wanyu/IteraTeR_v2 huggingface dataset:
Original:  We introduce a " Markov chains of Markov chains " model such that with probably (1-\omega) \in[0,1]  random walkers remain in the same layer and follow (layer-specific) \emph{intralayer Markov chains , whereas with probability \omega they move to different layers following (node-specific) interlayer Markov chains .
Revised:  We introduce a " Markov chains of Markov chains " model such that with probably (1-\omega) \in[0,1] random walkers remain in the same layer and follow (layer-specific) \emph{intralayer Markov chains , whereas with probability \omega they move to different layers following (node-specific) .
Edit intent: clarity
Original:  The smallest value k for which G admits such coloring is denoted by \chi  a (G).
Revised:  The smallest value k for which G admits such coloring is denoted by \chi ' a (G).
Edit intent: fluency
Original:  We prove that \chi  a (G) = 2R + 1 for most circulant graphs C n ([[1, R]]).
Revised:  We prove that \chi ' a (G) = 2R + 1 for most circulant graphs C n ([[1, R]]).
Edit intent: fluency
Original:  We prove that \chi  a (G) = 2R + 1 for most circulant graphs C n ([[1, R]]).
Revised:  We prove that \chi a (G) = 2R + 1 for most circulant graphs Cn ([[1, R]]).
Edit intent: fluency
Original:  Contrary to this widespread assumption, this paper shows that modern embeddings contain information that distinguishes synonyms and antonyms despite small cosine similarities between corresponding vectors.
Revised:  Contrary to this widespread assumption, this paper shows that , modern word embeddings contain information that distinguishes synonyms and antonyms despite small cosine similarities between corresponding vectors.
Edit intent: clarity
Original:  This information is encoded in the geometry of the embeddings and could be extracted with a  manifold learning procedure or %DIFDELCMD < {\em %%% contrasting map . Such a map is trained on a small labeled subset of the data and can produce new empeddings that explicitly highlight specific semantic attributes of the word.
Revised:  This information is encoded in the geometry of the embeddings and could be extracted with a manifold learning procedure or %DIFDELCMD < {\em %%% a contrasting map . Such a map is trained on a small labeled subset of the data and can produce new empeddings that explicitly highlight specific semantic attributes of the word.
Edit intent: clarity
Original:  This information is encoded in the geometry of the embeddings and could be extracted with a  manifold learning procedure or %DIFDELCMD < {\em %%% contrasting map . Such a map is trained on a small labeled subset of the data and can produce new empeddings that explicitly highlight specific semantic attributes of the word.
Revised:  This information is encoded in the geometry of the embeddings and could be extracted with a manifold learning procedure or %DIFDELCMD < {\em %%% contrasting map . Such a map is trained on a small labeled subset of the data and can produce new embeddings that explicitly highlight specific semantic attributes of the word.
Edit intent: fluency
Original:  We aim to estimate the total number of COVID-19 infections in a straightforward manner using a demographic scaling model.
Revised:  We introduce a demographic scaling model to estimate COVID-19 infections in a straightforward manner using a demographic scaling model.
Edit intent: clarity
Original:  This model is broadly applicable as it is based on little input data : deaths attributable to COVID-19 , COVID-19 infection fatality rates  , and life tables.
Revised:  This model is broadly applicable as it is based on minimal data requirements: COVID-19 , COVID-19 infection fatality rates , and life tables.
Edit intent: clarity
Original:  As many countries lack reliable estimates of age-specific infection fatality rates, we map them from a reference country onto countries of interest based on remaining life expectancy .
Revised:  As many countries lack reliable estimates of age-specific IFRs, we scale IFRs between countries using remaining life expectancy .
Edit intent: clarity
Original:  Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, our estimates suggest that the total number of infected is approximately 4 times the number of  ]  confirmed cases.
Revised:  Across the 10 countries with most COVID-19 deaths as of April 17 , 2020, the number of ] confirmed cases.
Edit intent: clarity
Original:  Most prominently, the grandfather antinomy describes the potentiality to inconsistencies ;
Revised:  Most prominently, the grandfather antinomy describes the potentiality to inconsistencies :
Edit intent: fluency
Original:  Yet, it describes a variant of the former: There are not too few consistent solutions---namely none---, but too many.
Revised:  Yet, it describes a variant of the former: There are not too few consistent solutions -- namely none -- but too many.
