wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s  on a core area of only 1.33 MGE  and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W  and 1135 GOp/s/MGE  , respectively.
Revised:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s at 1.2 V on a core area of only 1.33 MGE and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W and 1135 GOp/s/MGE , respectively.
Edit intent: fluency
Original:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s  on a core area of only 1.33 MGE  and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W  and 1135 GOp/s/MGE  , respectively.
Revised:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s on a core area of only 1.33 MGE and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator significantly outperforms the state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W and 1135 GOp/s/MGE , respectively.
Edit intent: clarity
Original:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s  on a core area of only 1.33 MGE  and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W  and 1135 GOp/s/MGE  , respectively.
Revised:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s on a core area of only 1.33 MGE and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W and 1135 GOp/s/MGE , respectively.
Edit intent: clarity
Original:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s  on a core area of only 1.33 MGE  and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W  and 1135 GOp/s/MGE  , respectively.
Revised:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s on a core area of only 1.33 MGE and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of energy and area efficiency achieving 61.2 TOp/s/W and 1135 GOp/s/MGE , respectively.
Edit intent: clarity
Original:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s  on a core area of only 1.33 MGE  and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W  and 1135 GOp/s/MGE  , respectively.
Revised:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s on a core area of only 1.33 MGE and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W and 1135 GOp/s/MGE @1.2 V , respectively.
Edit intent: clarity
Original:  Recent developments in CNN accelerators for system-on-chip integration have achieved very promising energy reduction .
Revised:  Recent developments in CNN accelerators for system-on-chip integration have reduced energy consumption significantly .
Edit intent: clarity
Original:  This prevents the adoption of CNNs in future ultra-low power internet of things end-nodes for near-sensor analytics.
Revised:  This prevents the adoption of CNNs in future ultra-low power Internet of Things end-nodes for near-sensor analytics.
Edit intent: fluency
Original:  These new findings bring major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as in the I/O and weights storagecosts .
Revised:  These new findings bring major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as reducing I/O and weights storagecosts .
Edit intent: clarity
Original:  These new findings bring major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as in the I/O and weights storagecosts .
Revised:  These new findings bring major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as in the I/O bandwidth and storage .
Edit intent: clarity
Original:  In this work, we present an accelerator optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm2 and with a power dissipation of 895 uW  in UMC 65 nm technology at 0.6 V.
Revised:  In this work, we present an accelerator optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm^2 and with a power dissipation of 895 uW in UMC 65 nm technology at 0.6 V.
Edit intent: clarity
Original:  In this work, we present an accelerator optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm2 and with a power dissipation of 895 uW  in UMC 65 nm technology at 0.6 V.
Revised:  In this work, we present an accelerator optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm2 and with a power dissipation of 895 \mu in UMC 65 nm technology at 0.6 V.
Edit intent: clarity
Original:  In this work, we present an accelerator optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm2 and with a power dissipation of 895 uW  in UMC 65 nm technology at 0.6 V.
Revised:  In this work, we present an accelerator optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm2 and with a power dissipation of 895 uW W in UMC 65 nm technology at 0.6 V.
Edit intent: fluency
Original:  Such gradient schemes may also have standalone uses, e.g. in post-processing .
Revised:  Such gradient schemes may also have standalone uses, e.g. \ in postprocessing .
Edit intent: fluency
Original: Finite volume methods, especially methods for complex geometries which employ curvilinear or unstructured grids, often make significant use of numerical schemes for computing the gradients of the dependent variables.
Revised: The discretisation of the dependent variables.
Edit intent: clarity
Original:  The most popular such schemes rely on the divergence theorem and on least squares minimisation.
Revised:  The most popular gradient schemes are the divergence theorem and on least squares minimisation.
Edit intent: clarity
Original:  These results are reproduced also with the popular public domain PDE solver OpenFOAM, which uses the Green-Gauss gradient scheme by default.
Revised:  These results are reproduced also with the popular public domain PDE solver OpenFOAM, which uses the divergence theorem gradient by default.
Edit intent: clarity
Original: The discretisation of the gradient operator is an important aspect of finite volume methods  that has not received as much attention as the discretisation of other terms of partial differential equations.
Revised: The discretisation of the gradient operator is an important aspect of finite volume methods (FVMs) that has not received as much attention as the discretisation of other terms of partial differential equations.
Edit intent: clarity
Original:  The most popular gradient schemes are the divergence theorem (  or Green-Gauss ) scheme, and the least-squares  scheme.
Revised:  The most popular gradient schemes are the divergence theorem ( or Green-Gauss , scheme, and the least-squares scheme.
Edit intent: fluency
Original:  The most popular gradient schemes are the divergence theorem (  or Green-Gauss ) scheme, and the least-squares  scheme.
