wanyu/IteraTeR_v2 huggingface dataset:
Original:  Specifically, we firstly benchmark the baseline Inception-v3 models on commonly used crowd counting datasets and show its superiority to other existing models.
Revised:  Specifically, we firstly benchmark the baseline Inception-v3 models on commonly used crowd counting datasets and achieve surprisingly good performance comparable with or better than most existing crowd counting models.
Edit intent: clarity
Original:  Subsequently, we present a Segmentation Guided Attention Network (SGANet) with theInception-v3 as the backbone for crowd counting.
Revised:  Subsequently, we present a Segmentation Guided Attention Network (SGANet) with Inception-v3 as the backbone for crowd counting.
Edit intent: fluency
Original:  Subsequently, we present a Segmentation Guided Attention Network (SGANet) with theInception-v3 as the backbone for crowd counting.
Revised:  Subsequently, we present a Segmentation Guided Attention Network (SGANet) with theInception-v3 as the backbone for crowd counting.
Edit intent: fluency
Original: Speaker diarization relies on the assumption that acoustic embeddings from speech segments corresponding to a particular speaker share common characteristics.
Revised: Speaker diarization relies on the assumption that speech segments corresponding to a particular speaker share common characteristics.
Edit intent: clarity
Original:  Those identities however are not known a priori, so a clustering algorithm is  employed, which is typically based solely on audio.
Revised:  These identities are not known a priori, so a clustering algorithm is employed, which is typically based solely on audio.
Edit intent: coherence
Original:  Those identities however are not known a priori, so a clustering algorithm is  employed, which is typically based solely on audio.
Revised:  Those identities however are not known a priori, so a clustering algorithm is typically employed, which is typically based solely on audio.
Edit intent: fluency
Original:  Those identities however are not known a priori, so a clustering algorithm is  employed, which is typically based solely on audio.
Revised:  Those identities however are not known a priori, so a clustering algorithm is employed, which is traditionally based solely on audio.
Edit intent: clarity
Original:   In this work we explore conversational scenarios where the speakers play distinct roles and are expected to follow different linguistic patterns.
Revised:  Under noisy conditions, however, such an approach poses the risk of generating unreliable speaker clusters. In this work we explore conversational scenarios where the speakers play distinct roles and are expected to follow different linguistic patterns.
Edit intent: clarity
Original:   In this work we explore conversational scenarios where the speakers play distinct roles and are expected to follow different linguistic patterns.
Revised:  In this work we explore conversational scenarios where the speakers assume distinct roles and are expected to follow different linguistic patterns.
Edit intent: clarity
Original:  We aim to exploit this distinct linguistic variability and build a language-based segmenter and a role recognizer which can be used to construct the speaker identities.
Revised:  This distinct linguistic variability and build a language-based segmenter and a role recognizer which can be used to construct the speaker identities.
Edit intent: clarity
Original:  We aim to exploit this distinct linguistic variability and build a language-based segmenter and a role recognizer which can be used to construct the speaker identities.
Revised:  We aim to exploit this distinct linguistic variability can be exploited to help us construct the speaker identities.
Edit intent: clarity
Original: Mining natural associations from high-dimensional spatiotemporal signals have received significant attention in various fields including biology, climatology  and financial analysis , etcetera.
Revised: Mining natural associations from high-dimensional spatiotemporal signals plays an important role in various fields including biology, climatology and financial analysis , etcetera.
Edit intent: clarity
Original: Mining natural associations from high-dimensional spatiotemporal signals have received significant attention in various fields including biology, climatology  and financial analysis , etcetera.
Revised: Mining natural associations from high-dimensional spatiotemporal signals have received significant attention in various fields including biology, climatology , and financial analysis , etcetera.
Edit intent: fluency
Original:  In this paper , we study the problem of uncovering graphs that better reveal the relations behind data , with the help of long and short term correlated property in spatiotemporal signals.
Revised:  In this paper , we study the problem of uncovering graphs that better reflect underlying data relations by leveraging the long- and short-term characteristics of spatiotemporal signals.
Edit intent: clarity
Original:  A spatiotemporal signal model considering both spatial and temporal relationship is firstly presented.
Revised:  First, a spatiotemporal signal model considering both spatial and temporal relationship is firstly presented.
Edit intent: coherence
Original:  A spatiotemporal signal model considering both spatial and temporal relationship is firstly presented.
Revised:  A spatiotemporal signal model is presented that considers both spatial and temporal relationship is firstly presented.
Edit intent: clarity
Original:  Particularly, a low-rank representation together with a Gaussian Markov process is adopted to describe the signals' time-correlated behavior.
Revised:  Particularly, a low-rank representation and a Gaussian Markov process is adopted to describe the signals' time-correlated behavior.
