wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper, we introduce MLPerf HPC, a benchmark suite of largescale scientific machine learning training applications , driven by the MLCommonsTM Association.
Revised:  In this paper, we introduce MLPerf HPC, a benchmark suite of largescale scientific machine learning training applications , driven by the MLCommons Association.
Edit intent: clarity
Original:  We present the results from the first submission round  including a diverse set of some of the world's largest HPC systems.
Revised:  We present the results from the first submission round , including a diverse set of some of the world's largest HPC systems.
Edit intent: fluency
Original:  As a result, we gain a quantitative understanding of optimizations on different subsystems such as staging and on-node loading of data, compute-unit utilization, and communication scheduling  enabling overall > 10x (end-to-end) performance improvements through system scaling.
Revised:  As a result, we gain a quantitative understanding of optimizations on different subsystems such as staging and on-node loading of data, compute-unit utilization, and communication scheduling , enabling overall > 10x (end-to-end) performance improvements through system scaling.
Edit intent: fluency
Original:  As a result, we gain a quantitative understanding of optimizations on different subsystems such as staging and on-node loading of data, compute-unit utilization, and communication scheduling  enabling overall > 10x (end-to-end) performance improvements through system scaling.
Revised:  As a result, we gain a quantitative understanding of optimizations on different subsystems such as staging and on-node loading of data, compute-unit utilization, and communication scheduling enabling overall > 10 \times (end-to-end) performance improvements through system scaling.
Edit intent: fluency
Original:  Notably, our analysis shows a scale-dependent interplay between the dataset size, a system's memory hierarchy, and training convergence that underlines the importance of near compute storage.
Revised:  Notably, our analysis shows a scale-dependent interplay between the dataset size, a system's memory hierarchy, and training convergence that underlines the importance of near-compute storage.
Edit intent: fluency
Original:  On the other hand,  decisions are not always based on reusable, multi-purpose, and explainable knowledge.
Revised:  On the other hand, such decisions are not always based on reusable, multi-purpose, and explainable knowledge.
Edit intent: clarity
Original:  This contribution aims to introduce a multidimensional framework to deal with the notion of Value in the AI context.
Revised:  This contribution aims to introduce a formal framework to deal with the notion of Value in the AI context.
Edit intent: clarity
Original:  this form of uncertainty is specified in analogy with two scenarios concerning decision-making and non-classical measurements .
Revised:  this form of uncertainty related to data observability .
Edit intent: clarity
Original:  On the other hand, such decisions are not always based on reusable , multi-purpose, and explainable knowledge  .
Revised:  On the other hand, such decisions are not always based on reusable and explainable knowledge .
Edit intent: clarity
Original:  Therefore, it is necessary to define new models to describe and manage this new (re)source of uncertainty.
Revised:  Therefore, it is necessary to define new models to describe and manage this type of uncertainty.
Edit intent: clarity
Original:  This contribution aims to introduce a formal framework to deal with the notion of Value in the AI-BD context , embracing both the multiplicity of Value dimensions and the uncertainty in their visibility as the foundations for a dynamic, relational representation of Value.
Revised:  This contribution introduces a conceptual framework to deal with the notion of Value in the AI-BD context , embracing both the multiplicity of Value dimensions and the uncertainty in their visibility as the foundations for a dynamic, relational representation of Value.
Edit intent: clarity
Original:  This contribution aims to introduce a formal framework to deal with the notion of Value in the AI-BD context , embracing both the multiplicity of Value dimensions and the uncertainty in their visibility as the foundations for a dynamic, relational representation of Value.
Revised:  This contribution aims to introduce a formal framework to deal with the notion of Value in AI-BD context , embracing both the multiplicity of Value dimensions and the uncertainty in their visibility as the foundations for a dynamic, relational representation of Value.
Edit intent: fluency
Original:  This contribution aims to introduce a formal framework to deal with the notion of Value in the AI-BD context , embracing both the multiplicity of Value dimensions and the uncertainty in their visibility as the foundations for a dynamic, relational representation of Value.
Revised:  This contribution aims to introduce a formal framework to deal with the notion of Value in the AI-BD contexts , embracing both the multiplicity of Value dimensions and the uncertainty in their visibility as the foundations for a dynamic, relational representation of Value.
