wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our experiments  show that fixing the attention heads on the encoder side of the Transformer at training time does not impact the translation quality and even increases BLEU scores by up to 3 points in low-resource scenarios.
Revised:  Our experiments with different data sizes and multiple language pairs show that fixing the attention heads on the encoder side of the Transformer at training time does not impact the translation quality and even increases BLEU scores by up to 3 points in low-resource scenarios.
Edit intent: meaning-changed
Original:  Here, we study the dynamics of a population of two strains, one growing slightly faster than the other, competing for resources in a time-varying binary environment modeled by a carrying capacity that switches either randomly or periodically between states of abundance and scarcity.
Revised:  Here, we study the dynamics of a population of two strains, one growing slightly faster than the other, competing for resources in a time-varying binary environment modeled by a carrying capacity switching either randomly or periodically between states of abundance and scarcity.
Edit intent: clarity
Original:  The population dynamics is characterized by demographic noise (birth and death events) coupled to the fluctuating population size. By combining analytical and simulation methods, we elucidate the similarities and differences of evolving subject to stochastic and periodic switching.
Revised:  The population dynamics is characterized by demographic noise (birth and death events) coupled to a varying environment. We elucidate the similarities and differences of evolving subject to stochastic and periodic switching.
Edit intent: clarity
Original:  By combining analytical and simulation methods, we elucidate the similarities and differences of evolving subject to stochastic and periodic switching. We show that the population size distribution is  broader under intermediate and fast random switching than under periodic variations, with periodic changes leading to an abrupt transition from slow to fast switching regimes.
Revised:  By combining analytical and simulation methods, we elucidate the similarities and differences of the evolution subject to a stochastically- and periodically-varying environment. Importantly, the population size distribution is broader under intermediate and fast random switching than under periodic variations, with periodic changes leading to an abrupt transition from slow to fast switching regimes.
Edit intent: clarity
Original:  We show that the population size distribution is  broader under intermediate and fast random switching than under periodic variations, with periodic changes leading to an abrupt transition from slow to fast switching regimes. The fixation probability under intermediate/fast random and periodic switching can hence vary significantly, with markedly different asymptotic behaviors .
Revised:  We show that the population size distribution is broader under intermediate and fast random switching than under periodic variations, which results in markedly different asymptotic behaviors between the fixation probability of random and periodic switching can hence vary significantly, with markedly different asymptotic behaviors .
Edit intent: clarity
Original:  We formulate this idea using a supervised deep similarity matching cost function and derive from it deep neural networks with feedforward, lateral and feedback connections, and neurons that exhibit biologically-plausible Hebbian and anti-Hebbian plasticity.
Revised:  We formulate this idea using a contrastive similarity matching objective function and derive from it deep neural networks with feedforward, lateral and feedback connections, and neurons that exhibit biologically-plausible Hebbian and anti-Hebbian plasticity.
Edit intent: meaning-changed
Original:  We formulate this idea using a contrastive similarity matching objective function and derive from it deep neural networks with feedforward, lateral  and feedback connections, and neurons that exhibit biologically-plausible Hebbian and anti-Hebbian plasticity.
Revised:  We formulate this idea using a contrastive similarity matching objective function and derive from it deep neural networks with feedforward, lateral , and feedback connections, and neurons that exhibit biologically-plausible Hebbian and anti-Hebbian plasticity.
Edit intent: fluency
Original:  We show that, if a cell is exposed to a widely variable environment, cells may gain additional chemotactic accuracy by expressing multiple types of receptors with varying affinities for the same signal . As the uncertainty of the environment is increased, there is a transition between cells preferring to express a single receptor type and a mixture of types - hedging their bets against the possibility of an unfavorable environment.
Revised:  We show that, if a cell is exposed to a widely variable environment, cells may gain additional chemotactic accuracy by expressing multiple types of receptors with varying affinities for the same signal , as found commonly in chemotaxing cells like Dictyostelium. As environment uncertainty is increased, there is a transition between cells preferring to express a single receptor type and a mixture of types - hedging their bets against the possibility of an unfavorable environment.
Edit intent: meaning-changed
Original:  Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single receptor type - it is possible for cells to extract orders of magnitude more information out of a signal by using a maximum likelihood estimate than naive time integration  .
Revised:  Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single type -- cells may extract orders of magnitude more information out of a signal by using a maximum likelihood estimate than naive time integration .
