wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We first observe that it can be viewed as a structural variant of tensor PCA in which the hypergraph parameters k and h determine the structure of the signal to be recovered when the observations are contaminated by Gaussian noise. In this work, we provide tight information-theoretic upper and lower bounds for the recovery problem , as well as the first non-trivial algorithmic bounds based on approximate message passing algorithms.
Revised:  We first observe that it can be viewed as a structural variant of tensor-PCA problem. We provide tight upper and lower bounds for the recovery problem , as well as the first non-trivial algorithmic bounds based on approximate message passing algorithms.
Edit intent: clarity
Original:  In this work, we provide tight information-theoretic upper and lower bounds for the recovery problem , as well as the first non-trivial algorithmic bounds based on approximate message passing algorithms.
Revised:  In this work, we provide tight information-theoretic upper and lower bounds for the exact recovery threshold by the maximum-likelihood estimator , as well as the first non-trivial algorithmic bounds based on approximate message passing algorithms.
Edit intent: meaning-changed
Original:  Interestingly, the bounds show that the structure of the signal does have an impact on the existing bounds of tensor PCA that the unstructured planted signal does not capture .
Revised:  Interestingly, the bounds show that the signal structure impacts the location of the statistical and computational phase transition that the known existing bounds for the tensor-PCA model do not capture. This effect is due to the generic planted signal prior that this latter model addresses .
Edit intent: meaning-changed
Original:  We present EdgeBERT  an in-depth and principled algorithm and hardware design methodology to achieve minimal latency and energy consumption on multi-task NLP inference. Compared to the ALBERT baseline, we achieve up to 2.4x and 13.4x inference latencyand memory savings, respectively, with less than 1\%-pt drop in accuracy on several GLUE benchmarks by employing a calibrated combination of 1) entropy-based early stopping, 2) adaptive attention span, 3) movement and magnitude pruning, and 4) floating-point quantization.
Revised:  We present EdgeBERT an in-depth and principled algorithm and hardware design methodology to achieve minimal latency and energy consumption on multi-task NLP . EdgeBERT employs entropy-based early exit predication in order to perform dynamic voltage-frequency scaling (DVFS), at a sentence granularity, for minimal energy consumption while adhering to a prescribed target latency. Computation and memory footprint overheads are further alleviated by employing a calibrated combination of 1) entropy-based early stopping, 2) adaptive attention span, 3) movement and magnitude pruning, and 4) floating-point quantization.
Edit intent: meaning-changed
Original:  Compared to the ALBERT baseline, we achieve up to 2.4x and 13.4x inference latencyand memory savings, respectively, with less than 1\%-pt drop in accuracy on several GLUE benchmarks by employing a calibrated combination of 1) entropy-based early stopping, 2) adaptive attention span, 3) movement and magnitude pruning, and 4) floating-point quantization.
Revised:  Compared to the ALBERT baseline, we achieve up to 2.4x and 13.4x inference latencyand memory savings, respectively, with less than 1\%-pt drop in accuracy on several GLUE benchmarks by employing a calibrated combination of adaptive attention span, 3) movement and magnitude pruning, and 4) floating-point quantization.
Edit intent: meaning-changed
Original:  Compared to the ALBERT baseline, we achieve up to 2.4x and 13.4x inference latencyand memory savings, respectively, with less than 1\%-pt drop in accuracy on several GLUE benchmarks by employing a calibrated combination of 1) entropy-based early stopping, 2) adaptive attention span, 3) movement and magnitude pruning, and 4) floating-point quantization.
Revised:  Compared to the ALBERT baseline, we achieve up to 2.4x and 13.4x inference latencyand memory savings, respectively, with less than 1\%-pt drop in accuracy on several GLUE benchmarks by employing a calibrated combination of 1) entropy-based early stopping, 2) adaptive attention span, selective network pruning, and 4) floating-point quantization.
Edit intent: clarity
Original:  Compared to the ALBERT baseline, we achieve up to 2.4x and 13.4x inference latencyand memory savings, respectively, with less than 1\%-pt drop in accuracy on several GLUE benchmarks by employing a calibrated combination of 1) entropy-based early stopping, 2) adaptive attention span, 3) movement and magnitude pruning, and 4) floating-point quantization.