Revised:  The most popular gradient schemes are the divergence theorem ( or Green-Gauss ) scheme, and the least-squares (LS) scheme.
Edit intent: clarity
Original:  Both schemes are generally believed to be second-order accurate, but the present study shows that in fact the divergence theorem gradient is second-order accurate only on structured meshes whereas it is zeroth-order accurate on general unstructured meshes, and the least-squares gradient is second-order and first-order accurate, respectively.
Revised:  Both are generally believed to be second-order accurate, but the present study shows that in fact the divergence theorem gradient is second-order accurate only on structured meshes whereas it is zeroth-order accurate on general unstructured meshes, and the least-squares gradient is second-order and first-order accurate, respectively.
Edit intent: clarity
Original:  Both schemes are generally believed to be second-order accurate, but the present study shows that in fact the divergence theorem gradient is second-order accurate only on structured meshes whereas it is zeroth-order accurate on general unstructured meshes, and the least-squares gradient is second-order and first-order accurate, respectively.
Revised:  Both schemes are generally believed to be second-order accurate, but the present study shows that in fact the divergence theorem gradient is second-order accurate only on structured meshes whereas it is zeroth-order accurate on general unstructured meshes, and the LS gradient is second-order and first-order accurate, respectively.
Edit intent: clarity
Original:  Furthermore, the schemes are used within a finite volume method to solve a simple diffusion equation on unstructured grids generated by several methods;
Revised:  Furthermore, the schemes are used within a FVM to solve a simple diffusion equation on unstructured grids generated by several methods;
Edit intent: clarity
Original:  the results reveal that the zeroth-order accuracy of the divergence theorem gradient scheme is inherited by the finite volume method as a whole, and the discretisation error does not decrease with grid refinement.
Revised:  the results reveal that the zeroth-order accuracy of the DT gradient is inherited by the finite volume method as a whole, and the discretisation error does not decrease with grid refinement.
Edit intent: clarity
Original:  the results reveal that the zeroth-order accuracy of the divergence theorem gradient scheme is inherited by the finite volume method as a whole, and the discretisation error does not decrease with grid refinement.
Revised:  the results reveal that the zeroth-order accuracy of the divergence theorem gradient scheme is inherited by the FVM as a whole, and the discretisation error does not decrease with grid refinement.
Edit intent: clarity
Original: The discretisation of the gradient operator is an important aspect of finite volume methods (FVMs) that has not received as much attention as the discretisation of other terms of partial differential equations .
Revised: Finite volume methods (FVMs) that has not received as much attention as the discretisation of other terms of partial differential equations .
Edit intent: clarity
Original:  The most popular gradient schemes are the divergence theorem (DT) , or Green-Gauss , scheme, and the least-squares (LS) scheme.
Revised:  The most popular gradient schemes are the divergence theorem (DT) ( or Green-Gauss , scheme, and the least-squares (LS) scheme.
Edit intent: fluency
Original:  The most popular gradient schemes are the divergence theorem (DT) , or Green-Gauss , scheme, and the least-squares (LS) scheme.
Revised:  The most popular gradient schemes are the divergence theorem (DT) , or Green-Gauss ) scheme, and the least-squares (LS) scheme.
Edit intent: fluency
Original:  Both are generally believed to be second-order accurate, but the present study shows that in fact the common variant of the DT gradient is second-order accurate only on structured meshes whereas it is zeroth-order accurate on general unstructured meshes, and the LS gradient is second-order and first-order accurate, respectively.
Revised:  Both are widely believed to be second-order accurate, but the present study shows that in fact the common variant of the DT gradient is second-order accurate only on structured meshes whereas it is zeroth-order accurate on general unstructured meshes, and the LS gradient is second-order and first-order accurate, respectively.
Edit intent: clarity
Original:  Furthermore, the schemes are used within a FVM to solve a simple diffusion equation on unstructured grids generated by several methods;
Revised:  The schemes are then used within a FVM to solve a simple diffusion equation on unstructured grids generated by several methods;
Edit intent: coherence
Original:  The numerical tests are performed using both an in-house code and the popular public domain PDE solver OpenFOAM , which uses the DT gradient by default .
Revised:  The numerical tests are performed using both an in-house code and the popular public domain PDE solver OpenFOAM .
Edit intent: coherence
Original:  In this paper, we continue the study of P^QMA[log], obtaining the following results : (1) The P^QMA[log]-completeness result of [Ambainis, CCC 2014] above requires O(log(n))-local Hamiltonians and O(log(n))-local observables .
Revised:  In this paper, we continue the study of P^QMA[log], obtaining the following results . (1) The P^QMA[log]-completeness result of [Ambainis, CCC 2014] above requires O(log(n))-local Hamiltonians and O(log(n))-local observables .
