wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Finally, a reduced brain network model (1400 neurons and 45000 synapses) is synthesized on the basis of real brain structural data, and the resulting model activity is compared with associated brain functional (source-space MEG) data .
Revised:  Finally, a reduced brain network model (1400 neurons and 45000 synapses) is synthesized on the basis of real brain structural data, and the resulting simulated activity is compared with associated brain functional (source-space MEG) data .
Edit intent: clarity
Original:  Finally, a reduced brain network model (1400 neurons and 45000 synapses) is synthesized on the basis of real brain structural data, and the resulting model activity is compared with associated brain functional (source-space MEG) data . The conducted test shows a good matching between the activity of  model and that of the emulated subject, in outstanding simulation times (about 20s for simulating 4s of activity with a normal PC).
Revised:  Finally, a reduced brain network model (1400 neurons and 45000 synapses) is synthesized on the basis of real brain structural data, and the resulting model activity is compared with associated brain functional (source-space MEG) data , demonstrating a good matching between the activity of model and that of the emulated subject, in outstanding simulation times (about 20s for simulating 4s of activity with a normal PC).
Edit intent: coherence
Original:  The conducted test shows a good matching between the activity of  model and that of the emulated subject, in outstanding simulation times (about 20s for simulating 4s of activity with a normal PC). Dedicated sections of stimuli editing and output synthesis allow the neuroscientist to introduce and extract brain-like signals, respectively.. .
Revised:  The conducted test shows a good matching between the activity of model and that of the experimetal data. This work aims to lay the groundwork for future event-driven based personalised brain models .
Edit intent: clarity
Original:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory  types, we construct a model including this biological feature.
Revised:  Here, such models are employed to investigate neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory types, we construct a model including this biological feature.
Edit intent: clarity
Original:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory  types, we construct a model including this biological feature.
Revised:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory types, we construct a model including this biological feature.
Edit intent: clarity
Original:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory  types, we construct a model including this biological feature.
Revised:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey cortex . Taking advantage of the separation of cells into excitatory and inhibitory types, we construct a model including this biological feature.
Edit intent: clarity
Original:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory  types, we construct a model including this biological feature.
Revised:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of excitatory and inhibitory types, we construct a model including this biological feature.
Edit intent: clarity
Original:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory  types, we construct a model including this biological feature.
Revised:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory neuron types, we construct a model including this biological feature.
Edit intent: fluency
Original:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory  types, we construct a model including this biological feature. By comparing the performances of Maximum Entropy models at predicting neural activity in wakefulness and deep sleep, we identify the dominant interactions between neurons in each brain state.
Revised:  Here, we employ such methods to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep . Taking advantage of the separation of cells into excitatory and inhibitory types, we construct a model including this distinction. This approach allows to shed light upon differences between excitatory and inhibitory activity across different brain states such as wakefulness and deep sleep, we identify the dominant interactions between neurons in each brain state.
Edit intent: clarity
Original:  By comparing the performances of Maximum Entropy models at predicting neural activity in wakefulness and deep sleep, we identify the dominant interactions between neurons in each brain state. We find that during wakefulness, dominant functional interactions are pairwise while during sleep, interactions are population-wide  .
Revised:  By comparing the performances of Maximum Entropy models at predicting neural activity in wakefulness and deep sleep, in agreement with previous findings. Additionally, Maximum Entropy models can also unveil novel features of neuronal interactions, which are found to be dominated by pairwise interactions during wakefulness, but are pairwise while during sleep, interactions are population-wide .
Edit intent: meaning-changed
Original:  This shows that Maximum Entropy models can be useful to analyze data-sets with excitatory and inhibitory cells, and can reveal the role of inhibitory cells URLanizing coherent dynamics in  cerebral cortex.
Revised:  Overall, we demonstrate Maximum Entropy models can be useful to analyze data-sets with excitatory and inhibitory cells, and can reveal the role of inhibitory cells URLanizing coherent dynamics in cerebral cortex.
Edit intent: clarity
Original:  This shows that Maximum Entropy models can be useful to analyze data-sets with excitatory and inhibitory cells, and can reveal the role of inhibitory cells URLanizing coherent dynamics in  cerebral cortex.
Revised:  This shows that Maximum Entropy models can be useful to analyze data-sets with classified neuron types, and to reveal the respective roles of excitatory and inhibitory neurons URLanizing coherent dynamics in cerebral cortex.
Edit intent: meaning-changed
Original:  This shows that Maximum Entropy models can be useful to analyze data-sets with excitatory and inhibitory cells, and can reveal the role of inhibitory cells URLanizing coherent dynamics in  cerebral cortex.
Revised:  This shows that Maximum Entropy models can be useful to analyze data-sets with excitatory and inhibitory cells, and can reveal the role of inhibitory cells URLanizing coherent dynamics in the cerebral cortex.
Edit intent: fluency
Original:  This capacity has been adequately demonstrated by imaging various types of samples, such as USAF resolution target, human pathological slides and fluorescence-labelled fibroblast cells . Their gigapixel, multi-color reconstructions verify a successful GAN-based single image super-resolution procedure.
Revised:  This capacity has been adequately demonstrated by imaging various types of samples, such as USAF resolution target, human pathological slides and fluorescence-labelled fibroblast cells , and deep tissues in transgenic mouse brain, by both wide-field and light-sheet microscopes. The gigapixel, multi-color reconstructions verify a successful GAN-based single image super-resolution procedure.
