wanyu/IteraTeR_v2 huggingface dataset:
Original:  We present  a model quantization technique that compresses the vast majority (typically 99.9\%) of the 32-bit floating-point parameters of state-of-the-art BERT models and its variants to 3 bits while maintaining their accuracy.
Revised:  We present a model quantization technique that compresses the vast majority (typically 99.9\%) of the 32-bit floating-point parameters of state-of-the-art BERT models and their variants to 3 bits while maintaining their accuracy.
Edit intent: fluency
Original:  Unlike other quantization methods, our technique does not require fine-tuning nor retraining to compensate for the quantization error  .
Revised:  Unlike other quantization methods, GOBO does not require fine-tuning nor retraining to compensate for the quantization error .
Edit intent: clarity
Original: We derive a convergent (up to a subsequence) numerical method for conservative solutions of the Hunter--Saxton equation  .
Revised: In the article a convergent numerical method for conservative solutions of the Hunter--Saxton equation .
Edit intent: clarity
Original: We derive a convergent (up to a subsequence) numerical method for conservative solutions of the Hunter--Saxton equation  .
Revised: We derive a convergent (up to a subsequence) numerical method for conservative solutions of the Hunter--Saxton equation is derived .
Edit intent: clarity
Original: Starting from a contact Hamiltonian description of L\`ienard systems, we introduce a new family of explicit geometric integrators for these nonlinear dynamical systems.
Revised: Starting from a contact Hamiltonian description of Li\'enard systems, we introduce a new family of explicit geometric integrators for these nonlinear dynamical systems.
Edit intent: fluency
Original: Motivation: It is known that the structure of transcription and protein interaction networks is informative of its biological function at multiple scales.
Revised: Motivation: It is known that the structure of transcription and protein interaction networks is informative of biological function at multiple scales.
Edit intent: clarity
Original: \ coli . Graph neural networks use a message passing framework to learn an embedding of a graph in a continuous space , either using network topology alone, or including additional node features.
Revised: \ coli . Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features.
Edit intent: clarity
Original:  Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query.
Revised:  It leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query.
Edit intent: clarity
Original:  Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query.
Revised:  Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from existing literature given a query.
Edit intent: fluency
Original:  To bootstrap the further study, the code for our system is available at URL
Revised:  To bootstrap the further study, the code for our system is also open-sourced to bootstrap further study.
Edit intent: clarity
Original:  The code for our system is also open-sourced to bootstrap further study  .
Revised:  The code for our system , to bootstrap further study .
Edit intent: clarity
Original: In this paper, we present the formulas of different types (explicit and recursive) for the determinant, other characteristic polynomial coefficients, adjugate, and inverses in real Clifford algebras over vector space of arbitrary dimension n using only the operations of multiplication, summation, and operations of conjugation without using the corresponding matrix representations .
Revised: In this paper, we present the formulas of different types (explicit and recursive) for the determinant, other characteristic polynomial coefficients, adjugate, and inverse in real Clifford algebras over vector space of arbitrary dimension n using only the operations of multiplication, summation, and operations of conjugation without using the corresponding matrix representations .
Edit intent: fluency
Original: In this paper, we present the formulas of different types (explicit and recursive) for the determinant, other characteristic polynomial coefficients, adjugate, and inverses in real Clifford algebras over vector space of arbitrary dimension n using only the operations of multiplication, summation, and operations of conjugation without using the corresponding matrix representations .
Revised: In this paper, we present the formulas of different types (explicit and recursive) for the determinant, other characteristic polynomial coefficients, adjugate, and inverses in real Clifford algebras over vector space of arbitrary dimension n . The formulas involve only the operations of multiplication, summation, and operations of conjugation without using the corresponding matrix representations .
Edit intent: clarity
Original: In this paper, we present the formulas of different types (explicit and recursive) for the determinant, other characteristic polynomial coefficients, adjugate, and inverses in real Clifford algebras over vector space of arbitrary dimension n using only the operations of multiplication, summation, and operations of conjugation without using the corresponding matrix representations .
Revised: In this paper, we present the formulas of different types (explicit and recursive) for the determinant, other characteristic polynomial coefficients, adjugate, and inverses in real Clifford algebras over vector space of arbitrary dimension n using only the operations of multiplication, summation, and operations of conjugation without explicit use of matrix representation .
