wanyu/IteraTeR_v2 huggingface dataset:
Original:  As using this information for importance sampling includes information about visibility, too, the number of light transport paths with non-zero contribution is dramatically increased , resulting in much less noisy images within a fixed time budget.
Revised:  As using this information for importance sampling includes information about visibility, too, the number of light transport paths with non-zero contribution is dramatically reduced , resulting in much less noisy images within a fixed time budget.
Edit intent: clarity
Original:  Daily national police data to which we had access have allowed us to take advantage of this natural experiment to explore the dynamics of riot propagation.
Revised:  Daily national police data has allowed us to take advantage of this natural experiment to explore the dynamics of riot propagation.
Edit intent: clarity
Original:  Daily national police data to which we had access have allowed us to take advantage of this natural experiment to explore the dynamics of riot propagation.
Revised:  Daily national police data to which we had access have allowed us to explore the dynamics of riot propagation.
Edit intent: clarity
Original:  Here we show that an epidemic-like model, with less than 10 free parameters and a single sociological variable characterizing neighborhood deprivation, accounts quantitatively for the full spatio-temporal dynamics of the riots.
Revised:  Here we show that an epidemic-like model, with just a few parameters and a single sociological variable characterizing neighborhood deprivation, accounts quantitatively for the full spatio-temporal dynamics of the riots.
Edit intent: clarity
Original:  The remarkable agreement between model and data demonstrates that geographic proximity played a major role in the riot propagation, even though information was readily available everywhere through media.
Revised:  The remarkable agreement between model and data demonstrates that geographic proximity played a major role in the propagation, even though information was readily available everywhere through media.
Edit intent: clarity
Original: As a large-scale instance of dramatic collective behavior , the 2005 French riots started in a poor suburb of Paris, then spread in all of France, lasting about three weeks.
Revised: As a large-scale instance of dramatic collective behaviour , the 2005 French riots started in a poor suburb of Paris, then spread in all of France, lasting about three weeks.
Edit intent: fluency
Original:  Here we show that an epidemic-like model, with just a few parameters and a single sociological variable characterizing neighborhood deprivation, accounts quantitatively for the full spatio-temporal dynamics of the riots.
Revised:  Here we show that an epidemic-like model, with just a few parameters and a single sociological variable characterizing neighbourhood deprivation, accounts quantitatively for the full spatio-temporal dynamics of the riots.
Edit intent: fluency
Original:  This is the first time that such data-driven modeling involving contagion both within and between cities (through geographic proximity or media) at the scale of a country, and on a daily basis, is performed.
Revised:  This is the first time that such data-driven modelling involving contagion both within and between cities (through geographic proximity or media) at the scale of a country, and on a daily basis, is performed.
Edit intent: fluency
Original:  Finally, we argue that our approach gives a general framework for the modeling of the dynamics of spontaneous collective uprisings.
Revised:  Finally, we argue that our approach gives a general framework for the modelling of the dynamics of spontaneous collective uprisings.
Edit intent: fluency
Original:  We introduce the M{}^\natural-convex completion problem, and show that a function f satisfying  JWP is Z-free if and only if a certain function f associated with f is M{}^\natural-convex completable.
Revised:  We introduce the M{}^\natural-convex completion problem, and show that a function f satisfying the JWP is Z-free if and only if a certain function f associated with f is M{}^\natural-convex completable.
Edit intent: fluency
Original:  Considering the ANN problem from an information-theoretic perspective, we interpret it as an encoding  which maps the original feature vectors to a less-entropic sparse representation while requiring them to be as informative as possible.
Revised:  Considering the ANN problem from an information-theoretic perspective, we interpret it as an encoding , which maps the original feature vectors to a less-entropic sparse representation while requiring them to be as informative as possible.
Edit intent: fluency
Original:  Considering the ANN problem from an information-theoretic perspective, we interpret it as an encoding  which maps the original feature vectors to a less-entropic sparse representation while requiring them to be as informative as possible.
