wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Considering the expansion of robot applications in more complex and dynamic environments,it is evident that it is impossible to pre-program all possible object categories . Robots will have to be able to learn new object categories in the field.The network architecture proposed in this work expands from the OrthographicNet, an approach recently proposed by Kasaei et al. , using a deep transfer learning strategy which not only meets the aforementioned requirements but additionally generates a scale and rotation-invariant reference frame for the classification of objects. In its current iteration, the OrthographicNet only uses shape-information. With the addition of multiple color spaces, the upgraded network architecture proposed here, can achieve an even higher descriptiveness while simultaneously increasing the robustness of predictions for similarly shaped objects .
Revised:  Considering the expansion of robot applications in more complex and dynamic environments,it is evident that it is impossible to pre-program all possible object categories . Robots will have to be able to learn new object categories in an open-ended fashion while working in the environment.Towards this goal, we propose a deep transfer learning approach to generate a scale- and pose-invariant object representation by considering shape and texture information in multiple colorspaces. The obtained global object representation is then fed to an instance-based object category learning and recognition,where a non-expert human user exists in the learning loop and can interactively guide the process of experience acquisition by teaching new object categories, or by correcting insufficient or erroneous categories. In this work, shape information encodes the common patterns of all categories, while texture information is used to describes the appearance of each instance in detail .
Edit intent: clarity
Original:  Experimental results show that the proposed network architecture ranks competitively among other state-of-the-art algorithms .
Revised:  Experimental results show that the proposed network architecture ranks competitively among other state-of-the-art approaches in terms of object classification accuracy and scalability. Furthermore, we performed a real robot experiment in the context of serve-a-beer scenario to show the real-time performance of the proposed approach .
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Original:  Therefore, we present a systematic reconciliation of multiple disease-drug connectivity scores by defining them using consistent notation and terminology.
Revised:  Therefore, we present a systematic reconciliation of multiple disease-drug similarity metrics (ES, css, Sum, Cosine, XSum, XCor, XSpe, XCos, EWCos) and connectivity scores (CS, RGES, NCS, WCS, Tau, CSS, EMUDRA) by defining them using consistent notation and terminology.
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Original:  In addition to providing clarity and deeper insights, this coherent definition of connectivity scores and their relationships provides a unified scheme that newer methods can adopt, enabling the computational drug-development community to compare and benchmark different approaches easily.
Revised:  In addition to providing clarity and deeper insights, this coherent definition of connectivity scores and their relationships provides a unified scheme that newer methods can adopt, enabling the computational drug-development community to compare and investigate different approaches easily.
Edit intent: clarity
Original:  Existing multimodal neural machine translation  (MNMT) methods achieve knowledge transfer by enforcing one encoder to learn shared representation across textual and visual modalities. However, the training and inference process heavily relies on well-aligned bilingual sentence - image triplets as input, which are often limited in quantity .
Revised:  Existing multimodal neural machine translation (MNMT) require triplets of bilingual sentence - image for training and tuples of source sentence - image triplets as input, which are often limited in quantity .
Edit intent: clarity
Original:  In this paper, we hypothesize that visual imagination via synthesizing visual representation from source text could help the neural model map two languages with different symbols, thus helps the translation task. Our proposed end-to-end imagination-based machine translation model (ImagiT ) first learns to generate semantic-consistent visual representation from  source sentence, and then generate target sentence based on both text representation and imagined visual representation.
Revised:  In this paper, we propose ImagiT, a novel machine translation method via visual imagination. ImagiT first learns to generate semantic-consistent visual representation from source sentence, and then generate target sentence based on both text representation and imagined visual representation.
Edit intent: clarity
Original:  Our proposed end-to-end imagination-based machine translation model (ImagiT ) first learns to generate semantic-consistent visual representation from  source sentence, and then generate target sentence based on both text representation and imagined visual representation.
Revised:  Our proposed end-to-end imagination-based machine translation model (ImagiT ) first learns to generate visual representation from source sentence, and then generate target sentence based on both text representation and imagined visual representation.
Edit intent: clarity
Original:  Our proposed end-to-end imagination-based machine translation model (ImagiT ) first learns to generate semantic-consistent visual representation from  source sentence, and then generate target sentence based on both text representation and imagined visual representation.
Revised:  Our proposed end-to-end imagination-based machine translation model (ImagiT ) first learns to generate semantic-consistent visual representation from the source sentence, and then generate target sentence based on both text representation and imagined visual representation.
Edit intent: fluency
Original:  Our proposed end-to-end imagination-based machine translation model (ImagiT ) first learns to generate semantic-consistent visual representation from  source sentence, and then generate target sentence based on both text representation and imagined visual representation.
Revised:  Our proposed end-to-end imagination-based machine translation model (ImagiT ) first learns to generate semantic-consistent visual representation from source sentence, and then utilizes both source sentence and the "imagined representation" to produce a target translation. Unlike previous methods, it only needs the source sentence at the inference time.
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Original:  Experiments demonstrate that our translation model benefits from visual imagination and significantly outperforms the text-only neural machine translation (NMT) baseline. We also conduct analyzing experiments, and the results show that imagination can help fill in missing information when performing the degradation strategy.
