wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this way, our prompt tuning approach enables strong few-shot and even zero-shot visual grounding capabilities of VL-PTMs. Comprehensive experimental results show that prompt tuned VL-PTMs outperform their fine-tuned counterparts by a large margin (e.g., 17.3\% absolute accuracy improvement, and 73.8\% relative standard deviation reduction on average with one shot in RefCOCO evaluation).
Revised:  In this way, our prompt tuning approach enables strong few-shot and even zero-shot visual grounding capabilities of VL-PTMs. Comprehensive experimental results show that the prompt-tuned VL-PTMs outperform their fine-tuned counterparts by a large margin (e.g., 17.3\% absolute accuracy improvement, and 73.8\% relative standard deviation reduction on average with one shot in RefCOCO evaluation).
Edit intent: fluency
Original:  All the data and code will be available to facilitate future research.
Revised:  All the data and codes will be available to facilitate future research.
Edit intent: fluency
Original:  Since this process is time-consuming and sometimes requires additional knowledge about the software project, current literature proposes several information retrieval techniques which can aid the bug localization process.
Revised:  Since this process is time-consuming and requires additional knowledge about the software project, current literature proposes several information retrieval techniques which can aid the bug localization process.
Edit intent: coherence
Original:  Since this process is time-consuming and sometimes requires additional knowledge about the software project, current literature proposes several information retrieval techniques which can aid the bug localization process.
Revised:  Since this process is time-consuming and sometimes requires additional knowledge about the software project, information retrieval techniques which can aid the bug localization process.
Edit intent: clarity
Original:  Since this process is time-consuming and sometimes requires additional knowledge about the software project, current literature proposes several information retrieval techniques which can aid the bug localization process.
Revised:  Since this process is time-consuming and sometimes requires additional knowledge about the software project, current literature proposes several information retrieval techniques can aid the bug localization process.
Edit intent: coherence
Original:  However, recent research questioned the state-of-the-art approaches, since they are barely adopted in practical scenarios.
Edit intent: coherence
Original:  In the second case , the existence of universal extensions follows from a symmetry property of Purification, whereby all pure bipartite states with the same marginal on one system are  interconvertible.
Revised:  In case (2) , the existence of universal extensions follows from a symmetry property of Purification, whereby all pure bipartite states with the same marginal on one system are interconvertible.
Edit intent: coherence
Original:  We propose a distributed stochastic generalized Nash equilibrium seeking algorithm in a partial-decision information setting based on the Douglas-Rachford operator splitting scheme, which significantly relaxes assumptions on co-coercivity and contractiveness in the existing literature.
Revised:  We propose a distributed stochastic generalized Nash equilibrium seeking algorithm in a partial-decision information setting based on the Douglas-Rachford operator splitting scheme, which notably relaxes assumptions on co-coercivity and contractiveness in the existing literature.
Edit intent: clarity
Original:  The proposed algorithm updates players' local decisions through box-constrained augmented best-response schemes and subsequent projections onto the local feasible sets, which occupy most of the computational workload.
Revised:  The proposed algorithm updates players' local decisions through augmented best-response schemes and subsequent projections onto the local feasible sets, which occupy most of the computational workload.
Edit intent: clarity
Original:  The Robbins-Siegmund theorem is leveraged to establish the main convergence results to a true Nash equilibrium and sufficient conditions to be satisfied by the inexact solver used .
Revised:  The Robbins-Siegmund theorem is leveraged to establish the main convergence results to a true Nash equilibrium using the proposed inexact solver .
Edit intent: clarity
Original:  The proposed method enjoys desirable scalability by only requiring each agent to keep local estimates that grow linearly with the number of its neighbors.
Revised:  The proposed method enjoys desirable scalability by only requiring each agent to keep local estimates whose number grows linearly with the number of its neighbors.
Edit intent: fluency
Original:  a few exceptions investigated special game classes such as zero-sum games and second-price auctions but have all focused only on optimal public signaling and exhibit sweepingly negative results .
