wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We present a complete wireless neural stimulation system consisting of a 2.2 mm3 wireless, batteryless, leadless implantable stimulator implant (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Revised:  We present a complete wireless neural stimulation system consisting of a 2.2 mm3 wireless, batteryless, leadless implantable stimulator (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Edit intent: clarity
Original:  The mote consists of a  stimulator integrated circuit (IC) , which performs high-efficiency ultrasonic power harvesting , decodes stimulation parameter downlink data, and generates current-controlled stimulation pulses.
Revised:  The mote consists of a piezoceramic transducer, an energy storage capacitor, and a stimulator integrated circuit (IC) , which performs high-efficiency ultrasonic power harvesting , decodes stimulation parameter downlink data, and generates current-controlled stimulation pulses.
Edit intent: meaning-changed
Original:  The mote consists of a  stimulator integrated circuit (IC) , which performs high-efficiency ultrasonic power harvesting , decodes stimulation parameter downlink data, and generates current-controlled stimulation pulses.
Revised:  The mote consists of a stimulator integrated circuit (IC) . The IC harvests ultrasonic power with high efficiency , decodes stimulation parameter downlink data, and generates current-controlled stimulation pulses.
Edit intent: clarity
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and  demodulated at the external receiver.
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with high-temporal resolution and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and demodulated at the external receiver.
Edit intent: clarity
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and  demodulated at the external receiver.
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability while reducing power consumption and on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and demodulated at the external receiver.
Edit intent: clarity
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and  demodulated at the external receiver.
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory requirements . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and demodulated at the external receiver.
Edit intent: clarity
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and  demodulated at the external receiver.
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether the mote is currently stimulating and is encoded by the mote via backscatter modulation and demodulated at the external receiver.
Edit intent: coherence
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and  demodulated at the external receiver.
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating ; it is encoded by the mote via backscatter modulation and demodulated at the external receiver.
Edit intent: coherence
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and  demodulated at the external receiver.
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and is demodulated at the external receiver.
Edit intent: fluency
Original:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and  demodulated at the external receiver. We characterize system power, stimulation performance, and acoustic link robustness under misalignment.
Revised:  Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with precise timing and closed-loop capability and without the need for on-chip memory and power consumption . Uplink data indicates whether or not the mote is currently stimulating and is encoded by the mote via backscatter modulation and demodulated at the external transceiver. We show that the system operates at an acoustic power one fifth the FDA limit for diagnostic ultrasound and is robust to expected real-world acoustic link misalignment.
Edit intent: meaning-changed
Original:  Example applications include the first constant-factor approximation algorithms whose running-time is polynomial in n for the following fundamental problems: (i) Constrained \ell_z Linear Regression: Given z>0, n vectors p_1,\cdots,p_n on the plane, and a vector b\in\mathbb{R^n, compute a unit vector x and a permutation \pi:
Revised:  Example applications include the first constant-factor approximation algorithms whose running-time is polynomial in n for the fundamental problem of p_1,\cdots,p_n on the plane, and a vector b\in\mathbb{R^n, compute a unit vector x and a permutation \pi:
Edit intent: clarity
Original:  Given z>0, n vectors p_1,\cdots,p_n on the plane, and a vector b\in\mathbb{R^n, compute a unit vector x and a permutation \pi:
Revised:  Given z>0, n vectorsPoints-to-Lines alignment p_1,\cdots,p_n on the plane, and a vector b\in\mathbb{R^n, compute a unit vector x and a permutation \pi:
Edit intent: meaning-changed
Original:  Given z>0, n vectors p_1,\cdots,p_n on the plane, and a vector b\in\mathbb{R^n, compute a unit vector x and a permutation \pi:
Revised:  Given z>0, n vectors: Given n points p_1,\cdots,p_n on the plane, and a vector b\in\mathbb{R^n, compute a unit vector x and a permutation \pi:
Edit intent: clarity
Original: }%DIFDELCMD < [%%% n%DIFDELCMD < ]\to[n] %%% that minimizes \sum_{i=1^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n }  lines \ell_1,\cdots,\ell_n on the plane  , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% ^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Revised: }%DIFDELCMD < [%%% %DIFDELCMD < ]\to[n] %%% that minimizes \sum_{i=1^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n } lines \ell_1,\cdots,\ell_n on the plane , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% ^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Edit intent: clarity
