wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper, we explore the extent to which adding semialgebraic conditions arising from the restriction to parameters with statistical meaning can improve existing methods of phylogenetic reconstruction.
Revised:  In this paper, we explore the extent to which adding semi-algebraic conditions arising from the restriction to parameters with statistical meaning can improve existing methods of phylogenetic reconstruction.
Edit intent: fluency
Original:  To this end, our aim is to compute the distance of data points to algebraic varieties and to the stochastic part of theses varieties.
Revised:  To this end, our aim is to compute the distance of data points to algebraic varieties and to the stochastic part of these varieties.
Edit intent: fluency
Original: We present VOQC, the first fully verified compiler for quantum circuits, written using the Coq proof assistant.
Revised: We present VOQC, the first fully verified optimizer for quantum circuits, written using the Coq proof assistant.
Edit intent: clarity
Original:  SQIR's careful design and our provided automation make it easy to write and verify a broad range of optimizations in VOQC, and even allow us to verify correctness properties of interesting source SQIR programs .
Revised:  SQIR's careful design and our provided automation make it possible to write and verify a broad range of optimizations in VOQC, and even allow us to verify correctness properties of interesting source SQIR programs .
Edit intent: clarity
Original:  Therefore, we conclude with a real RAM analogue to the Cook-Levin Theorem .
Revised:  Therefore, we conclude with a real RAM .
Edit intent: clarity
Original:  This gives an easy proof of ER-membership, as real verification algorithms are much more versatile than ETR-formulas  .
Revised:  This gives an easy proof of ER-membership, as verification algorithms on a real RAM are much more versatile than ETR-formulas .
Edit intent: clarity
Original:  In this work, we show that it is possible to attack neural networks in a highly restricted threat setting, where attackers have no knowledge of the neural network (i.e., in a black-box setting) and can only modify highly localized adversarial noise in the form of randomly chosen \\%DIF > \\%DIF > and \\%DIF > on ImageNet and CIFAR-10 trained ResNet-50 models, respectively.
Revised:  In this work, we show that it is possible for adversaries to generate localized \\%DIF > \\%DIF > and \\%DIF > on ImageNet and CIFAR-10 trained ResNet-50 models, respectively.
Edit intent: clarity
Original:  In this work, we show that it is possible to attack neural networks in a highly restricted threat setting, where attackers have no knowledge of the neural network (i.e., in a black-box setting) and can only modify highly localized adversarial noise in the form of randomly chosen \\%DIF > \\%DIF > and \\%DIF > on ImageNet and CIFAR-10 trained ResNet-50 models, respectively.
Revised:  In this work, we show that it is possible to attack neural networks in a highly restricted threat setting, where attackers have no knowledge of the neural network (i.e., in a black-box setting) and can only modify highly localized adversarial noise in the form of randomly chosenadversarial scratches \\%DIF > \\%DIF > and \\%DIF > on ImageNet and CIFAR-10 trained ResNet-50 models, respectively.
Edit intent: fluency
Original:  Against ImageNet models, Adversarial Scratches requires 3 times\a\\%DIF > on CIFAR-10 with an order of magnitude fewer queries than GenAttack (without any optimizations) and 73 times fewer queries than ZOO, both prior state-of-the-art black-box attacks.
Revised:  Against ImageNet models, Adversarial Scratches requires 3 times\a\\%DIF > on CIFAR-10 with an order of magnitude fewer queries than comparable attacks.
Edit intent: clarity
Original: Deep learning models are increasingly used for end-user applications, supporting both novel features , such as facial recognition, and traditional features, such as web search.
Revised: Deep learning models are increasingly used for end-user applications, supporting both novel features such as facial recognition, and traditional features, such as web search.
Edit intent: fluency
Original: Deep learning models are increasingly used for end-user applications, supporting both novel features , such as facial recognition, and traditional features, such as web search.
Revised: Deep learning models are increasingly used for end-user applications, supporting both novel features , such as facial recognition, and traditional features, e.g. web search.
Edit intent: clarity
Original:  Under-utilized server resources brought about by dynamic workloadscan influence provisioning decisions , which may result in inflated serving costs.