Revised:  a few exceptions investigated truly strategic games such as zero-sum games and second-price auctions but have all focused only on optimal public signaling and exhibit sweepingly negative results .
Edit intent: clarity
Original:  a few exceptions investigated special game classes such as zero-sum games and second-price auctions but have all focused only on optimal public signaling and exhibit sweepingly negative results .
Revised:  a few exceptions investigated special game classes such as zero-sum games and second-price auctions but have all focused only on optimal public signaling .
Edit intent: clarity
Original:  This paper initiates the algorithmic information design of both public and private signaling in a fundamental class of games with negative externalities, i.e., atomic singleton congestion games, with wide application in today's digital economy, machine scheduling, routing, etc.
Revised:  This paper initiates the algorithmic information design of both public and private signaling in a fundamental class of games with negative externalities, i.e., singleton congestion games, with wide application in today's digital economy, machine scheduling, routing, etc.
Edit intent: clarity
Original:  To our knowledge, this is the first set of computationally efficient algorithms for information design in succinctly representable many-player games.
Revised:  To our knowledge, this is the first set of efficient algorithms for information design in succinctly representable many-player games.
Edit intent: clarity
Original:  To overcome the challenge of multiple equilibria, here we introduce a new notion of equilibrium-oblivious NP-hardness , which rules out any possibility of computing a good signaling scheme, irrespective of the equilibrium selection rule.
Revised:  To overcome the issue of multiple equilibria, here we introduce a new notion of equilibrium-oblivious NP-hardness , which rules out any possibility of computing a good signaling scheme, irrespective of the equilibrium selection rule.
Edit intent: clarity
Original:  To overcome the challenge of multiple equilibria, here we introduce a new notion of equilibrium-oblivious NP-hardness , which rules out any possibility of computing a good signaling scheme, irrespective of the equilibrium selection rule.
Revised:  To overcome the challenge of multiple equilibria, here we introduce a new notion of equilibrium-oblivious hardness , which rules out any possibility of computing a good signaling scheme, irrespective of the equilibrium selection rule.
Edit intent: clarity
Original: Auditing trained deep learning (DL) models prior to deployment is vital in preventing unintended consequences.
Revised: Auditing trained deep learning (DL) models prior to deployment is vital for preventing unintended consequences.
Edit intent: fluency
Original:  One of the biggest challenges in auditing is in understanding how we can obtain human-interpretable specifications  that are directly useful to the end-user .
Revised:  One of the biggest challenges in auditing is the lack of human-interpretable specifications that are directly useful to the end-user .
Edit intent: clarity
Original:  One of the biggest challenges in auditing is in understanding how we can obtain human-interpretable specifications  that are directly useful to the end-user .
Revised:  One of the biggest challenges in auditing is in understanding how we can obtain human-interpretable specifications that are directly useful to the auditor .
Edit intent: clarity
Original:  We perform these unit tests by directly verifying the semantically aligned variations in an interpretable latent space of a generative model.
Revised:  We enable such unit tests through variations in a semantically-interpretable latent space of a generative model.
Edit intent: clarity
Original:  With evaluations on four different datasets, covering images of towers, chest X-rays, human faces, and ImageNet classes,  we show how AuditAI allows us to obtain controlled variations for verification and certified trainingwhile addressing the limitations of verifying using only pixel-space perturbations .
Revised:  With evaluations on four different datasets, covering images of chest X-rays, human faces, and ImageNet classes, we show how AuditAI allows us to obtain controlled variations for verification and certified trainingwhile addressing the limitations of verifying using only pixel-space perturbations .
Edit intent: clarity
Original:  With evaluations on four different datasets, covering images of towers, chest X-rays, human faces, and ImageNet classes,  we show how AuditAI allows us to obtain controlled variations for verification and certified trainingwhile addressing the limitations of verifying using only pixel-space perturbations .
