wanyu/IteraTeR_full_sent huggingface dataset:
Original:  in general, models learn shape at least as easily as  texture . Moreover, although ImageNet training leads to classifier weights that classify ambiguous images according to texture, shape is decodable from the hidden representations of ImageNet networks. Turning to the question of the origin of texture bias , we identify consistent effects of task, architecture, preprocessing, and hyperparameters. Different self-supervised training objectives and different architectures have  significant and largely independent effects on the shape bias of the learned representations.
Revised:  in general, models learn shape at least as easily as texture bias in CNNs trained on ImageNet? Different unsupervised training objectives and different architectures have significant and largely independent effects on the shape bias of the learned representations.
Edit intent: clarity
Original:  Different self-supervised training objectives and different architectures have  significant and largely independent effects on the shape bias of the learned representations. Among modern ImageNet architectures, we find that shape bias is positively correlated with ImageNet accuracy. Random-crop data augmentation encourages reliance on texture: Models trained without crops have lower accuracy but higher shape bias. Finally, hyperparameter combinations that yield similar accuracy are associated with vastly different levels of shape bias .
Revised:  Different self-supervised training objectives and different architectures have significant and largely independent effects on the level of texture bias. However, all objectives and architectures still lead to models that make texture-based classification decisions a majority of the time, even if shape information is decodable from their hidden representations. The effect of data augmentation is much larger. By taking less aggressive random crops at training time and applying simple, naturalistic augmentation (color distortion, noise, and blur), we train models that classify ambiguous images by shape a majority of the time, and outperform baselines on out-of-distribution test sets .
Edit intent: meaning-changed
Original:  We present a conjectural list of inequalities , which are satisfied by a  profile if and only if it is manipulable by a coalition Coal_{\mathcal{A_k} (of members of electorate who prefer A_k over the winner).
Revised:  For any such rule and any number of alternatives, we introduce a new approach allowing us to characterize the list of inequalities , which are satisfied by a profile if and only if it is manipulable by a coalition Coal_{\mathcal{A_k} (of members of electorate who prefer A_k over the winner).
Edit intent: meaning-changed
Original:  We present a conjectural list of inequalities , which are satisfied by a  profile if and only if it is manipulable by a coalition Coal_{\mathcal{A_k} (of members of electorate who prefer A_k over the winner).
Revised:  We present a conjectural list of inequalities which are satisfied by a profile if and only if it is manipulable by a coalition Coal_{\mathcal{A_k} (of members of electorate who prefer A_k over the winner).
Edit intent: fluency
Original:  We present a conjectural list of inequalities , which are satisfied by a  profile if and only if it is manipulable by a coalition Coal_{\mathcal{A_k} (of members of electorate who prefer A_k over the winner).
Revised:  We present a conjectural list of inequalities , which are satisfied by a preference profile if and only if it is manipulable by a coalition Coal_{\mathcal{A_k} (of members of electorate who prefer A_k over the winner).
Edit intent: clarity
Original:  We present a conjectural list of inequalities , which are satisfied by a  profile if and only if it is manipulable by a coalition Coal_{\mathcal{A_k} (of members of electorate who prefer A_k over the winner).
Revised:  We present a conjectural list of inequalities , which are satisfied by a profile if and only if it is manipulable by a coalition _k} (of members of electorate who prefer A_k over the winner).
Edit intent: clarity
Original:  The shares } of  manipulable outcomes for plurality, anti-plurality and Borda count in case of m=4 alternatives are presented and the Python code for carrying out the computation in case of Borda count is provided.
Revised:  The shares } of voters. Furthermore, the probability of manipulable outcomes for plurality, anti-plurality and Borda count in case of m=4 alternatives are presented and the Python code for carrying out the computation in case of Borda count is provided.
Edit intent: meaning-changed
Original:  The shares } of  manipulable outcomes for plurality, anti-plurality and Borda count in case of m=4 alternatives are presented and the Python code for carrying out the computation in case of Borda count is provided.
Revised:  The shares } of manipulable outcomes for some well-studied scoring voting rules in case of m=4 alternatives are presented and the Python code for carrying out the computation in case of Borda count is provided.
Edit intent: meaning-changed
Original:  The shares } of  manipulable outcomes for plurality, anti-plurality and Borda count in case of m=4 alternatives are presented and the Python code for carrying out the computation in case of Borda count is provided. We also study a specific type of manipulation (SM), which includes not only the fact of manipulability but the manipulability which leads to the winning of the desired alternative. The shares of SM results for plurality, anti-plurality, Borda and Coombs' procedures in case of m=3 alternatives are computed .
