wanyu/IteraTeR_v2 huggingface dataset:
Original:  In 1981, Myerson answered this question with an emphatic "No" for a monopolist selling a single product .
Revised:  In 1981, Myerson answered this question with an emphatic "No" for a monopolist selling a single good .
Edit intent: clarity
Original:  By contrast, there is no crisp answer for multiple products , and a major focus of Bayesian mechanism design has been understanding the increase in revenue from pricing lotteries over products instead of deterministic products, under different classes of valuation functions and different families of prior customer distributions .
Revised:  By contrast, there is no crisp answer for multiple goods , and a major focus of Bayesian mechanism design has been understanding the increase in revenue from pricing lotteries over products instead of deterministic products, under different classes of valuation functions and different families of prior customer distributions .
Edit intent: clarity
Original:  By contrast, there is no crisp answer for multiple products , and a major focus of Bayesian mechanism design has been understanding the increase in revenue from pricing lotteries over products instead of deterministic products, under different classes of valuation functions and different families of prior customer distributions .
Revised:  By contrast, there is no crisp answer for multiple products , and a major focus of Bayesian mechanism design since then has been to understand the power of randomized lotteries over deterministic pricing in different settings .
Edit intent: clarity
Original:  In this paper, we ask the same question for assortment optimization, where products have exogenously-fixed prices, and the decision is on a set of substitute products to offer.
Revised:  In this paper, we ask the same question for assortment optimization, where goods have exogenously-fixed prices, and the decision is on a set of substitute products to offer.
Edit intent: fluency
Original:  In this paper, we ask the same question for assortment optimization, where products have exogenously-fixed prices, and the decision is on a set of substitute products to offer.
Revised:  In this paper, we ask the same question for assortment optimization, where products have exogenously-fixed prices, and the decision is a set of substitute products to offer.
Edit intent: fluency
Original:  In this paper, we ask the same question for assortment optimization, where products have exogenously-fixed prices, and the decision is on a set of substitute products to offer.
Revised:  In this paper, we ask the same question for assortment optimization, where products have exogenously-fixed prices, and the decision is on a set of substitute goods to offer.
Edit intent: clarity
Original:  To formalize such a question, we introduce a Bayesian mechanism design problem where customers have ordinal preferences for products with fixed prices  , in which assortments correspond to deterministic mechanisms.
Revised:  To formalize this question, we introduce a Bayesian mechanism design problem where customers have ordinal preferences for products with fixed prices , in which assortments correspond to deterministic mechanisms.
Edit intent: clarity
Original:  To formalize such a question, we introduce a Bayesian mechanism design problem where customers have ordinal preferences for products with fixed prices  , in which assortments correspond to deterministic mechanisms.
Revised:  To formalize such a question, we introduce a Bayesian mechanism design problem with fixed prices , in which assortments correspond to deterministic mechanisms.
Edit intent: coherence
Original:  Meanwhile, randomized mechanisms correspond to lotteries whose payment is restricted  equal the fixed price of the randomly-allocated product , a significant departure from the lotteries currently studied in Bayesian mechanism design .
Revised:  Meanwhile, randomized mechanisms correspond to lotteries whose payment is restricted to equal the fixed price of the randomly-allocated product , a significant departure from the lotteries currently studied in Bayesian mechanism design .
Edit intent: fluency
Original:  We first show that for general ordinal preference distributions, lotteries under this restriction can still earn higher revenue than any deterministic assortment.
Revised:  We first show that for general ordinal distributions, lotteries under this restriction can still earn higher revenue than any deterministic assortment.
Edit intent: clarity
Original:  We first show that for general ordinal preference distributions, lotteries under this restriction can still earn higher revenue than any deterministic assortment.
Revised:  We first show that for general ordinal preference distributions, lotteries under this restriction can still earn greater revenue than any deterministic assortment.
Edit intent: clarity
Original:  Importantly, this sufficient condition captures common distributions used in the assortment optimization literature, including Multi-Nomial Logit (MNL) , Markov Chain choice models , Tversky's Elimination by Aspects model , and mixtures of any one of these with Independent Demand Models.