Revised:  With evaluations on four different datasets, covering images of towers, chest X-rays, human faces, ImageNet classes, we show how AuditAI allows us to obtain controlled variations for verification and certified trainingwhile addressing the limitations of verifying using only pixel-space perturbations .
Edit intent: fluency
Original: We provide a new and simplified proof of Winter's measurement compression [2004] via likelihood POVMs. Secondly, we provide an alternate proof of the central tool at the heart of this theorem - the Quantum covering lemma - that does not rely on the Ahlswede Winter's operator Chernoff bound [2002] , thereby requires only pairwise independence of the involved random operators .
Revised: We provide a new and simplified proof of Winter's measurement compression [2004] via likelihood POVMs. Secondly, we provide an alternate proof of the central tool at the heart of this theorem - the Quantum covering lemma . Our proof does not rely on the Ahlswede Winter's operator Chernoff bound [2002] , thereby requires only pairwise independence of the involved random operators .
Edit intent: coherence
Original: We provide a new and simplified proof of Winter's measurement compression [2004] via likelihood POVMs. Secondly, we provide an alternate proof of the central tool at the heart of this theorem - the Quantum covering lemma - that does not rely on the Ahlswede Winter's operator Chernoff bound [2002] , thereby requires only pairwise independence of the involved random operators .
Revised: We provide a new and simplified proof of Winter's measurement compression [2004] via likelihood POVMs. Secondly, we provide an alternate proof of the central tool at the heart of this theorem - the Quantum covering lemma - that does not rely on the Ahlswede Winter's operator Chernoff bound [2002] and is applicable even when the random operators are only pairwise independent .
Edit intent: coherence
Original:  However, because of the severe mismatch between input and target speech, many previous studies only focus on  magnitude spectrum estimation and remain the phase unaltered, resulting in the degraded speech quality under low signal-to-noise ratio conditions.
Revised:  However, because of the severe mismatch between input and target speech, many previous studies only focus on the magnitude spectrum estimation and remain the phase unaltered, resulting in the degraded speech quality under low signal-to-noise ratio conditions.
Edit intent: fluency
Original:  To tackle this problem, we decouple the difficult target w.r.t. original spectrum optimization into spectral magnitude and phase, and propose a novel Cycle-in-cycle generative adversarial network (dubbed CinCGAN)  to jointly estimate the spectral magnitude and phase information stage by stage  .
Revised:  To tackle this problem, we decouple the difficult target w.r.t. original spectrum optimization into spectral magnitude and phase, and propose a novel Cycle-in-cycle generative adversarial network (dubbed CinCGAN) to jointly estimate the spectral magnitude and phase information stage by stage .
Edit intent: fluency
Original:  To tackle this problem, we decouple the difficult target w.r.t. original spectrum optimization into spectral magnitude and phase, and propose a novel Cycle-in-cycle generative adversarial network (dubbed CinCGAN)  to jointly estimate the spectral magnitude and phase information stage by stage  .
Revised:  To tackle this problem, we decouple the difficult target w.r.t. original spectrum optimization into spectral magnitude and phase, and a novel Cycle-in-Cycle generative adversarial network (dubbed CinCGAN) to jointly estimate the spectral magnitude and phase information stage by stage .
Edit intent: coherence
Original:  To tackle this problem, we decouple the difficult target w.r.t. original spectrum optimization into spectral magnitude and phase, and propose a novel Cycle-in-cycle generative adversarial network (dubbed CinCGAN)  to jointly estimate the spectral magnitude and phase information stage by stage  .
Revised:  To tackle this problem, we decouple the difficult target w.r.t. original spectrum optimization into spectral magnitude and phase, and propose a novel Cycle-in-cycle generative adversarial network (dubbed CinCGAN) is proposed to jointly estimate the spectral magnitude and phase information stage by stage .
Edit intent: clarity
Original:  In the first stage, we pretrain a magnitude CycleGAN to coarsely denoise the spectral magnitude spectrum .
Revised:  In the first stage, we pretrain a magnitude CycleGAN to coarsely estimate the spectral magnitude spectrum .