Revised:  The shares } of manipulable outcomes for plurality, anti-plurality and Borda count in case of small number of alternatives and large electorates are presented under a well-known assumption on individual preference profiles .
Edit intent: meaning-changed
Original: Coalitional manipulation in voting is considered to be any scenario in which a group of voters decide to vote in a way that does not represent their true preferences in order to improve the outcome of the election  .
Revised: Coalitional manipulation in voting is considered to be any scenario in which a group of voters decide to vote in a way that does not represent their true preferences in order to improve the outcome of the election when they vote honestly .
Edit intent: meaning-changed
Original:  Furthermore, the probability of manipulable outcomes for some well-studied scoring voting rules in case of small number of alternatives and large electorates are presented under a well-known assumption on individual preference profiles.
Revised:  This gives us the possibility to find the probability of manipulable outcomes for some well-studied scoring voting rules in case of small number of alternatives and large electorates are presented under a well-known assumption on individual preference profiles.
Edit intent: coherence
Original:  In many such platforms prices are set by revenue-maximizing sellers, but the platform may influence prices through its promotion policy (e.g., increasing demand to a certain product by assigning to it a prominent position on the webpage ), and the information it reveals about the additional demand associated with being promoted.
Revised:  Even though in many such marketplaces prices are set by revenue-maximizing sellers, but the platform may influence prices through its promotion policy (e.g., increasing demand to a certain product by assigning to it a prominent position on the webpage ), and the information it reveals about the additional demand associated with being promoted.
Edit intent: clarity
Original:  In many such platforms prices are set by revenue-maximizing sellers, but the platform may influence prices through its promotion policy (e.g., increasing demand to a certain product by assigning to it a prominent position on the webpage ), and the information it reveals about the additional demand associated with being promoted.
Revised:  In many such platforms prices are set by revenue-maximizing sellers, platforms can influence prices through its promotion policy (e.g., increasing demand to a certain product by assigning to it a prominent position on the webpage ), and the information it reveals about the additional demand associated with being promoted.
Edit intent: coherence
Original:  In many such platforms prices are set by revenue-maximizing sellers, but the platform may influence prices through its promotion policy (e.g., increasing demand to a certain product by assigning to it a prominent position on the webpage ), and the information it reveals about the additional demand associated with being promoted.
Revised:  In many such platforms prices are set by revenue-maximizing sellers, but the platform may influence prices through (i) price-dependent promotion policies that can increase demand for a product by featuring it in a prominent position on the webpage ), and the information it reveals about the additional demand associated with being promoted.
Edit intent: meaning-changed
Original:  In many such platforms prices are set by revenue-maximizing sellers, but the platform may influence prices through its promotion policy (e.g., increasing demand to a certain product by assigning to it a prominent position on the webpage ), and the information it reveals about the additional demand associated with being promoted.
Revised:  In many such platforms prices are set by revenue-maximizing sellers, but the platform may influence prices through its promotion policy (e.g., increasing demand to a certain product by assigning to it a prominent position on the webpage and (ii) the information revealed to sellers about the value of being promoted.
Edit intent: clarity
Original:  We introduce the notion of confounding promotion polices , which are designed to prevent a Bayesian seller from learning the promotion value (at the cost of diverting consumers away from the best product offering).
Revised:  We introduce the notion of confounding promotion polices , which are designed to prevent a Bayesian seller from learning the promotion value (at the expense of the short-run loss of diverting consumers from the best product offering).
Edit intent: clarity
Original:  Leveraging this notion , we characterize the maximum long-run average consumer surplus that is achievable by the platform when the seller is myopic. We then establish that long-run average optimality can be maintained by optimizing over a class of joint information design and promotion policies under which the platform provides the seller with a (random) information signal at the beginning of the horizon, and then uses the best confounding promotion policy, which prevents the sellerfrom further learning.
Revised:  Leveraging this notion , we characterize the maximum long-run average consumer surplus that is achievable through joint information design and promotion policies under which the platform provides the seller with a (random) information signal at the beginning of the horizon, and then uses the best confounding promotion policy, which prevents the sellerfrom further learning.
Edit intent: clarity
Original:  We then establish that long-run average optimality can be maintained by optimizing over a class of joint information design and promotion policies under which the platform provides the seller with a (random) information signal at the beginning of the horizon, and then uses the best confounding promotion policy, which prevents the sellerfrom further learning. Additionally, we show that myopic pricing is a best response to such a platformstrategy, thereby establishing an approximate Bayesian Nash equilibrium between the platform and the seller .
