wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To begin, we start by considering the baseline where service times are known. We find that this allows for significant improvements. In particular, not only can the queue being joined be chosen based on the total work at the queue instead of the number of jobs, but also the jobs in the queue can be served using strategies that take advantage of the service times such as shortest job first or shortest remaining processing time. Such strategies greatly improve performance under high load. We then examine the impact of using predictions in place of true service times. Our main takeaway is that using even seemingly weak predictions of service times can yield significant benefits over blind First In First Out queueing in this context.
Revised:  Our primary takeaway is that using even seemingly weak predictions of service times can yield significant benefits over blind First In First Out queueing in this context.
Edit intent: clarity
Original:  We propose to view the hidden states of the encoder and the decoder as spatio-temporal snapshots of network dynamics and to apply proper orthogonal decomposition to their concatenation to compute a low-dimensional embedding for hidden state dynamics. Projection of the decoder states onto such interpretable embedding space shows that Seq2Seq training to predict sequences using gradient-descent back propagation effectively performs dimension reduction consisting of only a small percentage of dimensions of the network's hidden units.
Revised:  Furthermore, we show that the embedding is an effective method for unsupervised learning and can be utilized to estimate the optimality of model training. In particular, we demonstrate that embedding space projections of the decoder states onto such interpretable embedding space shows that Seq2Seq training to predict sequences using gradient-descent back propagation effectively performs dimension reduction consisting of only a small percentage of dimensions of the network's hidden units.
Edit intent: coherence
Original:  Projection of the decoder states onto such interpretable embedding space shows that Seq2Seq training to predict sequences using gradient-descent back propagation effectively performs dimension reduction consisting of only a small percentage of dimensions of the network's hidden units.
Revised:  Projection of the decoder states of RNN Seq2Seq training to predict sequences using gradient-descent back propagation effectively performs dimension reduction consisting of only a small percentage of dimensions of the network's hidden units.
Edit intent: clarity
Original:  Projection of the decoder states onto such interpretable embedding space shows that Seq2Seq training to predict sequences using gradient-descent back propagation effectively performs dimension reduction consisting of only a small percentage of dimensions of the network's hidden units. Furthermore, sequences are being clustered into well separable clusters in the low dimensional space each of which corresponds to a different type of dynamics .
Revised:  Projection of the decoder states onto such interpretable embedding space shows that Seq2Seq model trained on sequences prediction URLanized in clusters capturing similarities and differences in the dynamics of these sequences. Such performance corresponds to an unsupervised clustering of any spatio-temporal features and can be employed for time-dependent problems such as temporal segmentation, clustering of dynamic activity, self-supervised classification, action recognition, failure prediction, etc. We test and demonstrate the application of the low dimensional space each of which corresponds to a different type of dynamics .
Edit intent: meaning-changed
Original:  Furthermore, sequences are being clustered into well separable clusters in the low dimensional space each of which corresponds to a different type of dynamics . The projection methodology also clarifies the roles of the encoder and the decoder components of the network. We show that the projection of encoder hidden states onto the low dimensional space provides an initializing trajectory directing the sequence to the cluster which corresponds to that particular type of distinct dynamics and the projection of the decoder hidden states constitutes the embedded cluster attractor. Inspection of the low dimensional space and the projections onto it during training shows that the estimation of clusters separability in the embedding can be utilized to estimate the optimality of model training. We test and demonstrate our proposed interpretability methodology on synthetic examples (dynamics on a circle and an ellipse) and on 3D human body movement data .
Revised:  Furthermore, sequences are being clustered into well separable clusters in the embedding methodology to time-sequences of 3D human body movement data .
Edit intent: clarity
Original: A growing cell population such as cancer or bacteria accumulates point mutations (single nucleotide changes in DNA). To model this process, we consider that cellsdivide and die as a branching process, and that each cell contains a sequence of nucleotides which can mutate at cell division.
Revised: We study a fundamental model of DNA evolution in a growing population of cells: cell divisions follow the Yule process, and that each cell contains a sequence of nucleotides which can mutate at cell division.