Edit intent: clarity
Original:  Particularly, a low-rank representation together with a Gaussian Markov process is adopted to describe the signals' time-correlated behavior.
Revised:  Particularly, a low-rank representation together with a Gaussian Markov process to describe the signals' time-correlated behavior.
Edit intent: clarity
Original:  A Low-Rank and Spatiotemporal Smoothness-based graph learning method (GL-LRSS) is proposed, which novelly introduces spatiotemporal smooth prior to the field of time-vertex signal analysis.
Revised:  A Low-Rank and Spatiotemporal Smoothness-based graph learning method (GL-LRSS) , which introduces a spatiotemporal smoothness prior into time-vertex signal analysis.
Edit intent: clarity
Original:  Through jointly exploiting the low-rank property of long-time observations and the smoothness of short-time observations, the overall performance is effectively improved.
Revised:  By jointly exploiting the low-rank property of long-time observations and the smoothness of short-time observations, the overall performance is effectively improved.
Edit intent: fluency
Original:  Through jointly exploiting the low-rank property of long-time observations and the smoothness of short-time observations, the overall performance is effectively improved.
Revised:  Through jointly exploiting the low rank of long-time observations and the smoothness of short-time observations, the overall performance is effectively improved.
Edit intent: clarity
Original:  Through jointly exploiting the low-rank property of long-time observations and the smoothness of short-time observations, the overall performance is effectively improved.
Revised:  Through jointly exploiting the low-rank property of long-time observations and the smoothness of short-time observations, the overall learning performance can be effectively improved.
Edit intent: clarity
Original:  Experiments on both synthetic and real-world datasets demonstrate the significant improvement on learning accuracy of the proposed GL-LRSS over current state-of-the-art low-rank  estimation and graph learning methods.
Revised:  Experiments on both synthetic and real-world datasets demonstrate substantial improvements in the learning accuracy of the proposed GL-LRSS over current state-of-the-art low-rank estimation and graph learning methods.
Edit intent: clarity
Original:  Experiments on both synthetic and real-world datasets demonstrate the significant improvement on learning accuracy of the proposed GL-LRSS over current state-of-the-art low-rank  estimation and graph learning methods.
Revised:  Experiments on both synthetic and real-world datasets demonstrate the significant improvement on learning accuracy of the proposed method over the state-of-the-art low-rank estimation and graph learning methods.
Edit intent: clarity
Original: In this paper, we present a machine learning-powered iterative combinatorial auction (CA).
Revised: We present a machine learning-powered iterative combinatorial auction (CA).
Edit intent: clarity
Original:  While using ML inside an auction introduces new challenges, we demonstrate how we obtain a design that is individually rational, has good incentives, and is computationally practical.
Revised:  While using ML inside a CA introduces new challenges, we demonstrate how we obtain a design that is individually rational, has good incentives, and is computationally practical.
Edit intent: clarity
Original:  Our results indicate that, especially in large domains, our ML-powered auction can achieve higher allocative efficiency than the CCA, even with only a small number of value queries.
Revised:  Our results indicate that, especially in large domains, MLCA can achieve significantly higher allocative efficiency than the CCA, even with only a small number of value queries.
Edit intent: clarity
Original: We discuss several mappings from well-known NP-hard problems to Quadratic Unconstrained Binary Optimisation problems which are treated incorrectly by Lucas in \mbox{ Luc14 .
Revised: We discuss several mappings from well-known NP-hard problems to Quadratic Unconstrained Binary Optimisation problems which are treated incorrectly by Lucas .
Edit intent: coherence
Original:  To show that our proposed algorithm learns accurate and fair ranking policies from biased and noisy feedback , we provide empirical results beyond the theoretical justification of the framework .
Revised:  To show that our proposed algorithm learns accurate and fair ranking policies from biased and noisy feedback .
Edit intent: clarity
Original: We analyze a wireless line network employing wireless network coding , i. e., the reverse carpooling setting.
Revised: We analyze a wireless line network employing wireless network coding .
Edit intent: clarity
Original:  To strike a balance between the reduced transmission cost and the cost incurred by the packet delay, a scheduling algorithm for determining either to transmit an uncoded packet or to wait for coding is needed.
Revised:  To strike a balance between the reduced transmission cost and the cost incurred by the delay, a scheduling algorithm for determining either to transmit an uncoded packet or to wait for coding is needed.
Edit intent: clarity
Original:  To strike a balance between the reduced transmission cost and the cost incurred by the packet delay, a scheduling algorithm for determining either to transmit an uncoded packet or to wait for coding is needed.
Revised:  To strike a balance between the reduced transmission cost and the cost incurred by the packet delay, a scheduling algorithm determining either to transmit an uncoded packet or to wait for coding is needed.