Revised:  We then establish that long-run average optimality can be maintained by optimizing over a class of joint information design and promotion policies when the seller sets prices myopically. We then establish a Bayesian Nash equilibrium by showing that the seller's best response to such a platformstrategy, thereby establishing an approximate Bayesian Nash equilibrium between the platform and the seller .
Edit intent: clarity
Original:  Additionally, we show that myopic pricing is a best response to such a platformstrategy, thereby establishing an approximate Bayesian Nash equilibrium between the platform and the seller .
Revised:  Additionally, we show that myopic pricing is a best response to the platform's optimal policy is to price myopically at every history. Moreover, the equilibrium we identify is platform-optimal within the class of horizon-maximin equilibria, in which strategies are not predicated on precise knowledge of the horizon length, and are designed to maximize payoff over the worst-case horizon .
Edit intent: meaning-changed
Original:  This paper presents its design and feasibility study, which showed a plausible acceptance level from the participating teachers and students. We also reveal students' concerns on the abuse or misuse of this system : e.g., for checking attendance. Taken together, we discuss the opportunities and risks in design, and elaborate on the plan of a follow-up, in-depth implementation to further evaluate NaMemo's impacts on learning and teaching, as well as to probe design implications including privacy considerations.
Revised:  This paper presents the system design, the pilot feasibility test, and our plan for the following study, which aims to evaluate NaMemo's impacts on learning and teaching, as well as to probe design implications including privacy considerations.
Edit intent: coherence
Original:  We tested nine different models, which employed different architectures and recognition algorithms, including discriminative and generative models, all trained to recognize handwritten digits ( from the MNISTset of digit images ). We synthesized controversial stimuli to maximize the disagreement among the models . Human subjects viewed hundreds of these stimuli  and judged the probability of presence of each digit  in each image.
Revised:  We tested nine different models, which employed different architectures and recognition algorithms, including discriminative and generative models, all trained to recognize handwritten digits ( MNIST) and objects in small natural images (CIFAR-10). For each task, we synthesized controversial stimuli to maximize the disagreement among the models . Human subjects viewed hundreds of these stimuli and judged the probability of presence of each digit in each image.
Edit intent: meaning-changed
Original:  The best performing model was the generative Analysis-by-Synthesis model (based on variational autoencoders) . However, a simpler generative model (based on Gaussian-kernel-density estimation) also performed better than each of the discriminative models . None of the candidate models fully explained the human responses.
Revised:  The best performing model was the generative Analysis-by-Synthesis model (based on variational autoencoders) for MNIST and a hybrid discriminative-generative Joint Energy Model for CIFAR-10. These DNNs, which model the distribution of images, performed better than each of the discriminative models . None of the candidate models fully explained the human responses.
Edit intent: meaning-changed
Original:  However, a simpler generative model (based on Gaussian-kernel-density estimation) also performed better than each of the discriminative models . None of the candidate models fully explained the human responses.
Revised:  However, a simpler generative model (based on Gaussian-kernel-density estimation) also performed better than purely discriminative DNNs, which learn only to map images to labels . None of the candidate models fully explained the human responses.
Edit intent: meaning-changed
Original:  We discuss the advantages and limitations of controversial stimuli as an experimental paradigm and how they generalize and improve onadversarial examples as probes of discrepancies between models and human perception.
Revised:  Controversial stimuli generalize the concept of adversarial examples, obviating the need to assume a ground-truth model. Unlike natural images, controversial stimuli are not constrained to the stimulus distribution models are trained on, thus providing severe out-of-distribution tests that reveal the models' inductive biases. Controversial stimuli therefore provide powerful probes of discrepancies between models and human perception.
Edit intent: meaning-changed
Original: In this article, we are going to review a brief history of the field of Virtual Reality (VR) %DIFDELCMD < \footnotemark%%% Circa 2018 , VR systems  and applications and discuss how they evolved over time . After that, we will familiarize ourselves with key components of VR experiences and common VR terminology.
Revised: In this article, we are going to review a brief history of the field of Virtual Reality (VR) %DIFDELCMD < \footnotemark%%% , VR systems and applications and discuss how they evolved over time . After that, we will familiarize ourselves with key components of VR experiences and common VR terminology.
Edit intent: coherence
Original: In this article, we are going to review a brief history of the field of Virtual Reality (VR) %DIFDELCMD < \footnotemark%%% Circa 2018 , VR systems  and applications and discuss how they evolved over time . After that, we will familiarize ourselves with key components of VR experiences and common VR terminology.
