wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The proposed architecture accommodates diverse temporal resolutions in multiple streams to achieve robustness in acoustic modeling. For the diversity of temporal resolution in embedding processing, we consider dilation on TDNN-F, a variant of 1D-CNN .  Each stream stacks narrower TDNN-F layers whose kernel has a unique, stream-specific dilation rate when processing input speech frames in parallel.
Revised:  The proposed architecture accommodates diverse temporal resolutions in multiple streams to achieve the robustness . Each stream stacks narrower TDNN-F layers whose kernel has a unique, stream-specific dilation rate when processing input speech frames in parallel.
Edit intent: clarity
Original:  For the diversity of temporal resolution in embedding processing, we consider dilation on TDNN-F, a variant of 1D-CNN .  Each stream stacks narrower TDNN-F layers whose kernel has a unique, stream-specific dilation rate when processing input speech frames in parallel.
Revised:  For the diversity of temporal resolution in embedding processing, we consider dilation on TDNN-F, a variant of 1D-CNN . The dilation rates are selected from the multiples of a sub-sampling rate of 3 frames. Each stream stacks narrower TDNN-F layers whose kernel has a unique, stream-specific dilation rate when processing input speech frames in parallel.
Edit intent: meaning-changed
Original:  For the diversity of temporal resolution in embedding processing, we consider dilation on TDNN-F, a variant of 1D-CNN .  Each stream stacks narrower TDNN-F layers whose kernel has a unique, stream-specific dilation rate when processing input speech frames in parallel.
Revised:  For the diversity of temporal resolution in embedding processing, we consider dilation on TDNN-F, a variant of 1D-CNN . Each stream stacks TDNN-F layers whose kernel has a unique, stream-specific dilation rate when processing input speech frames in parallel.
Edit intent: clarity
Original:  For the diversity of temporal resolution in embedding processing, we consider dilation on TDNN-F, a variant of 1D-CNN .  Each stream stacks narrower TDNN-F layers whose kernel has a unique, stream-specific dilation rate when processing input speech frames in parallel. Hence it can better represent acoustic events without the increase of model complexity .
Revised:  For the diversity of temporal resolution in embedding processing, we consider dilation on TDNN-F, a variant of 1D-CNN . Each stream stacks narrower TDNN-F layers (a variant of 1D CNN), and output embedding vectors from the streams are concatenated then projected to the final layer .
Edit intent: clarity
Original:  On custom data from ASAPP's production  system for a contact center, it records a relative WER improvement of 11\% for the customer channel audios (10\% on average for the agent and customer channel recordings) to prove the superiority of the proposed model architecture in the wild.
Revised:  On custom data from ASAPP's production ASR system for a contact center, it records a relative WER improvement of 11\% for the customer channel audios (10\% on average for the agent and customer channel recordings) to prove the superiority of the proposed model architecture in the wild.
Edit intent: meaning-changed
Original:  On custom data from ASAPP's production  system for a contact center, it records a relative WER improvement of 11\% for the customer channel audios (10\% on average for the agent and customer channel recordings) to prove the superiority of the proposed model architecture in the wild.
Revised:  On custom data from ASAPP's production system for a contact center, it records a relative WER improvement of 11\% for customer channel audio to prove its robustness to data in the wild.
Edit intent: clarity
Original:  In terms of real-time factor (RTF) , multistream CNN outperforms the normal TDNN-F by 15\%, which also suggests its practicality on production systems or applications .
Revised:  In terms of real-time factor (RTF) , multistream CNN outperforms the normal TDNN-F by 15\%, which also suggests its practicality on production systems . When combined with self-attentive SRU LM rescoring, multistream CNN contributes for ASAPP to achieve the best WER of 1.75\% on test-clean in LibriSpeech .
Edit intent: meaning-changed
Original: On different time-intervals it can be useful to empirically determine whether the measurement process being observed is fundamental and representative of actual discrete events, or whether these measurements can still be faithfully represented as random samples of some underlying continuous process.
Revised: The Epps effect is key phenomenology relating to high frequency correlation dynamics in the financial markets. We argue that it can be useful to empirically determine whether the measurement process being observed is fundamental and representative of actual discrete events, or whether these measurements can still be faithfully represented as random samples of some underlying continuous process.