Revised:  Importantly, this sufficient condition captures commonly-used distributions in the assortment optimization literature, including Multi-Nomial Logit (MNL) , Markov Chain choice models , Tversky's Elimination by Aspects model , and mixtures of any one of these with Independent Demand Models.
Edit intent: fluency
Original:  Importantly, this sufficient condition captures common distributions used in the assortment optimization literature, including Multi-Nomial Logit (MNL) , Markov Chain choice models , Tversky's Elimination by Aspects model , and mixtures of any one of these with Independent Demand Models.
Revised:  Importantly, this sufficient condition captures common distributions used in the assortment optimization literature, including Multi-Nomial Logit , Markov Chain choice models , Tversky's Elimination by Aspects model , and mixtures of any one of these with Independent Demand Models.
Edit intent: clarity
Original:  Importantly, this sufficient condition captures common distributions used in the assortment optimization literature, including Multi-Nomial Logit (MNL) , Markov Chain choice models , Tversky's Elimination by Aspects model , and mixtures of any one of these with Independent Demand Models.
Revised:  Importantly, this sufficient condition captures common distributions used in the assortment optimization literature, including Multi-Nomial Logit (MNL) , Markov Chain , Tversky's Elimination by Aspects model , and mixtures of any one of these with Independent Demand Models.
Edit intent: clarity
Original:  Importantly, this sufficient condition captures common distributions used in the assortment optimization literature, including Multi-Nomial Logit (MNL) , Markov Chain choice models , Tversky's Elimination by Aspects model , and mixtures of any one of these with Independent Demand Models.
Revised:  Importantly, this sufficient condition captures common distributions used in the assortment optimization literature, including Multi-Nomial Logit (MNL) , Markov Chain choice models , Tversky's Elimination by Aspects , and mixtures of any one of these with Independent Demand Models.
Edit intent: fluency
Original:  Importantly, this sufficient condition captures common distributions used in the assortment optimization literature, including Multi-Nomial Logit (MNL) , Markov Chain choice models , Tversky's Elimination by Aspects model , and mixtures of any one of these with Independent Demand Models.
Revised:  Importantly, this sufficient condition captures common distributions used in the assortment optimization literature, including Multi-Nomial Logit (MNL) , Markov Chain choice models , Tversky's Elimination by Aspects model , and mixtures with Independent Demand Models.
Edit intent: clarity
Original:  The takeaway is that unless a firm has a very sophisticated model for consumer choice, assortments are optimal .
Revised:  The takeaway is that unless a firm has a sophisticated model for consumer choice, assortments are optimal .
Edit intent: clarity
Original:  The takeaway is that unless a firm has a very sophisticated model for consumer choice, assortments are optimal .
Revised:  The takeaway is that unless a firm has a very sophisticated model for consumer choice, fixed-price lotteries are no better than assortments .
Edit intent: clarity
Original:  We show that for high-dimensional multi-class data, the optimal linear discriminative representation maximizes the coding rate difference between the whole dataset and the average of all the subsets.
Revised:  We argue that for high-dimensional multi-class data, the optimal linear discriminative representation maximizes the coding rate difference between the whole dataset and the average of all the subsets.
Edit intent: clarity
Original:  We show that the basic iterative gradient ascent scheme for optimizing the rate reduction objective naturally leads to a multi-layer deep network, named ReduNet, that shares common characteristics of modern deep networks.
Revised:  We show that the basic iterative gradient ascent scheme for optimizing the rate reduction objective naturally leads to a multi-layer deep network, named ReduNet, which shares common characteristics of modern deep networks.
Edit intent: fluency
Original:  All components of so-obtained " white-box " network have precise optimization, statistical, and geometric interpretation.
Revised:  All components of so-obtained `` white-box " network have precise optimization, statistical, and geometric interpretation.
Edit intent: fluency
Original:  All components of so-obtained " white-box " network have precise optimization, statistical, and geometric interpretation.