Edit intent: clarity
Original:  Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single receptor type - it is possible for cells to extract orders of magnitude more information out of a signal by using a maximum likelihood estimate than naive time integration  .
Revised:  Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single receptor type - it is possible for cells to extract orders of magnitude more chemotactic information than expected by naive time integration .
Edit intent: clarity
Original:  Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single receptor type - it is possible for cells to extract orders of magnitude more information out of a signal by using a maximum likelihood estimate than naive time integration  .
Revised:  Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single receptor type - it is possible for cells to extract orders of magnitude more information out of a signal by using a maximum likelihood estimate than naive time integration . Our results show when cells should express multiple receptor types to chemotax, and how cells can efficiently interpret the data from these receptors .
Edit intent: meaning-changed
Original:  By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that would otherwise be intractable.
Revised:  By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable.
Edit intent: clarity
Original:  establish some common terminology, and review the immense scope of HMMs for applied ecological research  . By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.
Revised:  establish some common terminology, and review the immense scope of HMMs for applied ecological research . We also provide a supplemental tutorial on some of the more technical aspects of HMM implementation and interpretation . By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.
Edit intent: meaning-changed
Original: We combine stochastic thermodynamics, large deviation theory, and information theory to derive fundamental limits on the accuracy with which single cells can detect external chemical concentrationsthrough arbitrarily complex, energy consuming nonequilibrium cell-surface receptors .
Revised: We combine stochastic thermodynamics, large deviation theory, and information theory to derive fundamental limits on the accuracy with which single cell receptors can detect external chemical concentrationsthrough arbitrarily complex, energy consuming nonequilibrium cell-surface receptors .
Edit intent: fluency
Original:  We show that if estimation is performed by an ideal observer of the entire trajectory of receptor states, then no energy consuming non-equilibrium receptor that can be divided into two pools of bound signaling and unbound non-signaling states can outperform a simple equilibrium two-state receptor.
Revised:  We show that if estimation is performed by an ideal observer of the entire trajectory of receptor states, then no energy consuming non-equilibrium receptor that can be divided into signaling and non-signaling states can outperform a simple equilibrium two-state receptor.
Edit intent: clarity
Original:  We show that if estimation is performed by an ideal observer of the entire trajectory of receptor states, then no energy consuming non-equilibrium receptor that can be divided into two pools of bound signaling and unbound non-signaling states can outperform a simple equilibrium two-state receptor.
Revised:  We show that if estimation is performed by an ideal observer of the entire trajectory of receptor states, then no energy consuming non-equilibrium receptor that can be divided into two pools of bound signaling and unbound non-signaling states can outperform an equilibrium two-state receptor.
Edit intent: clarity
Original:  Moreover, we derive an energy accuracy tradeoff for such general non-equilibrium receptors when the estimation is performed by a simple observer of the duration the receptor is bound. Our tradeoff reveals that the simple observer can only attain the performance of the ideal observer in the limit of large receptor energy consumption and size.
Revised:  Moreover, we derive an energy accuracy tradeoff for such general non-equilibrium receptors when the estimation is performed by a simple observer of the duration the receptor is in signaling states. This tradeoff reveals that the simple observer can only attain the performance of the ideal observer in the limit of large receptor energy consumption and size.
Edit intent: clarity
Original:  Our derivations generalize the classic 1977 Berg-Purcell limit on cellular chemosensation along multiple dimensions, and also yield a novel thermodynamic uncertainty relation for the time a physical system spends in a pool of states .
Revised:  Our results generalize the classic 1977 Berg-Purcell limit on cellular chemosensation along multiple dimensions, and also yield a novel thermodynamic uncertainty relation for the time a physical system spends in a pool of states .
Edit intent: clarity
Original:  Our derivations generalize the classic 1977 Berg-Purcell limit on cellular chemosensation along multiple dimensions, and also yield a novel thermodynamic uncertainty relation for the time a physical system spends in a pool of states .
Revised:  Our derivations generalize the classic 1977 Berg-Purcell limit on cellular sensing along multiple dimensions, and also yield a novel thermodynamic uncertainty relation for the time a physical system spends in a pool of states .
Edit intent: meaning-changed
Original:  Our derivations generalize the classic 1977 Berg-Purcell limit on cellular chemosensation along multiple dimensions, and also yield a novel thermodynamic uncertainty relation for the time a physical system spends in a pool of states .
