wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our analysis shows that Hi--C maps display a non-trivial multifractal spectrum.We introduce a simple analytical model that describes the structure of chromosomes as a hierarchical set of domains nested in each other and we solve it exactly.
Revised:  Our analysis shows that Hi--C maps are bifractal, i.e. they are complex geometrical objects characterized by two distinct fractal dimensions. To rationalize this observation, we introduce a model that describes the structure of chromosomes as a hierarchical set of domains nested in each other and we solve it exactly.
Edit intent: meaning-changed
Original:  Contact maps are characterized by self-similar blocks along the diagonal, corresponding to a hierarchy of tightly packed chromosomal domains. Statistical properties of these maps, and thus of chromosomal interactions, are usually summarized by the scaling of the contact probability as a function of the genomic distance. This approach is fraught with limitations, since such scaling is usually rather noisy. Here, we introduce a multifractal analysis of chromosomal contact maps.
Revised:  In this paper, we introduce a multifractal analysis of chromosomal contact maps.
Edit intent: coherence
Original: The performance of biomolecular molecular dynamics (MD) simulations has steadily increased on modern high performance computing (HPC) resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is increasingly becoming a bottleneck.
Revised: The performance of biomolecular molecular dynamics simulations has steadily increased on modern high performance computing (HPC) resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is increasingly becoming a bottleneck.
Edit intent: clarity
Original: The performance of biomolecular molecular dynamics (MD) simulations has steadily increased on modern high performance computing (HPC) resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is increasingly becoming a bottleneck.
Revised: The performance of biomolecular molecular dynamics (MD) simulations has steadily increased on modern high performance computing resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is increasingly becoming a bottleneck.
Edit intent: clarity
Original: The performance of biomolecular molecular dynamics (MD) simulations has steadily increased on modern high performance computing (HPC) resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is increasingly becoming a bottleneck.
Revised: The performance of biomolecular molecular dynamics (MD) simulations has steadily increased on modern high performance computing (HPC) resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is becoming a bottleneck.
Edit intent: clarity
Original:  We found that strong scaling performance was impeded by stragglers, MPI processes that were slower than the typical process and that therefore dominated the overall run time . Stragglers were less prevalent for compute-bound workloads, thus pointing to file reading as a crucial bottleneck for scaling.
Revised:  Strong scaling performance was impeded by stragglers, MPI processes that were slower than the typical process and that therefore dominated the overall run time . Stragglers were less prevalent for compute-bound workloads, thus pointing to file reading as a crucial bottleneck for scaling.
Edit intent: coherence
Original:  We found that strong scaling performance was impeded by stragglers, MPI processes that were slower than the typical process and that therefore dominated the overall run time . Stragglers were less prevalent for compute-bound workloads, thus pointing to file reading as a crucial bottleneck for scaling.
Revised:  We found that strong scaling performance was impeded by stragglers, MPI processes that were slower than the typical process . Stragglers were less prevalent for compute-bound workloads, thus pointing to file reading as a crucial bottleneck for scaling.
Edit intent: coherence
Original:  We found that strong scaling performance was impeded by stragglers, MPI processes that were slower than the typical process and that therefore dominated the overall run time . Stragglers were less prevalent for compute-bound workloads, thus pointing to file reading as a crucial bottleneck for scaling.
Revised:  We found that strong scaling performance was impeded by stragglers, MPI processes that were slower than the typical process and that therefore dominated the overall run time . Stragglers were less prevalent for compute-bound workloads, thus pointing to file reading as a bottleneck for scaling.
Edit intent: clarity
Original:  However, a more complicated picture emerged in which both the computation and the ingestion of data exhibited close to ideal strong scaling behavior whereas stragglers were primarily caused by either large MPI communication costs or long times to open the single shared trajectory file.
Revised:  However, a more complicated picture emerged in which both the computation and the data ingestion exhibited close to ideal strong scaling behavior whereas stragglers were primarily caused by either large MPI communication costs or long times to open the single shared trajectory file.
Edit intent: clarity
Original:  We improved overall strong scaling performance by two different approaches to file access, namely subfiling (splitting the trajectory into as many trajectory segments as number of processes) and MPI-IO with Parallel HDF5 trajectory files.
Revised:  We improved overall strong scaling performance by two different approaches to file access, namely subfiling (splitting the trajectory into separate files) or MPI-IO with Parallel HDF5 trajectory files.
Edit intent: meaning-changed
Original:  Applying these strategies, we obtained near ideal strong scaling on up to 384 cores (16 nodes) . We summarize our lessons-learned in guidelines and strategies on how to take advantage of the available HPC resources to gain good scalability and potentially reduce trajectory analysis times by two orders of magnitude compared to the prevalent serial approach.