Edit intent: meaning-changed
Original:  Assuming that the population grows from one to many cells and that mutation rates are small, we prove limit theorems. At a single site (position) on the genetic sequence, the number of mutated cellsfollows a Luria-Delbr\"uck distribution. Across multiple sites , the joint distribution of mutation frequencies reflects the structure of the evolutionary tree. Taking the number of sites to infinity, the site frequency spectrum is deterministic at small frequencies, transitioning to random at large frequencies. Heterogeneous mutation ratesand selection are discussed. For an example application we estimate mutation rates in a lung adenocarcinoma.
Revised:  Following typical parameter values in bacteria and cancer cell populations, we take the mutation rate to zero and the final number of cells to infinity. We prove that almost every site (entry of the sequence) is mutated in only a finite number of cells, and these numbers are independent across sites. However independence breaks down for the rare sites which are mutated in a positive fraction of the population. The model is free from the popular but disputed infinite sites assumption. Violations of the infinite sites assumption are widespread while their impact on mutation frequencies is negligible at the scale of population fractions. Some results are generalised to allow for cell death, selection, and site-specific mutation rates. We also estimate mutation rates in a lung adenocarcinoma.
Edit intent: meaning-changed
Original: Extracellular recordings using modern, dense probes provide detailed footprints of action potentials (spikes) from thousands of neurons simultaneously. Inferring the activity of single neurons from these recordings, however, is a complex blind source separation problem, complicated both by the high intrinsic data dimensionality and large data volume. Despite these complications, dense probes can allow for the estimation of a spike's source location, a powerful feature for determining the firing neuron's position and identity in the recording.
Revised: Determining the positions of neurons in an extracellular recording is useful for investigating the functional properties of the estimation of a spike's source location, a powerful feature for determining the firing neuron's position and identity in the recording.
Edit intent: clarity
Original:  Despite these complications, dense probes can allow for the estimation of a spike's source location, a powerful feature for determining the firing neuron's position and identity in the recording. Here we present a novel, generative model for inferring the source of individual spikes given observed electrical traces .
Revised:  Despite these complications, dense probes can allow for the underlying neural circuitry. In this work, we present a novel, generative model for inferring the source of individual spikes given observed electrical traces .
Edit intent: clarity
Original:  We evaluate our method on  biophysically realistic simulated datasets, showing that our method outperforms heuristic localization methods such as center of mass and can improve spike sorting performance significantly.
Revised:  We evaluate our method on biophysically realistic simulated and real extracellular datasets, demonstrating that it is more accurate than and can improve spike sorting performance over heuristic localization methods such as center of mass and can improve spike sorting performance significantly.
Edit intent: meaning-changed
Original:  We evaluate our method on  biophysically realistic simulated datasets, showing that our method outperforms heuristic localization methods such as center of mass and can improve spike sorting performance significantly. We further apply our model to real data to show that it is an effective, interpretable tool for analyzing large-scale extracellular recordings .
Revised:  We evaluate our method on biophysically realistic simulated datasets, showing that our method outperforms heuristic localization methods such as center of mass .
Edit intent: coherence
Original: For a many-to-one matching market where firms have strict and q-responsive preferences, we give a characterization of the  strongly stable fractional matching set as the union of the convex hull of  connected sets of stable matchings  .
Revised: For a many-to-one matching market where firms have strict and -responsive preferences, we give a characterization of the strongly stable fractional matching set as the union of the convex hull of connected sets of stable matchings .
Edit intent: clarity
Original: For a many-to-one matching market where firms have strict and q-responsive preferences, we give a characterization of the  strongly stable fractional matching set as the union of the convex hull of  connected sets of stable matchings  .
Revised: For a many-to-one matching market where firms have strict and q-responsive preferences, we give a characterization of the set of strongly stable fractional matching set as the union of the convex hull of connected sets of stable matchings .
Edit intent: clarity
Original: For a many-to-one matching market where firms have strict and q-responsive preferences, we give a characterization of the  strongly stable fractional matching set as the union of the convex hull of  connected sets of stable matchings  .
Revised: For a many-to-one matching market where firms have strict and q-responsive preferences, we give a characterization of the strongly stable fractional matchings as the union of the convex hull of connected sets of stable matchings .