Revised:  All components of so-obtained " white-box '' network have precise optimization, statistical, and geometric interpretation.
Edit intent: fluency
Original:  All components of so-obtained `` white-box '' network have precise optimization, statistical, and geometric interpretation.
Revised:  All components of so-obtained " white-box '' network have precise optimization, statistical, and geometric interpretation.
Edit intent: fluency
Original:  All components of so-obtained `` white-box '' network have precise optimization, statistical, and geometric interpretation.
Revised:  All components of so-obtained `` white-box " network have precise optimization, statistical, and geometric interpretation.
Edit intent: fluency
Original: In  intelligent transportation system, the key problem of traffic forecasting is how to extract the periodic temporal dependencies and complex spatial correlation.
Revised: In an intelligent transportation system, the key problem of traffic forecasting is how to extract the periodic temporal dependencies and complex spatial correlation.
Edit intent: fluency
Original:  Current state-of-the-art methods for traffic flow prediction are based on graph architectures and sequence learning models, but they do not fully exploit spatial-temporal dynamic information in  traffic system.
Revised:  Current state-of-the-art methods for traffic flow forecasting are based on graph architectures and sequence learning models, but they do not fully exploit spatial-temporal dynamic information in traffic system.
Edit intent: clarity
Original:  Current state-of-the-art methods for traffic flow prediction are based on graph architectures and sequence learning models, but they do not fully exploit spatial-temporal dynamic information in  traffic system.
Revised:  Current state-of-the-art methods for traffic flow prediction are based on graph architectures and sequence learning models, but they do not fully exploit spatial-temporal dynamic information in the traffic system.
Edit intent: fluency
Original:  Specifically, the temporal dependence of  short-range is diluted by recurrent neural networks, and  existing sequence model ignores local spatial information because the convolution operation uses global average pooling.
Revised:  Specifically, the temporal dependence of the short-range is diluted by recurrent neural networks, and existing sequence model ignores local spatial information because the convolution operation uses global average pooling.
Edit intent: fluency
Original:  Specifically, the temporal dependence of  short-range is diluted by recurrent neural networks, and  existing sequence model ignores local spatial information because the convolution operation uses global average pooling.
Revised:  Specifically, the temporal dependence of short-range is diluted by recurrent neural networks, and the existing sequence model ignores local spatial information because the convolution operation uses global average pooling.
Edit intent: fluency
Original:  Moreover, the accidents features impact on local traffic congestion  and position encoding is employed to detect anomalies in complex traffic situations.
Revised:  Moreover, the accidents features impact on local traffic congestion , and position encoding is employed to detect anomalies in complex traffic situations.
Edit intent: fluency
Original:  We conduct extensive experiments on large-scale real-world tasks and verify the effectiveness of our proposed method.
Revised:  We conduct a large number of experiments on real-world tasks and verify the effectiveness of our proposed method.
Edit intent: clarity
Original:  We show that the new analog computing techniques like Coherent Ising Machines (CIM) and Oscillator-based Ising Machines (OIM) are promising candidates for performing near-optimal MIMO detection.
Revised:  We show that the new analog computing techniques like Coherent Ising Machines (CIM) are promising candidates for performing near-optimal MIMO detection.
Edit intent: coherence
Original:  We propose a novel regularized Ising formulation for MIMO detection that mitigates the error floor and further evolves it into an algorithm that achieves near-optimal MIMO detection.
Revised:  We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor problem and further evolves it into an algorithm that achieves near-optimal MIMO detection.
Edit intent: clarity
Original:  Massive MIMO systems, that have a much larger number of antennas at the Access point (AP) than at the users , allow linear detectors to be near-optimal.
Revised:  Massive MIMO systems, that have a large number of antennas at the Access point (AP) than at the users , allow linear detectors to be near-optimal.
Edit intent: clarity
Original:  Massive MIMO systems, that have a much larger number of antennas at the Access point (AP) than at the users , allow linear detectors to be near-optimal.