Edit intent: meaning-changed
Original: On different time-intervals it can be useful to empirically determine whether the measurement process being observed is fundamental and representative of actual discrete events, or whether these measurements can still be faithfully represented as random samples of some underlying continuous process. As sampling time-scales become smaller for a continuous-time process one can expect to be able to continue to measure correlations, even as the sampling intervals become very small. However, with a discrete event process one can expect the correlation measurements to quickly break-down. This is a theoretically well explored problem. Here we concern ourselves with a simulation based empirical investigation that uses the Epps effect as a discriminator between situations where the underlying system is discrete e.g. a D-type Hawkes process , and when it can still be appropriate to represent the problem with a continuous-time random process that is being asynchronously sampled e. g. an asynchronously sampled set of correlated Brownian motions.
Revised: On different time-intervals it can be used to determine whether trades at a tick-by-tick scale are best represented as samples from a Brownian diffusion, perhaps dressed with jumps; or as samples from truly discrete events represented as connected point processes. This can answer the question of whether correlations are better understood as an emergent time scale dependent property. In other words: Is the Epps effect a bias? To this end, we derive the Epps effect as a discriminator between situations where the underlying system is discrete e.g. a D-type Hawkes process , and when it can still be appropriate to represent the problem with a continuous-time random process that is being asynchronously sampled e. g. an asynchronously sampled set of correlated Brownian motions.
Edit intent: meaning-changed
Original:  Here we concern ourselves with a simulation based empirical investigation that uses the Epps effect as a discriminator between situations where the underlying system is discrete e.g. a D-type Hawkes process , and when it can still be appropriate to represent the problem with a continuous-time random process that is being asynchronously sampled e. g. an asynchronously sampled set of correlated Brownian motions.
Revised:  Here we concern ourselves with a simulation based empirical investigation that uses the Epps effect arising from asynchrony and provide a refined method to correct for the effect. The correction is compared against two existing methods correcting for asynchrony. We propose three experiments to discriminate between possible underlying representations: whether the data is best thought to be generated by discrete connected events (as proxied by a D-type Hawkes process , and when it can still be appropriate to represent the problem with a continuous-time random process that is being asynchronously sampled e. g. an asynchronously sampled set of correlated Brownian motions.
Edit intent: meaning-changed
Original:  Here we concern ourselves with a simulation based empirical investigation that uses the Epps effect as a discriminator between situations where the underlying system is discrete e.g. a D-type Hawkes process , and when it can still be appropriate to represent the problem with a continuous-time random process that is being asynchronously sampled e. g. an asynchronously sampled set of correlated Brownian motions. We derive a method aimed to compensate for the Epps effect from asynchrony and then use this to discriminate. We are able to compare the correction on a simple continuous Brownian price path model with a Hawkes price model when the sampling is either a simple homogeneous Poisson Process or a Hawkes sampling process. This suggests that Epps curves can sometimes provide insight into whether discrete data are in fact observables realised from fundamental co-dependent discrete processes, or when they are merely samples of some correlated continuous-time process .
Revised:  Here we concern ourselves with a simulation based empirical investigation that uses the Epps effect as a discriminator between situations where the underlying system is discrete e.g. a D-type Hawkes process ), or if they can be approximated to arise from Brownian diffusions, with or without jumps. We then demonstrate how the Hawkes representation easily recovers the phenomenology reported in the literature; phenomenology that cannot be recovered using a Brownian representation, without additional ad-hoc model complexity, even with jumps. The experiments are applied to trade and quote data from the Johannesburg Stock Exchange. We find evidence suggesting that high frequency correlation dynamics are most faithfully recovered when tick-by-tick data is represented as a web of inter-connected discrete events rather than sampled or averaged from underlying continuous Brownian diffusions irrespective of whether or not they are dressed with jumps .
Edit intent: meaning-changed
Original:  However, in order to optimize for hardware with different resource specifications and for applications that have various latency requirements , models with varying sparsity levels usually need to be trained and deployed separately .
Revised:  However, multiple models with different sparsity levels usually need to be separately trained and deployed to heterogeneous target hardware with different resource specifications and for applications that have various latency requirements , models with varying sparsity levels usually need to be trained and deployed separately .
Edit intent: clarity
Original:  In this paper, generalizing from slimmable neural networks, we present dynamic sparsity neural networks (DSNN) that, once trained, can instantly switch to execute at any given sparsity level at run-time.
Revised:  In this paper, we present Dynamic Sparsity Neural Networks (DSNN) that, once trained, can instantly switch to execute at any given sparsity level at run-time.