Revised:  Our derivations generalize the classic 1977 Berg-Purcell limit on cellular chemosensation along multiple dimensions, and yield a novel thermodynamic uncertainty relation for the time a physical system spends in a pool of states .
Edit intent: clarity
Original:  Our derivations generalize the classic 1977 Berg-Purcell limit on cellular chemosensation along multiple dimensions, and also yield a novel thermodynamic uncertainty relation for the time a physical system spends in a pool of states . This relation itself is of independent interest, with applications not only to cellular chemosensation, but also to the reliability of other biological processes, like clocks and motors, as a function of energy consumption .
Revised:  Our derivations generalize the classic 1977 Berg-Purcell limit on cellular chemosensation along multiple dimensions, and also yield a novel thermodynamic uncertainty relation for the time a physical system spends in a pool of states .
Edit intent: coherence
Original:  Current analysis is often hindered due to difficulties in estimating human posture . We introduce a new approach to the problem of human posture estimation for teleoperation tasks which relies solely on a haptic-input device for generating observations.
Revised:  Current posture estimation approaches face occlusion challenges in teleoperation and physical human-robot interaction. We investigate if the leader robot is an adequate sensor for posture estimation in teleoperation and we introduce a new probabilistic approach which relies solely on a haptic-input device for generating observations.
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Original:  We evaluate our posture estimation approach from a haptic-input device by comparing it with the human posture estimates from a commercial motion capture system . Our results show that the proposed algorithm successfully estimates human posture based only on the trajectory of the haptic-input device stylus.
Revised:  We evaluate our posture estimation approach from a haptic-input device by comparing it with the human posture estimates from a commercial motion capture system and also two least-squares optimization approaches for human inverse kinematics. The results reveal that the proposed algorithm successfully estimates human posture based only on the trajectory of the haptic-input device stylus.
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Original:  Our results show that the proposed algorithm successfully estimates human posture based only on the trajectory of the haptic-input device stylus. We additionally show that ergonomic risk estimates derived from our posture estimation approach are comparable to those estimates from gold-standard , motion-capture based pose estimates.
Revised:  Our results show that the proposed algorithm successfully estimates human postures and ergonomic risk scores comparable to those estimates from gold-standard , motion-capture based pose estimates.
Edit intent: clarity
Original:  In this paper, we present a new telerobotic training simulator for the Canadarm2 on the International Space Station (ISS), and record physiological data from subjects as they perform a task from the simulator under conditions which increased workload (e. g. latency and time pressure).
Revised:  In this paper, we present a new telerobotic training simulator for the Canadarm2 on the International Space Station (ISS), which is able to modulate workload through the addition of confounding factors such as latency, obstacles, and time pressure. In addition, multimodal physiological data is collected from subjects as they perform a task from the simulator under conditions which increased workload (e. g. latency and time pressure).
Edit intent: meaning-changed
Original:  In this paper, we present a new telerobotic training simulator for the Canadarm2 on the International Space Station (ISS), and record physiological data from subjects as they perform a task from the simulator under conditions which increased workload (e. g. latency and time pressure).
Revised:  In this paper, we present a new telerobotic training simulator for the Canadarm2 on the International Space Station (ISS), and record physiological data from subjects as they perform a task from the simulator under these different conditions.
Edit intent: clarity
Original:  Furthermore, EEG classification using a Riemannian classifier and Leave-One-Subject-Out cross-validation showed promising classification performance . EEG results also reveal that Independent Component Analysis (ICA) preprocessing and centrally located channels are more discriminative for 5-class discrimination, whereas information derived from the EEG parietal channels was more accurate in two-class classification of latency and time pressure paradigms .
Revised:  Furthermore, EEG classification using a Riemannian classifier and Leave-One-Subject-Out cross-validation showed promising classification performance and allowed for comparison of different channel configurations and preprocessing methods. Additionally, Riemannian distance and beta power of EEG data were investigated as potential cross-trial and continuous workload measures .
Edit intent: clarity
Original: Pre-trained language models such as BERT have recently contributed to significant advances in Natural Language Processing tasks. Interestingly, while multilingual BERT models have demonstrated impressive results, recent works have shown how monolingual BERT can also be competitive in zero-shot cross-lingual settings. This suggests that the abstractions learned by these models can transfer across languages, even when trained on monolingual data .