Revised:  Considering the ANN problem from an information-theoretic perspective, we interpret it as an encoding which maps the original feature vectors to a less entropic sparse representation while requiring them to be as informative as possible.
Edit intent: fluency
Original:  We next show that the classical approach to this problem  which consists of binarization of the projected vectors is sub-optimal.
Revised:  We next show that the classical approach to this problem , which consists of binarization of the projected vectors is sub-optimal.
Edit intent: fluency
Original:  Instead, we show that a recently proposed ternary encoding achieves higher coding gains  .
Revised:  Instead, a properly designed ternary encoding achieves higher coding gains .
Edit intent: clarity
Original: Evolutionary event relations such as orthology, paralogy and xenology provide many information on the evolutionary history of the investigated genes.
Revised: Evolutionary event relations such as orthology, paralogy and xenology provide important information on the evolutionary history of the investigated genes.
Edit intent: clarity
Original:  In particular, the respective  event-labeled  gene trees can then be constructed in polynomial time by utilizing solely the information of these event relations .
Revised:  In particular, the respectiveevent-labeled event-labeled gene trees can then be constructed in polynomial time by utilizing solely the information of these event relations .
Edit intent: fluency
Original:  In this contribution, we are concerned with the next conceptual step: the derivation of a species tree from an event-labeled gene tree T that contains horizontal gene transfer , speciation and duplication events .
Revised:  In this contribution, we first characterize whether there is a valid reconciliation map for binary event-labeled gene tree T that contains horizontal gene transfer , speciation and duplication events .
Edit intent: clarity
Original:  However, informative triples as well as DTL-scenarios have its limitations when  used to explain the biological feasibility of gene trees.
Revised:  However, informative triples as well as DTL-scenarios have its limitations when they are used to explain the biological feasibility of gene trees.
Edit intent: fluency
Original:  We pinpoint the regime of magnetization dynamics leading to highest performance in the bias conditions space .
Revised:  We pinpoint the regime of magnetization dynamics leading to highest performance .
Edit intent: clarity
Original: In event-driven programming frameworks, such as Android, the client and the framework interact using callins (framework methods that the client invokes) and callbacks (client methods that the framework invokes) .
Revised: Event-driven programming frameworks, such as Android, the client and the framework interact using callins (framework methods that the client invokes) and callbacks (client methods that the framework invokes) .
Edit intent: coherence
Original:  The protocols for interacting with these frameworks can often be described by finite-state machines we dub *asynchronous typestates*.
Revised:  The protocols for interacting with these components can often be described by finite-state machines we dub *asynchronous typestates*.
Edit intent: clarity
Original:  Asynchronous typestates are akin to classical typestates, with the key difference that their outputs (callbacks) are produced asynchronously.
Revised:  Callback typestates are akin to classical typestates, with the key difference that their outputs (callbacks) are produced asynchronously.
Edit intent: clarity
Original:  Asynchronous typestates are akin to classical typestates, with the key difference that their outputs (callbacks) are produced asynchronously.
Revised:  Asynchronous typestates are akin to classical typestates, with the difference that their outputs (callbacks) are produced asynchronously.
Edit intent: clarity
Original:  Additionally , in several cases, the synthesized asynchronous typestates uncovered surprising and undocumented behaviors.
Revised:  Moreover , in several cases, the synthesized asynchronous typestates uncovered surprising and undocumented behaviors.
Edit intent: fluency
Original:  Additionally , in several cases, the synthesized asynchronous typestates uncovered surprising and undocumented behaviors.
Revised:  Additionally , in several cases, the synthesized callback typestates uncovered surprising and undocumented behaviors.