Edit intent: meaning-changed
Original:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Revised:  We also propose an image degrading model to generate low resolution images for training, making our approach free from the complex image registration during training dataset preparation. After a welltrained network being created, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Edit intent: meaning-changed
Original:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Revised:  Furthermore, this deep learning-based imaging approach is capable of recovering a large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Edit intent: clarity
Original:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Revised:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( ~ 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Edit intent: clarity
Original:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Revised:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Edit intent: clarity
Original:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Revised:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( ~ 1.7 {\mu}m) image at a high speed ( in 1 second) .
Edit intent: clarity
Original:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Revised:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at high speed ( in 1 second) .
Edit intent: fluency
Original:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) .
Revised:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( within 1 second) .
Edit intent: clarity
Original:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) . As a result, GAN-microscopy opens a new way to computationally overcome the general challenge of high-throughput , high-resolution microscopy that is originally coupled to the physical limitation of system's optics .
Revised:  Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) , high-resolution microscopy that is originally coupled to the physical limitation of system's optics .
Edit intent: coherence
Original:  As a result, GAN-microscopy opens a new way to computationally overcome the general challenge of high-throughput , high-resolution microscopy that is originally coupled to the physical limitation of system's optics .
Revised:  As a result, GAN-microscopy opens a new way to computationally overcome the general challenge of high-throughput , while not necessarily introducing any changes to the setup of existing microscopes .
Edit intent: meaning-changed
Original: The past decades witness FLOPS (Floating-point Operations per Second) , as an important computation-centric performance metric , guides computer architecture evolution, bridges hardware and software co-design, and provides quantitative performance number for system optimization .
Revised: The past decades witness FLOPS (Floating-point Operations per Second) as an important computation-centric performance metric , guides computer architecture evolution, bridges hardware and software co-design, and provides quantitative performance number for system optimization .
Edit intent: fluency
Original:  However, for emerging datacenter computing (in short, DC)  workloads, such as internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%.
Revised:  However, for datacenter (in short, DC) workloads, such as internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%.
Edit intent: clarity
Original:  However, for emerging datacenter computing (in short, DC)  workloads, such as internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%.
Revised:  However, for emerging datacenter computing (in short, DC) computing workloads, such as internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%.
Edit intent: fluency
Original:  However, for emerging datacenter computing (in short, DC)  workloads, such as internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%.
Revised:  However, for emerging datacenter computing (in short, DC) workloads, such as Internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%.
Edit intent: fluency
Original:  However, for emerging datacenter computing (in short, DC)  workloads, such as internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%.
Revised:  However, for emerging datacenter computing (in short, DC) workloads, such as internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity, and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%.
Edit intent: clarity
Original:  However, for emerging datacenter computing (in short, DC)  workloads, such as internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%. These contradicting performance numbers imply that FLOPS is inappropriate for evaluating DC computer systems.
Revised:  However, for emerging datacenter computing (in short, DC) workloads, such as internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform). Furthermore, we reveal that the traditional FLOPS based Roofline performance model is not suitable for modern DC workloads, and gives misleading information for system optimization. These observations imply that FLOPS is inappropriate for evaluating DC computer systems.
Edit intent: meaning-changed
Original:  To address the above issue, we propose a new computation-centric metric BOPS (Basic OPerations per Second) . In our definition, Basic Operations include all of arithmetic, logical, comparing  and array addressing operations for integer and floating point.
Revised:  To address the above issue, we propose a new computation-centric metric BOPS (Basic OPerations ) that measures the efficient work defined by the source code, includes floating-point operations and the arithmetic, logical, comparing and array addressing operations for integer and floating point.
Edit intent: meaning-changed
Original:  In our definition, Basic Operations include all of arithmetic, logical, comparing  and array addressing operations for integer and floating point. BOPS is the average number of BOPs (Basic OPerations) completed each second.
Revised:  In our definition, Basic Operations include all of arithmetic, logical, comparing and array addressing parts of integer operations. We define BOPS as the average number of BOPs (Basic OPerations) completed each second.
Edit intent: clarity
Original:  On the basis of BOPS, also we propose a new roofline performance model for DC computing . Through the experiments, we demonstrate that our new metrics--BOPS, measuring tool, and new performance modelindeed facilitate DC computer system design and optimization .
Revised:  On the basis of BOPS, also we propose a new roofline performance model for DC computing , which we call DC-Roofline model, with which we optimize DC workloads with the improvement varying from 119\% to 325\% .
Edit intent: meaning-changed
Original: The past decades witness FLOPS (Floating-point Operations per Second) as an important computation-centric performance metric. However, for datacenter (in short, DC) computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity , and the average FLOPS efficiency  is only 0.1\% , while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform).
Revised: For datacenter computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity , and the average FLOPS efficiency is only 0.1\% , while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform).
Edit intent: coherence
Original:  However, for datacenter (in short, DC) computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity , and the average FLOPS efficiency  is only 0.1\% , while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform).
Revised:  However, for datacenter (in short, DC) computing workloads, previous work reports that they have extremely low floating point operation intensity , and the average FLOPS efficiency is only 0.1\% , while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform).
Edit intent: clarity
Original:  However, for datacenter (in short, DC) computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity , and the average FLOPS efficiency  is only 0.1\% , while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform).
Revised:  However, for datacenter (in short, DC) computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity . Our experiments also show that the average FLOPS efficiency is only 0.1\% , while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform).
Edit intent: coherence
Original:  However, for datacenter (in short, DC) computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity , and the average FLOPS efficiency  is only 0.1\% , while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform).
Revised:  However, for datacenter (in short, DC) computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity , and the average FLOPS efficiency of the DC workloads is only 0.1\% , while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform).
Edit intent: meaning-changed