Edit intent: clarity
Original: For a many-to-one matching market where firms have strict and q-responsive preferences, we give a characterization of the  strongly stable fractional matching set as the union of the convex hull of  connected sets of stable matchings  .
Revised: For a many-to-one matching market where firms have strict and q-responsive preferences, we give a characterization of the strongly stable fractional matching set as the union of the convex hull of all connected sets of stable matchings .
Edit intent: clarity
Original: For a many-to-one matching market where firms have strict and q-responsive preferences, we give a characterization of the  strongly stable fractional matching set as the union of the convex hull of  connected sets of stable matchings  .
Revised: For a many-to-one matching market where firms have strict and q-responsive preferences, we give a characterization of the strongly stable fractional matching set as the union of the convex hull of connected sets of stable matchings . Also, we prove that a strongly stable fractional matching is represented as a convex combination of stable matchings that are ordered in the common preferences of all firms .
Edit intent: meaning-changed
Original: We propose a new, complementary approach to interpretability, in which machines are not considered as experts whose role it is to suggest what should be done and why, but rather as advisers.
Revised: The task of optimizing machines to support human decision-making is often conflated with that of optimizing machines for accuracy even though they are materially different. Whereas it is typical for learning systems to prescribe actions through prediction, here we propose an approach in which the role of machines is to reframe problems in order to directly support human decisions. Inspired by the success of representation learning in promoting machine performance, we frame the problem as one of learning representations that are not considered as experts whose role it is to suggest what should be done and why, but rather as advisers.
Edit intent: meaning-changed
Original: We propose a new, complementary approach to interpretability, in which machines are not considered as experts whose role it is to suggest what should be done and why, but rather as advisers. The objective of these models is to communicate to a human decision-maker not what to decide but how to decide. In this way, we propose that machine learning pipelines will be more readily adopted, since they allow a decision-maker to retain agency. Specifically, we develop a framework for learning representations by humans, for humans, in which we learn representations of inputs ( " advice") that are effective for human decision-making.
Revised: We propose a new, complementary approach to interpretability, in which machines are conducive to good human performance. This " advice") that are effective for human decision-making.
Edit intent: clarity
Original:  Specifically, we develop a framework for learning representations by humans, for humans, in which we learn representations of inputs ( " advice") that are effective for human decision-making. Representation-generating models are trained with humans-in-the-loop, implicitly incorporating the human decision-making model .
Revised:  Specifically, we develop a framework for learning representations by humans, for humans, in which we learn representations of inputs ( " Man Composed with Machine'' framework incorporates a human decision-making model .
Edit intent: meaning-changed
Original:  Representation-generating models are trained with humans-in-the-loop, implicitly incorporating the human decision-making model . We show that optimizing for human decision-making rather than accuracy is effective in promoting good decisionsin various classification tasks while inherently maintaining a sense of interpretability .
Revised:  Representation-generating models are trained with humans-in-the-loop, implicitly incorporating the human decision-making model directly into the representation learning paradigm with optimization achieved through a novel human-in-the-loop training procedure. We empirically demonstrate on various tasks and representational forms that the framework is capable of learning representations that better coincide with human decision-making rather than accuracy is effective in promoting good decisionsin various classification tasks while inherently maintaining a sense of interpretability .
Edit intent: meaning-changed
Original:  Extending classic results from an earlier literature, we determine conditions under which (a) solutions exist, are unique and are globally computable, and (b) the resulting wealth dynamics are stationary, ergodic and geometric mixing .
Revised:  Extending classic results from an earlier literature, we determine conditions under which (a) solutions exist, are unique and are globally computable, (b) the resulting wealth dynamics are stationary, ergodic and geometric mixing .
Edit intent: fluency
Original:  Extending classic results from an earlier literature, we determine conditions under which (a) solutions exist, are unique and are globally computable, and (b) the resulting wealth dynamics are stationary, ergodic and geometric mixing .
Revised:  Extending classic results from an earlier literature, we determine conditions under which (a) solutions exist, are unique and are globally computable, and (b) the resulting wealth dynamics are stationary, ergodic and geometrically mixing, and (c) the wealth distribution has a Pareto tail .