Revised:  These resources could also be under-utilized facing dynamic workloads , which may result in inflated serving costs.
Edit intent: clarity
Original:  Leveraging Perseus, we evaluated the inference throughput and cost for various serving deployments and demonstrated that multi-tenant model serving can lead to up to 12\% cost reduction.
Revised:  Leveraging Perseus, we evaluated the inference throughput and cost for serving various models and demonstrated that multi-tenant model serving can lead to up to 12\% cost reduction.
Edit intent: clarity
Original:  Leveraging Perseus, we evaluated the inference throughput and cost for various serving deployments and demonstrated that multi-tenant model serving can lead to up to 12\% cost reduction.
Revised:  Leveraging Perseus, we evaluated the inference throughput and cost for various serving deployments and demonstrated that multi-tenant model serving led to up to 12\% cost reduction.
Edit intent: clarity
Original:  We confirm well know market phenomenology with the aim of providing some standardised R based simulation tools.
Revised:  We confirm well known market phenomenology with the aim of providing some standardised R based simulation tools.
Edit intent: fluency
Original:  However, in the encounter of data distribution mismatch between training and inference, neural vocoders trained on real data often degrade in voice quality for unseen scenarios.
Revised:  However, when encountering data distribution mismatch between training and inference, neural vocoders trained on real data often degrade in voice quality for unseen scenarios.
Edit intent: fluency
Original:  Furthermore, we present results with considerable reference value of subjective human evaluation for future studies.
Revised:  Great amount of subjective MOS results in naturalness for all vocoders are presented for future studies.
Edit intent: coherence
Original:  The company exploits two of its own broodstocks - NL and TR, originating from the chars from the Nuayak Lake and the Three River (Nunavut, Canada).
Revised:  The company exploits two of its own broodstocks - NL and TR, originating from the chars from the Nauyuk Lake and the Three River (Nunavut, Canada).
Edit intent: fluency
Original:  In this paper, we propose a perfectly parallel static analysis for certifying causal fairness of feed-forward neural networks used for classification tasks .
Revised:  In this paper, we propose a perfectly parallel static analysis for certifying causal fairness of feed-forward neural networks used for classification of tabular data .
Edit intent: clarity
Original:  We train our models on raw complex-valued STFT outputs and achieve state-of-the-art SDR performance in the MUSDB18 singing voice separation task by a large margin of 1.0 dB.
Revised:  We train our models on raw complex-valued STFT outputs and achieve state-of-the-art SDR performance on the MUSDB singing voice separation task by a large margin of 1.0 dB.
Edit intent: clarity
Original: Musical Source Separation (MSS) is a signal processing task that tries to separate the mixed musical signal into each acoustic sound source, such as singing voice or drums.
Revised: Singing Voice Separation (SVS) tries to separate the mixed musical signal into each acoustic sound source, such as singing voice or drums.
Edit intent: clarity
Original: Musical Source Separation (MSS) is a signal processing task that tries to separate the mixed musical signal into each acoustic sound source, such as singing voice or drums.
Revised: Musical Source Separation (MSS) is a signal processing task that tries to separate singing voice from a given mixed musical signal into each acoustic sound source, such as singing voice or drums.
Edit intent: clarity
Original:  Recentlymany machine learning-based methods have been proposed for the MSS task, but there were no existing works that evaluate and directly compare various types of networks .
Revised:  Recentlymany machine learning-based methods have been proposed for the MSS task, but there were no existing works that evaluate and compare various types of networks .
Edit intent: clarity
Original:  In this paper, we aim to design a variety of neural transformation methods, including time-invariant methods, time-frequency methods, and mixtures of two different transformations.
Revised:  In this paper, we introduce a variety of neural transformation methods, including time-invariant methods, time-frequency methods, and mixtures of two different transformations.
Edit intent: clarity
Original:  We train our models on raw complex-valued STFT outputs and achieve state-of-the-art SDR performance on the MUSDB singing voice separation task by a large margin of 1.0 dB .
Revised:  We train our models on raw complex-valued STFT outputs and achieve state-of-the-art SDR on the MUSDB singing voice separation task by a large margin of 1.0 dB .
