wanyu/IteraTeR_full_sent huggingface dataset:
Original:  An analytic method is then described in which a variable acting on the self-regulating terms of a community matrix enables one, depending on the field of the maximal eigenvalue, to either determine the size of the key feedback that leads to the system's tipping point, or narrows down the search space .
Revised:  This leads to quite rich combinatorial structure among the destabilising cycle sets as set size grows within the coefficients of the characteristic polynomial .
Edit intent: meaning-changed
Original:  Most prominently, the top six AMMs -- Uniswap, Balancer, Curve, Dodo , Bancor and Sushiswap -- hold in aggregate 15 billion USD worth of crypto-assets as of March 2021.
Revised:  Most prominently, the top six AMMs -- Uniswap, Balancer, Curve, DODO , Bancor and Sushiswap -- hold in aggregate 15 billion USD worth of crypto-assets as of March 2021.
Edit intent: clarity
Original:  We employ our framework to systematically compare the mechanics of the top AMM protocols  , deriving their slippage and divergence loss functions  .
Revised:  We employ our framework to systematically compare the mechanics of the top AMM protocols , deriving their slippage and divergence loss functions . We further discuss security and privacy concerns associated with AMM DEXs, and conduct a comprehensive literature review on related work covering both DeFi and conventional market microstructure .
Edit intent: meaning-changed
Original:  For either quasi-uniform grids or graded bisection grids, we show that the condition numbers of the resulting systems remain uniformly bounded with respect to both the number of levels and the fractional power  .
Revised:  For either quasi-uniform grids or graded bisection grids, we show that the condition numbers of the resulting systems remain uniformly bounded with respect to both the number of levels and the fractional power . The results apply also to the spectral and censored fractional Laplacians .
Edit intent: meaning-changed
Original: The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is required for quantum and digital annealers whose goal is the optimization of a well defined metric, the objective function.
Revised: The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is a requirement for quantum and digital annealers whose goal is the optimization of a well defined metric, the objective function.
Edit intent: fluency
Original: The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is required for quantum and digital annealers whose goal is the optimization of a well defined metric, the objective function. However , diverse suboptimal solutions may be preferred over harder to implement strict optimal ones. In addition, the decision-maker usually has insightsthat are not always efficiently translated into the optimization model, such as acceptable target , interval or range values. Multi-criteria decision making is an example of involving the user in the decision process .
Revised: The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is required for quantum and digital annealers .
Edit intent: coherence
Original:   In this paper, we present two variants of goal-seeking QUBO that minimize the deviation from the goal through a tabu-search based greedy one-flip heuristic . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
Revised:  However optimality for QUBO problems of any practical size is extremely difficult to achieve. In order to incorporate the problem-specific insights, a diverse set of solutions meeting an acceptable target metric or goal is the preference in high level decision making. In this paper, we present two variants of goal-seeking QUBO that minimize the deviation from the goal through a tabu-search based greedy one-flip heuristic . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
Edit intent: meaning-changed
Original:   In this paper, we present two variants of goal-seeking QUBO that minimize the deviation from the goal through a tabu-search based greedy one-flip heuristic . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
Revised:  In this paper, we present two alternatives for goal-seeking QUBO that minimize the deviation from the goal through a tabu-search based greedy one-flip heuristic . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
Edit intent: clarity
Original:   In this paper, we present two variants of goal-seeking QUBO that minimize the deviation from the goal through a tabu-search based greedy one-flip heuristic . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
Revised:  In this paper, we present two variants of goal-seeking QUBO for minimizing the deviation from the goal through a tabu-search based greedy one-flip heuristic . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
Edit intent: coherence
Original:   In this paper, we present two variants of goal-seeking QUBO that minimize the deviation from the goal through a tabu-search based greedy one-flip heuristic . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
Revised:  In this paper, we present two variants of goal-seeking QUBO that minimize the deviation from a given target as well as a range of values around a target . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
Edit intent: meaning-changed
Original: Motivation: Several accurate deep learning models have been proposed to predict drug-target affinity (DTA). However, all of these models are black box hence are difficult to interpret and verify its result, and thus risking acceptance. Explanation is necessary to allow the DTA model more trustworthy. Explanation with counterfactual provides human-understandable examples.