Revised:  Applying these strategies, we obtained near ideal strong scaling on up to 384 cores (16 nodes) , thus reducing trajectory analysis times by two orders of magnitude compared to the prevalent serial approach.
Edit intent: coherence
Original:  We summarize our lessons-learned in guidelines and strategies on how to take advantage of the available HPC resources to gain good scalability and potentially reduce trajectory analysis times by two orders of magnitude compared to the prevalent serial approach.
Revised:  We summarize our lessons-learned in guidelines and strategies on how to take advantage of the available HPC resources to gain good scalability and potentially reduce trajectory analysis times by two orders of magnitude compared to the serial approach.
Edit intent: clarity
Original:  All products must fulfill the exclusion restriction, for the instrument to be valid. I propose applying methods which can preselect invalid products when either more than half or the largest group of products is valid. I discuss extensions of these methods for fixed effects models. I illustrate the procedures with three applications: a simulation study, the labor market effect of Chinese import competition and the effect of immigration to the US. My results help explain why previous studies have found low effects of immigration  .
Revised:  All products must fulfill the exclusion restriction, making the identifying assumption very strict. In this paper, I propose the use of machine learning methods that allow to substantially relax the exclusion restriction, by selecting invalid shares. I apply the methods to simulated data and the estimation of the effects of trade exposure and immigration on wages. After removing shares selected as endogenous, the positive effects of immigration .
Edit intent: meaning-changed
Original: Many economic studies use shift-share instruments to estimate causal effects of a treatment . Often, this type of instrument implies that all shares fulfill an exclusion restriction, making the identifying assumption very strict. In this paper , I propose the use of machine learning methods that allow to substantially relax the exclusion restriction , by selecting invalid shares.
Revised: Many economic studies use shift-share instruments to estimate causal effects of a treatment . Often, this type of instrument implies that all shares fulfill an exclusion restriction, making the identifying assumption strict. This paper proposes to use methods that relax the exclusion restriction , by selecting invalid shares.
Edit intent: clarity
Original:  I apply the methods to simulated data and the estimation of the effects of trade exposure and immigration on wages . After removing shares selected as endogenous, the positive effects of immigration decrease while the effects of Chinese import exposure are not affected .
Revised:  I apply the methods in simulations and two empirical examples: the effect of immigration on wages . After removing shares selected as endogenous, the positive effects of immigration decrease while the effects of Chinese import exposure are not affected .
Edit intent: clarity
Original:  I apply the methods to simulated data and the estimation of the effects of trade exposure and immigration on wages . After removing shares selected as endogenous, the positive effects of immigration decrease while the effects of Chinese import exposure are not affected .
Revised:  I apply the methods to simulated data and the estimation of the effects of trade exposure and immigration on wages . After removing shares selected as endogenous, the positive effects of immigration decrease while the effects of Chinese import exposure on employment. I find that weak instruments and strong violations of the exclusion restriction do not worsen the performance of the estimators. In both applications, the coefficients change considerably but this is reconcilable with the arguments made in the literature .
Edit intent: meaning-changed
Original:  In the two dimensional case our analysis shows that both the local and global shift of the front exhibit intermittent dynamics and follow the same scaling laws as in one dimension, apart from a logarithmic correction for global shifts. In one dimension, we show that determining the front position is related to an extremal problem of random walks, shedding further light on the origin of the jumps. Our results are in stark contrast to the behavior of the model without disorder, suggesting that current observations might misleadingly show little to no climate response for an extended period of time, concealing the long-term effects of climate change.
Revised:  Our results suggest that current observations might misleadingly show little to no climate response for an extended period of time, concealing the long-term effects of climate change.
Edit intent: coherence
Original: We report the first implementation of the Lattice Boltzmann method (LBM) to integrate the Bloch-Torrey equation, which describes the evolution of the transverse magnetization vector and the fate of the signal of diffusion magnetic resonance imaging (dMRI).
Revised: We report the first implementation of the lattice Boltzmann method (LBM) to integrate the Bloch-Torrey equation, which describes the evolution of the transverse magnetization vector and the fate of the signal of diffusion magnetic resonance imaging (dMRI).
Edit intent: fluency
Original:  Motivated by the need to interpret dMRI experiments on skeletal muscle , and to offset the small time step limitation of classical LBM, a hybrid LBM scheme is introduced and implemented to solve the Bloch-Torrey equation in a tissue model consisting of cylindrical inclusions (representing bundles of parallel myocytes), delineated by thin permeable membranes (sarcolemma) and surrounded by an extracellular domain (endomysium) .