Edit intent: coherence
Original:  In this paper, generalizing from slimmable neural networks, we present dynamic sparsity neural networks (DSNN) that, once trained, can instantly switch to execute at any given sparsity level at run-time.
Revised:  In this paper, generalizing from slimmable neural networks, we present dynamic sparsity neural networks (DSNN) that, once trained, can instantly switch to any predefined sparsity configuration at run-time.
Edit intent: clarity
Original:  We show the efficacy of such models on ASR through comprehensive experiments and demonstrate that the performance of a dynamic sparsity model is on par with , and in some cases exceeds, the performance of individually trained single sparsity networks.
Revised:  We demonstrate the effectiveness and flexibility of DSNN using experiments on internal production datasets with Google Voice Search data, and show that the performance of a dynamic sparsity model is on par with , and in some cases exceeds, the performance of individually trained single sparsity networks.
Edit intent: meaning-changed
Original:  We show the efficacy of such models on ASR through comprehensive experiments and demonstrate that the performance of a dynamic sparsity model is on par with , and in some cases exceeds, the performance of individually trained single sparsity networks.
Revised:  We show the efficacy of such models on ASR through comprehensive experiments and demonstrate that the performance of a DSNN model is on par with , and in some cases exceeds, the performance of individually trained single sparsity networks.
Edit intent: meaning-changed
Original:  We show the efficacy of such models on ASR through comprehensive experiments and demonstrate that the performance of a dynamic sparsity model is on par with , and in some cases exceeds, the performance of individually trained single sparsity networks.
Revised:  We show the efficacy of such models on ASR through comprehensive experiments and demonstrate that the performance of a dynamic sparsity model is on par with that of individually trained single sparsity networks.
Edit intent: clarity
Original:  We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as users selection for model seeding, weigh users' edges ,  users' activation timing, etc.
Revised:  We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as users selection for model seeding, weigh users' edges , graph edge weighting, users' activation timing, etc.
Edit intent: meaning-changed
Original:  Based on the proposed approach, the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on overlaps of their social circles  , and activate users while considering their aggression levels.
Revised:  It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on overlaps of their social circles , and activate users while considering their aggression levels.
Edit intent: coherence
Original:  Based on the proposed approach, the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on overlaps of their social circles  , and activate users while considering their aggression levels.
Revised:  Based on the proposed approach, the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles , and activate users while considering their aggression levels.
Edit intent: clarity
Original:  Based on the proposed approach, the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on overlaps of their social circles  , and activate users while considering their aggression levels.
Revised:  Based on the proposed approach, the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on overlaps of their social circles ' overlaps , and activate users while considering their aggression levels.
Edit intent: meaning-changed
Original:  Based on the proposed approach, the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on overlaps of their social circles  , and activate users while considering their aggression levels.
Revised:  Based on the proposed approach, the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on overlaps of their social circles , and activate users according to their aggression levels.
Edit intent: clarity
Original:  We also establish the connections with the risk-sensitive zero-sum stochastic differential games over an infinite horizon with ergodic payoff criteria, as well as with the classical power robust expected utility for long time horizons  .
Revised:  We also establish the connections with the risk-sensitive zero-sum stochastic differential games over an infinite horizon with ergodic payoff criteria, as well as with the classical robust expected utilities for long time horizons .
Edit intent: fluency
Original:  We also establish the connections with the risk-sensitive zero-sum stochastic differential games over an infinite horizon with ergodic payoff criteria, as well as with the classical power robust expected utility for long time horizons  .
Revised:  We also establish the connections with the risk-sensitive zero-sum stochastic differential games over an infinite horizon with ergodic payoff criteria, as well as with the classical power robust expected utility for long time horizons . Finally, we give an example to illustrate that our approach can be applied to address a type of robust forward investment performance processes with negative realization processes .
Edit intent: meaning-changed
Original: We study the design of a decentralized platform in which workers and jobs repeatedly match, and their future engagement with the platform depends on whether they successfully find a match . The platform offers two types of matches to workers: an " adopted match " which entails repeatedly matching with the same job or a one-time match.
Revised: Volunteer crowdsourcing platforms, such as food URLanizations, match volunteers with tasks which are often recurring. To ensure completion of such tasks, platforms frequently use a commitment lever known as " adopted match " which entails repeatedly matching with the same job or a one-time match.
Edit intent: meaning-changed
Original:  The platform offers two types of matches to workers: an " adopted match " which entails repeatedly matching with the same job or a one-time match.Due to randomness in match compatibility, adoption seems favorable as it reduces uncertaintyin matching. However, high adoption levels reduce the number of available jobs, which in turn can suppress future worker engagementif the remaining workers cannot find a match.