Edit intent: clarity
Original:  Previous population-based probabilistic (PPC) theories share some basic properties: 1) continuous  neurons;
Revised:  Most previous population-based probabilistic (PPC) theories share some basic properties: 1) continuous neurons;
Edit intent: fluency
Original:  Previous population-based probabilistic (PPC) theories share some basic properties: 1) continuous  neurons;
Revised:  Previous population-based probabilistic (PPC) theories share several basic properties: 1) continuous neurons;
Edit intent: clarity
Original:  3) decoding requires either graded synapsesor rate coding ;
Revised:  3) graded synapses ;
Edit intent: clarity
Original:  4) generally assume rate-coded signaling ;
Revised:  4) rate coding ;
Edit intent: clarity
Original:  individual neurons are generally assumed to 5) have unimodal, e.g., bell-shaped, tuning functions (TFs) and 6) be fundamentally noisy;
Revised:  individual neurons are assumed to 5) have unimodal, e.g., bell-shaped, tuning functions (TFs) and 6) be fundamentally noisy;
Edit intent: coherence
Original:  and 7) noise/correlation are generally viewed as degrading computation .
Revised:  and 7) noise/correlation are generally viewed as problems requiring mitigation .
Edit intent: clarity
Original:  4) signaling via waves of contemporaneously arriving first-spikes ;
Revised:  4) formally requires only single, i.e., first, spikes ;
Edit intent: clarity
Original:  individual neurons 5)  have completely flat TFs (all weights initially zero) and 6) are not noisy;
Revised:  individual neurons 5) initially have completely flat TFs (all weights initially zero) and 6) are not noisy;
Edit intent: fluency
Original:  individual neurons 5)  have completely flat TFs (all weights initially zero) and 6) are not noisy;
Revised:  individual neurons 5) have completely flat TFs (all weights zero) and 6) are not noisy;
Edit intent: clarity
Original:  Thus, noise controls a tradeoff between storage capacity and embedding the input space statistics ( in the pattern of code intersections ) , indirectly yielding particular correlation patterns.
Revised:  Thus, noise controls a tradeoff between storage capacity and embedding the input space statistics in the pattern of code intersections ) , indirectly yielding particular correlation patterns.
Edit intent: fluency
Original:  Thus, noise controls a tradeoff between storage capacity and embedding the input space statistics ( in the pattern of code intersections ) , indirectly yielding particular correlation patterns.
Revised:  Thus, noise controls a tradeoff between storage capacity and embedding the input space statistics ( in the pattern of intersections over the codes , indirectly yielding particular correlation patterns.
Edit intent: clarity
Original:  The theory, Sparsey, was introduced 20 years ago as a canonical cortical circuit/algorithm model, but not elaborated as a probabilistic model.
Revised:  The theory, Sparsey, was introduced 20 years ago as a canonical cortical circuit/algorithm model, but not discussed as a probabilistic model.
Edit intent: clarity
Original:  Finally, consistent with moving beyond the Neuron Doctrine to the view that the SDC (cell assembly, ensemble) is the atomic unit of neural representation, Sparsey explains how classical unimodal TFs emerge as an artifact of a single/few-trial learning process in which SDC codes are laid down in superposition.
Revised:  Finally, consistent with moving beyond the Neuron Doctrine to the view that the SDC (cell assembly, ensemble) is the atomic unit of neural representation, Sparsey suggests that classical unimodal TFs emerge as an artifact of a single/few-trial learning process in which SDC codes are laid down in superposition.
Edit intent: clarity
Original: It is widely acknowledged that the brain i) implements probabilistic reasoning , and ii) represents information via population /distributed coding.
Revised: The brain is believed to implement probabilistic reasoning and to represent information via population /distributed coding.
Edit intent: clarity
Original: It is widely acknowledged that the brain i) implements probabilistic reasoning , and ii) represents information via population /distributed coding.
Revised: It is widely acknowledged that the brain i) implements probabilistic reasoning , and ii) represents information via population , or distributed, coding.
