wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The time required for the neuron to charge and for the neuron to recover to the resting potential are numerically fitted to functions of the Izhikevich neuron model parameters and the  input current.
Revised:  The time required for the neuron to charge and for the neuron to recover to the resting potential are numerically fitted to functions of the Izhikevich neuron model parameters and the peak input current.
Edit intent: meaning-changed
Original:  We demonstrated the scaling of FFN training up to 1024 Intel Knights Landing (KNL) nodes at Argonne Leadership Computing Facility. We investigated the training accuracy with different optimizers, learning rates, and optional warm-up periods. We discovered that square root scaling for learning rate works best beyond 16 nodes, which is contrary to the case of smaller number of nodes, where linear learning rate scaling with warm-up performs the best. Our distributed training reaches 95\% accuracy in approximately 4.5 hours on 1024 KNL nodes using Adam optimizer .
Revised:  We demonstrated the scaling of FFN training up to 1024 Intel Knights Landing (KNL) nodes on the Theta supercomputer. Our trained models achieved similar level of inference performance but took less training time compared to previous methods. Our study on the effect of batch size on the FFN training suggests ways to further improve training efficiency. Our findings on the optimal learning rate and batch size agree with previous works .
Edit intent: clarity
Original:  In order to reduce the training time, we implemented synchronous and data-parallel distributed training using the Horovod framework , which is different from the asynchronous training scheme used in the published FFN code.
Revised:  In order to reduce the training time, we implemented synchronous and data-parallel distributed training using the Horovod library , which is different from the asynchronous training scheme used in the published FFN code.
Edit intent: clarity
Original: We consider an optimal stopping problem of a d-dimensional Brownian motion, where the payoff at stopping is the maximum component of the Brownian motion, and there is a running cost before stopping. Applications include choosing one among several alternatives while learning simultaneously about all the alternatives (parallel search), and exercising an option based on several assets.
Revised: We consider an optimal stopping problem of a d-dimensional Brownian motion, where the payoff at stopping is the maximum component of the Brownian motion, and there is a running cost of searching for information, and has to decide when to stop searching for information and choose one alternative. The expected payoff of each alternative evolves as a diffusion process when information is being learned.
Edit intent: clarity
Original: Employment gratuity is the money companies typically give to their employees at the end of their contracts as a legal requirement. Like pension, it is a form of retirement plan and is often given as an alternative to a pension plan.
Revised: Employment gratuity is the money companies typically give to their employees at the end of their contracts or at the time of leaving the company. It is a defined benefit plan and is often given as an alternative to a pension plan.
Edit intent: clarity
Original:  This paper sheds light on this question , highlighting the need for analytically informed decisions. It quantifies the economic benefits of the tax relief provided by government relative to investing the monthly-received funds in a risk-free savings account or in helping repay a loan.
Revised:  This paper sheds light on this question by quantifying the economic benefits of the tax relief provided by government relative to investing the monthly-received funds in a risk-free savings account or in helping repay a loan.
Edit intent: coherence
Original:  However, because these models have often very different assumptions , sometimes arbitrary, it is difficult to evaluate what are the real key factors that determine food webs evolution, and whether these models' results are robust or not .
Revised:  However, these models generally make strong arbitrary assumptions on how traits evolve, casting doubts on their robustness. In particular, biomass conversion efficiency is always considered independent of the predator and prey size, which contradicts with the literature .
Edit intent: meaning-changed
Original:  Second, we propose a general model which encompasses all previous models . We show that our model recovers all previous models' results under identical assumptions. However, most importantly, we also show that, when relaxing some of their hypotheses, previous models give rise to degenerate food webs.
Revised:  Second, we propose a general model encompassing most previous models which allows to show that relaxing arbitrary assumptions gives rise to unrealistic food webs.
Edit intent: clarity
Original:  Third, we show that the assumptions made regarding the form of biomass conversion efficiency are key for food webs evolution, a parameter which was neglected in previous models. We conclude by discussing the implication of biomass conversion efficiency, and by questioning the relevance of such models to study the evolution of food webs.
Revised:  Third, we show that the assumptions made regarding the form of biomass conversion efficiency are key for food webs adaptive evolution because realistic food webs can evolve, making obsolete the need of arbitrary constraints on traits' evolution. We finally conclude that, on the one hand, ecologists should pay attention to how biomass flows into food webs in models. On the other hand, we question more generally the robustness of evolutionary models for the study of food webs.