Revised: Motivation: Several accurate deep learning models have been proposed , but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more trustworthy, and can also enable scientists to distill biological knowledge from the models. Counterfactual explanation is one popular approach to explaining the behaviour of a deep neural network, which works by systematically answering the question "How would the model output change if the inputs were changed in this way?".
Edit intent: coherence
Original:  Most counterfactual explanation methods only operate on single input data , which are in tabular or continuous forms. In contrast, the DTA model has two discrete inputs. It is challenging for the counterfactual generation framework to optimize both discrete inputs at the same time. Results : We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug-target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex.
Revised:  Most counterfactual explanation methods only operate on single input data . It remains an open problem how to extend counterfactual-based XAI methods to DTA models, which have two inputs, one for drug and one for target, that also happen to be discrete in nature. Methods : We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug-target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex.
Edit intent: clarity
Original:  Results : We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug-target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex.
Revised:  Results : We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex.
Edit intent: fluency
Original:  The result on the Davis dataset shows the advantages of the proposed MACDA framework compared with previous works .
Revised:  Results: We benchmark the proposed MACDA framework using the Davis dataset and find that our framework produces more parsimonious explanations with no loss in explanation validity, as measured by encoding similarity and QED. We then present a case study involving ABL1 and Nilotinib to demonstrate how MACDA can explain the behaviour of a DTA model in the underlying substructure interaction between inputs in its prediction, revealing mechanisms that align with prior domain knowledge .
Edit intent: meaning-changed
Original:  However, in the literature, in addition to improvements to the model architecture , there are a number of improvements either briefly mentioned as implementation details or visible only in source code, and these overlooked techniques may play a pivotal role in their practical use .
Revised:  However, in addition to their reliance on elaborate architectures and algorithms , there are a number of improvements either briefly mentioned as implementation details or visible only in source code, and these overlooked techniques may play a pivotal role in their practical use .
Edit intent: meaning-changed
Original:  However, in the literature, in addition to improvements to the model architecture , there are a number of improvements either briefly mentioned as implementation details or visible only in source code, and these overlooked techniques may play a pivotal role in their practical use .
Revised:  However, in the literature, in addition to improvements to the model architecture , there are several key technical details that are frequently overlooked, and yet nonetheless can play a vital role in achieving satisfactory performance .
Edit intent: clarity
Original:  We empirically evaluate their impacts on the final model accuracy through ablation studies , and show that we are able to significantly improve various GNN models to the extent that they outweigh the gains from model architectureimprovement . Notably, many of the top-ranked models on  Open Graph Benchmark  benefit from our techniques.
Revised:  We empirically evaluate their impact on final node classification accuracy by conducting ablation studies and demonstrate consistently-improved performance, often to an extent that outweighs the gains from model architectureimprovement . Notably, many of the top-ranked models on Open Graph Benchmark benefit from our techniques.
Edit intent: clarity
Original:  We empirically evaluate their impacts on the final model accuracy through ablation studies , and show that we are able to significantly improve various GNN models to the extent that they outweigh the gains from model architectureimprovement . Notably, many of the top-ranked models on  Open Graph Benchmark  benefit from our techniques.
Revised:  We empirically evaluate their impacts on the final model accuracy through ablation studies , and show that we are able to significantly improve various GNN models to the extent that they outweigh the gains from more dramatic changes in the underlying GNN architecture . Notably, many of the top-ranked models on Open Graph Benchmark benefit from our techniques.
Edit intent: meaning-changed
Original:  We empirically evaluate their impacts on the final model accuracy through ablation studies , and show that we are able to significantly improve various GNN models to the extent that they outweigh the gains from model architectureimprovement . Notably, many of the top-ranked models on  Open Graph Benchmark  benefit from our techniques.
Revised:  We empirically evaluate their impacts on the final model accuracy through ablation studies , and show that we are able to significantly improve various GNN models to the extent that they outweigh the gains from model architectureimprovement . Notably, many of the top-ranked models on the Open Graph Benchmark benefit from our techniques.