Edit intent: fluency
Original:  In a very recent paper Physical Review A 101, 022325 (2020)%DIFDELCMD < ]%%% , Chen, Ye and Li provided a characterization of exact one-query quantum algorithms .
Revised:  In a very recent paper %DIFDELCMD < ]%%% , Chen, Ye and Li provided a characterization of exact one-query quantum algorithms .
Edit intent: coherence
Original:  We first note that this result is an immediate corollary of what was proved five years back by Montanaro, Jozsa and Mitchison Algorithmica 71, pp. 775-796 (2015) .
Revised:  We first Algorithmica 71, pp. 775-796 (2015) .
Edit intent: clarity
Original:  Our results classify different classes of Boolean functions on n-variables (n being a function of k), that can be evaluated (i) with exactly k queries in both classical and exact quantum model or (ii) with k queries in classical model and (k-1) queries in exact quantum model .
Revised:  Our results highlight how different classes of Boolean functions on n-variables (n being a function of k), that can be evaluated (i) with exactly k queries in both classical and exact quantum model or (ii) with k queries in classical model and (k-1) queries in exact quantum model .
Edit intent: clarity
Original:  We provide an inapproximability result for this objective, and propose a new election rule inspired in Phragmen's methods that achieves a) a constant-factor approximation guarantee for the objective , and b) the PJR property .
Revised:  We provide an inapproximability result for this objective, we propose a new election rule inspired in Phragmen's methods that achieves a) a constant-factor approximation guarantee for the objective , and b) the PJR property .
Edit intent: fluency
Original:  We provide an inapproximability result for this objective, and propose a new election rule inspired in Phragmen's methods that achieves a) a constant-factor approximation guarantee for the objective , and b) the PJR property .
Revised:  We provide an inapproximability result for this objective, and propose a new efficient election rule that simultaneously achieves a) a constant-factor approximation guarantee for the objective , and b) the PJR property .
Edit intent: clarity
Original:  We provide an inapproximability result for this objective, and propose a new election rule inspired in Phragmen's methods that achieves a) a constant-factor approximation guarantee for the objective , and b) the PJR property .
Revised:  We provide an inapproximability result for this objective, and propose a new election rule inspired in Phragmen's methods that achieves a) a constant-factor approximation guarantee for it , and b) the PJR property .
Edit intent: fluency
Original:  Furthermore, a structural property allows one to quickly verify that the winning committee satisfies the two aforementioned properties, even if the algorithm is executed by an untrusted party who only communicates the output.
Revised:  Furthermore, a structural property allows one to quickly verify that the winning committee satisfies the two aforementioned guarantees, even when the algorithm is executed by an untrusted party who only communicates the output.
Edit intent: clarity
Original:  Finally, we present an efficient post-computation that, when paired with any approximation algorithm for Maximin Support, returns a new solution that a) preserves the approximation guarantee, b) satisfies PJR, and c) can be efficiently verified to satisfy PJR.
Revised:  As a result, the rule can be adapted into a verifiable computing scheme. Moreover, its verification procedure easily admits parallel processing for further efficiency.
Edit intent: coherence
Original:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Revised:  Our work is motivated by an application on blockchain networks that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Edit intent: clarity
Original:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Revised:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake , where the community must elect a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Edit intent: clarity
Original:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Revised:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community elects a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Edit intent: clarity
Original:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Revised:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in the consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Edit intent: clarity
Original:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Revised:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in its consensus protocol, and where preventing overrepresentation protects the system against attacks by an adversarial minority.
Edit intent: clarity
Original:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the system against attacks by an adversarial minority.
Revised:  Our work is motivated by an application on blockchains that implement Nominated Proof-of-Stake (NPoS) , where the community must elect a committee of validators to participate in its consensus protocol, and where fighting overrepresentation protects the network against attacks by an adversarial minority.
Edit intent: clarity
Original:  Our election rule enables a validator election protocol with formal and verifiable guarantees on security and proportionality .
Revised:  Our election rule enables a validator selection protocol with formal and verifiable guarantees on security and proportionality .
Edit intent: clarity
Original:  Our election rule enables a validator election protocol with formal and verifiable guarantees on security and proportionality .
Revised:  Our election rule enables a validator election protocol with formal guarantees on security and proportionality .
Edit intent: clarity
Original:  Policy makers rely on measuring how quickly the disease is spreading to make decisions about mitigation strategies .