Edit intent: clarity
Original:  We use the methods of Clifford algebras (including the method of quaternion typification suggested by the author in previous papers and the method of operations of conjugation of special type presented in this paper) and the matrix methods (related to the Cayley-Hamilton theorem , the Faddeev-LeVerrier algorithm, and the Bell polynomials) , which we generalize to the case of Clifford algebras in this paper.
Revised:  We use methods of Clifford algebras (including the method of quaternion typification suggested by the author in previous papers and the method of operations of conjugation of special type presented in this paper) and the matrix methods (related to the Cayley-Hamilton theorem , the Faddeev-LeVerrier algorithm, and the Bell polynomials) , which we generalize to the case of Clifford algebras in this paper.
Edit intent: fluency
Original:  We use the methods of Clifford algebras (including the method of quaternion typification suggested by the author in previous papers and the method of operations of conjugation of special type presented in this paper) and the matrix methods (related to the Cayley-Hamilton theorem , the Faddeev-LeVerrier algorithm, and the Bell polynomials) , which we generalize to the case of Clifford algebras in this paper.
Revised:  We use the methods of Clifford algebras (including the method of quaternion typification proposed by the author in previous papers and the method of operations of conjugation of special type presented in this paper) and the matrix methods (related to the Cayley-Hamilton theorem , the Faddeev-LeVerrier algorithm, and the Bell polynomials) , which we generalize to the case of Clifford algebras in this paper.
Edit intent: fluency
Original:  We use the methods of Clifford algebras (including the method of quaternion typification suggested by the author in previous papers and the method of operations of conjugation of special type presented in this paper) and the matrix methods (related to the Cayley-Hamilton theorem , the Faddeev-LeVerrier algorithm, and the Bell polynomials) , which we generalize to the case of Clifford algebras in this paper.
Revised:  We use the methods of Clifford algebras (including the method of quaternion typification suggested by the author in previous papers and the method of operations of conjugation of special type presented in this paper) and the matrix methods (related to the Cayley-Hamilton theorem , the Faddeev-LeVerrier algorithm, and the Bell polynomials) to the case of Clifford algebras in this paper.
Edit intent: coherence
Original:  We use this construction in the analytical proof of the formulas for the determinant, other characteristic polynomial coefficients, adjugate, and inverses in Clifford algebras  .
Revised:  We use this construction in the analytical proof of formulas for the determinant, other characteristic polynomial coefficients, adjugate, and inverses in Clifford algebras .
Edit intent: fluency
Original:  We use this construction in the analytical proof of the formulas for the determinant, other characteristic polynomial coefficients, adjugate, and inverses in Clifford algebras  .
Revised:  We use this construction in the analytical proof of the formulas for the determinant, other characteristic polynomial coefficients, adjugate, and inverse in Clifford algebras .
Edit intent: fluency
Original:  To demonstrate this strategy, we develop an agent-based model describing the time-evolution of infection patterns and their response to vaccination .
Revised:  To demonstrate this strategy, we develop a simple statistical model describing the time-evolution of infection patterns and their response to vaccination .
Edit intent: clarity
Original: In this letter, we present a novel upper-bound for the hitting time of continuous-time quantum walks, based on the spectrum and eigenstates of the Hamiltonian.
Revised: Continuous-time quantum walks have proven to be an extremely useful framework for the design of several quantum algorithms. Often, the Hamiltonian.
Edit intent: clarity
Original:  We apply the new bound to the quantum walk algorithm for the glued-trees problem by Childs et al. (2002) , improving their hitting time upper bound by a polynomial factor .
Revised:  We apply the new bound to the quantum walk algorithm for the glued-trees problem , improving their hitting time upper bound by a polynomial factor .