Revised:  Motivated by the need to interpret dMRI experiments in biological tissues , and to offset the small time step limitation of classical LBM, a hybrid LBM scheme is introduced and implemented to solve the Bloch-Torrey equation in a tissue model consisting of cylindrical inclusions (representing bundles of parallel myocytes), delineated by thin permeable membranes (sarcolemma) and surrounded by an extracellular domain (endomysium) .
Edit intent: clarity
Original:  Motivated by the need to interpret dMRI experiments on skeletal muscle , and to offset the small time step limitation of classical LBM, a hybrid LBM scheme is introduced and implemented to solve the Bloch-Torrey equation in a tissue model consisting of cylindrical inclusions (representing bundles of parallel myocytes), delineated by thin permeable membranes (sarcolemma) and surrounded by an extracellular domain (endomysium) .
Revised:  Motivated by the need to interpret dMRI experiments on skeletal muscle , and to offset the small time step limitation of classical LBM, a hybrid LBM scheme is introduced and implemented to solve the Bloch-Torrey equation . A membrane boundary condition is presented which is able to accurately represent the effects of thin curvilinear membranes typically found in biological tissues .
Edit intent: clarity
Original:  As implemented, the hybrid LBM scheme accommodates piece-wise uniform transport, dMRI parameters, periodic  outer boundary conditions, and finite membrane permeabilities on non-boundary-conforming inner boundaries.
Revised:  As implemented, the hybrid LBM scheme accommodates piece-wise uniform transport, dMRI parameters, periodic and mirroring outer boundary conditions, and finite membrane permeabilities on non-boundary-conforming inner boundaries.
Edit intent: meaning-changed
Original:  The parallel implementation of the hybrid LBM code in a multi-CPU computer system with MPI is straightforward. For a domain decomposition algorithm on one million lattice nodes, the speedup is linear up to 168 MPI processes. While offering certain advantages over finite element or Monte Carlo schemes, the proposed hybrid LBM constitutes a sui generis scheme that can be easily adapted to model more complex interfacial conditions and physics in heterogeneous multiphase tissue models and to accommodate sophisticated dMRI sequences.
Revised:  The parallel implementation of the hybrid LBM code in a multi-CPU computer system , as well as on GPUs, is straightforward and efficient. Along with offering certain advantages over finite element or Monte Carlo schemes, the proposed hybrid LBM constitutes a sui generis scheme that can be easily adapted to model more complex interfacial conditions and physics in heterogeneous multiphase tissue models and to accommodate sophisticated dMRI sequences.
Edit intent: meaning-changed
Original:  While offering certain advantages over finite element or Monte Carlo schemes, the proposed hybrid LBM constitutes a sui generis scheme that can be easily adapted to model more complex interfacial conditions and physics in heterogeneous multiphase tissue models and to accommodate sophisticated dMRI sequences.
Revised:  While offering certain advantages over finite element or Monte Carlo schemes, the proposed hybrid LBM constitutes a flexible scheme that can be easily adapted to model more complex interfacial conditions and physics in heterogeneous multiphase tissue models and to accommodate sophisticated dMRI sequences.
Edit intent: clarity
Original:  While offering certain advantages over finite element or Monte Carlo schemes, the proposed hybrid LBM constitutes a sui generis scheme that can be easily adapted to model more complex interfacial conditions and physics in heterogeneous multiphase tissue models and to accommodate sophisticated dMRI sequences.
Revised:  While offering certain advantages over finite element or Monte Carlo schemes, the proposed hybrid LBM constitutes a sui generis scheme that can by easily adapted to model more complex interfacial conditions and physics in heterogeneous multiphase tissue models and to accommodate sophisticated dMRI sequences.
Edit intent: fluency
Original: The Algonauts challenge require to construct an multi-subject encoder of images to brain activity signals . Deep networks such as ResNet-50 and AlexNet trained for image classification are known to produce feature representations along their intermediate stages which closely mimic the visual hierarchy.
Revised: The Algonauts challenge requires to construct an multi-subject encoder of images to brain activity signals . Deep networks such as ResNet-50 and AlexNet trained for image classification are known to produce feature representations along their intermediate stages which closely mimic the visual hierarchy.
Edit intent: fluency
Original: The Algonauts challenge require to construct an multi-subject encoder of images to brain activity signals . Deep networks such as ResNet-50 and AlexNet trained for image classification are known to produce feature representations along their intermediate stages which closely mimic the visual hierarchy.