Revised:  The platform offers two types of matches to workers: an " adopted match " Despite being effective in reducing match uncertainty, high levels of adoption reduce match availability for volunteers which in turn can suppress future worker engagementif the remaining workers cannot find a match.
Edit intent: clarity
Original:  However, high adoption levels reduce the number of available jobs, which in turn can suppress future worker engagementif the remaining workers cannot find a match.To optimally resolve the trade-off between adoption and maintaining available options, we develop a random market model that captures the heterogeneity in workers' future engagementbased on match type.
Revised:  However, high adoption levels reduce the number of available jobs, which in turn can suppress future engagement. We study how platforms should balance these two opposing factors. Our research is motivated by a collaboration with Food Rescue U.S. (FRUS), a volunteer-based food URLanization active in over 33 locations across the U.S. For platforms such as FRUS, success crucially depends on efficient volunteer utilization and engagement. Consequently, effectively utilizing non-monetary levers, such as adoption, is critical. Based on our analysis of fine-grained FRUS data, we develop a random market model that captures the heterogeneity in workers' future engagementbased on match type.
Edit intent: meaning-changed
Original: To optimally resolve the trade-off between adoption and maintaining available options, we develop a random market model that captures the heterogeneity in workers' future engagementbased on match type.
Revised: To optimally resolve the trade-off between adoption and maintaining available options, we develop a model for a repeated two-sided matching market consisting of tasks (prearranged donations) and volunteers. Our model incorporates the uncertainty in match compatibility as well as the negative impact of failing to match on future engagement. We study the platform's optimal policy for setting the adoption level to maximize the total discounted number of matches.
Edit intent: meaning-changed
Original:  In thinner markets, even though a static policy of full or no adoption can be suboptimal,  it achieves a constant-factor approximation where the factor improves with market thickness  .
Revised:  In thinner markets, even though a static policy of full or no adoption can be suboptimal, it achieves a constant-factor approximation where the factor improves with market thickness . Using our analytical and empirical results, we revisit the current design of the FRUS platform and make location-specific policy recommendations. More broadly, our work sheds light on how other two-sided platforms can control the double-edged impacts that commitment levers have on growth and engagement .
Edit intent: meaning-changed
Original: Public goods are often either over-consumed in the absence of regulatory mechanisms, or remain completely unused, as in the case of the Covid-19 pandemic, where social distance constraints are enforced to limit the number of people who can share public spaces.
Revised: Public goods are often either over-consumed in the absence of regulatory mechanisms, or remain completely unused, as in the Covid-19 pandemic, where social distance constraints are enforced to limit the number of people who can share public spaces.
Edit intent: coherence
Original:  The presence of additional physical constraints that arise in public goods allocation problems motivates the need for modifications to the Fisher market framework to account for these more general set of constraints . In our market mechanism, we specifically modify the Fisher market social optimization problem through a perturbation of the budgets of agents .
Revised:  The presence of additional physical constraints that arise in public goods allocation problems motivates the need for modifications to the Fisher market framework to account for additional constraints we introduce a social convex optimization problem where we perturb the budgets of agents .
Edit intent: clarity
Original:  In our market mechanism, we specifically modify the Fisher market social optimization problem through a perturbation of the budgets of agents . The budget perturbations are set based on the dual variables of the additional physical constraints such that the KKT conditions of the newly defined social and individual optimization problems are equivalent.
Revised:  In our market mechanism, we specifically modify the Fisher market social optimization problem through a perturbation of the budgets of agents such that the KKT conditions of the newly defined social and individual optimization problems are equivalent.
Edit intent: coherence
Original:  The budget perturbations are set based on the dual variables of the additional physical constraints such that the KKT conditions of the newly defined social and individual optimization problems are equivalent. This establishes that our mechanism sets market clearing pricessuch that each agent obtains their maximum possible utilities subject to their constraints .
Revised:  The budget perturbations are set based on the dual variables of the additional physical constraints such that the KKT conditions of the perturbed social problem establishes equilibrium prices .
Edit intent: clarity
Original:  We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections, and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007-February 2020), over 50 thousand accounts, 17 countries, and different account types including both trolls and bots.
Revised:  We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections, and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007 to February 2020), over 50 thousand accounts, 17 countries, and different account types including both trolls and bots.
Edit intent: fluency