Edit intent: clarity
Original:  In the first stage, we pretrain a magnitude CycleGAN to coarsely denoise the spectral magnitude spectrum .
Revised:  In the first stage, we pretrain a magnitude CycleGAN to coarsely denoise the spectral magnitude of clean speech .
Edit intent: clarity
Original:  In the second stage, we couple the pretrained CycleGAN with a complex-valued CycleGAN as a cycle-in-cycle structure to  recover phase information and refine the spectral magnitude simultaneously.
Revised:  In the second stage, we incorporate the pretrained CycleGAN with a complex-valued CycleGAN as a cycle-in-cycle structure to recover phase information and refine the spectral magnitude simultaneously.
Edit intent: clarity
Original:  In the second stage, we couple the pretrained CycleGAN with a complex-valued CycleGAN as a cycle-in-cycle structure to  recover phase information and refine the spectral magnitude simultaneously.
Revised:  In the second stage, we couple the pretrained CycleGAN in a complex-valued CycleGAN as a cycle-in-cycle structure to recover phase information and refine the spectral magnitude simultaneously.
Edit intent: clarity
Original:  In the second stage, we couple the pretrained CycleGAN with a complex-valued CycleGAN as a cycle-in-cycle structure to  recover phase information and refine the spectral magnitude simultaneously.
Revised:  In the second stage, we couple the pretrained CycleGAN with a complex-valued CycleGAN as a cycle-in-cycle structure to simultaneously recover phase information and refine the spectral magnitude simultaneously.
Edit intent: fluency
Original:  Experiments on training the models with standard paired data also show that the proposed method can achieve remarkable performance .
Revised:  The evaluation on training the models with standard paired data also show that the proposed method can achieve remarkable performance .
Edit intent: clarity
Original: In this paper we introduce the Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from the Faster Regional Convolutional Neural Network (Faster R-CNN ) to generate logos .
Revised: In this paper we introduce Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from the Faster Regional Convolutional Neural Network (Faster R-CNN ) to generate logos .
Edit intent: fluency
Original: In this paper we introduce the Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from the Faster Regional Convolutional Neural Network (Faster R-CNN ) to generate logos .
Revised: In this paper we introduce the Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from Faster R-CNN ) to generate logos .
Edit intent: coherence
Original: In this paper we introduce the Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from the Faster Regional Convolutional Neural Network (Faster R-CNN ) to generate logos .
Revised: In this paper we introduce the Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from the Faster Regional Convolutional Neural Network (Faster R-CNN for logo generation .
Edit intent: clarity
Original:  Our approach beats the state-of-the-art models ( StyleGAN2 , Self-Attention GANs) that suffer from mode collapse due to the size of the data .
Revised:  Our approach beats the state-of-the-art models StyleGAN2 , Self-Attention GANs) that suffer from mode collapse due to the size of the data .
Edit intent: fluency
Original:  Our approach beats the state-of-the-art models ( StyleGAN2 , Self-Attention GANs) that suffer from mode collapse due to the size of the data .
Revised:  Our approach beats the state-of-the-art models ( StyleGAN2 and Self-Attention GANs) that suffer from mode collapse due to the size of the data .
Edit intent: fluency
Original:  Our approach beats the state-of-the-art models ( StyleGAN2 , Self-Attention GANs) that suffer from mode collapse due to the size of the data .
Revised:  Our approach beats the state-of-the-art models ( StyleGAN2 , Self-Attention GAN .
Edit intent: clarity
Original:  (1) Reduce solving a general linear system A   to solving a weighted Laplacian system L of size O(nnz(A)).
Revised:  (1) Reduce solving a general linear system A = to solving a weighted Laplacian system L of size O(nnz(A)).
Edit intent: fluency
Original: The recursive and hierarchical structure of full rooted trees is used in various areas  such as data compression, image processing, and machine learning.
Revised: The recursive and hierarchical structure of full rooted trees is used in various areas , such as data compression, image processing, and machine learning.