Edit intent: clarity
Original: This work aims to examine one of the cornerstone problems of Musical Instrument Recognition , in particular  instrument classification.
Revised: This work aims to examine one of the cornerstone problems of Musical Instrument Recognition , in particular , instrument classification.
Edit intent: fluency
Original:  IRMAS (Instrument recognition in Musical Audio Signals) data set is chosen  .
Revised:  IRMAS (Instrument recognition in Musical Audio Signals) data set is chosen for this purpose .
Edit intent: clarity
Original:  The data includes music obtained from various decades in the last century, thus having a wide variety in audio quality.
Revised:  The data includes musical clips recorded from various sources in the last century, thus having a wide variety in audio quality.
Edit intent: clarity
Original:  The data includes music obtained from various decades in the last century, thus having a wide variety in audio quality.
Revised:  The data includes music obtained from various decades in the last century, thus having a wide variety of audio quality.
Edit intent: clarity
Original:  The classifier had a major challenge distinguishing between flute and organ.
Edit intent: coherence
Original: The main challenge of speaker verification in the wild is the interference caused by irrelevant information in speech and the lack of speaker labels in speech datasets .
Revised: Irrelevant information in speeches can seriously interfere with the performance of speaker verification. Particularly, the most popular datasets do not contain enough labels to overcome this challenge .
Edit intent: clarity
Original:  In order to solve the above problems , we propose a novel speaker embedding training framework based on adversarially disentangled identity representation.
Revised:  In order to solve this problem , we propose a novel speaker embedding training framework based on adversarially disentangled identity representation.
Edit intent: coherence
Original:  Experiments prove that the framework we propose can significantly improve the performance of speaker verification from the original models without adjusting the structure and hyper-parameters of them.
Revised:  Experiments prove that the framework we propose can significantly improve the performance of speaker verification from the original models without adjusting the structure or hyper-parameters of them.
Edit intent: fluency
Original:  This suggests that adversarially disentangled representation is extremely useful for alleviating the lack of speaker labels .
Revised:  Furthermore, porting an existing model into the proposed framework is straightforward and cost-efficient, with very little effort from the model owners due to the modular design of the framework .
Edit intent: coherence
Original: With the pervasiveness of voice control on smart devices , speaker verification is widely used as the preferred identity authentication mechanism due to its convenience .
Revised: With the pervasiveness of voice control on smart devices .
Edit intent: clarity
Original:  However, the task of "in-the-wild" speaker verification is  challenging, considering the speech samples may contain lots of identity-unrelated information, e.g., background noise, reverberation, emotion, etc. Previous works focus on optimizing the model to improve verification accuracy, without considering eliminating the impact from the identity-unrelated information.
Revised:  However, the task of "in-the-wild" speaker verification is still challenging, considering the speech samples may contain lots of identity-unrelated information, e.g., background noise, reverberation, emotion, etc. Previous works focus on optimizing the model to improve verification accuracy, without considering eliminating the impact from the identity-unrelated information.
Edit intent: coherence
Original:  However, the task of "in-the-wild" speaker verification is  challenging, considering the speech samples may contain lots of identity-unrelated information, e.g., background noise, reverberation, emotion, etc. Previous works focus on optimizing the model to improve verification accuracy, without considering eliminating the impact from the identity-unrelated information.
Revised:  However, the task of "in-the-wild" speaker verification is challenging, considering the speech samples may contain lots of identity-unrelated information, e.g., background noise, reverberation, emotion, etc. Previous works focus on optimizing the model to improve verification accuracy, without taking into account the elimination of the impact from the identity-unrelated information.
Edit intent: clarity
Original:  To solve this problem, we propose SEF-ALDR , a novel Speaker Embedding  Framework via Adversarial Learning based Disentangled Representation, to further improve the performance of existing models on speaker verification.
Revised:  To solve the above problem, we propose SEF-ALDR , a novel Speaker Embedding Framework via Adversarial Learning based Disentangled Representation, to further improve the performance of existing models on speaker verification.
Edit intent: coherence
Original:  To solve this problem, we propose SEF-ALDR , a novel Speaker Embedding  Framework via Adversarial Learning based Disentangled Representation, to further improve the performance of existing models on speaker verification.