Revised:  Experiments demonstrate that our translation model benefits from visual imagination and significantly outperforms the text-only neural machine translation baselines. Further analysis reveals that the imagination process in ImagiT helps fill in missing information when performing the degradation strategy.
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Original: There is a growing body of literature that formulates social human-robot interactions as sequential decision-making tasks. In such cases, reinforcement learning arises naturally since the interaction is a key component in both reinforcement learning and social robotics .
Revised: There is a growing body of literature that formulates social human-robot interactions as sequential decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics .
Edit intent: clarity
Original:  In such cases, reinforcement learning arises naturally since the interaction is a key component in both reinforcement learning and social robotics . This article surveys reinforcement learning approaches in social robotics. We propose a taxonomy that categorizes reinforcement learning methods in social robotics according to the nature of the reward function.
Revised:  In such cases, reinforcement learning arises naturally since the interaction is a key component in both reinforcement learning and social robotics , it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. In addition to a survey, we categorize existent RL approaches based on the design of the reward function.
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Original:  We propose a taxonomy that categorizes reinforcement learning methods in social robotics according to the nature of the reward function. We discuss the benefits and challenges of such methodsand outline possible future directions .
Revised:  We propose a taxonomy that categorizes reinforcement learning methods in social robotics according to the nature of the reward mechanisms. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. Thus, this paper aims to become a starting point for researchers interested to use and apply reinforcement learning methods in this particular research field .
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Original:   This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods.
Revised:  Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods.
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Original:   Thus, this paper aims to become a starting point for researchers interested to use and apply reinforcement learning methods in this particular research field.
Revised:  The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested to use and apply reinforcement learning methods in this particular research field.
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Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information , we propose LUT, Listen-Understand-Translate,  a unified framework with triple supervision  to decouple the end-to-end speech-to-text translation task.
Revised:  Existing methods are limited by the amount of parallel corpus. Can we build a system to fully utilize signals in a parallel ST corpus? We are inspired by human understanding system which is composed of auditory perception and cognitive processing. In this paper , we propose LUT, Listen-Understand-Translate, a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: meaning-changed
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information , we propose LUT, Listen-Understand-Translate,  a unified framework with triple supervision  to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information , we propose Listen-Understand-Translate, a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: clarity
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information , we propose LUT, Listen-Understand-Translate,  a unified framework with triple supervision  to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information , we propose LUT, Listen-Understand-Translate, (LUT), a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: clarity
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information , we propose LUT, Listen-Understand-Translate,  a unified framework with triple supervision  to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information , we propose LUT, Listen-Understand-Translate, a unified framework with triple supervision signals to decouple the end-to-end speech-to-text translation task.
Edit intent: clarity
Original:  In addition to the target language sentence translation loss, LUT includes two auxiliary supervising signals to guide the acoustic encoder to extracts acoustic features from the input, and the semantic encoder to extract semantic features relevant to the source transcription text. We do experiments on English-French,  English-German and  English-Chinese speech translation benchmarks and the results demonstrate the reasonability of LUT . Our code is available at URL
Revised:  In addition to the target language sentence translation loss, LUT includes two auxiliary supervising signals to guide the acoustic encoder to extract as much information from the auditory input. In addition, LUT utilizes a pre-trained BERT model to enforce the upper encoder to produce as much semantic information as possible, without extra data. We perform experiments on a diverse set of speech translation benchmarks, including Librispeech English-French, English-German and English-Chinese speech translation benchmarks and the results demonstrate the reasonability of LUT . Our code is available at URL
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Original:  To reduce the learning difficulty, we propose SDST , an integral framework withS uccessiveD ecoding for end-to-endS peech-to-textT ranslation task.
Revised:  To reduce the learning difficulty, we propose COnSecutive Transcription and Translation (COSTT) , an integral framework withS uccessiveD ecoding for end-to-endS peech-to-textT ranslation task.
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Original:  To reduce the learning difficulty, we propose SDST , an integral framework withS uccessiveD ecoding for end-to-endS peech-to-textT ranslation task. This method is verified in two mainstream datasets . Experiments show that our proposed %DIFDELCMD < \method %%% improves the previous state-of-the-art methods by big margins.
Revised:  To reduce the learning difficulty, we propose SDST , an integral framework withS uccessiveD ecoding for end-to-endS peech-to-textT for speech-to-text translation. Our method is verified in two mainstream datasets . Experiments show that our proposed %DIFDELCMD < \method %%% improves the previous state-of-the-art methods by big margins.
Edit intent: clarity
Original:  This method is verified in two mainstream datasets . Experiments show that our proposed %DIFDELCMD < \method %%% improves the previous state-of-the-art methods by big margins.
Revised:  This method is verified on three mainstream datasets, including Augmented LibriSpeech English-French dataset, TED English-German dataset, and TED English-Chinese dataset . Experiments show that our proposed %DIFDELCMD < \method %%% improves the previous state-of-the-art methods by big margins.
