architecture perspective .
Edit intent: meaning-changed
Original:  BigDataBench 4.0 is publicly available from URL Also, for the first time, we comprehensively characterize the  benchmarks of seven workload types in BigDataBench 4.0 in addition to traditional benchmarks like SPECCPU, PARSEC and HPCC in a hierarchical manner and drill down on five levels, using the Top-Down analysis from an architecture perspective .
Revised:  BigDataBench 4.0 is publicly available from URL Also, for the first time, we comprehensively characterize the benchmarks of seven workload types in BigDataBench 4.0 .
Edit intent: coherence
Original: From a public-health perspective, the occurrence of drug-drug-interactions (DDI) from multiple drug prescriptions is a serious problem, especially in the elderly population. This is true both for individuals and the system itself since patients with complications due to DDI will likely re-enter the system at a costlier level.
Revised: From a public-health perspective, the occurrence of drug-drug-interactions (DDI) from multiple drug prescriptions is a serious problem, both for individuals and the system itself since patients with complications due to DDI will likely re-enter the system at a costlier level.
Edit intent: coherence
Original:  We conducted an 18-month study of DDI occurrence in Blumenau (Brazil ; pop. 340,000) using city-wide drug dispensing data from both primary and secondary-care level.
Revised:  We present a large-scale longitudinal study of the DDI phenomenon at the primary- and secondary-care level using electronic health records from the city of Blumenau in Southern Brazil (pop. ~ 340,000) using city-wide drug dispensing data from both primary and secondary-care level.
Edit intent: clarity
Original:   We found  181 distinct DDI being prescribed concomitantly to almost 5 \% of the city population. We also discovered that women are at a 60\% risk increase of DDI when compared to men , while only having a 6\% co-administration risk increase.
Revised:  We found 181 distinct DDI being prescribed concomitantly to almost 5 \% of the patients in the city's public health-care system. Further, 4\% of the patients were dispensed major DDI combinations, likely to result in very serious adverse reactions and costs we estimate to be larger than previously reported. DDI results are integrated into associative networks for inference and visualization, revealing key medications and interactions. Analysis reveals that women have a 60\% risk increase of DDI when compared to men , while only having a 6\% co-administration risk increase.
Edit intent: meaning-changed
Original:  We also discovered that women are at a 60\% risk increase of DDI when compared to men , while only having a 6\% co-administration risk increase. Analysis of the DDI co-occurrence network reveals which DDI pairs are most associated with the observed greater DDI risk for females, demonstrating that contraception and hormone therapy are not the main culprits of the gender disparity, which is maximized after the reproductive years .
Revised:  We also discovered that women are at a 60\% risk increase of DDI when compared to men ; the increase becomes 90\% when only major DDI are considered .
Edit intent: meaning-changed
Original:  Furthermore, DDI risk increases dramatically with age, with patients age 70-79 having a 50-fold risk increase in comparison to patients aged 0-19 . Interestingly, several null models demonstrate that this risk increase is not due to increased polypharmacy with age.
Revised:  Furthermore, DDI risk increases substantially with age. Patients aged 70-79 having a 50-fold risk increase in comparison to patients aged 0-19 . Interestingly, several null models demonstrate that this risk increase is not due to increased polypharmacy with age.
Edit intent: clarity
Original:  Furthermore, DDI risk increases dramatically with age, with patients age 70-79 having a 50-fold risk increase in comparison to patients aged 0-19 . Interestingly, several null models demonstrate that this risk increase is not due to increased polypharmacy with age.
Revised:  Furthermore, DDI risk increases dramatically with age, with patients age 70-79 years have a 34\% risk of DDI when they are prescribed two or more drugs concomitantly . Interestingly, several null models demonstrate that this risk increase is not due to increased polypharmacy with age.
Edit intent: meaning-changed
Original:  Furthermore, DDI risk increases dramatically with age, with patients age 70-79 having a 50-fold risk increase in comparison to patients aged 0-19 . Interestingly, several null models demonstrate that this risk increase is not due to increased polypharmacy with age.
Revised:  Furthermore, DDI risk increases dramatically with age, with patients age 70-79 having a 50-fold risk increase in comparison to patients aged 0-19 . Interestingly, a null model demonstrates that age and women-specific risks from increased polypharmacy far exceed expectations in those populations. This suggests that social and biological factors are at play.
Edit intent: meaning-changed
Original:  Finally, we demonstrate that while the number of drugs and co-administrations help predict a patient's number of DDI (R^2=.413), they are not sufficient to flag these patients accurately, which we achieve by training classifiers with additional data (MCC= .83,F1 = .72 ).
Revised:  Finally, we demonstrate that machine learning classifiers accurately predict patients likely to be administered DDI given their history of prescribed drugs, gender, and age (MCC= .83,F1 = .72 ).
Edit intent: clarity
Original:  Finally, we demonstrate that while the number of drugs and co-administrations help predict a patient's number of DDI (R^2=.413), they are not sufficient to flag these patients accurately, which we achieve by training classifiers with additional data (MCC= .83,F1 = .72 ).
Revised:  Finally, we demonstrate that while the number of drugs and co-administrations help predict a patient's number of DDI (R^2=.413), they are not sufficient to flag these patients accurately, which we achieve by training classifiers with additional data (MCC= .7,AUC = .72 ).
Edit intent: meaning-changed
Original:  Finally, we demonstrate that while the number of drugs and co-administrations help predict a patient's number of DDI (R^2=.413), they are not sufficient to flag these patients accurately, which we achieve by training classifiers with additional data (MCC= .83,F1 = .72 ).
Revised:  Finally, we demonstrate that while the number of drugs and co-administrations help predict a patient's number of DDI (R^2=.413), they are not sufficient to flag these patients accurately, which we achieve by training classifiers with additional data (MCC= .83,F1 = .97 ).
Edit intent: meaning-changed
Original: Fast constant factor approximation algorithms are devised for a ] problem of intersecting a set of straight line segments with the smallest cardinality set of disks of fixed radii r>0, where the set of segments forms a straight line drawing G=(V,E) of a planar graph without edge crossings.
Revised: Fast constant factor approximation algorithms are devised for an NP- and W ] problem of intersecting a set of straight line segments with the smallest cardinality set of disks of fixed radii r>0, where the set of segments forms a straight line drawing G=(V,E) of a planar graph without edge crossings.
Edit intent: meaning-changed
Original: Fast constant factor approximation algorithms are devised for a ] problem of intersecting a set of straight line segments with the smallest cardinality set of disks of fixed radii r>0, where the set of segments forms a straight line drawing G=(V,E) of a planar graph without edge crossings.
Revised: Fast constant factor approximation algorithms are devised for a 1] problem of intersecting a set of straight line segments with the smallest cardinality set of disks of fixed radii r>0, where the set of segments forms a straight line drawing G=(V,E) of a planar graph without edge crossings.
Edit intent: meaning-changed
Original: Fast constant factor approximation algorithms are devised for a ] problem of intersecting a set of straight line segments with the smallest cardinality set of disks of fixed radii r>0, where the set of segments forms a straight line drawing G=(V,E) of a planar graph without edge crossings.
Revised: Fast constant factor approximation algorithms are devised for a ]-hard problem of intersecting a set of straight line segments with the smallest cardinality set of disks of fixed radii r>0, where the set of segments forms a straight line drawing G=(V,E) of a planar graph without edge crossings.
Edit intent: meaning-changed