Revised:  To solve this problem, we propose SEEF-ALDR , a novel Speaker Embedding Framework via Adversarial Learning based Disentangled Representation, to further improve the performance of existing models on speaker verification.
Edit intent: fluency
Original:  To solve this problem, we propose SEF-ALDR , a novel Speaker Embedding  Framework via Adversarial Learning based Disentangled Representation, to further improve the performance of existing models on speaker verification.
Revised:  To solve this problem, we propose SEF-ALDR , a novel Speaker Embedding Framework via Adversarial Learning based Disentangled Representation, to reinforce the performance of existing models on speaker verification.
Edit intent: clarity
Original:  The key idea is to retrieve as much speaker identity information as possible from the original speech, i.e., minimizing the impact of identity-unrelated information on the speaker verification task .
Revised:  The key idea is to retrieve as much speaker identity information as possible from the original speech, thus minimizing the impact of identity-unrelated information on the speaker verification task .
Edit intent: clarity
Original:  Experimental results demonstrate that the proposed framework can significantly improve the performance of the speaker verification by 20.3\% and 28.7 \% on average over 13 tested baselines on dataset Voxceleb1 and 9 tested baselines on dataset Voxceleb2 respectively, without adjusting the structure or hyper-parameters of them.
Revised:  Experimental results demonstrate that the proposed framework can significantly improve the performance of speaker verification by 20.3\% and 28.7 \% on average over 13 tested baselines on dataset Voxceleb1 and 9 tested baselines on dataset Voxceleb2 respectively, without adjusting the structure or hyper-parameters of them.
Edit intent: fluency
Original: We consider the  complexity of reasoning in extensions of the language of \Delta_0-formulas with non-standard list terms, which represent bounded list search, bounded recursion , and bounded iteration.
Revised: We consider the complexity of reasoning in extensions of the language of \Delta_0-formulas with non-standard list terms, which represent bounded list search, bounded iteration , and bounded iteration.
Edit intent: clarity
Original:  We provide a number of complexity bounds on model checking and satisfiability for these formulas.
Revised:  We provide a number of results on the complexity of model checking and satisfiability for these formulas.
Edit intent: clarity
Original:  In particular, we show that the set of \Delta_0-formulas with bounded recursive terms true in a given list superstructure HW(M) is non-elementary ( i.e., it contains the class kEXPTIME, for all k\geqslant 1).
Revised:  In particular, we show that the set of \Delta_0-formulas with bounded recursive terms true in a given list superstructure HW(M) is non-elementary ( it contains the class kEXPTIME, for all k\geqslant 1).
Edit intent: coherence
Original:  For \Delta_0-formulas with bounded iteration or restricted usage of recursive terms, we show lower complexity.
Revised:  For \Delta_0-formulas with restrictions on the usage of iterative and recursive terms, we show lower complexity.
Edit intent: clarity
Original: We show that the (degree+1)-list coloring problem can be solved deterministically in O(D \cdot \log n \cdot\log ^3 \Delta) in the CONGEST\CONGEST model, where D is the diameter of the graph, n the number of nodes, and \Delta is the maximum degree.
Revised: We show that the (degree+1)-list coloring problem can be solved deterministically in O(D \cdot \log n \cdot\log ^3 \Delta) in the CONGEST\CONGEST model, where D is the diameter of the graph, n the number of nodes, and \Delta the maximum degree.
Edit intent: fluency
Original:  Using the  network decomposition algorithm from Rozhon and Ghaffari ] this implies the first efficient deterministic, that is, poly\poly \log n-time , CONGEST\CONGEST algorithm for the  \Delta+ 1-coloring and the ( degree +1)-list coloring problem.
Revised:  Using the network decomposition algorithm from Rozhon and Ghaffari ], this implies the first efficient deterministic, that is, poly\poly \log n-time , CONGEST\CONGEST algorithm for the \Delta+ 1-coloring and the ( degree +1)-list coloring problem.
Edit intent: fluency
Original:  Using the  network decomposition algorithm from Rozhon and Ghaffari ] this implies the first efficient deterministic, that is, poly\poly \log n-time , CONGEST\CONGEST algorithm for the  \Delta+ 1-coloring and the ( degree +1)-list coloring problem.