Edit intent: fluency
Original:  The focus on reusable knowledge is exploited in the relation between Human and Artificial intelligences, which is characterised by a " non-classical " form of uncertainty related to data observability.
Revised:  The focus on reusable knowledge is exploited in the relation between Human and Artificial intelligences, which is characterised by a non-classical " form of uncertainty related to data observability.
Edit intent: fluency
Original:  The focus on reusable knowledge is exploited in the relation between Human and Artificial intelligences, which is characterised by a " non-classical " form of uncertainty related to data observability.
Revised:  The focus on reusable knowledge is exploited in the relation between Human and Artificial intelligences, which is characterised by a " non-classical form of uncertainty related to data observability.
Edit intent: fluency
Original:  The focus on reusable knowledge is exploited in the relation between Human and Artificial intelligences, which is characterised by a " non-classical " form of uncertainty related to data observability.
Revised:  The focus on reusable knowledge is exploited in the relation between Human and Artificial intelligences, which is characterised by a " non-classical " form of uncertainty regarding data observability.
Edit intent: fluency
Original:  Finally, we identify applicative domains for future investigation, in order to address the impact of the dynamic behaviour of Value dimensions on strategies and decision-making , enhancing the adaptability and , hence, the sustainability of AI-BD initiatives over time.
Revised:  The impact of the dynamic behaviour of Value dimensions on strategies and decision-making , enhancing the adaptability and , hence, the sustainability of AI-BD initiatives over time.
Edit intent: coherence
Original:  Finally, we identify applicative domains for future investigation, in order to address the impact of the dynamic behaviour of Value dimensions on strategies and decision-making , enhancing the adaptability and , hence, the sustainability of AI-BD initiatives over time.
Revised:  Finally, we identify applicative domains for future investigation, in order to address the impact of the dynamic behaviour of Value dimensions on decision-making , enhancing the adaptability and , hence, the sustainability of AI-BD initiatives over time.
Edit intent: clarity
Original:  We show how underlying technologies such as Nand Flash and3DXP differentiate, and relate to real world scenarios, enabling from 5\% to 29\% power savings.
Revised:  We show how underlying technologies such as Nand Flash and 3DXP differentiate, and relate to real world scenarios, enabling from 5\% to 29\% power savings.
Edit intent: fluency
Original:  The key innovation of our approach is the design of the active object detection policy that uses an internal representation called the Relational Box Field, which allows for every pixel to regress an improved location of an active objectbounding box, essentially giving every pixel the ability to vote for a better bounding boxlocation.
Revised:  The key innovation of our approach is the Relational Box Field, which allows for every pixel to regress an improved location of an active objectbounding box, essentially giving every pixel the ability to vote for a better bounding boxlocation.
Edit intent: coherence
Original:  While existing defense methods have demonstrated promising results on detecting and erasing backdoor triggers , it is still not clear if measures can be taken to avoid the triggers from being learned into the model in the first place.
Revised:  While existing defense methods have demonstrated promising results on detecting or erasing backdoors , it is still not clear if measures can be taken to avoid the triggers from being learned into the model in the first place.
Edit intent: fluency
Original:  While existing defense methods have demonstrated promising results on detecting and erasing backdoor triggers , it is still not clear if measures can be taken to avoid the triggers from being learned into the model in the first place.
Revised:  While existing defense methods have demonstrated promising results on detecting and erasing backdoor triggers , it is still not clear whether robust training methods can be devised to prevent the backdoor triggers being injected into the trained model in the first place.
Edit intent: clarity
Original:  In this paper, we introduce the concept of anti-backdoor learning, of which the aim is to train clean models on  backdoor-poisoned data.
Revised:  In this paper, we introduce the concept of anti-backdoor learning, aiming to train backdoor-poisoned data.
Edit intent: coherence
Original:  In this paper, we introduce the concept of anti-backdoor learning, of which the aim is to train clean models on  backdoor-poisoned data.
Revised:  In this paper, we introduce the concept of anti-backdoor learning, of which the aim is to train clean models on models given backdoor-poisoned data.