Revised: The Algonauts challenge require to construct an multi-subject encoder of images to brain activity . Deep networks such as ResNet-50 and AlexNet trained for image classification are known to produce feature representations along their intermediate stages which closely mimic the visual hierarchy.
Edit intent: clarity
Original:  However the challenges introduced in the Algonauts project, including combining data from multiple subjects, relying on very few similarity data points, solving for various ROIs, and multi-modality, require devising a flexible framework which can efficiently accommodate that .
Revised:  However the challenges introduced in the Algonauts project, including combining data from multiple subjects, relying on very few similarity data points, solving for various ROIs, and multi-modality, require devising a flexible framework which can efficiently accommodate them .
Edit intent: clarity
Original:  More precisely,  they set prices at which they agree to buy and sell the assets they cover . Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money out of their bid-ask spread while mitigating the risk associated with holding inventory in a volatile market.
Revised:  More precisely, for a list of assets, they set prices at which they agree to buy and sell the assets they cover . Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money out of their bid-ask spread while mitigating the risk associated with holding inventory in a volatile market.
Edit intent: meaning-changed
Original:  More precisely,  they set prices at which they agree to buy and sell the assets they cover . Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money out of their bid-ask spread while mitigating the risk associated with holding inventory in a volatile market.
Revised:  More precisely, they set prices at which they agree to buy and sell . Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money out of their bid-ask spread while mitigating the risk associated with holding inventory in a volatile market.
Edit intent: clarity
Original:  More precisely,  they set prices at which they agree to buy and sell the assets they cover . Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money out of their bid-ask spread while mitigating the risk associated with holding inventory in a volatile market.
Revised:  More precisely, they set prices at which they agree to buy and sell the assets they cover . Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money while mitigating the risk associated with holding inventory in a volatile market.
Edit intent: clarity
Original:  Moreover, we generalize existing market making models by the addition of an important feature for OTC markets: the variability of transaction sizes and the possibility for the market maker to answer different prices to requests with different sizes.
Revised:  Moreover, we generalize existing market making models by the addition of an important feature for OTC markets: the variability of transaction sizes and the possibility for the market makers in OTC markets to answer different prices to requests with different sizes.
Edit intent: clarity
Original:  We  also show that the VC-dimension is unbounded for pairwise disjoint convex sets in \mathbb{R ^d , for d \geq 3.
Revised:  We provide a quadratic lower bound in the number of pairs of intersecting sets in a shattered family of convex sets in the plane. We also show that the VC-dimension is unbounded for pairwise disjoint convex sets in \mathbb{R ^d , for d \geq 3.
Edit intent: meaning-changed
Original: This paper is concerned with a two-fold objective. Firstly, we establish elicitability and identifiability results for systemic risk measures introduced in Feinstein, Rudloff and Weber (2017 ).
Revised: Identification and scoring functions are statistical tools to assess the calibration and the relative performance of risk measure estimates, e.g., in backtesting. A risk measures is called identifiable (elicitable) it it admits a strict identification function (strictly consistent scoring function). We consider measures of systemic risk introduced in Feinstein, Rudloff and Weber (2017 ).
Edit intent: coherence
Original:  Firstly, we establish elicitability and identifiability results for systemic risk measures introduced in Feinstein, Rudloff and Weber (2017 ). Specifying the entire set of capital allocations adequate to render a financial system acceptable, these systemic risk measures are examples of set-valued functionals.
Revised:  Firstly, we establish elicitability and identifiability results for systemic risk measures introduced in Feinstein, Rudloff and Weber (2017 ). Since these are set-valued functionals.
Edit intent: coherence
Original:  Specifying the entire set of capital allocations adequate to render a financial system acceptable, these systemic risk measures are examples of set-valued functionals.A functional is elicitable (identifiable ) if it is the unique minimiser (zero) of an expected scoring function(identification function). Elicitability and identifiability are essential for forecast ranking and validation, M- and Z-estimation, both possibly in a regression framework. To account for the set-valued nature of the systemic risk measures mentioned above, we secondly introduce a theoretical framework of elicitability and identifiability of set-valued functionals.
Revised:  Specifying the entire set of capital allocations adequate to render a financial system acceptable, these systemic risk measures are examples of set-valued , we work within the theoretical framework of elicitability and identifiability of set-valued functionals.
Edit intent: coherence
Original:  To account for the set-valued nature of the systemic risk measures mentioned above, we secondly introduce a theoretical framework of elicitability and identifiability of set-valued functionals.
Revised:  To account for the set-valued nature of the systemic risk measures mentioned above, we secondly introduce a theoretical framework of Fissler, Hlavinov\'a and Rudloff (2019) for forecast evaluation of set-valued functionals.
Edit intent: meaning-changed