Revised:  Compared to the ALBERT baseline, we achieve up to 2.4x and 13.4x inference latencyand memory savings, respectively, with less than 1\%-pt drop in accuracy on several GLUE benchmarks by employing a calibrated combination of 1) entropy-based early stopping, 2) adaptive attention span, 3) movement and magnitude pruning, and floating-point quantization.
Edit intent: fluency
Original:  Furthermore, in order to maximize the  benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a scalable hardware architecture wherein floating-point bit encodings of the shareable multi-task embedding parameters are stored in high-density non-volatile memory .
Revised:  Furthermore, in order to maximize the synergistic benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a scalable hardware architecture wherein floating-point bit encodings of the shareable multi-task embedding parameters are stored in high-density non-volatile memory .
Edit intent: meaning-changed
Original:  Furthermore, in order to maximize the  benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a scalable hardware architecture wherein floating-point bit encodings of the shareable multi-task embedding parameters are stored in high-density non-volatile memory .
Revised:  Furthermore, in order to maximize the benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a 12nm scalable hardware accelerator system, integrating a fast-switching low-dropout voltage regulator (LDO), an all-digital phase-locked loop (ADPLL), as well as, high-density embedded non-volatile memories (eNVMs) wherein the sparse floating-point bit encodings of the shareable multi-task embedding parameters are stored in high-density non-volatile memory .
Edit intent: meaning-changed
Original:  Furthermore, in order to maximize the  benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a scalable hardware architecture wherein floating-point bit encodings of the shareable multi-task embedding parameters are stored in high-density non-volatile memory .
Revised:  Furthermore, in order to maximize the benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a scalable hardware architecture wherein floating-point bit encodings of the shared multi-task embedding parameters are stored in high-density non-volatile memory .
Edit intent: clarity
Original:  Furthermore, in order to maximize the  benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a scalable hardware architecture wherein floating-point bit encodings of the shareable multi-task embedding parameters are stored in high-density non-volatile memory .
Revised:  Furthermore, in order to maximize the benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a scalable hardware architecture wherein floating-point bit encodings of the shareable multi-task parameters are carefully stored .
Edit intent: clarity
Original:  Altogether, EdgeBERT enables fully on-chip inference acceleration of NLP workloads with 5.2 x, and 157x lower energy than that of an un-optimized accelerator and CUDA adaptations on an Nvidia Jetson Tegra X2 mobile GPU, respectively.
Revised:  Altogether, latency-aware multi-task NLP inference acceleration on the EdgeBERT hardware system generates up to 7x, 2.5 x, and 157x lower energy than that of an un-optimized accelerator and CUDA adaptations on an Nvidia Jetson Tegra X2 mobile GPU, respectively.
Edit intent: meaning-changed
Original:  Altogether, EdgeBERT enables fully on-chip inference acceleration of NLP workloads with 5.2 x, and 157x lower energy than that of an un-optimized accelerator and CUDA adaptations on an Nvidia Jetson Tegra X2 mobile GPU, respectively.
Revised:  Altogether, EdgeBERT enables fully on-chip inference acceleration of NLP workloads with 5.2 x, and 53x lower energy compared to the conventional inference without early stopping, the latency-unbounded early exit approach, and CUDA adaptations on an Nvidia Jetson Tegra X2 mobile GPU, respectively.
Edit intent: meaning-changed
Original:  Using more than 40,000 rigorously selected tweets (from over six million tweets collected using keywords) posted by over 20,000 distinct Twitter users , we adopt a human-guided machine learning framework to capture the public opinions on the potential vaccines for SARS-CoV-2 and classify them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Revised:  We adopt a human-guided machine learning framework (using more than 40,000 rigorously selected tweets (from over six million tweets collected using keywords) posted by over 20,000 distinct Twitter users , we adopt a human-guided machine learning framework to capture the public opinions on the potential vaccines for SARS-CoV-2 and classify them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Edit intent: fluency
Original:  Using more than 40,000 rigorously selected tweets (from over six million tweets collected using keywords) posted by over 20,000 distinct Twitter users , we adopt a human-guided machine learning framework to capture the public opinions on the potential vaccines for SARS-CoV-2 and classify them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Revised:  Using more than 40,000 rigorously selected tweets from more than 20,000 distinct Twitter users , we adopt a human-guided machine learning framework to capture the public opinions on the potential vaccines for SARS-CoV-2 and classify them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Edit intent: clarity
Original:  Using more than 40,000 rigorously selected tweets (from over six million tweets collected using keywords) posted by over 20,000 distinct Twitter users , we adopt a human-guided machine learning framework to capture the public opinions on the potential vaccines for SARS-CoV-2 and classify them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Revised:  Using more than 40,000 rigorously selected tweets (from over six million tweets collected using keywords) posted by over 20,000 distinct Twitter users ) to capture public opinion on the potential vaccines for SARS-CoV-2 and classify them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Edit intent: coherence
Original:  Using more than 40,000 rigorously selected tweets (from over six million tweets collected using keywords) posted by over 20,000 distinct Twitter users , we adopt a human-guided machine learning framework to capture the public opinions on the potential vaccines for SARS-CoV-2 and classify them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Revised:  Using more than 40,000 rigorously selected tweets (from over six million tweets collected using keywords) posted by over 20,000 distinct Twitter users , we adopt a human-guided machine learning framework to capture the public opinions on the potential vaccines for SARS-CoV-2 , classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine.