Edit intent: clarity
Original:  We frame the overall learning process as a dual-task of learning the clean portion of data and learning the backdoor portion   of data.
Revised:  We frame the overall learning process as a dual-task of learning the clean of data.
Edit intent: coherence
Original:  We frame the overall learning process as a dual-task of learning the clean portion of data and learning the backdoor portion   of data.
Revised:  We frame the overall learning process as a dual-task of learning the clean portion of data and learning the backdoor portion portions of data.
Edit intent: clarity
Original:  From this view, we identify two inherent characteristics of backdoor attacks as their weaknesses: 1) the models learn backdoored data at a much faster rate than learning clean data, and the stronger the attack the faster the model converges on backdoored data;
Revised:  From this view, we identify two inherent characteristics of backdoor attacks as their weaknesses: 1) the models learn backdoored data much faster than learning with clean data, and the stronger the attack the faster the model converges on backdoored data;
Edit intent: clarity
Original:  and 2) the backdoor task is tied to a specific class (the backdoor target class).
Revised:  2) the backdoor task is tied to a specific class (the backdoor target class).
Edit intent: coherence
Original:  ABL introduces a two-stage \emph{gradient ascent} mechanism into standard training to 1) help isolate backdoor examples at an early training stage, and 2) break the correlation between backdoor examples and the target class at a later training stage.
Revised:  ABL introduces a two-stage \emph{gradient ascent} mechanism for standard training to 1) help isolate backdoor examples at an early training stage, and 2) break the correlation between backdoor examples and the target class at a later training stage.
Edit intent: clarity
Original:  Bernstein fits have been implemented into R and provide another route for development of power study tools .
Revised:  Bernstein fits have been implemented into R provide another route for development of power study tools .
Edit intent: coherence
Original:  Bernstein fits have been implemented into R and provide another route for development of power study tools .
Revised:  Bernstein fits have been implemented into R and provide another route for power-study tool development .
Edit intent: clarity
Original:  Methods. Monte Carlo (M.C.) Kruskal-Wallis power studies were compared with measured power and  ANOVA equivalents ( M.C. plus analytical), with or without normalization, using simulated datasets with a rangeof Pearson types;
Revised:  Methods. Monte-Carlo Kruskal-Wallis power studies were compared with measured power and ANOVA equivalents ( M.C. plus analytical), with or without normalization, using simulated datasets with a rangeof Pearson types;
Edit intent: clarity
Original:  Methods. Monte Carlo (M.C.) Kruskal-Wallis power studies were compared with measured power and  ANOVA equivalents ( M.C. plus analytical), with or without normalization, using simulated datasets with a rangeof Pearson types;
Revised:  Methods. Monte Carlo (M.C.) Kruskal-Wallis power studies were compared with measured power and Monte-Carlo ANOVA equivalents ( M.C. plus analytical), with or without normalization, using simulated datasets with a rangeof Pearson types;
Edit intent: fluency
Original:  Methods. Monte Carlo (M.C.) Kruskal-Wallis power studies were compared with measured power and  ANOVA equivalents ( M.C. plus analytical), with or without normalization, using simulated datasets with a rangeof Pearson types;
Revised:  Methods. Monte Carlo (M.C.) Kruskal-Wallis power studies were compared with measured power and ANOVA equivalents ( plus analytical), with or without normalization, using simulated datasets with a rangeof Pearson types;
Edit intent: fluency
Original:  Methods. Monte Carlo (M.C.) Kruskal-Wallis power studies were compared with measured power and  ANOVA equivalents ( M.C. plus analytical), with or without normalization, using simulated datasets with a rangeof Pearson types;
Revised:  Methods. Monte Carlo (M.C.) Kruskal-Wallis power studies were compared with measured power and ANOVA equivalents ( M.C. plus analytical), with / without normalization, using simulated datasets with a rangeof Pearson types;
Edit intent: fluency
Original:  With three from four simulated runs (  Pearson types pooled ), M.C. Kruskal-Wallis gave predicted sample sizes significantly slightly lower than that measured and more accurate than M.C. ANOVA(which gave significantly higher predictions;
Revised:  With 3/4 simulated runs ( Pearson types pooled ), M.C. Kruskal-Wallis gave predicted sample sizes significantly slightly lower than that measured and more accurate than M.C. ANOVA(which gave significantly higher predictions;
Edit intent: clarity
Original:  With three from four simulated runs (  Pearson types pooled ), M.C. Kruskal-Wallis gave predicted sample sizes significantly slightly lower than that measured and more accurate than M.C. ANOVA(which gave significantly higher predictions;
Revised:  With three from four simulated runs ( Pearson types pooled ), M.C. Kruskal-Wallis gave predicted sample sizes significantly lower than measured but more accurate than M.C. ANOVA(which gave significantly higher predictions;
Edit intent: clarity
Original:  With three from four simulated runs (  Pearson types pooled ), M.C. Kruskal-Wallis gave predicted sample sizes significantly slightly lower than that measured and more accurate than M.C. ANOVA(which gave significantly higher predictions;
Revised:  With three from four simulated runs ( Pearson types pooled ), M.C. Kruskal-Wallis gave predicted sample sizes significantly slightly lower than that measured and more accurate than ANOVA; the latter gave significantly higher predictions;
Edit intent: clarity
Original:  with run_two  both gave significantly lower predictions than measured with M.C. ANOVA more accurate).