Revised:  Policy makers rely on measuring how quickly the disease is spreading in their communities .
Edit intent: clarity
Original:  We analyze US county-level data about confirmed COVID-19 infections and deaths  to show that its impact is heterogeneous.
Revised:  We analyze confirmed infections and deaths to show that its impact is heterogeneous.
Edit intent: clarity
Original:  In addition, data are often incomplete and heterogeneous, so a high degree of uncertainty must naturally be incorporated into the models .
Revised:  In addition, data are often incomplete and heterogeneous, so a high degree of uncertainty must be incorporated into the models .
Edit intent: clarity
Original:  In addition, data are often incomplete and heterogeneous, so a high degree of uncertainty must naturally be incorporated into the models .
Revised:  In addition, data are often incomplete and heterogeneous, so a high degree of uncertainty must naturally be incorporated into the model from the beginning .
Edit intent: clarity
Original:  In this work we address both these aspects, through an optimal control formulation of the epidemiological model in presence of uncertain data.
Revised:  In this work we address these aspects, through an optimal control formulation of the epidemiological model in presence of uncertain data.
Edit intent: clarity
Original:  The importance of the timing and intensity of interventions  is particularly relevant in the case of uncertain parameters on the actual number of infected people.
Revised:  The timing and intensity of interventions is particularly relevant in the case of uncertain parameters on the actual number of infected people.
Edit intent: clarity
Original:  The importance of the timing and intensity of interventions  is particularly relevant in the case of uncertain parameters on the actual number of infected people.
Revised:  The importance of the timing and intensity of interventions , however, is particularly relevant in the case of uncertain parameters on the actual number of infected people.
Edit intent: coherence
Original:  However, significant knowledge can be harvested from older applications or versions and applied in the context of newer applicationsor versions.
Revised:  However, significant knowledge can be harvested from a set of applications and applied in the context of newer applicationsor versions.
Edit intent: clarity
Original:  Therefore , we propose to employ continual lifelong learning to dynamically tune hardware configurations based on application behavior .
Revised:  Therefore , we propose to use the harvested knowledge to tune hardware configurations based on application behavior .
Edit intent: clarity
Original:  Therefore , we propose to employ continual lifelong learning to dynamically tune hardware configurations based on application behavior .
Revised:  Therefore , we propose to employ continual lifelong learning to dynamically tune hardware configurations .
Edit intent: clarity
Original:  The goal of such tuning is to maximize hardware efficiency (i.e., maximize an application performance while minimizing the hardware energy consumption).
Revised:  The goal of such tuning is to maximize hardware efficiency (i.e., maximize an applications performance while minimizing the hardware energy consumption).
Edit intent: fluency
Original:  The goal of such tuning is to maximize hardware efficiency (i.e., maximize an application performance while minimizing the hardware energy consumption).
Revised:  The goal of such tuning is to maximize hardware efficiency (i.e., maximize an application performance while minimizing the energy consumption).
Edit intent: clarity
Original:  Our proposed approach,  FORECASTER, uses deep reinforcement learning to continually learn during the execution of an application as well as propagate and utilize the accumulated knowledge during subsequent executions of the same or new application.
Revised:  Our proposed approach, called FORECASTER, uses deep reinforcement learning to continually learn during the execution of an application as well as propagate and utilize the accumulated knowledge during subsequent executions of the same or new application.
Edit intent: clarity
Original:  Our results show that FORECASTER can save an average 16\% of system power over the baseline setup with full usage of hardware while sacrificing an average of 4.7\% of execution time .
Revised:  Our results show that FORECASTER can save an average 16\% system power over the baseline setup with full usage of hardware while sacrificing an average of 4.7\% of execution time .
Edit intent: clarity
Original: This paper presents a generic convolutional neural network accelerator (CNNA) for a system on chip design (SoC).
Revised: This paper presents a configurable Convolutional Neural Network Accelerator (CNNA) for a system on chip design (SoC).
Edit intent: clarity
Original: This paper presents a generic convolutional neural network accelerator (CNNA) for a system on chip design (SoC).
Revised: This paper presents a generic convolutional neural network accelerator (CNNA) for a System on Chip design (SoC).
Edit intent: fluency
Original:  The presented CNNA has a scalable architecture which uses high level synthesis (HLS) and SystemC for the hardware accelerator.
Revised:  The presented CNNA has a scalable architecture which uses High Level Synthesis (HLS) and SystemC for the hardware accelerator.