Edit intent: fluency
Original:  2) full/dense distributed coding: all/mostcoding field neurons formally participate in every code;
Revised:  2) fully(densely)-distributed codes, i.e., all(most) units participate in every code;
Edit intent: clarity
Original:  individual neurons are assumed to 5) have unimodal, e.g., bell-shaped, tuning functions (TFs) and 6) be fundamentally noisy ;
Revised:  5) have unimodal, e.g., bell-shaped, tuning functions (TFs) and 6) be fundamentally noisy ;
Edit intent: clarity
Original:  individual neurons are assumed to 5) have unimodal, e.g., bell-shaped, tuning functions (TFs) and 6) be fundamentally noisy ;
Revised:  individual neurons are assumed to 5) units have innate unimodal tuning functions (TFs) and 6) be fundamentally noisy ;
Edit intent: clarity
Original:  individual neurons are assumed to 5) have unimodal, e.g., bell-shaped, tuning functions (TFs) and 6) be fundamentally noisy ;
Revised:  individual neurons are assumed to 5) have unimodal, e.g., bell-shaped, tuning functions (TFs) ; 6) be fundamentally noisy ;
Edit intent: coherence
Original:  individual neurons are assumed to 5) have unimodal, e.g., bell-shaped, tuning functions (TFs) and 6) be fundamentally noisy ;
Revised:  individual neurons are assumed to 5) have unimodal, e.g., bell-shaped, tuning functions (TFs) and 6) intrinsically noisy units ;
Edit intent: clarity
Original:  In contrast, our theory assumes: 1) binary neurons ;
Revised:  In contrast, our theory assumes: 1) binary units ;
Edit intent: clarity
Original:  2) only a small subset of neurons , i.e., a sparse distributed code (SDC) , comprises any individual code;
Revised:  2) only a small subset of units , i.e., a sparse distributed code (SDC) , comprises any individual code;
Edit intent: clarity
Original:  4)  formally requires only single , i.e., first, spikes;
Revised:  4) signaling formally requires only single , i.e., first, spikes;
Edit intent: clarity
Original:  individual neurons 5)  initially have completely flat TFs (all weights zero) and 6) are not noisy;
Revised:  individual neurons 5) initially have completely flat TFs (all weights zero) ; 6) are not noisy;
Edit intent: coherence
Original:  and 7) noise is a resource generated and used to cause similar inputs to map to similar codes, which controls a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over the codes, indirectly yielding particular correlation patterns.
Revised:  and 7) noise is a resource generated / used to cause similar inputs to map to similar codes, which controls a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over the codes, indirectly yielding particular correlation patterns.
Edit intent: coherence
Original:  and 7) noise is a resource generated and used to cause similar inputs to map to similar codes, which controls a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over the codes, indirectly yielding particular correlation patterns.
Revised:  and 7) noise is a resource generated and used to cause similar inputs to map to similar codes, controlling a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over the codes, indirectly yielding particular correlation patterns.
Edit intent: coherence
Original:  and 7) noise is a resource generated and used to cause similar inputs to map to similar codes, which controls a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over the codes, indirectly yielding particular correlation patterns.
Revised:  and 7) noise is a resource generated and used to cause similar inputs to map to similar codes, which controls a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over stored codes, indirectly yielding particular correlation patterns.
Edit intent: fluency
Original:  and 7) noise is a resource generated and used to cause similar inputs to map to similar codes, which controls a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over the codes, indirectly yielding particular correlation patterns.
Revised:  and 7) noise is a resource generated and used to cause similar inputs to map to similar codes, which controls a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over the codes, indirectly yielding correlation patterns.
Edit intent: clarity
Original:  Assuming input similarity correlates with likelihood, the active SDC code simultaneously represents both the most probable hypothesis and the full probability distribution .
Revised:  Assuming input similarity correlates with likelihood, the active SDC simultaneously represents both the most probable hypothesis and the full probability distribution .