Revised: Pre-trained language models such as BERT have recently contributed to significant advances in NLP tasks. Recently, multi-modal versions of BERT have been developed, using heavy pre-training relying on vast corpora of aligned textual and image data, primarily applied to classification tasks such as VQA .
Edit intent: meaning-changed
Original:  In this paper, we investigate whether such generalization potential applies to other modalities, such as vision: does BERT contain abstractions that generalize beyond text?
Revised:  In this paper, we are interested in evaluating the visual capabilities of BERT out-of-the-box, by avoiding pre-training made on supplementary data. We choose to study Visual Question Generation, a task of great interest for grounded dialog, that enables to study the impact of each modality (as input can be visual and/or textual). Moreover, the generation aspect of the task requires an adaptation since BERT is primarily designed as an encoder.
Edit intent: meaning-changed
Original:  The results reported under different configurations indicate a positive answer to our research question, and the proposed model obtains substantial improvements over the state-of-the-art on two established Visual Question Generation datasets.
Revised:  The results reported under different configurations indicate an innate capacity for BERT-gen to adapt to multi-modal data and text generation, even with few data available, avoiding expensive pre-training. The proposed model obtains substantial improvements over the state-of-the-art on two established Visual Question Generation datasets.
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Original: We address the problem of finding a minimal separator in an Andersson-Madigan-Perlman chain graph (AMP CG), namely, finding a set Z of nodes that separate a given non-adjacent pair of nodes such that no proper subset of Z separates that pair.
Revised: We address the problem of finding a minimal separator in an Andersson-Madigan-Perlman chain graph (AMP CG), namely, finding a set Z of nodes that separates a given nonadjacent pair of nodes such that no proper subset of Z separates that pair.
Edit intent: fluency
Original:  We provide an extension of the decomposition approach for learning Bayesian networks (BNs) proposed by (Xie et. al.) to learn AMP CGs , which include BNs as a special case, under the faithfulness assumption and prove its correctness using the minimal separator results. The advantages of this decomposition approach hold in the more general setting: reduced complexity and increased power of computational independence tests. In addition, we show that the PC-like algorithm  is order-dependent, in the sense that the output can depend on the order in which the variables are given.
Revised:  To address the problem of learning the structure of AMP CGs from data, we show that the PC-like algorithm is order-dependent, in the sense that the output can depend on the order in which the variables are given.
Edit intent: clarity
Original:  Simulations under a variety of settings demonstrate the competitive performance of our decomposition-based method, called LCD-AMP, in comparison with the (modified version of) PC-like algorithm.
Revised:  We also extend the decomposition-based approach for learning Bayesian networks (BNs) proposed by (Xie et al., 2006) to learn AMP CGs, which include BNs as a special case, under the faithfulness assumption. We prove the correctness of our extension using the minimal separator results. Using standard benchmarks and synthetically generated models and data in our experiments demonstrate the competitive performance of our decomposition-based method, called LCD-AMP, in comparison with the (modified version of) PC-like algorithm.
Edit intent: meaning-changed
Original:  In fact, the decomposition-based algorithm usually outperforms the PC-like algorithm . We empirically show that the results of both algorithms are more accurate and stable when the sample size is reasonably large and the underlying graph is sparse.
Revised:  In fact, the decomposition-based algorithm usually outperforms the PC-like algorithm , and our modifications of the PC-like algorithm learn structures that are more similar to the underlying ground truth graphs than the original PC-like algorithm, especially in high-dimensional settings. In particular, we empirically show that the results of both algorithms are more accurate and stable when the sample size is reasonably large and the underlying graph is sparse.
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Original:  A quantized representation of probability in the brain was also posited and supported by experimental data [7].  ][  In this paper, we hypothesize that intertemporal choices may also be quantized.
Revised:  A quantized representation of probability in the brain was also posited and supported by experimental data [7]. Value and probability are inter-related via Prospect Theory ][ In this paper, we hypothesize that intertemporal choices may also be quantized.
Edit intent: meaning-changed
Original:  A quantized representation of probability in the brain was also posited and supported by experimental data [7].  ][  In this paper, we hypothesize that intertemporal choices may also be quantized.
Revised:  A quantized representation of probability in the brain was also posited and supported by experimental data [7]. 4][ In this paper, we hypothesize that intertemporal choices may also be quantized.
Edit intent: meaning-changed