Edit intent: coherence
Original:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower  acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Revised:  We compare demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Edit intent: coherence
Original:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower  acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Revised:  To investigate the scope and causes of public opinions, we compare the demographics, social capital , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Edit intent: fluency
Original:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower  acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Revised:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious status, and political affiliations of these three groups and find significant differences . A lower acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Edit intent: coherence
Original:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower  acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Revised:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups to investigate the scope and causes of public opinions . A lower acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Edit intent: coherence
Original:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower  acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Revised:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower vaccine acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Edit intent: fluency
Original:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower  acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Revised:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower acceptance level for potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Edit intent: fluency
Original:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower  acceptance level for the potential COVID-19 vaccines is observed among the socioeconomically disadvantaged groups.
Revised:  To investigate the scope and causes of public opinions, we compare the demographics, social capitals , income, religious statuses as well as the political affiliations of these three groups and find significant differences . A lower acceptance level for the potential COVID-19 vaccines is observed among socioeconomically disadvantaged groups.
Edit intent: fluency
Original: We consider an infinite-horizon optimal consumption problem for an individual who forms a consumption habit based on an exponentially-weighted average of her past rate of consumption. The novelty of our approach is in introducing habit formation through a constraint, rather than through the objective function, as is customary in the existing habit-formation literature. Specifically, we require that the individual  consume at a rate that is greater than a certain proportion \alpha\le1 of her consumption habit .
Revised: We propose a new optimal consumption model in which the degree of addictiveness of habit formation is directly controlled through a consumption constraint. In particular, we assume that the individual consume at a rate that is greater than a certain proportion \alpha\le1 of her consumption habit .
Edit intent: clarity
Original:  Specifically, we require that the individual  consume at a rate that is greater than a certain proportion \alpha\le1 of her consumption habit . Our habit-formation model allows for both addictive ( \alpha=1 ) and non-addictive ( 0<\alpha<1 ) habits.
Revised:  Specifically, we require that the individual consume at a rate that is greater than a certain proportion \alpha\le1 of her consumption habit , which is the exponentially-weighted average of past consumption rates. \alpha=1 ) and non-addictive ( 0<\alpha<1 ) habits.
Edit intent: meaning-changed
Original:  Our habit-formation model allows for both addictive ( \alpha=1 ) and non-addictive ( 0<\alpha<1 ) habits.
Revised:  Our habit-formation model allows for both addictive ( \alpha=1 prohibits the habit process to decrease and corresponds to the completely addictive model. \alpha=0 makes the habit-formation constraint moot and corresponds to the non-addictive ( 0<\alpha<1 ) habits.
Edit intent: meaning-changed
Original:  Our habit-formation model allows for both addictive ( \alpha=1 ) and non-addictive ( 0<\alpha<1 ) habits.
Revised:  Our habit-formation model allows for both addictive ( \alpha=1 ) and non-addictive ( 0<\alpha<1 leads to partially addictive models, with the level of addictiveness increasing with \alpha. In contrast to the existing habit-formation literature, our constraint cannot be incorporated in the objective function through infinite marginal utility.
Edit intent: meaning-changed
Original:  Assuming that the individual invests in a risk-free market, we formulate and solve a deterministic control problem to maximize the discounted CRRA utility of the individual's consumption-to-habit process subject to the said habit-formation constraint.