Original: }%DIFDELCMD < [%%% n%DIFDELCMD < ]\to[n] %%% that minimizes \sum_{i=1^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n }  lines \ell_1,\cdots,\ell_n on the plane  , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% ^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Revised: }%DIFDELCMD < [%%% n%DIFDELCMD < ]\to[n] %%% ^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n } lines \ell_1,\cdots,\ell_n on the plane , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% ^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Edit intent: clarity
Original: }%DIFDELCMD < [%%% n%DIFDELCMD < ]\to[n] %%% that minimizes \sum_{i=1^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n }  lines \ell_1,\cdots,\ell_n on the plane  , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% ^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Revised: }%DIFDELCMD < [%%% n%DIFDELCMD < ]\to[n] %%% that minimizes \sum_{i=1^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n } and n lines \ell_1,\cdots,\ell_n on the plane , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% ^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Edit intent: clarity
Original: }%DIFDELCMD < [%%% n%DIFDELCMD < ]\to[n] %%% that minimizes \sum_{i=1^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n }  lines \ell_1,\cdots,\ell_n on the plane  , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% ^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Revised: }%DIFDELCMD < [%%% n%DIFDELCMD < ]\to[n] %%% that minimizes \sum_{i=1^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n } lines \ell_1,\cdots,\ell_n on the plane and z>0 , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% ^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Edit intent: meaning-changed
Original: }%DIFDELCMD < [%%% n%DIFDELCMD < ]\to[n] %%% that minimizes \sum_{i=1^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n }  lines \ell_1,\cdots,\ell_n on the plane  , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% ^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Revised: }%DIFDELCMD < [%%% n%DIFDELCMD < ]\to[n] %%% that minimizes \sum_{i=1^n |p_ix-b_{\pi(i)}|^z. (ii) Points-to-Lines alignment: Given n } lines \ell_1,\cdots,\ell_n on the plane , compute the matching \pi:[n]\to[n] and alignment (rotation matrix R and a translation vector t) that minimize the sum of Euclidean distances %DIFDELCMD < \[ i=1^n dist(Rp_i-t,\pi(i))^z \] %%% \sum_{i=1^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z } between each point to its corresponding line.
Edit intent: fluency
Original:  In this case , the running time of our algorithms reduces to O(n ) using core-sets that support: streaming, dynamic, and distributed parallel computations (e.g. on the cloud) in poly-logarithmic update time.
Revised:  If \pi is given , the running time of our algorithms reduces to O(n ) using core-sets that support: streaming, dynamic, and distributed parallel computations (e.g. on the cloud) in poly-logarithmic update time.
Edit intent: meaning-changed
Original:  In this case , the running time of our algorithms reduces to O(n ) using core-sets that support: streaming, dynamic, and distributed parallel computations (e.g. on the cloud) in poly-logarithmic update time.
Revised:  In this case , the running time of our algorithms is O(n ) using core-sets that support: streaming, dynamic, and distributed parallel computations (e.g. on the cloud) in poly-logarithmic update time.
Edit intent: clarity
Original:  In this case , the running time of our algorithms reduces to O(n ) using core-sets that support: streaming, dynamic, and distributed parallel computations (e.g. on the cloud) in poly-logarithmic update time.
Revised:  In this case , the running time of our algorithms reduces to O(n ^3), and even near-linear in n using core-sets that support: streaming, dynamic, and distributed parallel computations (e.g. on the cloud) in poly-logarithmic update time.
Edit intent: meaning-changed
Original:  In this case , the running time of our algorithms reduces to O(n ) using core-sets that support: streaming, dynamic, and distributed parallel computations (e.g. on the cloud) in poly-logarithmic update time.
Revised:  In this case , the running time of our algorithms reduces to O(n ) using core-sets that support: streaming, dynamic, and distributed parallel computations in poly-logarithmic update time.
Edit intent: clarity
Original: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" ) domains .
Revised: Reconstituted nicotinic acetylcholine receptors ( nAChRs ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" ) domains .
Edit intent: fluency
Original: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" ) domains .
Revised: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and cholesterol URLanization of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" ) domains .
Edit intent: clarity
Original: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" ) domains .
Revised: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within domain-forming membranes, but whether nAChR partitions to cholesterol-rich liquid ordered ("raft" ) domains .
Edit intent: clarity
Original: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" ) domains .
Revised: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid-ordered ("raft" ) domains .