Edit intent: meaning-changed
Original:  Near thermodynamic equilibrium, response is completely determined by the nature of spontaneous fluctuations, according to the fluctuation-dissipation theorem (FDT). This deep connection forms the basis of powerful experimental techniques, but also implies that highly-responsive equilibrium devices are always plagued by noise .
Revised:  Near thermodynamic equilibrium, response is completely determined by the nature of spontaneous fluctuations, according to the fluctuation-dissipation theorem provides a powerful theoretical and experimental tool to determine the nature of response by observing spontaneous equilibrium fluctuations .
Edit intent: clarity
Original:  In this work, we present equalities and inequalities--akin to the FDT but valid arbitrarily far from equilibrium--that link response to the strength of nonequilibrium driving.
Revised:  In this work, we present equalities and inequalities--akin to the FDT but valid arbitrarily far from equilibrium that constrain the response of nonequilibrium steady states in terms of the strength of nonequilibrium driving.
Edit intent: clarity
Original: The form of genotype-phenotype maps are typically modular. However, the form of the genotype-phenotype map in human brain connectivity is unknown. A modular mapping could exist, in which distinct sets of genes' coexpression across brain regions is similar to distinct brain regions' functional or structural connectivity, and in which single nucleotide polymorphisms (SNPs) at distinct sets of genes alter distinct brainregions'functional or structural connectivity .
Revised: For much of biology, the manner in which genotype maps to phenotype remains a fundamental mystery. The few maps that are known tend to show modular pleiotropy: sets of phenotypes are determined by distinct sets of genes alter distinct brainregions'functional or structural connectivity .
Edit intent: clarity
Original:  A modular mapping could exist, in which distinct sets of genes' coexpression across brain regions is similar to distinct brain regions' functional or structural connectivity, and in which single nucleotide polymorphisms (SNPs) at distinct sets of genes alter distinct brainregions'functional or structural connectivity .
Revised:  A modular mapping could exist, in which distinct sets of genes' coexpression across brain regions is similar to distinct brain regions' functional or structural connectivity, and in which single nucleotide polymorphisms (SNPs) at distinct sets of genes . One key map that has evaded discovery is that of the human brain's network architecture .
Edit intent: clarity
Original:  Here, we leverage multimodal human neuroimaging, genotype, and post-mortem gene expression datasets to determine the form of the genotype-phenotype map of human brain connectivity. Across multiple analytic approaches, we find that both gene coexpression and SNPs are consistently more strongly related to functional brain connectivity than to structural brain connectivity.
Revised:  Here, we leverage multimodal human neuroimaging, genotype, and post-mortem gene expression datasets to determine the form of this map for gene coexpression and SNPs are consistently more strongly related to functional brain connectivity than to structural brain connectivity.
Edit intent: clarity
Original:  Across multiple analytic approaches, we find that both gene coexpression and SNPs are consistently more strongly related to functional brain connectivity than to structural brain connectivity. Critically, network analyses of genes and brain connectivity demonstrate that different sets of genes account for the connectivity of different regions in the brain .
Revised:  Across multiple analytic approaches, we find that both gene coexpression and single nucleotide polymorphisms. We discover that mostly non-overlapping sets of genes account for the connectivity of different regions in the brain .
Edit intent: clarity
Original:  Critically, network analyses of genes and brain connectivity demonstrate that different sets of genes account for the connectivity of different regions in the brain . Moreover, the genetic signature of each brain region reflects its community affiliation and role in network communication. Connector hubs have genetic signatures that are similarly connector-like, in that they are representative of the genetic signature of nodes in multiple other modules. Remarkably, we find a tight relationship between gene coexpression and genetic variance: SNPs that are located at the genes whose coexpression is similar to a region's connectivity across cortex tend to predict more variance in that region's connectivity across subjects than SNPs at other genes . Finally, brain regions whose connectivity are well explained by gene coexpression and genetic variance (SNPs) also display connectivity variance across subjects that tracks variance in human performance on cognitively demanding tasks, heritability, development, and evolutionary expansion .
Revised:  Critically, network analyses of genes and brain connectivity demonstrate that different sets of genes account for the connectivity of brain network modules (or so-called communities), suggesting that brain network communities demarcate genetic transitions. We find that these clean boundaries break down at connector hubs, whose integrative connectivity is encoded by pleiotropic genes from mostly non-overlapping sets. Broadly, this study opens fundamentally new directions in the study of genetic encoding of brain development, evolution, and disease .
