wanyu/IteraTeR_full_sent huggingface dataset:
Original:   In this work, we present Deep Canonical Correlation Sensor Alignment (DCCA), a method that is specifically tailored to address these problems. It exploits common properties specific to misalignments produced by sensor circuitry, such as clock-drift and offsets .
Revised:  In this work, we present Deep Canonical Correlation Sensor Alignment (DCCA), a novel, generally applicable solution for the temporal alignment of raw (biomedical) sensor signals. DCCA allows practitioners to directly align raw signals, from distinct sensors, without requiring deep domain knowledge .
Edit intent: clarity
Original:  On a selection of artificial and real datasets  we demonstrate the performance  of DCCA under a variety of conditions  .
Revised:  On a selection of artificial and real datasets we demonstrate the performance of DCCA under a variety of conditions . We compare the DCCA algorithm to other warping based methods, DCCA outperforms dynamic time warping and cross correlation based methods by an order of magnitude in terms of alignment error. DCCA performs especially well on almost periodic biomedical signals such as heart-beats and breathing patterns. In comparison to existing approaches, that are not tailored towards raw sensor data, DCCA is not only fast enough to work on signals with billions of data points but also provides automatic filtering and transformation functionalities, allowing it to deal with very noisy and even morphologically distinct signals .
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Original:  Our method outperforms all reference architectures on 4 out of 8 tasks in the ATOM3D benchmark and broadly improves over rotation-invariant graph neural networks . We also demonstrate that transfer learning can improve performance in learning from macromolecular structure.
Revised:  Our method outperforms all reference architectures on 4 out of 8 tasks in the ATOM3D benchmark , is tied for first on two others, and is competitive with equivariant networks using higher-order representations and spherical harmonic convolutions. In addition, we demonstrate that transfer learning can improve performance in learning from macromolecular structure.
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Original:  Here, we use a model of the Ih current model with either fast or slow kinetics to determine its influence on the membrane time constant (\tau_m) of a CA1 pyramidal cell model.
Revised:  Here, we use a model of the I_{\rm h current model with either fast or slow kinetics to determine its influence on the membrane time constant (\tau_m) of a CA1 pyramidal cell model.
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Original:  Our simulation results show that the Ih with fast kinetics decreases \tau_m and attenuates and shortens the excitatory postsynaptic potentials more than the slow Ih .
Revised:  Our simulation results show that the I_{\rm h with fast kinetics decreases \tau_m and attenuates and shortens the excitatory postsynaptic potentials more than the slow Ih .
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Original:  Our simulation results show that the Ih with fast kinetics decreases \tau_m and attenuates and shortens the excitatory postsynaptic potentials more than the slow Ih .
Revised:  Our simulation results show that the Ih with fast kinetics decreases \tau_m and attenuates and shortens the excitatory postsynaptic potentials more than the slow I_{\rm h .
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Original:  We conclude that the Ih activation kinetics is able to modulate \tau_m and the temporal properties of excitatory postsynaptic potentials (EPSPs) in CA1 pyramidal cells.
Revised:  We conclude that the I_{\rm h activation kinetics is able to modulate \tau_m and the temporal properties of excitatory postsynaptic potentials (EPSPs) in CA1 pyramidal cells.
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Original:  We utilize a diverse dataset collected at the University of Illinois Hospital and Health to successfully develop a robust deep learning model for prediction and also develop a clinically inspired model that calculates the marginal reflex distance and iris ratio.
Revised:  We utilize a diverse dataset collected from the Illinois Ophthalmic Database Atlas (I-ODA) to develop a robust deep learning model for prediction and also develop a clinically inspired model that calculates the marginal reflex distance and iris ratio.
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Original: Morphing between two planar drawings using an extra dimension is relatively new area of research. Also, the morphs with polynomially-bounded resolution has gained a lot of attention in recent years. Recent results use linear number of morphing stepsto morph between two planar straight-line grid drawings of an n-vertex tree while maintaining the resolution.
Revised: We study crossing-free grid morphs for planar tree drawings using 3D. A morph consists of morphing steps, where vertices move simultaneously along straight-line grid drawings of an n-vertex tree while maintaining the resolution.