Revised:  with run_two both gave significantly lower predictions ; Monte-Carlo ANOVA more accurate).
Edit intent: clarity
Original:  With run_one populations where ANOVA could not be used, Kruskal-Wallis predictions were similar to that measured.
Revised:  With run_one populations where ANOVA could not be used, Kruskal-Wallis predictions similar to measured.
Edit intent: clarity
Original:  In two from three medical studies, Kruskal-Wallis predictions ( with "dialysis" with similar predictionsand "marital" higher than measured) were more accurate than M.C. ANOVA (both higher than measured) but in one ( "diabetes" ) the reverse was the case (Kruskal-Wallis giving lower;
Revised:  In two from three medical studies, Kruskal-Wallis predictions ( Dialysis: similar predictions; Marital: higher than measured) were more accurate than M.C. ANOVA (both higher than measured) but in one ( "diabetes" ) the reverse was the case (Kruskal-Wallis giving lower;
Edit intent: clarity
Original:  In two from three medical studies, Kruskal-Wallis predictions ( with "dialysis" with similar predictionsand "marital" higher than measured) were more accurate than M.C. ANOVA (both higher than measured) but in one ( "diabetes" ) the reverse was the case (Kruskal-Wallis giving lower;
Revised:  In two from three medical studies, Kruskal-Wallis predictions ( with "dialysis" with similar predictionsand "marital" higher than measured) were more accurate than ANOVA (both higher than measured) but in one ( "diabetes" ) the reverse was the case (Kruskal-Wallis giving lower;
Edit intent: clarity
Original:  In two from three medical studies, Kruskal-Wallis predictions ( with "dialysis" with similar predictionsand "marital" higher than measured) were more accurate than M.C. ANOVA (both higher than measured) but in one ( "diabetes" ) the reverse was the case (Kruskal-Wallis giving lower;
Revised:  In two from three medical studies, Kruskal-Wallis predictions ( with "dialysis" with similar predictionsand "marital" higher than measured) were more accurate than M.C. ANOVA (both higher than measured) but in one ( Diabetes ) the reverse was the case (Kruskal-Wallis giving lower;
Edit intent: fluency
Original:  In two from three medical studies, Kruskal-Wallis predictions ( with "dialysis" with similar predictionsand "marital" higher than measured) were more accurate than M.C. ANOVA (both higher than measured) but in one ( "diabetes" ) the reverse was the case (Kruskal-Wallis giving lower;
Revised:  In two from three medical studies, Kruskal-Wallis predictions ( with "dialysis" with similar predictionsand "marital" higher than measured) were more accurate than M.C. ANOVA (both higher than measured) but in one ( "diabetes" ) the reverse was found (Kruskal-Wallis giving lower;
Edit intent: clarity
Original:  M.C. Kruskal-Wallis power studies based on Bernstein fits can be used with continuous variables (regardless of ties) to predict power and sample size.
Revised:  M.C. Kruskal-Wallis power studies based on Bernstein fits can be used with continuous variables to predict power and sample size.