Edit intent: clarity
Original:  However, privacy concerns require the strict protection of a user's data which leads to a dilemma between pursuing better intelligent services and privacy preservation. To solve this dilemma, this paper develops a decentralized learning framework to enable proactive social care equipped with artificial intelligence and data protection.
Revised:  However, privacy concerns require the strict protection of a user's data become an isolated island and limits the predictive performance of standalone clients. To enable effective proactive social care equipped with artificial intelligence and data protection.
Edit intent: meaning-changed
Original:  To solve this dilemma, this paper develops a decentralized learning framework to enable proactive social care equipped with artificial intelligence and data protection. The proposed learning framework trains a secure local model for each user using their own datasets. Each user's device only shares its local model's parameters with the central model server without exposing any data, and the server integrates multiple users' models to learn a global data-free model .
Revised:  To solve this dilemma, this paper develops a decentralized learning framework to enable proactive social care and the server integrates multiple users' models to learn a global data-free model .
Edit intent: coherence
Original:  Each user's device only shares its local model's parameters with the central model server without exposing any data, and the server integrates multiple users' models to learn a global data-free model .
Revised:  Each user's device only shares its local model's parameters with the central model server without exposing any data, and knowledge sharing within intelligent agents, this paper develops a knowledge transferring framework via model aggregation. Under this framework, distributed clients perform on-device training, and a third-party server integrates multiple users' models to learn a global data-free model .
Edit intent: meaning-changed
Original:  In particular, to evaluate the effectiveness of the learning algorithm, we use a case study on the early detection and prevention of suicidal ideation, and the experiment results on four datasets derived from social media demonstrate the effectiveness of the proposed learning method.
Revised:  In particular, to evaluate the effectiveness of the learning algorithm, we use a case study on the early detection and prevention of suicidal ideation, and the experiment results on four datasets derived from social communities demonstrate the effectiveness of the proposed learning method.
Edit intent: clarity
Original:  This allows us to connect the previous control problem to a  quadratic BSDE with jumps that is new to the literature , in which the driver term has non-standard structures and carries the conditional filter as an infinite-dimensional parameter.
Revised:  This allows us to connect the control problem to a quadratic BSDE with jumps that is new to the literature , in which the driver term has non-standard structures and carries the conditional filter as an infinite-dimensional parameter.
Edit intent: clarity
Original:  This allows us to connect the previous control problem to a  quadratic BSDE with jumps that is new to the literature , in which the driver term has non-standard structures and carries the conditional filter as an infinite-dimensional parameter.
Revised:  This allows us to connect the previous control problem to a new type of quadratic BSDE with jumps that is new to the literature , in which the driver term has non-standard structures and carries the conditional filter as an infinite-dimensional parameter.
Edit intent: clarity
Original:  This allows us to connect the previous control problem to a  quadratic BSDE with jumps that is new to the literature , in which the driver term has non-standard structures and carries the conditional filter as an infinite-dimensional parameter.
Revised:  This allows us to connect the previous control problem to a quadratic BSDE with jumps , in which the driver term has non-standard structures and carries the conditional filter as an infinite-dimensional parameter.
Edit intent: clarity
Original:  By proposing some novel truncation techniques , we obtain the existence of  solution to this new BSDE using the delicate convergence of solutions associated to some truncated BSDEs.
Revised:  By proposing some truncation techniques and establishing a uniform a priori estimates , we obtain the existence of solution to this new BSDE using the delicate convergence of solutions associated to some truncated BSDEs.
Edit intent: meaning-changed
Original:  By proposing some truncation techniques and establishing a uniform a priori estimates, we obtain the existence of a solution to this BSDE using the convergence of solutions associated to some truncated BSDEs.
Revised:  By proposing some truncation techniques and establishing a uniform a priori estimates, we obtain the existence of a solution to the BSDE using the convergence of solutions associated to some truncated BSDEs.
Edit intent: coherence
Original:  By proving a martingale representation theorem based on incomplete and phasing out filtration, we characterize the value function in an equivalent form. This allows us to connect the control problem to a quadratic BSDE with jumps, in which the driver term has non-standard structures and carries the conditional filter as an infinite-dimensional parameter.