Edit intent: clarity
Original:  Recent results use linear number of morphing stepsto morph between two planar straight-line grid drawings of an n-vertex tree while maintaining the resolution.In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [  between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Revised:  Recent results use linear number of morphing stepsto morph between two planar straight-line grid drawings of an n-vertex planar graph G with \mathcal{O(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [ between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Edit intent: meaning-changed
Original: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [  between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Revised: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[Alamdari et al.\ 2017 (\log n) for an n-vertex tree }[ [ between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Edit intent: meaning-changed
Original: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [  between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Revised: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ , while using 3D can reduce the number of steps to \mathcal{O(\log n) for an n-vertex tree }[ [ between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Edit intent: meaning-changed
Original: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [  between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Revised: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[Arseneva et al.\ 2019 [ between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Edit intent: meaning-changed
Original: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [  between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Revised: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ . However, these morphs do not bound one practical parameter, the resolution. Recently[ between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Edit intent: meaning-changed
Original: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [  between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Revised: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [Barrera-Cruz et al.\ 2019 between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Edit intent: meaning-changed
Original: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [  between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Revised: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [ a 2D crossing-free morph was devised between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Edit intent: meaning-changed
Original: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [  between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Revised: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [ between two planar drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution.
Edit intent: fluency
Original: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [  between two planar straight-line grid drawings of an n-vertex tree T in sublinear number of morphing steps such that each intermediate morphing step produces a 3D straight-line crossing free grid drawing of T while maintaining the resolution. To be precise, we first devise an algorithm that takes O(n) morphing steps but uses a grid of small volume.
Revised: In this paper, we bring together both the paradigms to develop an algorithm that morphs(n) morphing steps and sometimes \Omega(n) steps is necessary even when G is a path~}[ (\log n) for an n-vertex tree }[ [ between two planar straight-line grid drawings of an n-vertex tree in O(n) morphing steps but uses a grid of small volume.
Edit intent: coherence
Original:  To be precise, we first devise an algorithm that takes O(n) morphing steps but uses a grid of small volume. We then use this algorithm to devise a better algorithm that takes \mathcal{O}(\sqrt{n} \log n) morphing steps.
Revised:  To be precise, we first devise an algorithm that takes O(n) morphing steps but uses a grid of polynomial size. Can the number of steps in this setting be reduced substantially by using 3D? We answer this question in affirmative and present a 3D non-crossing morph between two planar grid drawings of an n-vertex tree in \mathcal{O}(\sqrt{n} \log n) morphing steps.
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Original:  However, the traditional benchmark dataset for this task, Docking Benchmark 5 (DB5), contains only a paltry 230 complexes for training, validating, and testing different machine learning algorithms.
Revised:  However, the traditional benchmark dataset for this task, Docking Benchmark 5 (DB5), contains only a modest 230 complexes for training, validating, and testing different machine learning algorithms.
Edit intent: clarity
Original:  The previous version of DIPS contains only the Cartesian coordinates and types of the atoms comprising a given protein complex, whereas DIPS-Plus now includes a plethora of new residue-level features including protrusion indices, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, giving researchers a large, well-curated feature bank for training protein interface prediction methods  .
Revised:  The previous version of DIPS contains only the Cartesian coordinates and types of the atoms comprising a given protein complex, whereas DIPS-Plus now includes a plethora of new residue-level features including protrusion indices, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, giving researchers a large, well-curated feature bank for training protein interface prediction methods . We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields SOTA results, surpassing the performance of all other models trained on residue-level and atom-level encodings of protein complexes to date .
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Original:  In this article, we present a non-symmetrical bidirectional recurrent neural network architecture called U-RNN as a sequence encoder and evaluate its relevance to replace LSTMs for various forecasting models.
Revised:  In this article, we present a non-symmetrical bidirectional recurrent neural network architecture called U-RNN to encode pedestrian trajectories and evaluate its relevance to replace LSTMs for various forecasting models.
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Original:  Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metric (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a variety of approaches and interaction modules  .
Revised:  Experimental results on the Trajnet++ benchmark show that the U-LSTM variant yields better results regarding every available metric (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a variety of approaches and interaction modules .
Edit intent: clarity
Original:  Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metric (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a variety of approaches and interaction modules  .
Revised:  Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metrics (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a variety of approaches and interaction modules .
Edit intent: fluency
Original:  Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metric (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a variety of approaches and interaction modules  .
Revised:  Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metric (ADE, FDE, Collision rate) than common trajectory encoders for a variety of approaches and interaction modules .
Edit intent: clarity
Original:  Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metric (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a variety of approaches and interaction modules  .
Revised:  Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metric (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a variety of approaches and interaction modules , suggesting that the proposed approach is a viable alternative to the de facto sequence encoding RNNs .
Edit intent: meaning-changed
Original:  In this work, we first propose a novel pure transformer-based mask vision transformer ( MViT ) for FER in the wild, which consists of two modules: a transformer-based mask generation network (MGN) to generate a mask that can filter out complex backgrounds and occlusion of face images, and a dynamic relabeling module to rectify incorrect labels in FER datasets in the wild.
Revised:  In this work, we first propose a novel pure transformer-based mask vision transformer ( MVT ) for FER in the wild, which consists of two modules: a transformer-based mask generation network (MGN) to generate a mask that can filter out complex backgrounds and occlusion of face images, and a dynamic relabeling module to rectify incorrect labels in FER datasets in the wild.