Edit intent: fluency
Original:  We empirically evaluate their impacts on the final model accuracy through ablation studies , and show that we are able to significantly improve various GNN models to the extent that they outweigh the gains from model architectureimprovement . Notably, many of the top-ranked models on  Open Graph Benchmark  benefit from our techniques.
Revised:  We empirically evaluate their impacts on the final model accuracy through ablation studies , and show that we are able to significantly improve various GNN models to the extent that they outweigh the gains from model architectureimprovement . Notably, many of the top-ranked models on Open Graph Benchmark (OGB) leaderboard benefit from our techniques.
Edit intent: meaning-changed
Original:  Soc. , in press] and Choudhury [2021 preprint]  .
Revised:  Soc. , in press] and Choudhury [2021 preprint] . Finally using a 1950 result of Gantmacher--Krein, we show that totally negative/non-positive matrices can not be detected by (single) test vectors from orthants other than the open bi-orthant that have coordinates with alternating signs, via the sign non-reversal property or the variation diminishing property .
Edit intent: meaning-changed
Original:  Despite the utility of tactile information for many robotics applications , tactile sensors have yet to be widely deployed in industrial  settings -- part of the challenge lies in identifying slip and other key events from the tactile data stream.
Revised:  Despite the utility of tactile information for many robotics applications , tactile sensors have yet to be widely deployed in industrial robotics settings -- part of the challenge lies in identifying slip and other key events from the tactile data stream.
Edit intent: fluency
Original:  Following demand for informal mathematical datasets , we describe a dataset creation method where we consider a derivation agent as a finite state machine which exists in equational states represented by strings, where transitions can occur through a combination of string operations that mimic mathematics, and defined computer algebra operations.
Revised:  Following demand , we describe a dataset creation method where we consider a derivation agent as a finite state machine which exists in equational states represented by strings, where transitions can occur through a combination of string operations that mimic mathematics, and defined computer algebra operations.
Edit intent: clarity
Original:  Following demand for informal mathematical datasets , we describe a dataset creation method where we consider a derivation agent as a finite state machine which exists in equational states represented by strings, where transitions can occur through a combination of string operations that mimic mathematics, and defined computer algebra operations. We present the novel dataset PhysAI-DS1 generated by this method, which consists of a curated derivation of a contemporary condensed matter physics result reconstructed using a computer algebra system .
Revised:  Following demand for informal mathematical datasets , we describe a method for converting informal hand-written derivations into datasets, and present an example dataset crafted from a contemporary result in condensed matter .
Edit intent: clarity
Original:  We define an equation reconstruction task based on formulating derivation segments as basic units of non-trivial state sequences, with the goal of reconstructing an unknown intermediate state equivalent to one-hop inference, extensible to the multi-hop case. We present a symbolic similarity-based heuristic approach to solve an equation reconstruction task on the PhysAI-DS1 dataset, which employs a set of actions, a knowledge base of symbols and equations, and a computer algebra system, to reconstruct an unknown intermediate state within a sequence of three equational states, grouped together as a derivation unit.
Revised:  We define an equation reconstruction task based on formulating derivation segments as basic units of non-trivial state sequences, with the goal of reconstructing an unknown intermediate equation posed as a state, taken from three consecutive equational states within a derivation. Derivation automation is achieved by applying string-based CAS-reliant actions to states, which mimic mathematical operations and induce state transitions. We implement a symbolic similarity-based heuristic approach to solve an equation reconstruction task on the PhysAI-DS1 dataset, which employs a set of actions, a knowledge base of symbols and equations, and a computer algebra system, to reconstruct an unknown intermediate state within a sequence of three equational states, grouped together as a derivation unit.
Edit intent: meaning-changed
Original:  We present a symbolic similarity-based heuristic approach to solve an equation reconstruction task on the PhysAI-DS1 dataset, which employs a set of actions, a knowledge base of symbols and equations, and a computer algebra system, to reconstruct an unknown intermediate state within a sequence of three equational states, grouped together as a derivation unit.
Revised:  We present a symbolic similarity-based heuristic search to solve the equation reconstruction task on the PhysAI-DS1 dataset, which employs a set of actions, a knowledge base of symbols and equations, and a computer algebra system, to reconstruct an unknown intermediate state within a sequence of three equational states, grouped together as a derivation unit.