Revised:  Massive MIMO systems, that have a much larger number of antennas at the Access point (AP) , allow linear detectors to be near-optimal.
Edit intent: clarity
Original:  However, the simplified detection in these systems comes at the cost of overall throughput, which can be improved by supporting more users.
Revised:  However, the simplified detection in these systems comes at the cost of overall throughput, which could be improved by supporting more users.
Edit intent: coherence
Original:  We demonstrate that, for a 16\times16 large MIMO system, our methods achieve around 2.5\times more throughput in mid-SNR regime (\approx 12 dB) and 2\times more throughput in high-SNR regime( > 20dB) than MMSE .
Revised:  We demonstrate that, for a 16\times16 large MIMO system, we estimate around 2.5\times more throughput in mid-SNR regime (\approx 12 dB) and 2\times more throughput in high-SNR regime( > 20dB) than MMSE .
Edit intent: clarity
Original: Analysis engines based on Graph Neural Networks (GNNs)
Revised: Graph neural networks (GNN) analysis engines
Edit intent: clarity
Original:  are vital for many real-world problems that model relationships using large graphs . Challenges for a GNN hardware platform include the ability to (a) host a variety of GNNs, (b) handle high sparsity in input node feature vectors and the graph adjacency matrix and the accompanying random memory access patterns, and (c) maintain load-balanced computation in the face of uneven workloads  induced by high sparsity and power-law vertex degree distributions in real datasets.
Revised:  are vital for real-world problems that model relationships using large graphs . Challenges for a GNN hardware platform include the ability to (a) host a variety of GNNs, (b) handle high sparsity in input node feature vectors and the graph adjacency matrix and the accompanying random memory access patterns, and (c) maintain load-balanced computation in the face of uneven workloads induced by high sparsity and power-law vertex degree distributions in real datasets.
Edit intent: clarity
Original:  are vital for many real-world problems that model relationships using large graphs . Challenges for a GNN hardware platform include the ability to (a) host a variety of GNNs, (b) handle high sparsity in input node feature vectors and the graph adjacency matrix and the accompanying random memory access patterns, and (c) maintain load-balanced computation in the face of uneven workloads  induced by high sparsity and power-law vertex degree distributions in real datasets.
Revised:  are vital for many real-world problems that use large graph models . Challenges for a GNN hardware platform include the ability to (a) host a variety of GNNs, (b) handle high sparsity in input node feature vectors and the graph adjacency matrix and the accompanying random memory access patterns, and (c) maintain load-balanced computation in the face of uneven workloads induced by high sparsity and power-law vertex degree distributions in real datasets.
Edit intent: clarity
Original:  are vital for many real-world problems that model relationships using large graphs . Challenges for a GNN hardware platform include the ability to (a) host a variety of GNNs, (b) handle high sparsity in input node feature vectors and the graph adjacency matrix and the accompanying random memory access patterns, and (c) maintain load-balanced computation in the face of uneven workloads  induced by high sparsity and power-law vertex degree distributions in real datasets.
Revised:  are vital for many real-world problems that model relationships using large graphs . Challenges for a GNN hardware platform include the ability to (a) host a variety of GNNs, (b) handle high sparsity in input vertex feature vectors and the graph adjacency matrix and the accompanying random memory access patterns, and (c) maintain load-balanced computation in the face of uneven workloads induced by high sparsity and power-law vertex degree distributions in real datasets.
Edit intent: clarity
Original:  are vital for many real-world problems that model relationships using large graphs . Challenges for a GNN hardware platform include the ability to (a) host a variety of GNNs, (b) handle high sparsity in input node feature vectors and the graph adjacency matrix and the accompanying random memory access patterns, and (c) maintain load-balanced computation in the face of uneven workloads  induced by high sparsity and power-law vertex degree distributions in real datasets.
Revised:  are vital for many real-world problems that model relationships using large graphs . Challenges for a GNN hardware platform include the ability to (a) host a variety of GNNs, (b) handle high sparsity in input node feature vectors and the graph adjacency matrix and the accompanying random memory access patterns, and (c) maintain load-balanced computation in the face of uneven workloads , induced by high sparsity and power-law vertex degree distributions in real datasets.