Edit intent: coherence
Original:  Here, Alice and Bob are given x\in \{0,1\}^n,  y\in \{0,1\}^n respectively and their goal is to compute F(x,y) subject to the  constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and input x (in particular, her reply is independent of the information from the previous rounds) ;
Revised:  Here, Alice is given x\in \{0,1\}^n, y\in \{0,1\}^n respectively and their goal is to compute F(x,y) subject to the constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and input x (in particular, her reply is independent of the information from the previous rounds) ;
Edit intent: coherence
Original:  Here, Alice and Bob are given x\in \{0,1\}^n,  y\in \{0,1\}^n respectively and their goal is to compute F(x,y) subject to the  constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and input x (in particular, her reply is independent of the information from the previous rounds) ;
Revised:  Here, Alice and Bob are given x\in \{0,1\}^n, Bob is given y\in \{0,1\}^n respectively and their goal is to compute F(x,y) subject to the constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and input x (in particular, her reply is independent of the information from the previous rounds) ;
Edit intent: coherence
Original:  Here, Alice and Bob are given x\in \{0,1\}^n,  y\in \{0,1\}^n respectively and their goal is to compute F(x,y) subject to the  constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and input x (in particular, her reply is independent of the information from the previous rounds) ;
Revised:  Here, Alice and Bob are given x\in \{0,1\}^n, y\in \{0,1\}^n and their goal is to compute F(x,y) subject to the constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and input x (in particular, her reply is independent of the information from the previous rounds) ;
Edit intent: clarity
Original:  Here, Alice and Bob are given x\in \{0,1\}^n,  y\in \{0,1\}^n respectively and their goal is to compute F(x,y) subject to the  constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and input x (in particular, her reply is independent of the information from the previous rounds) ;
Revised:  Here, Alice and Bob are given x\in \{0,1\}^n, y\in \{0,1\}^n respectively and their goal is to compute F(x,y) subject to the following constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and input x (in particular, her reply is independent of the information from the previous rounds) ;
Edit intent: clarity
Original:  Here, Alice and Bob are given x\in \{0,1\}^n,  y\in \{0,1\}^n respectively and their goal is to compute F(x,y) subject to the  constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and input x (in particular, her reply is independent of the information from the previous rounds) ;
Revised:  Here, Alice and Bob are given x\in \{0,1\}^n, y\in \{0,1\}^n respectively and their goal is to compute F(x,y) subject to the constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and her input x ;
Edit intent: coherence
Original:  In this paper, we also consider variants of our memoryless model wherein one party is allowed to have memory, the parties are allowed to communicate qubits , only one player is allowed to send messages and the relationship between our model and the garden-hose model of computation . Restricted versions of our communication model were studied before by Brody et al. (ITCS'13) and  Papakonstantinou et al. (CCC'14) , in the context of space-bounded communication complexity .
Revised:  In this paper, we also consider variants of our memoryless model wherein one party is allowed to have memory, the parties are allowed to communicate quantum bits , only one player is allowed to send messages and the relationship between our model and the garden-hose model of computation . Restricted versions of our communication model were studied before by Brody et al. (ITCS'13) and Papakonstantinou et al. (CCC'14) , in the context of space-bounded communication complexity .
Edit intent: fluency
Original:  In this paper, we also consider variants of our memoryless model wherein one party is allowed to have memory, the parties are allowed to communicate qubits , only one player is allowed to send messages and the relationship between our model and the garden-hose model of computation . Restricted versions of our communication model were studied before by Brody et al. (ITCS'13) and  Papakonstantinou et al. (CCC'14) , in the context of space-bounded communication complexity .
Revised:  In this paper, we also consider variants of our memoryless model wherein one party is allowed to have memory, the parties are allowed to communicate qubits , only one player is allowed to send messages . We show that our memoryless communication model capture the garden-hose model of computation . Restricted versions of our communication model were studied before by Brody et al. (ITCS'13) and Papakonstantinou et al. (CCC'14) , in the context of space-bounded communication complexity .
Edit intent: coherence
Original:  In this paper, we establish following: (1) We show that the memoryless communication complexity of F characterizes the logarithm of the size of the smallest bipartite branching program computing F (up to a factor 2);
Revised:  In this paper, we establish following: (1) We show that the memoryless communication complexity of F equals the logarithm of the size of the smallest bipartite branching program computing F (up to a factor 2);
Edit intent: clarity
Original:  (3) We exhibit exponential quantum-classical separations in the four variants of the memoryless communication model .
Revised:  (3) We exhibit various exponential separations between these memoryless communication models .
Edit intent: clarity
Original:  We end with an intriguing open question: can we find an explicit F and constant c>1 for which the memoryless communication complexity is at least c \log n? Note that c\geq 2+\varepsilon would imply a \Omega(n^{2+\varepsilon}) lower bound for general formula size, improving upon the best lower bound by Ne\v{c}iporuk Nec66 .