Edit intent: fluency
Original: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" ) domains .
Revised: Reconstituted nicotinic acetylcholine receptors ( nAChR ) exhibit significant gain-of-function upon addition of cholesterol to reconstitution mixtures, and experimentally observed clustering of nAChRs within membranes is cholesterol-sensitive, leading to a common expectation that nAChRs will partition into cholesterol-rich liquid ordered ("raft" or l_o) domains or cholesterol-poor liquid-disordered (l_{do .
Edit intent: meaning-changed
Original:  We use coarse-grained molecular dynamics simulations to observe spontaneous interactions of cholesterol, saturated lipids, and polyunsaturated  lipids with nAChRs.
Revised:  We use coarse-grained molecular dynamics simulations to observe spontaneous interactions of cholesterol, saturated lipids, and polyunsaturated (PUFA) lipids with nAChRs.
Edit intent: meaning-changed
Original:  In binary  DPPC:CHOL  mixtures, both cholesterol and DPPC acyl chains were observed spontaneously entering deep "non-annular" cavities in the nAChR TMD, particularly at the subunit interface and the \beta subunit center, facilitated by the low amino acid density in the cryo-EM structure of nAChR in a native membrane.
Revised:  In binary Dipalmitoylphosphatidylcholine:Cholesterol ( DPPC:CHOL mixtures, both cholesterol and DPPC acyl chains were observed spontaneously entering deep "non-annular" cavities in the nAChR TMD, particularly at the subunit interface and the \beta subunit center, facilitated by the low amino acid density in the cryo-EM structure of nAChR in a native membrane.
Edit intent: meaning-changed
Original:  In binary  DPPC:CHOL  mixtures, both cholesterol and DPPC acyl chains were observed spontaneously entering deep "non-annular" cavities in the nAChR TMD, particularly at the subunit interface and the \beta subunit center, facilitated by the low amino acid density in the cryo-EM structure of nAChR in a native membrane.
Revised:  In binary DPPC:CHOL ) mixtures, both cholesterol and DPPC acyl chains were observed spontaneously entering deep "non-annular" cavities in the nAChR TMD, particularly at the subunit interface and the \beta subunit center, facilitated by the low amino acid density in the cryo-EM structure of nAChR in a native membrane.
Edit intent: fluency
Original:  In binary  DPPC:CHOL  mixtures, both cholesterol and DPPC acyl chains were observed spontaneously entering deep "non-annular" cavities in the nAChR TMD, particularly at the subunit interface and the \beta subunit center, facilitated by the low amino acid density in the cryo-EM structure of nAChR in a native membrane.
Revised:  In binary DPPC:CHOL mixtures, both CHOL and DPPC acyl chains were observed spontaneously entering deep "non-annular" cavities in the nAChR TMD, particularly at the subunit interface and the \beta subunit center, facilitated by the low amino acid density in the cryo-EM structure of nAChR in a native membrane.
Edit intent: coherence
Original:  In  ternary mixtures containing polyunsaturated fatty acids , the presence of a single receptor did not significantly affect the likelihood of domain formation , and nAChR partitioned to the cholesterol-poor liquid-disordered domain whenever one was present.
Revised:  In domain-forming ternary mixtures containing polyunsaturated fatty acids , the presence of a single receptor did not significantly affect the likelihood of domain formation , and nAChR partitioned to the cholesterol-poor liquid-disordered domain whenever one was present.
Edit intent: clarity
Original:  In  ternary mixtures containing polyunsaturated fatty acids , the presence of a single receptor did not significantly affect the likelihood of domain formation , and nAChR partitioned to the cholesterol-poor liquid-disordered domain whenever one was present.
Revised:  In ternary mixtures containing PUFAs , the presence of a single receptor did not significantly affect the likelihood of domain formation , and nAChR partitioned to the cholesterol-poor liquid-disordered domain whenever one was present.
Edit intent: meaning-changed
Original:  In  ternary mixtures containing polyunsaturated fatty acids , the presence of a single receptor did not significantly affect the likelihood of domain formation , and nAChR partitioned to the cholesterol-poor liquid-disordered domain whenever one was present.
Revised:  In ternary mixtures containing polyunsaturated fatty acids , the presence of a single receptor did not significantly affect the likelihood of domain formation . nAChR partitioned to the cholesterol-poor liquid-disordered domain whenever one was present.
Edit intent: coherence