Edit intent: fluency
Original:  It tackles workload imbalance by (i) splitting node feature operands into blocks, (ii)  reordering and redistributing computations, and (iii) using a flexible MAC architecture with low communication overheads among the processing elements.
Revised:  It tackles workload imbalance by (i) splitting node feature operands into blocks, (ii) ~ reordering and redistributing computations, and (iii) using a flexible MAC architecture with low communication overheads among the processing elements.
Edit intent: fluency
Original:  It tackles workload imbalance by (i) splitting node feature operands into blocks, (ii)  reordering and redistributing computations, and (iii) using a flexible MAC architecture with low communication overheads among the processing elements.
Revised:  It tackles workload imbalance by (i) splitting node feature operands into blocks, (ii) reordering and redistributing computations, (iii) using a flexible MAC architecture with low communication overheads among the processing elements.
Edit intent: coherence
Original:  In addition, it adopts a graph partitioning scheme and a graph-specific  caching policy that efficiently uses off-chip memory bandwidth that is well suited to the characteristics of real-world graphs.
Revised:  In addition, it adopts a graph partitioning scheme and a graph-specific caching policy that is well suited to the characteristics of real-world graphs.
Edit intent: clarity
Original:  In addition, it adopts a graph partitioning scheme and a graph-specific  caching policy that efficiently uses off-chip memory bandwidth that is well suited to the characteristics of real-world graphs.
Revised:  In addition, it adopts a graph partitioning scheme and a graph-specific caching policy that efficiently uses off-chip memory bandwidth that is well suited to real-world graphs.
Edit intent: clarity
Original:  Random memory access effects are mitigated by partitioning and degree-aware caching to enable the reuse of high-degree vertices . GNNIE achieves average speedups of over 8890x over a CPU and 295x over a GPU over multiple datasets on graph attention networks (GATs), graph convolutional networks (GCNs), GraphSAGE, GINConv, and DiffPool , Compared to prior approaches, GNNIE achieves an average speedup of 9.74x over HyGCN for GCN, GraphSAGE, and GINConv ;
Revised:  Random memory access effects are mitigated by partitioning and degree-aware caching to enable the reuse of high-degree vertices . GNNIE achieves average speedups of over 8890x over a CPU and 295x over a GPU over multiple datasets on graph attention networks (GATs), graph convolutional networks (GCNs), GraphSAGE, GINConv, and DiffPool . Compared to prior approaches, GNNIE achieves an average speedup of 9.74x over HyGCN for GCN, GraphSAGE, and GINConv ;
Edit intent: fluency
Original:  GNNIE achieves an average speedup of 2.28 x over AWB-GCN (which runs only GCNs) , despite using 3.4x fewer processing units .
Revised:  GNNIE achieves an average speedup of 2.28 x over AWB-GCN (which runs only GCNs) .
Edit intent: clarity
Original:  To improve this tradeoff , prior work has looked at privacy amplification methods which analyze how common training operations such as iteration and subsampling the data can lead to higher privacy .
Revised:  One way to improve this tradeoff , prior work has looked at privacy amplification methods which analyze how common training operations such as iteration and subsampling the data can lead to higher privacy .
Edit intent: clarity
Original:  To improve this tradeoff , prior work has looked at privacy amplification methods which analyze how common training operations such as iteration and subsampling the data can lead to higher privacy .
Revised:  To improve this tradeoff for free is to leverage the noise in common data operations that already use randomness. Such operations include noisy SGD and data subsampling. The additional noise in these operations may amplify the privacy guarantee of the overall algorithm, a phenomenon known as privacy amplification .
Edit intent: clarity
Original:  We provide an algorithm to compute the amplification factor in this setting, and establish upper and lower bounds on this factor .
Revised:  We provide an algorithm to compute the amplification factor , and we establish upper and lower bounds on this factor .
Edit intent: clarity