Revised:  By proving a martingale representation theorem based on incomplete and phasing out filtration, we connect the control problem to a quadratic BSDE with jumps, in which the driver term has non-standard structures and carries the conditional filter as an infinite-dimensional parameter.
Edit intent: coherence
Original:  By proposing some truncation techniques and establishing a uniform a priori estimates, we obtain the existence of a solution to the BSDE using the convergence of solutions associated to some truncated BSDEs.
Revised:  By proposing some truncation techniques and proving a uniform a priori estimates, we obtain the existence of a solution to the BSDE using the convergence of solutions associated to some truncated BSDEs.
Edit intent: clarity
Original:  The complexity is adaptive and comparable to that of previous methods , namely linear in the number of sites, namely edges or facets resp  .
Revised:  The complexity is adaptive and comparable to that of previous methods . Under a mild assumption it is O(n / \Delta + 1 / \Delta^2) in 2D or O(n\alpha^2 / \Delta^2 +1 / \Delta^3) in 3D, where n is the number of sites, namely edges or facets resp .
Edit intent: meaning-changed
Original:  In this paper, we propose a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level.
Revised:  In this paper, we introduce a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level.
Edit intent: clarity
Original:  In this paper, we propose a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level.
Revised:  In this paper, we propose a very low overhead and portable analysis for exposing the latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level.
Edit intent: clarity
Original:  In this paper, we propose a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level.
Revised:  In this paper, we propose a very low overhead and portable analysis for exposing the hidden latency of each instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level.
Edit intent: clarity
Original:  In this paper, we propose a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level.
Revised:  In this paper, we propose a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the GPU pipeline(s) and the access overhead of the various memory hierarchies at the microarchitecture level.
Edit intent: meaning-changed
Original:  In this paper, we propose a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level. We also show the impact of the possible optimizations a CUDA compiler have over the various latencies.
Revised:  In this paper, we propose a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies found in GPUs at the micro-architecture level. Furthermore, we show the impact of the possible optimizations a CUDA compiler have over the various latencies.
Edit intent: meaning-changed
Original:  Current benchmarking tools suffer from limitations such as a) being either too granular like DeepBench  (or) b) mandate a working implementation that is either framework specific or hardware-architecture specific  (or) c) provide only high level benchmark metrics.
Revised:  Current benchmarking tools suffer from limitations such as a) being either too granular like DeepBench 1 (or) b) mandate a working implementation that is either framework specific or hardware-architecture specific (or) c) provide only high level benchmark metrics.
Edit intent: meaning-changed
Original:  Current benchmarking tools suffer from limitations such as a) being either too granular like DeepBench  (or) b) mandate a working implementation that is either framework specific or hardware-architecture specific  (or) c) provide only high level benchmark metrics.
Revised:  Current benchmarking tools suffer from limitations such as a) being either too granular like DeepBench (or) b) mandate a working implementation that is either framework specific or hardware-architecture specific or both (or) c) provide only high level benchmark metrics.
Edit intent: meaning-changed
Original:  In this paper, we present NTP (Neural Net Topology Profiler), a sophisticated benchmarking framework, to effectively identify memory and compute signature of an end-to-end topology on multiple hardware architectures, without the need to actually implement the topology in a framework . NTP is tightly integrated with hardware specific benchmark tools to enable exhaustive data collection and analysis.
Revised:  In this paper, we present NTP (Neural Net Topology Profiler), a sophisticated benchmarking framework, to effectively identify memory and compute signature of an end-to-end topology on multiple hardware architectures, without the need for an actual implementation . NTP is tightly integrated with hardware specific benchmark tools to enable exhaustive data collection and analysis.
Edit intent: clarity
Original:  In this paper, we present NTP (Neural Net Topology Profiler), a sophisticated benchmarking framework, to effectively identify memory and compute signature of an end-to-end topology on multiple hardware architectures, without the need to actually implement the topology in a framework . NTP is tightly integrated with hardware specific benchmark tools to enable exhaustive data collection and analysis.
Revised:  In this paper, we present NTP (Neural Net Topology Profiler), a sophisticated benchmarking framework, to effectively identify memory and compute signature of an end-to-end topology on multiple hardware architectures, without the need to actually implement the topology in a framework . NTP is tightly integrated with hardware specific benchmarking tools to enable exhaustive data collection and analysis.
Edit intent: fluency