Edit intent: fluency
Original:  In most of these studies , the full rooted tree is not a random variable .
Revised:  In most of these cases , the full rooted tree is not a random variable .
Edit intent: clarity
Original:  One method to solve it is to assume a prior distribution on the full rooted trees.
Revised:  One method to solve this problem is to assume a prior distribution on the full rooted trees.
Edit intent: clarity
Original:  It enables us to avoid overfitting based on the Bayes decision theory.
Revised:  This enables overfitting to be avoided based on the Bayes decision theory.
Edit intent: clarity
Original:  For example, by assigning a low prior probability on a complex model, the MAP estimator prevents the overfitting.
Revised:  For example, by assigning a low prior probability to a complex model, the MAP estimator prevents the overfitting.
Edit intent: clarity
Original:  Its parametric representation is well suited to calculate the properties of our distribution by recursive functions: the mode, the expectation, the posterior distribution, etc.
Revised:  Its parametric representation is suitable for calculating the properties of our distribution by recursive functions: the mode, the expectation, the posterior distribution, etc.
Edit intent: clarity
Original:  Its parametric representation is well suited to calculate the properties of our distribution by recursive functions: the mode, the expectation, the posterior distribution, etc.
Revised:  Its parametric representation is well suited to calculate the properties of our distribution using recursive functions, such as the mode, the expectation, the posterior distribution, etc.
Edit intent: clarity
Original:  Although some previous studies have proposed such distributions ,  they are for specific applications.
Revised:  Although some previous studies have proposed such distributions , they are only applicable to specific applications.
Edit intent: clarity
Original:  Therefore, we extract the mathematically essential part of them and derive new generalized methods to calculate the expectation, the posterior distribution, etc.
Revised:  Therefore, we extract their mathematically essential components and derive new generalized methods to calculate the expectation, the posterior distribution, etc.
Edit intent: clarity
Original:  Therefore, we extract the mathematically essential part of them and derive new generalized methods to calculate the expectation, the posterior distribution, etc.
Revised:  Therefore, we extract the mathematically essential part of them and derive new generalized methods to calculate the expectation, posterior distribution, etc.
Edit intent: fluency
Original:  In particular, we demonstrate the SPIR scheme on a simple fingerprint database, successfully retrieving a 582-byte fingerprint file from a database with 800 entries.
Revised:  In particular, we demonstrate the SPIR scheme on a simple fingerprint database, successfully retrieving a 582-byte fingerprint files from a database with 800 entries.
Edit intent: fluency
Original:  Our simulation and experimental results show the feasibility of the proposed SPIR scheme, presenting a promising way for practical IR applications with privacy requirements .
Revised:  Our experimental results clearly demonstrate the feasibility of the proposed SPIR scheme, presenting a promising way for practical IR applications with privacy requirements .
Edit intent: clarity
Original:  Finally, we propose  an image patch matching module to calculate the distance between dense local representations  to determine which category the query image belongs to in the support set.
Revised:  Finally, we propose to use an image patch matching module to calculate the distance between dense local representations to determine which category the query image belongs to in the support set.
Edit intent: coherence
Original:  Finally, we propose  an image patch matching module to calculate the distance between dense local representations  to determine which category the query image belongs to in the support set.
Revised:  Finally, we propose an image patch matching module for calculating the distance between dense local representations to determine which category the query image belongs to in the support set.
Edit intent: fluency
Original:  Finally, we propose  an image patch matching module to calculate the distance between dense local representations  to determine which category the query image belongs to in the support set.
Revised:  Finally, we propose an image patch matching module to calculate the distance between dense local representations , thus to determine which category the query image belongs to in the support set.
Edit intent: coherence
Original:  Hyperproperties are properties of systems that relate multiple computation traces, including many important  security and concurrency properties.
Revised:  Hyperproperties are properties of systems that relate multiple computation traces, including many important information-flow security and concurrency properties.
Edit intent: clarity