Edit intent: fluency
Original:  Extensive experimental results demonstrate that our MViT outperforms state-of-the-art methods on RAF-DB with 88.62\%, FERPlus with 89.22\%, and AffectNet-7 with 64.57\%, respectively, and achieves a comparable result on AffectNet-8 with 61.40\%.
Revised:  Extensive experimental results demonstrate that our MVT outperforms state-of-the-art methods on RAF-DB with 88.62\%, FERPlus with 89.22\%, and AffectNet-7 with 64.57\%, respectively, and achieves a comparable result on AffectNet-8 with 61.40\%.
Edit intent: fluency
Original:  Extensive results validate the effectiveness of our proposals on ImageNet with diverse ViT backbones . For instance, at 40\% structured sparsity, our sparsified DeiT-Base can achieve 0.42\% accuracy gain, at 33.13\% and 24.70\% running time savings, compared to its dense counterpart . Perhaps most surprisingly, we find that the proposed sparse (co-)training can even improve the ViT accuracy rather than compromising it, making sparsity a tantalizing "free lunch".
Revised:  Extensive results validate the effectiveness of our proposals on ImageNet with diverse ViT backbones savings, compared to its dense counterpart . Perhaps most surprisingly, we find that the proposed sparse (co-)training can even improve the ViT accuracy rather than compromising it, making sparsity a tantalizing "free lunch".
Edit intent: coherence
Original:  For instance, at 40\% structured sparsity, our sparsified DeiT-Base can achieve 0.42\% accuracy gain, at 33.13\% and 24.70\% running time savings, compared to its dense counterpart . Perhaps most surprisingly, we find that the proposed sparse (co-)training can even improve the ViT accuracy rather than compromising it, making sparsity a tantalizing "free lunch".
Revised:  For instance, at 40\% structured sparsity, our sparsified DeiT-Base can achieve 0.42\% accuracy gain, at 33.13\% and 24.70\% running time validate the effectiveness of our proposals which obtain significantly reduced computational cost and almost unimpaired generalization . Perhaps most surprisingly, we find that the proposed sparse (co-)training can even improve the ViT accuracy rather than compromising it, making sparsity a tantalizing "free lunch".
Edit intent: meaning-changed
Original:  For example, our sparsified DeiT-Small at  5\%, 50\%  sparsity for (data, architecture), improves 0.28\% top-1 accuracy  and meanwhile enjoys 49.32\% FLOPs and 4.40\% running time savings. 
Revised:  For example, our sparsified DeiT-Small at ( 5\%, 50\% sparsity for (data, architecture), improves 0.28\% top-1 accuracy and meanwhile enjoys 49.32\% FLOPs and 4.40\% running time savings. 
Edit intent: fluency
Original:  For example, our sparsified DeiT-Small at  5\%, 50\%  sparsity for (data, architecture), improves 0.28\% top-1 accuracy  and meanwhile enjoys 49.32\% FLOPs and 4.40\% running time savings. 
Revised:  For example, our sparsified DeiT-Small at 5\%, 50\% ) sparsity for (data, architecture), improves 0.28\% top-1 accuracy and meanwhile enjoys 49.32\% FLOPs and 4.40\% running time savings. 
Edit intent: fluency
Original:  For example, our sparsified DeiT-Small at  5\%, 50\%  sparsity for (data, architecture), improves 0.28\% top-1 accuracy  and meanwhile enjoys 49.32\% FLOPs and 4.40\% running time savings. 
Revised:  For example, our sparsified DeiT-Small at 5\%, 50\% sparsity for (data, architecture), improves 0.28\% top-1 accuracy , and meanwhile enjoys 49.32\% FLOPs and 4.40\% running time savings. 
Edit intent: fluency
Original:  For example, our sparsified DeiT-Small at  5\%, 50\%  sparsity for (data, architecture), improves 0.28\% top-1 accuracy  and meanwhile enjoys 49.32\% FLOPs and 4.40\% running time savings. 
Revised:  For example, our sparsified DeiT-Small at 5\%, 50\% sparsity for (data, architecture), improves 0.28\% top-1 accuracy and meanwhile enjoys 49.32\% FLOPs and 4.40\% running time savings. Our codes are available at URL
Edit intent: meaning-changed
Original:  Our starting point is to show that all continuous-time Follow-the-Regularized-Leader (FTRL) dynamics, which includes the well-known Replicator Dynamic, are lossless, i.e. it is passive with no energy dissipation.
Revised:  Our starting point is to show that all continuous-time Follow-the-Regularized-Leader (FTRL) dynamics, which include the well-known Replicator Dynamic, are lossless, i.e. it is passive with no energy dissipation.
Edit intent: fluency