Edit intent: clarity
Original:  Overall they do not seem less accurate than M.C. ANOVA where the latter can be used , and have a much wider range of applicability.
Revised:  Overall they do not seem less accurate than ANOVA power studies where the latter can be used , and have a much wider range of applicability.
Edit intent: clarity
Original:  Overall they do not seem less accurate than M.C. ANOVA where the latter can be used , and have a much wider range of applicability.
Revised:  Overall they do not seem less accurate than M.C. ANOVA where the latter are valid , and have a much wider range of applicability.
Edit intent: clarity
Original:  Overall they do not seem less accurate than M.C. ANOVA where the latter can be used , and have a much wider range of applicability.
Revised:  Overall they do not seem less accurate than M.C. ANOVA where the latter can be used , and have wider applicability.
Edit intent: clarity
Original:  M.C. Kruskal-Wallis will be useful for sample size prediction with three groups.
Revised:  M.C. Kruskal-Wallis is useful for sample size prediction with three groups.
Edit intent: clarity
Original: In this paper  we study the cooperative card game, The Crew: The Quest for Planet Nine from the viewpoint of algorithmic combinatorial game theory.
Revised: In this paper , we study the cooperative card game, The Crew: The Quest for Planet Nine from the viewpoint of algorithmic combinatorial game theory.
Edit intent: fluency
Original:  The Crew: The Quest for Planet Nine, is a game based off of traditional trick taking card games, like bridge or hearts.
Revised:  The Crew: The Quest for Planet Nine, is a game based on traditional trick-taking card games, like bridge or hearts.
Edit intent: fluency
Original:  The game also forces players to not talk about the cards in their hand , and has a number of "Task Tokens" which can modify the rules slightly.
Revised:  The game also forces players to not talk about the cards in their hand and has a number of "Task Tokens" which can modify the rules slightly.
Edit intent: fluency
Original:  In this work, we introduce and formally define a perfect-information model of this problem, and show that the general unbounded version , as well as the constant player count version are both intractable.
Revised:  In this work, we introduce and formally define a perfect-information model of this problem, and show that the general unbounded version is computationally intractable.
Edit intent: clarity
Original:  However, we also show that two bounded versions of this decision problem -- deciding whether or not all players can complete their objectives -- can be solved in polynomial time.
Revised:  However, we also show that two bounded versions of this decision problem - deciding whether or not all players can complete their objectives -- can be solved in polynomial time.
Edit intent: fluency
Original:  However, we also show that two bounded versions of this decision problem -- deciding whether or not all players can complete their objectives -- can be solved in polynomial time.
Revised:  However, we also show that two bounded versions of this decision problem -- deciding whether or not all players can complete their objectives - can be solved in polynomial time.
Edit intent: fluency
Original:  For the coherent noise model, we apply this approach to describe a simple switch between computation and storage of intermediate results in a decoherence-free subspace  .
Revised:  For the coherent noise model, we describe how to switch between computation and storage of intermediate results in a decoherence-free subspace .
Edit intent: clarity
Original:  The power of our approach to logical gate synthesis is demonstrated by two proofs of concept: the [[2^{l+1}-2,2,2]] triorthogonal code family, and the [[2^m,m{r},2^{\min\{r,m-r\}}]] quantum Reed-Muller code family.
Revised:  The power of our approach is demonstrated by two proofs of concept: the [[2^{l+1}-2,2,2]] triorthogonal code family, and the [[2^m,m{r},2^{\min\{r,m-r\}}]] quantum Reed-Muller code family.
Edit intent: clarity
Original:  This essay discusses what happens when, eventually, someone chooses to cancel their participation in a large SNP. Methodology/design: This is a theoretical essay, whose narrative resembles a theoretical-empirical manuscript, grounded on the author's experience and his subjective perceptions regarding being out of the WhatsApp  (nowadays, the main SNP instance in the world).
Revised:  This essay discusses what happens when, eventually, someone chooses to cancel their participation in a large SNP. Methodology/design: This is a theoretical essay, whose narrative resembles a theoretical-empirical manuscript, grounded on the author's experience and his subjective perceptions regarding being out of the WhatsApp network (nowadays, the main SNP instance in the world).
Edit intent: clarity