Edit intent: meaning-changed
Original:  While it is often prudent to avoid such projects, we argue it can be beneficial to engage them in some cases as the visualization process can help refine data collection, solving a ` chicken and egg ' problem of having the data and tools to analyze it.
Revised:  While it is often prudent to avoid such projects, we argue it can be beneficial to engage them in some cases as the visualization process can help refine data collection, solving a " chicken and egg ' problem of having the data and tools to analyze it.
Edit intent: fluency
Original:  While it is often prudent to avoid such projects, we argue it can be beneficial to engage them in some cases as the visualization process can help refine data collection, solving a ` chicken and egg ' problem of having the data and tools to analyze it.
Revised:  While it is often prudent to avoid such projects, we argue it can be beneficial to engage them in some cases as the visualization process can help refine data collection, solving a ` chicken and egg " problem of having the data and tools to analyze it.
Edit intent: fluency
Original:  We found this to be the case in the domain of task parallel computing . Parallel and distributed programs orchestrate the cooperation of many computation resources to produce results that would be impossible to collect on a single machine. The complexity of orchestrating and fine-tuning the execution of these parallel and distributed computations requires careful consideration from the programmer. However, few performance data collection and analysis tools are geared towards the specifics of task parallel paradigms. What changes need to be made are an open area of research.
Revised:  We found this to be the case in the domain of task parallel computing where such data and tooling is an open area of research.
Edit intent: clarity
Original: This paper develops a threshold regression model , where the threshold is determined by an unknown relation between two variables . The threshold function is estimated fully nonparametrically . The observations are allowed to be cross-sectionally dependent and the model can be applied to determine an unknown spatial border for sample splitting over a random field.
Revised: This paper develops a threshold regression model , where the threshold is determined by an unknown relation between two variables nonparametrically determines the threshold. We allow the observations to be cross-sectionally dependent and the model can be applied to determine an unknown spatial border for sample splitting over a random field.
Edit intent: clarity
Original:  The uniform rate of convergence and the nonstandard limiting distribution of the nonparametric threshold estimator are derived. The root-n consistency and the asymptotic normality of the regression slope parameter estimatorare also obtained.
Revised:  The uniform rate of convergence and the nonstandard limiting distribution of the nonparametric threshold estimator . We also obtain the root-n consistency and the asymptotic normality of the regression slope parameter estimatorare also obtained.
Edit intent: coherence
Original:  The root-n consistency and the asymptotic normality of the regression slope parameter estimatorare also obtained. Empirical relevance is illustrated by estimating an economic border induced by the housing price difference between Queens and Brooklyn in New York City, where the economic border deviates substantially from the administrative one .
Revised:  The root-n consistency and the asymptotic normality of the regression coefficient estimator. Our model has broad empirical relevance as illustrated by estimating an economic border induced by the housing price difference between Queens and Brooklyn in New York City, where the economic border deviates substantially from the administrative one .
Edit intent: clarity
Original:  Empirical relevance is illustrated by estimating an economic border induced by the housing price difference between Queens and Brooklyn in New York City, where the economic border deviates substantially from the administrative one .
Revised:  Empirical relevance is illustrated by estimating an economic border induced by the housing price difference between Queens and Brooklyn in New York City, where the economic border deviates substantially from the tipping point in social segregation problems as a function of demographic characteristics; and determining metropolitan area boundaries using nighttime light intensity collected from satellite imagery. We find that the new empirical results are substantially different from the existing studies .
Edit intent: meaning-changed
Original:  In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain  , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner  .
Revised:  In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Edit intent: meaning-changed
Original:  In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain  , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner  .
Revised:  In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner . Furthering the links between physics, complexity science and neuroscience promotes the understanding of the physical basis that supports the level of conscious arousal in URLanisms .
Edit intent: meaning-changed
Original:   In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Revised:  We then go on to show that, in addition to reducing complexity, anesthesia also modulates the informational structure between the forward- and reverse-time neural signals. Specifically, using three distinct notions of temporal asymmetry we show that anesthesia reduces temporal asymmetry on information-theoretic and information-geometric grounds. In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Edit intent: meaning-changed
Original:   In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Revised:  In contrast to prior work, our results show that : (1) Complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Edit intent: meaning-changed