Edit intent: clarity
Original:  2) only a small subset of units, i.e., a sparse distributed code (SDC ) (cell assembly , ensemble ), comprises any individual code;
Revised:  2) only a small subset of units, i.e., a sparse distributed representation (SDR ) (cell assembly , ensemble ), comprises any individual code;
Edit intent: clarity
Original:  2) only a small subset of units, i.e., a sparse distributed code (SDC ) (cell assembly , ensemble ), comprises any individual code;
Revised:  2) only a small subset of units, i.e., a sparse distributed code (SDC ) (cell assembly ), comprises any individual code;
Edit intent: clarity
Original:  4) signaling formally requires only single (  first) spikes;
Revised:  4) signaling formally requires only single ( i.e., first) spikes;
Edit intent: coherence
Original:  but rather 7) noise is a resource generated/used to cause similar inputs to map to similar codes, controlling a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over stored codes, indirectly yielding correlation patterns .
Revised:  but rather 7) noise is a resource generated/used to cause similar inputs to map to similar codes, controlling a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over stored codes, epiphenomenally determining correlation patterns across neurons .
Edit intent: clarity
Original:  Here, we show that the active SDC simultaneously represents both the most similar/likely input and the  coarsely-ranked  distribution over all stored inputs (hypotheses) . Crucially, Sparsey's code selection algorithm (CSA), used for both learning and inference, achieves this with a single pass over the weights for each successive item of a sequence, thus performing spatiotemporal pattern learning/inference with a number of steps that remains constant as the number of stored items increases .
Revised:  Here, we show that the active SDC simultaneously represents both the most similar/likely input and the entire ( coarsely-ranked distribution over all stored inputs (hypotheses) . Crucially, Sparsey's code selection algorithm (CSA), used for both learning and inference, achieves this with a single pass over the weights for each successive item of a sequence, thus performing spatiotemporal pattern learning/inference with a number of steps that remains constant as the number of stored items increases .
Edit intent: clarity
Original:  Here, we show that the active SDC simultaneously represents both the most similar/likely input and the  coarsely-ranked  distribution over all stored inputs (hypotheses) . Crucially, Sparsey's code selection algorithm (CSA), used for both learning and inference, achieves this with a single pass over the weights for each successive item of a sequence, thus performing spatiotemporal pattern learning/inference with a number of steps that remains constant as the number of stored items increases .
Revised:  Here, we show that the active SDC simultaneously represents both the most similar/likely input and the coarsely-ranked distribution over all stored inputs (hypotheses) . Crucially, Sparsey's code selection algorithm , which underlies both learning and inference, achieves this with a single pass over the weights for each successive item of a sequence, thus performing spatiotemporal pattern learning/inference with a number of steps that remains constant as the number of stored items increases .
Edit intent: clarity
Original: Erasure coding has been widely adopted in distributed storage systems for fault-tolerant storage with low redundancy.
Edit intent: coherence
Original:  It encodes objects in entirety, and incurs 60\% less storage redundancy for small objects than existing replication- and erasure-coding-based approaches.
Revised:  By encoding objects in entirety, and incurs 60\% less storage redundancy for small objects than existing replication- and erasure-coding-based approaches.
Edit intent: fluency
Original:  It also supports graceful transitions between decentralized requests in normal mode (i.e., no failures) and coordinated requests in degraded mode (i.e., with failures) , so as to maintain availability and consistency .
Revised:  It also supports graceful transitions between decentralized requests in normal mode (i.e., no failures) and coordinated requests in degraded mode (i.e., with failures) .
Edit intent: coherence
Original:  We evaluate our MemEC prototype via extensive testbed experiments under read-heavy and update-heavy YCSB workloads.
Revised:  We evaluate our MemEC prototype via testbed experiments under read-heavy and update-heavy YCSB workloads.
Edit intent: clarity
Original:   MemEC achieves high throughput and low latency in both normal and degraded modes, and also achieves fast transitions between the two modes.
Revised:  We show that MemEC achieves high throughput and low latency in both normal and degraded modes, and also achieves fast transitions between the two modes.
Edit intent: coherence
Original:   MemEC achieves high throughput and low latency in both normal and degraded modes, and also achieves fast transitions between the two modes.
Revised:  MemEC achieves high throughput and low latency in both normal and degraded modes, and supports fast transitions between the two modes.
Edit intent: clarity