Edit intent: fluency
Original:  In this paper, we continue the study of P^QMA[log], obtaining the following results : (1) The P^QMA[log]-completeness result of [Ambainis, CCC 2014] above requires O(log(n))-local Hamiltonians and O(log(n))-local observables .
Revised:  In this paper, we continue the study of P^QMA[log], obtaining the following results : (1) The P^QMA[log]-completeness result of [Ambainis, CCC 2014] requires O(log(n))-local Hamiltonians and O(log(n))-local observables .
Edit intent: coherence
Original:  We give a formal justification of this intuition by exploiting the technique of hierarchical voting of [Beigel, Hemachandra, and Wechsung, SCT 1989] to show P^QMA[log] is in PP.
Revised:  We justify this formally by exploiting the technique of hierarchical voting of [Beigel, Hemachandra, and Wechsung, SCT 1989] to show P^QMA[log] is in PP.
Edit intent: clarity
Original:  We give a formal justification of this intuition by exploiting the technique of hierarchical voting of [Beigel, Hemachandra, and Wechsung, SCT 1989] to show P^QMA[log] is in PP.
Revised:  We give a formal justification of this intuition by exploiting the hierarchical voting technique of [Beigel, Hemachandra, and Wechsung, SCT 1989] to show P^QMA[log] is in PP.
Edit intent: clarity
Original:  We give a formal justification of this intuition by exploiting the technique of hierarchical voting of [Beigel, Hemachandra, and Wechsung, SCT 1989] to show P^QMA[log] is in PP.
Revised:  We give a formal justification of this intuition by exploiting the technique of hierarchical voting of [Beigel, Hemachandra, Wechsung, SCT 1989] to show P^QMA[log] is in PP.
Edit intent: clarity
Original:  This improves the known containment that QMA is in PP [Kitaev, Watrous, STOC 2000].
Revised:  This improves the containment QMA is in PP [Kitaev, Watrous, STOC 2000].
Edit intent: clarity
Original:  ( 1) The P^QMA[log]-completeness result of [Ambainis, CCC 2014] requires O(log (n) )-local observables and Hamiltonians.
Revised:  ( 1) The P^QMA[log]-completeness result of [Ambainis, CCC 2014] requires O(log n )-local observables and Hamiltonians.
Edit intent: fluency
Original:  (4) A central theme of this work is the subtlety involved in the study of oracle classes in which the oracle solves a promise problem.
Revised:  This work contributes a rigorous treatment of the subtlety involved in the study of oracle classes in which the oracle solves a promise problem.
Edit intent: clarity
Original:  (4) A central theme of this work is the subtlety involved in the study of oracle classes in which the oracle solves a promise problem.
Revised:  (4) A central theme of this work is the subtlety involved in studying oracle classes in which the oracle solves a promise problem.
Edit intent: fluency
Original:  In this vein, we identify a flaw in Ambainis, CCC 2014%DIFDELCMD < ] %%% regarding a P^UQMA log%DIFDELCMD < ]%%% -hardness proof for estimating spectral gaps of local Hamiltonians.
Revised:  Ambainis, CCC 2014%DIFDELCMD < ] %%% regarding a P^UQMA log%DIFDELCMD < ]%%% -hardness proof for estimating spectral gaps of local Hamiltonians.
Edit intent: coherence
Original:  In this vein, we identify a flaw in Ambainis, CCC 2014%DIFDELCMD < ] %%% regarding a P^UQMA log%DIFDELCMD < ]%%% -hardness proof for estimating spectral gaps of local Hamiltonians.
Revised:  In this vein, we identify a flaw in Ambainis, CCC 2014%DIFDELCMD < ] %%% log%DIFDELCMD < ]%%% -hardness proof for estimating spectral gaps of local Hamiltonians.
Edit intent: coherence
Original:  By introducing a "query validation" technique, we build on Ambainis, CCC 2014%DIFDELCMD < ] %%% to obtain P^UQMA log%DIFDELCMD < ]%%% -hardness for estimating spectral gaps under polynomial-time Turing reductions .
Revised:  By introducing a "query validation" technique, we build on Ambainis, CCC 2014%DIFDELCMD < ] %%% log%DIFDELCMD < ]%%% -hardness for estimating spectral gaps under polynomial-time Turing reductions .
Edit intent: clarity
Original:  By introducing a "query validation" technique, we build on Ambainis, CCC 2014%DIFDELCMD < ] %%% to obtain P^UQMA log%DIFDELCMD < ]%%% -hardness for estimating spectral gaps under polynomial-time Turing reductions .
Revised:  By introducing a "query validation" technique, we build on Ambainis, CCC 2014%DIFDELCMD < ] %%% to obtain P^UQMA %DIFDELCMD < ]%%% -hardness for estimating spectral gaps under polynomial-time Turing reductions .