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Original:  This method is verified in two mainstream datasets . Experiments show that our proposed %DIFDELCMD < \method %%% improves the previous state-of-the-art methods by big margins.
Revised:  This method is verified in two mainstream datasets . Experiments show that our proposed %DIFDELCMD < \method %%% COSTT outperforms the previous state-of-the-art methods by big margins.
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Original:  This method is verified in two mainstream datasets . Experiments show that our proposed %DIFDELCMD < \method %%% improves the previous state-of-the-art methods by big margins.
Revised:  This method is verified in two mainstream datasets . Experiments show that our proposed %DIFDELCMD < \method %%% improves the previous state-of-the-art methods . Our code is available at URL
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Original:   Our method is verified on three mainstream datasets, including Augmented LibriSpeech English-French dataset, TED English-German dataset, and TED English-Chinese dataset.
Revised:  The key idea is to generate source transcript and target translation text with a single decoder. It benefits the model training so that additional large parallel text corpus can be fully exploited to enhance the speech translation training. Our method is verified on three mainstream datasets, including Augmented LibriSpeech English-French dataset, TED English-German dataset, and TED English-Chinese dataset.
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Original:  DiffWave produces high-fidelity audios in Different Waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation.
Revised:  DiffWave produces high-fidelity audios in different waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation.
Edit intent: fluency
Original:  We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality ~ (MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster.
Revised:  We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality (MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster.
Edit intent: fluency
Original: Set positive invariance is an important concept in the theory of dynamical systems and one which also has practical applications in areas of computer science, such as formal verification, as well as in control theory. Great progress has been made in understanding positively invariant sets in continuous dynamical systems and powerful computational tools have been developed for reasoning about them; however, many of the insights from recent developments in this area have largely remained folklore and are not elaborated in existing literature.
Revised: We present two characterizations of positive invariance of sets under the flow of systems of ordinary differential equations. The first characterization uses inward sets which intuitively collect those points from which the flow evolves within the set for a short period of time, whereas the second characterization uses the notion of exit sets, which intuitively collect those points from which the flow immediately leaves the set. Our proofs emphasize the use of the insights from recent developments in this area have largely remained folklore and are not elaborated in existing literature.
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Original:  however, many of the insights from recent developments in this area have largely remained folklore and are not elaborated in existing literature. This article contributes an explicit development of modern methods for checking positively invariant sets of ordinary differential equations and describes two possible characterizations of positive invariants: one based on the real induction principle , and a novel alternative based on topological notions .
Revised:  however, many of the real induction principle , and a novel alternative based on topological notions .
Edit intent: coherence
Original:  This article contributes an explicit development of modern methods for checking positively invariant sets of ordinary differential equations and describes two possible characterizations of positive invariants: one based on the real induction principle , and a novel alternative based on topological notions .
Revised:  This article contributes an explicit development of modern methods for checking positively invariant sets of ordinary differential equations and describes two possible characterizations of positive invariants: one based on the real induction principle as a generic and unifying proof technique that captures the essence of the formal reasoning justifying our results and provides cleaner alternative proofs of known results .
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Original:  The two characterizations , while in a certain sense equivalent, lead to two  different decision procedures  for checking whether a given semi-algebraic set is positively invariant under the flow of a system of polynomial ordinary differential equations  .
Revised:  The two characterizations presented in this article, while essentially equivalent, lead to two different decision procedures for checking whether a given semi-algebraic set is positively invariant under the flow of a system of polynomial ordinary differential equations .
Edit intent: meaning-changed
Original:  The two characterizations , while in a certain sense equivalent, lead to two  different decision procedures  for checking whether a given semi-algebraic set is positively invariant under the flow of a system of polynomial ordinary differential equations  .
Revised:  The two characterizations , while in a certain sense equivalent, lead to two rather different decision procedures for checking whether a given semi-algebraic set is positively invariant under the flow of a system of polynomial ordinary differential equations .
Edit intent: clarity
Original:  The two characterizations , while in a certain sense equivalent, lead to two  different decision procedures  for checking whether a given semi-algebraic set is positively invariant under the flow of a system of polynomial ordinary differential equations  .
Revised:  The two characterizations , while in a certain sense equivalent, lead to two different decision procedures (termed respectively LZZ and ESE) for checking whether a given semi-algebraic set is positively invariant under the flow of a system of polynomial ordinary differential equations .
Edit intent: meaning-changed
Original:  The two characterizations , while in a certain sense equivalent, lead to two  different decision procedures  for checking whether a given semi-algebraic set is positively invariant under the flow of a system of polynomial ordinary differential equations  .
Revised:  The two characterizations , while in a certain sense equivalent, lead to two different decision procedures for checking whether a given semi-algebraic set is positively invariant under the flow of a system of polynomial ordinary differential equations . The procedure LZZ improves upon the original work by Liu, Zhan and Zhao (EMSOFT 2011). The procedure ESE, introduced in this article, works by splitting the problem, in a principled way, into simpler sub-problems that are easier to check, and is shown to exhibit substantially better performance compared to LZZ on problems featuring semi-algebraic sets described by formulas with non-trivial Boolean structure .
Edit intent: meaning-changed