Revised: In this article, we are going to review a brief history of the field of Virtual Reality (VR) %DIFDELCMD < \footnotemark%%% Circa 2018 , VR systems , and applications and discuss how they evolved over time . After that, we will familiarize ourselves with key components of VR experiences and common VR terminology.
Edit intent: fluency
Original: In this article, we are going to review a brief history of the field of Virtual Reality (VR) %DIFDELCMD < \footnotemark%%% Circa 2018 , VR systems  and applications and discuss how they evolved over time . After that, we will familiarize ourselves with key components of VR experiences and common VR terminology.
Revised: In this article, we are going to review a brief history of the field of Virtual Reality (VR) %DIFDELCMD < \footnotemark%%% Circa 2018 , VR systems and applications and discuss how they evolved . After that, we will familiarize ourselves with key components of VR experiences and common VR terminology.
Edit intent: clarity
Original: In this article, we are going to review a brief history of the field of Virtual Reality (VR) %DIFDELCMD < \footnotemark%%% Circa 2018 , VR systems  and applications and discuss how they evolved over time . After that, we will familiarize ourselves with key components of VR experiences and common VR terminology.
Revised: In this article, we are going to review a brief history of the field of Virtual Reality (VR) %DIFDELCMD < \footnotemark%%% Circa 2018 , VR systems and applications and discuss how they evolved over time . After that, we will familiarize ourselves with the essential components of VR experiences and common VR terminology.
Edit intent: clarity
Original: Advances in deep learning and neural networks have resulted in the rapid development of hardware accelerators that support them. A large majority of ASIC accelerators, however, target a single hardware design point to accelerate the main computational kernels of deep neural networks such as convolutions or matrix multiplication. On the other hand, the spectrum of use-cases for neural network accelerators, ranging from edge devices to cloud, presents a prime opportunity for agile hardware design and generator methodologies. We present Gemmini-- an open source and agile systolic array generatorenabling systematic evaluations of deep-learning architectures . Gemmini generates a custom ASIC accelerator for matrix multiplication based on a systolic array architecture, complete with additional functions for neural network inference.
Revised: DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments. This makes it difficult to appreciate the impact of System-on-Chip (SoC) resource contention, OS overheads, and programming-stack inefficiencies on overall performance/energy-efficiency. To address this challenge, we present Gemmini, an open-source*, full-stack DNN accelerator generator . Gemmini generates a custom ASIC accelerator for matrix multiplication based on a systolic array architecture, complete with additional functions for neural network inference.
Edit intent: meaning-changed
Original:  We present Gemmini-- an open source and agile systolic array generatorenabling systematic evaluations of deep-learning architectures . Gemmini generates a custom ASIC accelerator for matrix multiplication based on a systolic array architecture, complete with additional functions for neural network inference. Gemmini runs with the RISC-V ISA, and is integrated with the Rocket Chip System-on-Chip generator ecosystem, including Rocket in-order cores and BOOM out-of-order cores. Through an elaborate design space exploration case study, this work demonstrates the selection processes of various parameters for the use-case of inference on edge devices. Selected design points achieve two to three orders of magnitude speedup in deep neural network inference compared to the baseline execution on a host processor.
Revised:  We present Gemmini-- an open source and agile systolic array generatorenabling systematic evaluations of deep-learning architectures . Gemmini generates a wide design-space of efficient ASIC accelerators from a flexible architectural template, together with flexible programming stacks and full SoCs with shared resources that capture system-level effects.
Edit intent: clarity
Original: In this work we address the problem of neuronal cell-type classification, and we employ a real-world dataset of raw neuronal activity measurements obtained with calcium imaging techniques. While neuronal cell-type classification is a crucial step in understanding the function of neuronal circuits, and thus a systematic classification of neurons is much needed, it still remains a challenge .
Revised: Identification of different neuronal cell types is critical for understanding their contribution to brain functions. Yet, automated and reliable classification of neurons is much needed, it still remains a challenge .
Edit intent: clarity
Original:  While neuronal cell-type classification is a crucial step in understanding the function of neuronal circuits, and thus a systematic classification of neurons is much needed, it still remains a challenge . In recent years, several approaches have been employed for a reliable neuronal cell-type recognition, such as immunohistochemical (IHC) analysis and feature extraction algorithms based on several characteristics of neuronal cells.
Revised:  While neuronal cell-type classification is a crucial step in understanding the function of neuronal circuits, and thus a systematic classification of neurons is much needed, it still remains a challenge , primarily because of their biological complexity. Typical approaches include laborious and expensive immunohistochemical analysis while feature extraction algorithms based on several characteristics of neuronal cells.
Edit intent: coherence