Edit intent: clarity
Original:  We present a symbolic similarity-based heuristic approach to solve an equation reconstruction task on the PhysAI-DS1 dataset, which employs a set of actions, a knowledge base of symbols and equations, and a computer algebra system, to reconstruct an unknown intermediate state within a sequence of three equational states, grouped together as a derivation unit. Informal derivation comprehension of contemporary results is an important step towards the comprehension and automation of modern physicsreasoners .
Revised:  We present a symbolic similarity-based heuristic approach to solve an equation reconstruction task as an early step towards multi-hop equational inference in physics .
Edit intent: clarity
Original: We will discuss how over the last 30 to 50 years,  systems that focused only on data have been handicapped with success focused on narrowly focused tasks, and knowledge has been critical in developing smarter, intelligent,  more effective systems.
Revised: We discuss how over the last 30 to 50 years, systems that focused only on data have been handicapped with success focused on narrowly focused tasks, and knowledge has been critical in developing smarter, intelligent, more effective systems.
Edit intent: clarity
Original: We will discuss how over the last 30 to 50 years,  systems that focused only on data have been handicapped with success focused on narrowly focused tasks, and knowledge has been critical in developing smarter, intelligent,  more effective systems.
Revised: We will discuss how over the last 30 to 50 years, Artificial Intelligence (AI) systems that focused only on data have been handicapped with success focused on narrowly focused tasks, and knowledge has been critical in developing smarter, intelligent, more effective systems.
Edit intent: meaning-changed
Original: We will discuss how over the last 30 to 50 years,  systems that focused only on data have been handicapped with success focused on narrowly focused tasks, and knowledge has been critical in developing smarter, intelligent,  more effective systems.
Revised: We will discuss how over the last 30 to 50 years, systems that focused only on data have been handicapped , and how knowledge has been critical in developing smarter, intelligent, more effective systems.
Edit intent: clarity
Original: We will discuss how over the last 30 to 50 years,  systems that focused only on data have been handicapped with success focused on narrowly focused tasks, and knowledge has been critical in developing smarter, intelligent,  more effective systems.
Revised: We will discuss how over the last 30 to 50 years, systems that focused only on data have been handicapped with success focused on narrowly focused tasks, and knowledge has been critical in developing smarter, intelligent, and more effective systems.
Edit intent: fluency
Original:  We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science .
Revised:  In fact, the vast progress in AI can be viewed in terms of the three waves of AI as identified by DARPA. During the first wave, handcrafted knowledge has been at the center-piece, while during the second wave, the data-driven approaches supplanted knowledge. Now we see a strong role and resurgence of knowledge fueling major breakthroughs in the third wave of AI underpinning future intelligent systems as they attempt human-like decision making, and seek to become trusted assistants and companions for humans. We find a wider availability of knowledge created from diverse sources, using manual to automated means both by repurposing as well as by extraction. Using knowledge with statistical learning is becoming increasingly indispensable to help make AI systems more transparent and auditable. We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science .
Edit intent: meaning-changed
Original:  We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science . And we will end with the recent interest in neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining  data-intensive statistical AI systems with  symbolic AI systems which results in more capable AI systems that support more human-like intelligence.
Revised:  We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science , and discuss emerging neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining data-intensive statistical AI systems with symbolic AI systems which results in more capable AI systems that support more human-like intelligence.
Edit intent: clarity
Original:  And we will end with the recent interest in neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining  data-intensive statistical AI systems with  symbolic AI systems which results in more capable AI systems that support more human-like intelligence.
Revised:  And we will end with the recent interest in neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining capabilities of the data-intensive statistical AI systems with symbolic AI systems which results in more capable AI systems that support more human-like intelligence.
Edit intent: clarity
Original:  And we will end with the recent interest in neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining  data-intensive statistical AI systems with  symbolic AI systems which results in more capable AI systems that support more human-like intelligence.
Revised:  And we will end with the recent interest in neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining data-intensive statistical AI systems with those of symbolic AI systems which results in more capable AI systems that support more human-like intelligence.
Edit intent: clarity