Edit intent: clarity
Original:  Because of highly uncertain traffic injections, a scheduling algorithm with no assumption of the traffic is not only criticalbut also practical.
Revised:  Because of highly uncertain traffic injections, scheduling with no assumption of the traffic is not only criticalbut also practical.
Edit intent: clarity
Original:  In this paper , we propose a randomized online scheduling algorithm for a relay in arbitrary traffic, which can be non-stationary or even adversarial.
Revised:  In this paper , we propose a randomized online scheduling algorithm for a relay in arbitrary traffic, which can be non-stationary or adversarial.
Edit intent: coherence
Original:  The expected total cost (including a transmission cost and a delay cost) incurred by the proposed algorithms is (asymptotically) at most e/(e-1) ~ 1.58 times the minimum total cost achieved by an optimal offline scheduling algorithm .
Revised:  The expected total cost (including a transmission cost and a delay cost) incurred by the proposed algorithm is at most e/(e-1) ~ 1.58 times the minimum total cost achieved by an optimal offline scheduling algorithm .
Edit intent: clarity
Original:  The expected total cost (including a transmission cost and a delay cost) incurred by the proposed algorithms is (asymptotically) at most e/(e-1) ~ 1.58 times the minimum total cost achieved by an optimal offline scheduling algorithm .
Revised:  The expected total cost (including a transmission cost and a delay cost) incurred by the proposed algorithms is (asymptotically) at most e/(e-1) ~ 1.58 times the minimum achievable total cost .
Edit intent: clarity
Original:  In particular, the proposed algorithm is universal in the sense that that ratio is independent of the traffic.
Revised:  In particular, the proposed algorithm is universal in the sense that the ratio is independent of the traffic.
Edit intent: fluency
Original:  With the universality, the proposed algorithm can be implemented at each relay distributedly  in a multi-relay network ;
Revised:  With the universality, the proposed algorithm can be implemented at each relay distributedly ( in a multi-relay network ;
Edit intent: fluency
Original:  The proposed algorithm turns out to generalize the classic ski-rental online algorithm from several perspectives .
Revised:  The proposed algorithm turns out to generalize the classic ski-rental online algorithm .
Edit intent: clarity
Original: We describe a two-message protocol that enables a purely classical verifier to delegate any quantum computation to an untrusted quantum prover.
Revised: We describe a two-message protocol that enables a purely classical party to delegate any quantum computation to an untrusted quantum prover.
Edit intent: clarity
Original:  Specifically, it is secure in the Quantum Random Oracle Model, and assuming the quantum hardness of the Learning with Errors problem.
Revised:  Specifically, our protocols are secure in the Quantum Random Oracle Model, and assuming the quantum hardness of the Learning with Errors problem.
Edit intent: clarity
Original:  Specifically, it is secure in the Quantum Random Oracle Model, and assuming the quantum hardness of the Learning with Errors problem.
Revised:  Specifically, it is secure in the Quantum Random Oracle Model, under the assumption that Learning with Errors problem.
Edit intent: clarity
Original: Understanding the hierarchical self-organization of living systems is one of the biggest conceptual challenges of the present century .
Revised: Understanding the hierarchical self-organization of living systems is a challenge that requires the development of a multiscale description of their constituents .
Edit intent: clarity
Original:  The number fluctuations within sub-regions of the system show a hyperuniform structure or exhibit giant number fluctuations, depending on whether or not the noise is conserved.
Revised:  The number fluctuations within subregions of the system show a hyperuniform structure or exhibit giant number fluctuations, depending on whether or not the noise is conserved.
Edit intent: fluency
Original: Understanding the hierarchical self-organization of living systems is a challenge that requires the development of a multiscale description of their constituents.
Edit intent: clarity
Original:  A generically observed mechanism that drives such organization is interaction among the individual elements---which may represent cells, bacteria, or even enzymes---via chemical signals.
Revised:  A generically observed mechanism that drives the self-organization of living systems is interaction via chemical signals among the individual elements---which may represent cells, bacteria, or even enzymes---via chemical signals.
Edit intent: clarity
Original:  A generically observed mechanism that drives such organization is interaction among the individual elements---which may represent cells, bacteria, or even enzymes---via chemical signals.
Revised:  A generically observed mechanism that drives such organization is interaction among the individual elements -- which may represent cells, bacteria, or even enzymes---via chemical signals.
Edit intent: fluency
Original:  This work proposes a %DIFDELCMD < {\em %%% hybrid representation approach for the conversion.
Revised:  This work proposes a %DIFDELCMD < {\em %%% hybrid representation approach for the conversion.