Revised:  We end with an intriguing open question: can we find an explicit function F and universal constant c>1 for which the memoryless communication complexity is at least c \log n? Note that c\geq 2+\varepsilon would imply a \Omega(n^{2+\varepsilon}) lower bound for general formula size, improving upon the best lower bound by Ne\v{c}iporuk Nec66 .
Edit intent: clarity
Original:  Traditionally, machine learning methods classify samples based solely on their features.
Revised:  Traditionally, machine learning methods classify samples based solely on sample features.
Edit intent: clarity
Original: We propose an incremental learning for end-to-end Automatic Speech Recognition (ASR) to extend the model's capacity on a new task while retaining the performance on existing ones.
Revised: We propose a new incremental learning for end-to-end Automatic Speech Recognition (ASR) to extend the model's capacity on a new task while retaining the performance on existing ones.
Edit intent: clarity
Original: We propose an incremental learning for end-to-end Automatic Speech Recognition (ASR) to extend the model's capacity on a new task while retaining the performance on existing ones.
Revised: We propose an incremental learning for end-to-end Automatic Speech Recognition (ASR) to extend the model's capacity on a new task while retaining the performance on previous ones.
Edit intent: clarity
Original:  The proposed method is effective without accessing to the old dataset to address the issues of high training cost and old dataset unavailability .
Revised:  The proposed method is effective without accessing to the old dataset to address the issues of high retraining cost and unavailable old dataset .
Edit intent: clarity
Original:  To achieve this, knowledge distillation is applied as a guidance to retain the recognition ability from the previous model , which is then combined with the new ASR task for model optimization .
Revised:  To achieve this, both attention distillation and knowledge distillation are applied to preserve the ability of the old model during the progressive learning .
Edit intent: clarity
Original: We propose a new incremental learning for end-to-end Automatic Speech Recognition (ASR) to extend the model's capacity on a new task while retaining the performance on previous ones.
Revised: In this paper, we propose an incremental learning method for end-to-end Automatic Speech Recognition (ASR) to extend the model's capacity on a new task while retaining the performance on previous ones.
Edit intent: clarity
Original: We propose a new incremental learning for end-to-end Automatic Speech Recognition (ASR) to extend the model's capacity on a new task while retaining the performance on previous ones.
Revised: We propose a new incremental learning for end-to-end Automatic Speech Recognition (ASR) which enables an ASR system to perform well on new tasks while maintaining the performance on previous ones.
Edit intent: clarity
Original: PubMed is an essential resource for the medical domain, but useful concepts are either difficult to extract from PubMed or are disambiguated , which has significantly hindered knowledge discovery.
Revised: PubMed is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguated , which has significantly hindered knowledge discovery.
Edit intent: coherence
Original:  To address this issue, we developed a solution by constructing a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER,  affiliation history and educational background of authors from ORCID, and  fine-grained affiliation data from MapAffil.
Revised:  To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, affiliation history and educational background of authors from ORCID, and fine-grained affiliation data from MapAffil.
Edit intent: clarity
Original:  To address this issue, we developed a solution by constructing a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER,  affiliation history and educational background of authors from ORCID, and  fine-grained affiliation data from MapAffil.
Revised:  To address this issue, we developed a solution by constructing a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID, and fine-grained affiliation data from MapAffil.
Edit intent: clarity
Original:  To address this issue, we developed a solution by constructing a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER,  affiliation history and educational background of authors from ORCID, and  fine-grained affiliation data from MapAffil.
Revised:  To address this issue, we developed a solution by constructing a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, affiliation history and educational background of authors from ORCID, and identifying fine-grained affiliation data from MapAffil.
Edit intent: clarity
Original:  By integrating the credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding.
Revised:  Through the integration of the credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding.
Edit intent: clarity
Original:  This method of learning differential equations from data fits into a new class of algorithms that replace pointwise derivative approximations with linear transformations and subsequent variance reduction techniques and improves on the standard SINDy algorithm by orders of magnitude.
Revised:  This method of learning differential equations from data fits into a new class of algorithms that replace pointwise derivative approximations with linear transformations and a variance reduction technique. Our approach improves on the standard SINDy algorithm by orders of magnitude.
Edit intent: clarity
Original:  We demonstrate a scaling relation that holds when selection for cooperation exactly balances out selection for cheating: N \propto m^{-\alpha, where \alpha(>0)increases with selection strength.
Revised:  , where \alpha(>0)increases with selection strength.
Edit intent: clarity