Revised:  Using the network decomposition algorithm from Rozhon and Ghaffari ] this implies the first efficient (i.e.,\poly \log n-time , CONGEST\CONGEST algorithm for the \Delta+ 1-coloring and the ( degree +1)-list coloring problem.
Edit intent: coherence
Original:  Using the  network decomposition algorithm from Rozhon and Ghaffari ] this implies the first efficient deterministic, that is, poly\poly \log n-time , CONGEST\CONGEST algorithm for the  \Delta+ 1-coloring and the ( degree +1)-list coloring problem.
Revised:  Using the network decomposition algorithm from Rozhon and Ghaffari ] this implies the first efficient deterministic, that is, poly\poly \log n-time , CONGEST\CONGEST algorithm for the ( \Delta+ 1-coloring and the ( degree +1)-list coloring problem.
Edit intent: fluency
Original:  Using the  network decomposition algorithm from Rozhon and Ghaffari ] this implies the first efficient deterministic, that is, poly\poly \log n-time , CONGEST\CONGEST algorithm for the  \Delta+ 1-coloring and the ( degree +1)-list coloring problem.
Revised:  Using the network decomposition algorithm from Rozhon and Ghaffari ] this implies the first efficient deterministic, that is, poly\poly \log n-time , CONGEST\CONGEST algorithm for the \Delta+ 1)-coloring and the ( degree +1)-list coloring problem.
Edit intent: fluency
Original:  Both bounds play a central role in the ongoing pursuit of the dynamic optimality conjecture ( Tarjan and Sleator , 1985), but their relationship remained unknown for more than three decades.
Revised:  Both bounds play a central role in the ongoing pursuit of the dynamic optimality conjecture ( Sleator and Tarjan , 1985), but their relationship remained unknown for more than three decades.
Edit intent: fluency
Original:  The main ingredient of the proof is a new *symmetric* characterization of Wilber's Funnel bound, which proves that it is invariant under rotations of X.
Revised:  The main ingredient of the proof is a new "symmetric" characterization of Wilber's Funnel bound, which proves that it is invariant under rotations of X.
Edit intent: fluency
Original:  Furthermore, we study its connections to diffusions on Riemannian manifolds and Wasserstein gradient flows.
Revised:  Furthermore, we study its connections to diffusion on Riemannian manifolds and Wasserstein gradient flows.
Edit intent: fluency
Original:  In case of a forward problem with additive Gaussian measurement errors, ALDI allows for a gradient-free implementation in the spirit of the ensemble Kalman filter.
Revised:  In case of a forward problem with additive Gaussian measurement errors, ALDI allows for a gradient-free approximation in the spirit of the ensemble Kalman filter.
Edit intent: clarity
Original: The famous dynamic optimality conjecture of Sleator and Tarjan from 1985 conjectures the existence of an O(1)-competitive  algorithm for binary search trees ( BST's).
Revised: The dynamic optimality conjecture of Sleator and Tarjan from 1985 conjectures the existence of an O(1)-competitive algorithm for binary search trees ( BST's).
Edit intent: clarity
Original: The famous dynamic optimality conjecture of Sleator and Tarjan from 1985 conjectures the existence of an O(1)-competitive  algorithm for binary search trees ( BST's).
Revised: The famous dynamic optimality conjecture , postulating the existence of an O(1)-competitive algorithm for binary search trees ( BST's).
Edit intent: clarity
Original:  Although several candidate lower bounds were suggested in the past, such as WB-1 and WB-2 bounds by Wilber, and Independent Rectangles bound by Demaine et al., the only currently known non-trivial approximation algorithm achieves an O(\log \log n) approximation factor by comparing OPT with a weak variant of WB-1, that uses afixed partitioning of the keys.
Revised:  Although several candidate lower bounds were suggested in the past, such as WB-1 and WB-2 bounds by Wilber, and Independent Rectangles bound by Demaine et al., the only currently known non-trivial approximation algorithm achieves an O(\log \log n) fixed partitioning of the keys.
Edit intent: clarity