Edit intent: fluency
Original: In type theory with an impredicative universe of propositions and a proof-irrelevant propositional equality the normalization property fails.
Revised: Normalization fails in type theory with an impredicative universe of propositions and a proof-irrelevant propositional equality the normalization property fails.
Edit intent: clarity
Original:  Experimental results demonstrate that our model achieves state-of-the-art performance.
Revised:  Experimental results show that our model achieves state-of-the-art performance.
Edit intent: fluency
Original:  In this paper, we showed that using a dictionary of complex sparse codes,  learned from natural scenes based on efficient coding theory, as the front-end for these methods can improve their performance in neural response prediction.
Revised:  In this paper, we show that using a dictionary of complex sparse codes, learned from natural scenes based on efficient coding theory, as the front-end for these methods can improve their performance in neural response prediction.
Edit intent: fluency
Original:  In this paper, we showed that using a dictionary of complex sparse codes,  learned from natural scenes based on efficient coding theory, as the front-end for these methods can improve their performance in neural response prediction.
Revised:  In this paper, we showed that using a dictionary of complex sparse codes, learned from natural scenes based on efficient coding theory, as the front-end for these methods can improve their performance in neuronal response prediction.
Edit intent: fluency
Original:  At the same time , this approach makes the component features of the models more transparent and interpretable.
Revised:  More importantly , this approach makes the component features of the models more transparent and interpretable.
Edit intent: coherence
Original:  At the same time , this approach makes the component features of the models more transparent and interpretable.
Revised:  At the same time , this approach makes the constituent kernels of these models substantially more coherent and interpretable.
Edit intent: clarity
Original:  This observation affirms the recent discovery of complex feature selectivity of V1 neurons and also supports the predictions of efficient coding theory under over-complete conditions .
Revised:  This observation affirms the recent discovery of complex feature selectivity in V1 neurons and also supports the predictions of efficient coding theory under over-complete conditions .
Edit intent: fluency
Original:  This observation affirms the recent discovery of complex feature selectivity of V1 neurons and also supports the predictions of efficient coding theory under over-complete conditions .
Revised:  This observation affirms the recent discovery of complex feature selectivity of V1 .
Edit intent: clarity
Original: System identification techniques---projection pursuit regression models ( PPR ) and convolutional neural networks (CNNs) ---provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli.
Revised: System identification techniques -- projection pursuit regression models ( PPR ) and convolutional neural networks (CNNs) ---provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli.
Edit intent: fluency
Original: System identification techniques---projection pursuit regression models ( PPR ) and convolutional neural networks (CNNs) ---provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli.
Revised: System identification techniques---projection pursuit regression models ( PPRs ) and convolutional neural networks (CNNs) ---provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli.
Edit intent: fluency
Original: System identification techniques---projection pursuit regression models ( PPR ) and convolutional neural networks (CNNs) ---provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli.
Revised: System identification techniques---projection pursuit regression models ( PPR ) and convolutional neural networks (CNNs) -- provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli.
Edit intent: fluency
Original:  However, the constituent kernels recovered by these methods , particularly those of CNNs, are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron's receptive field.
Revised:  However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron's receptive field.
Edit intent: clarity
Original:  In this paper, we show that using a dictionary of complex sparse codes, which are learned from natural scenes based on efficient coding theory, as the front-end for PPR and CNNs can improve their performance in neuronal response prediction .
Revised:  In this paper, we show that using a dictionary of complex sparse codes, which are learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction .
Edit intent: fluency
Original:  In addition, we find that models with a complex sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity.
Revised:  In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity.
Edit intent: clarity
Original:  This observation adds further credence to the sparse coding theory as well as empirical findings of complex feature selectivity in V1  .
Revised:  We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 .
Edit intent: clarity
Original:  This observation adds further credence to the sparse coding theory as well as empirical findings of complex feature selectivity in V1  .
Revised:  This observation adds further credence to the sparse coding theory as well as empirical findings of complex feature selectivity in V1 neurons .
Edit intent: clarity
Original:  In particular, we study the dynamic known asfictitious play (FP) in which each player simultaneously best-responds to the empirical distribution of the historical plays of their opponent.
Revised:  In particular, we study the dynamic known asFictitious Play (FP) in which each player simultaneously best-responds to the empirical distribution of the historical plays of their opponent.
Edit intent: fluency
Original:  However, Daskalakis and Pan disproved a version of this conjecture in 2014, showing that an exponentially-slow rate can occur, although their result relied on adversarial tie-breaking.
Revised:  However, Daskalakis and Pan disproved a version of this conjecture in 2014, showing that a slow rate can occur, although their result relied on adversarial tie-breaking.
Edit intent: clarity