Edit intent: fluency
Original:  A training method using fixed-point quantized weights is proposed and presented in the paper.
Revised:  A training method with fixed-point quantized weights is proposed and presented in the paper.
Edit intent: fluency
Original:  The CNNA is template-based, enabling it to scale for different targets of the Xilinx ZYNQ platform.
Revised:  The CNNA is template-based, enabling it to scale for different targets of the Xilinx Zynq platform.
Edit intent: fluency
Original:  The result gave a high  accuracy in training with an auto-scaled fixed-point Q2.14 format compared to a similar floating-point model.
Revised:  The result gave a high level of accuracy in training with an auto-scaled fixed-point Q2.14 format compared to a similar floating-point model.
Edit intent: clarity
Original:  It was able to perform inference in 2.0 seconds, while having an average power consumption of 2.63 W, which corresponds to a power efficiency of 6.0 GOPS/W for the CNN accelerator .
Revised:  It was able to perform inference in 2.0 seconds, while having an average power consumption of 2.63 W, which corresponds to a power efficiency of 6.0 GOPS/W .
Edit intent: clarity
Original:  Over the last two decades, DIDO's algorithms have evolved from their simple use of generic nonlinear programming solvers to a more sophisticated employment of fast spectral Hamiltonian programming techniques.
Revised:  Over the last two decades, DIDO's algorithms have evolved from their simple use of generic nonlinear programming solvers to a multifaceted engagement of fast spectral Hamiltonian programming techniques.
Edit intent: clarity
Original:  A challenge example problem from robotics is included to showcase how the latest version of DIDO is capable of escaping the trappings of a "local minimum" that ensnare many other trajectory optimization methods.
Revised:  A challenge example problem from robotics is included to showcase how the latest version of DIDO is capable of escaping the trappings of a ``local minimum'' that ensnare many other trajectory optimization methods.
Edit intent: fluency
Original: We introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of the same query.
Revised: We introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of the same query.
Edit intent: fluency
Original:  Furthermore, we advocate for the adoption of the principle of equal expected exposure: given a fixed information need, no item  receive more or less expected exposure compared to any other item of the same relevance grade.
Revised:  Furthermore, we advocate for the adoption of the principle of equal expected exposure: given a fixed information need, no item should receive more or less expected exposure compared to any other item of the same relevance grade.
Edit intent: clarity
Original:  Furthermore, we advocate for the adoption of the principle of equal expected exposure: given a fixed information need, no item  receive more or less expected exposure compared to any other item of the same relevance grade.
Revised:  Furthermore, we advocate for the adoption of the principle of equal expected exposure: given a fixed information need, no item receive more or less expected exposure than any other item of the same relevance grade.
Edit intent: coherence
Original:  Importantly, this methodology relaxes classic information retrieval assumptions, allowing a system, in response to a query, to produce a distribution over rankings instead of a single fixed ranking.
Revised:  Importantly, this methodology relaxes classic information retrieval assumptions, allowing a system, in response to a query, to produce a distribution over rankings instead of a single fixed ranking.
Edit intent: fluency
Original:  We study the behavior of the expected exposure metric and stochastic rankers across a variety of information access conditions, including ad hoc retrieval and recommendation.
Revised:  We study the behavior of the expected exposure metric and stochastic rankers across a variety of information access conditions, including ad hoc retrieval and recommendation.
Edit intent: fluency
Original:  For each country, the Susceptible-Infected-Removed (SIR ) models fitting cumulative infective case data within 5\% error are analysed.
Revised:  For each country, the SIR models fitting cumulative infective case data within 5\% error are analysed.
Edit intent: clarity
Original: Transmission rates in epidemic outbreaks  vary over time depending on the societal and government responseto infections and mortality data, as evidenced in the course of the COVID-19 pandemic.
Revised: Transmission rates in epidemic outbreaks may vary over time depending on the societal and government responseto infections and mortality data, as evidenced in the course of the COVID-19 pandemic.
Edit intent: fluency
Original: Transmission rates in epidemic outbreaks  vary over time depending on the societal and government responseto infections and mortality data, as evidenced in the course of the COVID-19 pandemic.
Revised: Transmission rates in epidemic outbreaks vary over time depending on the societal and government responseto infections and mortality data, as evidenced in the COVID-19 pandemic.
Edit intent: clarity