Revised:  Assuming that the individual invests in a risk-free market, we formulate and solve an infinite-horizon, deterministic control problem to maximize the discounted CRRA utility of the individual's consumption-to-habit process subject to the said habit-formation constraint.
Edit intent: meaning-changed
Original:  Assuming that the individual invests in a risk-free market, we formulate and solve a deterministic control problem to maximize the discounted CRRA utility of the individual's consumption-to-habit process subject to the said habit-formation constraint.
Revised:  Assuming that the individual invests in a risk-free market, we formulate and solve a deterministic control problem to maximize the discounted CRRA utility of the consumption-to-habit process subject to the said habit-formation constraint.
Edit intent: clarity
Original:  Assuming that the individual invests in a risk-free market, we formulate and solve a deterministic control problem to maximize the discounted CRRA utility of the individual's consumption-to-habit process subject to the said habit-formation constraint.
Revised:  Assuming that the individual invests in a risk-free market, we formulate and solve a deterministic control problem to maximize the discounted CRRA utility of the individual's consumption-to-habit process subject to the habit-formation constraint.
Edit intent: clarity
Original:  By consuming patiently, these individuals maintain a wealth-to-habit ratio that is greater than the minimum acceptable level  .
Revised:  By consuming patiently, these individuals maintain a wealth-to-habit ratio that is greater than the minimum acceptable level . Additionally, we prove that the optimal consumption path is hump-shaped if the initial wealth-to-habit ratio is either: (1) larger than a high threshold; or (2) below a low threshold and the agent is less risk averse. Thus, we provide a simple explanation for the consumption hump observed by various empirical studies .
Edit intent: meaning-changed
Original:  The first is the %DIFDELCMD < {\sc %%% Rich Lines problem, which asks for the set of all lines that each covers at least \lambda points from S, for a given integer parameter \lambda \geq 2;
Revised:  The first is the %DIFDELCMD < {\sc %%% Rich Lines problem, which asks for the set of all lines that each covers at least \lambda points from S, for a given integer parameter \lambda \geq 2;
Edit intent: fluency
Original:  this problem subsumes the %DIFDELCMD < {\sc %%% 3-Points-on-Line problem and the %DIFDELCMD < {\sc %%% Exact Fitting problem, which -- the latter -- asks for a line containing the maximum number of points.
Revised:  this problem subsumes the %DIFDELCMD < {\sc %%% 3-Points-on-Line problem and the %DIFDELCMD < {\sc %%% Exact Fitting problem, which -- the latter -- asks for a line containing the maximum number of points.
Edit intent: fluency
Original:  The second is the %DIFDELCMD < \NP%%% -hard problem%DIFDELCMD < {\sc %%% Line Cover , which asks for a set of k lines that cover the points of S, for a given parameter k \in N. Both problems have been extensively studied.
Revised:  The second is the %DIFDELCMD < \NP%%% %DIFDELCMD < {\sc %%% Line Cover , which asks for a set of k lines that cover the points of S, for a given parameter k \in N. Both problems have been extensively studied.
Edit intent: coherence
Original:  The second is the %DIFDELCMD < \NP%%% -hard problem%DIFDELCMD < {\sc %%% Line Cover , which asks for a set of k lines that cover the points of S, for a given parameter k \in N. Both problems have been extensively studied.
Revised:  The second is the %DIFDELCMD < \NP%%% -hard problem%DIFDELCMD < {\sc %%% NP-hard problem Line Cover , which asks for a set of k lines that cover the points of S, for a given parameter k \in N. Both problems have been extensively studied.
Edit intent: meaning-changed
Original:  For %DIFDELCMD < {\sc %%% Rich Lines and%DIFDELCMD < {\sc %%% Exact Fitting , we present a randomized Monte Carlo algorithm that achieves a lower running time than that of Guibas et al.'s algorithm [ %DIFDELCMD < {\it %%% Computational Geometry 1996], for a wide range of the parameter \lambda.
Revised:  For %DIFDELCMD < {\sc %%% and%DIFDELCMD < {\sc %%% Exact Fitting , we present a randomized Monte Carlo algorithm that achieves a lower running time than that of Guibas et al.'s algorithm [ %DIFDELCMD < {\it %%% Computational Geometry 1996], for a wide range of the parameter \lambda.
Edit intent: clarity