Edit intent: clarity
Original:  The ordered Ramsey number OR(G,< ) is the minimum number N such that every  ordered complete graph with N vertices and with edges colored by two colors contains a monochromatic copy of (G,<) .
Revised:  The ordered Ramsey number OR(G,< ) is the minimum number N such that every ordered complete graph on N vertices contains a monochromatic copy of (G,<) .
Edit intent: clarity
Original:  We show that for every integer d >2 , almost every d-regular graph G satisfies OR(G,<) >= n^{3/2-1/d) \geq \frac{n^{3/2-1/d}}{4\log{n}\log{\log{n}}} } for every ordering (G,<) of G. In particular, there are 3-regular graphs G on n vertices for which the numbers OR(G,< \overline{R} ) are superlinear in n, regardless of the ordering  of G. This solves a problem of Conlon, Fox, Lee, and Sudakov.
Revised:  We show that for every integer d \geq 3 , almost every d-regular graph G satisfies OR(G,<) >= n^{3/2-1/d) \geq \frac{n^{3/2-1/d}}{4\log{n}\log{\log{n}}} } for every ordering (G,<) of G. In particular, there are 3-regular graphs G on n vertices for which the numbers OR(G,< \overline{R} ) are superlinear in n, regardless of the ordering of G. This solves a problem of Conlon, Fox, Lee, and Sudakov.
Edit intent: clarity
Original:  We show that for every integer d >2 , almost every d-regular graph G satisfies OR(G,<) >= n^{3/2-1/d) \geq \frac{n^{3/2-1/d}}{4\log{n}\log{\log{n}}} } for every ordering (G,<) of G. In particular, there are 3-regular graphs G on n vertices for which the numbers OR(G,< \overline{R} ) are superlinear in n, regardless of the ordering  of G. This solves a problem of Conlon, Fox, Lee, and Sudakov.
Revised:  We show that for every integer d >2 , almost every d-regular graph G satisfies OR(G,<) >= n^{3/2-1/d) \geq \frac{n^{3/2-1/d}}{4\log{n}\log{\log{n}}} } for every ordering (G,<) of G. In particular, there are 3-regular graphs G on n vertices for which the numbers \overline{R} ) are superlinear in n, regardless of the ordering of G. This solves a problem of Conlon, Fox, Lee, and Sudakov.
Edit intent: fluency
Original:  We also show that almost every ordered matching (M,<) with n vertices and with interval chromatic number two satisfies OR(M,<) >= \overline{R}) \geq } cn^2/ log^2(n) for some absolute constant c.
Revised:  We also show that almost every ordered matching (M,<) with n vertices and with interval chromatic number two satisfies \overline{R}) \geq } cn^2/ log^2(n) for some absolute constant c.
Edit intent: clarity
Original:  We also show that almost every ordered matching (M,<) with n vertices and with interval chromatic number two satisfies OR(M,<) >= \overline{R}) \geq } cn^2/ log^2(n) for some absolute constant c.
Revised:  We also show that almost every ordered matching (M,<) with n vertices and with interval chromatic number two satisfies OR(M,<) >= \overline{R}) \geq } cn^2/ \log^2{n for some absolute constant c.
Edit intent: clarity
Original: Surprise is a ubiquitous concept describing a wide range of phenomena from unexpected events to behavioral responses.
Revised: Surprise describes a range of phenomena from unexpected events to behavioral responses.
Edit intent: clarity
Original:  We propose a measure of surprise , to arrive at a new framework for surprise-driven learning.
Revised:  We propose a measure of surprise and use it for surprise-driven learning.
Edit intent: clarity
Original:  There are two components to this framework: (i) a confidence-adjusted surprise measure to capture environmental statistics as well as subjective beliefs, (ii) a surprise-minimization learning rule, or SMiLe-rule, which dynamically adjusts the balance between new and old information without making prior assumptions about the temporal statistics in the environment.
Revised:  Our surprise measure takes into account data likelihood as well as subjective beliefs, (ii) a surprise-minimization learning rule, or SMiLe-rule, which dynamically adjusts the balance between new and old information without making prior assumptions about the temporal statistics in the environment.
Edit intent: clarity
Original:  There are two components to this framework: (i) a confidence-adjusted surprise measure to capture environmental statistics as well as subjective beliefs, (ii) a surprise-minimization learning rule, or SMiLe-rule, which dynamically adjusts the balance between new and old information without making prior assumptions about the temporal statistics in the environment.
Revised:  There are two components to this framework: (i) a confidence-adjusted surprise measure to capture environmental statistics as well as subjective beliefs, (ii) a surprise-minimization learning rule, or SMiLe-rule, which dynamically adjusts the balance between new and old information without the need of knowledge about the temporal statistics in the environment.
Edit intent: clarity